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github
|
martinarielhartmann/mirtooloct-master
|
evaleach.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirdesign/evaleach.m
| 33,230 |
utf_8
|
a6791354f1633edd3bc91578a489bed5
|
function [y d2] = evaleach(d,single,name)
% Top-down traversal of the design flowchart, at the beginning of the
% evaluation phase.
% Called by mirfunction, mireval, mirframe and mirsegment.
% This is during that traversal that we check whether a chunk decomposition
% needs to be performed or not, and carry out that chunk decomposition.
if nargin<3 || isempty(name)
if not(ischar(d.method))
name = func2str(d.method);
end
end
if nargin<2
single = 0;
end
CHUNKLIM = mirchunklim;
f = d.file;
fr = d.frame;
if ~isempty(fr) && length(fr.length.val)>1
fr.length.val = fr.length.val(d.scale);
if length(fr.hop.val)>1
fr.hop.val = fr.hop.val(d.scale);
end
end
frnochunk = isfield(d.frame,'dontchunk');
frchunkbefore = isfield(d.frame,'chunkbefore');
sg = d.segment;
sr = d.sampling;
sr2 = d.resampling;
w = d.size;
lsz = w(2)-w(1)+1;
len = lsz/sr;
if ischar(sg)
error('ERROR in MIREVAL: mirsegment of design object accepts only array of numbers as second argument.');
end
if not(isempty(sg))
if ~isnumeric(sg)
sg = sort(mirgetdata(sg));
sg = [0 sg';sg' len];
end
over = find(sg > len);
if not(isempty(over))
sg = sg(1:over-1);
end
end
a = d.argin;
ch = d.chunk;
chan = d.channel;
specif = d.specif;
if iscombinemethod(specif,'Average') || iscombinemethod(specif,'Sum')
specif.eachchunk = 'Normal';
end
if ischar(a)
% The top-down traversal of the design flowchart now reaches the lowest
% layer, i.e., audio file loading.
% Now the actual evaluation will be carried out bottom-up.
if isempty(ch)
% No chunk decomposition
y = miraudio(f,'Now',[w(:)' chan]);
else
% Chunk decomposition
y = miraudio(f,'Now',[ch(1),ch(2) chan]);
end
if not(isempty(d.postoption)) && d.postoption.mono
y = miraudio(y,'Mono',1);
end
y = set(y,'AcrossChunks',get(d,'AcrossChunks'));
y = set(y,'Extracted',1);
d2 = d;
elseif d.chunkdecomposed && isempty(d.tmpfile)
% Already in a chunk decomposition process
[y d2] = evalnow(d);
elseif isempty(fr) || frnochunk || not(isempty(sg)) %% WHAT ABOUT CHANNELS?
% Absence of frame decomposition or presence of segment decomposition in the design to evaluate
% (Or particular frame decomposition, where chunks are not distributed to children (frnochunk).)
if not(isempty(sg))
meth = 'Segment ';
if size(sg,1) == 1
chunks = floor(sg(1:end-1)*sr)+1;
chunks(2,:) = min( floor(sg(2:end)*sr)-1,lsz-1)+1;
else
% The following is used only for miremotion
chunks = floor(sg*sr);
chunks(1,:) = chunks(1,:)+1;
% Code below by Ming-Hsu Chang
chunks = chunks'; %%%
chunks(2, 1:size(chunks,2)-1) = chunks(1, 2:size(chunks,2)) + (sg(2,1)/fr.hop.val - sg(2,1))*sr; %%%
if chunks(2, size(chunks,2)-1) > len %%%
chunks = chunks(:, 1:size(chunks,2)-2);
else
chunks = chunks(:, 1:size(chunks,2)-1);
end
end
elseif not(isfield(specif,'eachchunk')) ...
|| d.nochunk ...
|| (not(isempty(single)) && isnumeric(single) && single > 1 ...
&& isfield(specif,'combinechunk') ...
&& iscell(specif.combinechunk))
chunks = [];
else
meth = 'Chunk ';
if isempty(fr)
if lsz > CHUNKLIM
% The required memory exceed the max memory threshold.
nch = ceil(lsz/CHUNKLIM);
%%% TAKE INTO CONSIDERATION NUMBER OF CHANNELS; ETC...
chunks = max(0,lsz-CHUNKLIM*(nch:-1:1))+w(1);
chunks(2,:) = lsz-CHUNKLIM*(nch-1:-1:0)+w(1)-1;
else
chunks = [];
end
else
chunks = compute_frames(fr,sr,sr2,w,lsz,...
CHUNKLIM/d.chunksizefactor,d.overlap);
end
end
if not(isempty(chunks))
% The chunk decomposition is performed.
nch = size(chunks,2);
d = callbeforechunk(d,d,w,lsz); % Some optional initialisation
tmp = [];
if mirwaitbar
h = waitbar(0,['Computing ' name]);
else
h = 0;
end
if not(isempty(d.tmpfile)) && d.tmpfile.fid == 0
% When applicable, a new temporary file is created.
tmpname = [f '.mirtmp'];
d.tmpfile.fid = fopen(tmpname,'w');
end
if not(d.ascending)
chunks = fliplr(chunks);
end
afterpostoption = d.postoption; % Used only when:
% - eachchunk is set to 'Normal',
% - combinechunk is not set to 'Average', and
% - no afterchunk field has been specified.
% afterpostoption will be used for the final call
% to the method after the chunk decomposition.
method = d.method;
if ~isfield(specif,'eachchunk')
specif.eachchunk = 'Normal';
end
if ischar(specif.eachchunk) && strcmpi(specif.eachchunk,'Normal')
if not(isempty(d.postoption))
pof = fieldnames(d.postoption);
for o = 1:length(pof)
if isfield(specif.option.(pof{o}),'chunkcombine')
afterpostoption = rmfield(afterpostoption,pof{o});
else
d.postoption = rmfield(d.postoption,pof{o});
end
end
end
else
method = specif.eachchunk;
end
d2 = d;
d2.method = method;
y = {};
for i = 1:size(chunks,2)
if mirverbose
disp([meth,num2str(i),'/',num2str(nch),'...'])
end
d2 = set(d2,'Chunk',[chunks(1,i) chunks(2,i) (i == size(chunks,2))]);
if not(ischar(specif.eachchunk) && ...
strcmpi(specif.eachchunk,'Normal'))
if frnochunk
d2.postoption = 0;
else
diffchunks = diff(chunks); % Usual chunk size
d2.postoption = max(diffchunks) - diffchunks(i);
% Reduction of the current chunk size to be taken into
% consideration in mirspectrum, for instance, using
% zeropadding
end
end
d2 = set(d2,'InterChunk',tmp);
d2.chunkdecomposed = 1;
[ss d3] = evalnow(d2);
if iscell(ss) && not(isempty(ss))
tmp = get(ss{1},'InterChunk');
elseif isstruct(ss)
tmp = [];
else
tmp = get(ss,'InterChunk');
end
% d2 is like d3 except that its argument is now evaluated.
d3.postoption = d.postoption; % Pas joli joli
d3.method = method;
d2 = d3; % This new argument is transfered to d
y = combinechunk_noframe(y,ss,sg,i,d2,chunks,single);
clear ss
if isa(h,'matlab.ui.Figure')
if not(d.ascending)
close(h)
h = waitbar((chunks(1,i)-chunks(1,end))/chunks(2,1),...
['Computing ' func2str(d.method) ' (backward)']);
else
waitbar((chunks(2,i)-chunks(1))/chunks(end),h)
end
end
end
if ~isstruct(y)
% Final operations to be executed after the chunk decomposition
if iscombinemethod(d2.specif,'Average')
y{1} = divideweightchunk(y{1},lsz);
elseif not(isempty(afterpostoption)) && isempty(d2.tmpfile)
y{1} = d.method(y{1},[],afterpostoption);
end
if not(isempty(d2.tmpfile))
adr = ftell(d2.tmpfile.fid);
fclose(d2.tmpfile.fid);
ytmpfile.fid = fopen(tmpname);
fseek(ytmpfile.fid,adr,'bof');
ytmpfile.data = y{1};
ytmpfile.layer = 0;
y{1} = set(y{1},'TmpFile',ytmpfile);
end
end
if isa(d,'mirstruct') && ...
(isempty(d.frame) || isfield(d.frame,'dontchunk'))
y = evalbranches(d,y);
end
if isa(h,'matlab.ui.Figure')
close(h)
end
drawnow
else
% No chunk decomposition
[y d2] = evalnow(d);
if isa(d,'mirstruct') && isfield(d.frame,'dontchunk')
y = evalbranches(d,y);
end
end
elseif d.nochunk
[y d2] = evalnow(d);
else
% Frame decomposition in the design to be evaluated.
chunks = compute_frames(fr,sr,sr2,w,lsz,CHUNKLIM,d.overlap);
if size(chunks,2)>1
% The chunk decomposition is performed.
if mirwaitbar
h = waitbar(0,['Computing ' name]);
else
h = 0;
end
inter = [];
d = set(d,'FrameDecomposition',1);
d2 = d;
nch = size(chunks,2);
y = {};
if frchunkbefore
d2after = d2;
d2.method = d2.argin.method;
d2.option = d2.argin.option;
d2.postoption = d2.argin.postoption;
d2.argin = d2.argin.argin;
end
for fri = 1:nch % For each chunk...
if mirverbose
disp(['Chunk ',num2str(fri),'/',num2str(nch),'...'])
end
d2 = set(d2,'Chunk',chunks(:,fri)');
d2 = set(d2,'InterChunk',inter);
%d2.postoption = [];
[res d2] = evalnow(d2);
if not(isempty(res))
if iscell(res)
inter = get(res{1},'InterChunk');
elseif not(isstruct(res))
inter = get(res,'InterChunk');
res = {res};
end
end
y = combinechunk_frame(y,res,d2,fri);
if ~isempty(h)
waitbar(chunks(2,fri)/chunks(end),h);
end
end
if frchunkbefore
y = d2after.method(y,d2after.option,d2after.postoption);
end
if isa(d,'mirstruct') && get(d,'Stat')
y = mirstat(y);
end
if ~isempty(h)
close(h)
end
else
% No chunk decomposition
[y d2] = evalnow(d);
end
end
if iscell(y)
for i = 1:length(y)
if not(isempty(y{i}) || isstruct(y{i}))
if iscell(y{i})
for j = 1:length(y{i})
y{i}{j} = set(y{i}{j},'InterChunk',[]);
end
else
y{i} = set(y{i},'InterChunk',[]);
end
end
end
end
function chunks = compute_frames(fr,sr,sr2,w,lsz,CHUNKLIM,frov)
if strcmpi(fr.length.unit,'s')
fl = fr.length.val*sr;
fl2 = fr.length.val*sr2;
elseif strcmpi(fr.length.unit,'sp')
fl = fr.length.val;
fl2 = fl;
end
if strcmpi(fr.hop.unit,'/1')
h = fr.hop.val*fl;
h2 = fr.hop.val*fl2;
elseif strcmpi(fr.hop.unit,'%')
h = fr.hop.val*fl*.01;
h2 = fr.hop.val*fl2*.01;
elseif strcmpi(fr.hop.unit,'s')
h = fr.hop.val*sr;
h2 = fr.hop.val*sr2;
elseif strcmpi(fr.hop.unit,'sp')
h = fr.hop.val;
h2 = fr.hop.val;
elseif strcmpi(fr.hop.unit,'Hz')
h = sr/fr.hop.val;
h2 = sr2/fr.hop.val;
end
if strcmpi(fr.phase.unit,'s')
ph = fr.phase.val*sr;
elseif strcmpi(fr.phase.unit,'sp')
ph = fr.phase.val;
elseif strcmpi(fr.phase.unit,'/1')
ph = fr.phase.val*h;
elseif strcmpi(fr.phase.unit,'%')
ph = fr.phase.val*h*.01;
end
n = floor((lsz-fl-ph)/h)+1; % Number of frames
if n < 1
%warning('WARNING IN MIRFRAME: Frame length longer than total sequence size. No frame decomposition.');
fp = w;
fp2 = (w-1)/sr*sr2+1;
else
st = floor(((1:n)-1)*h+ph)+w(1);
st2 = floor(((1:n)-1)*h2)+w(1)+ph;
fp = [st; floor(st+fl-1)];
fp(:,fp(2,:)>w(2)) = [];
fp2 = [st2; floor(st2+fl2-1)];
fp2(:,fp2(2,:)>(w(2)-w(1))/sr*sr2+w(2)) = [];
end
fpsz = (fp(2,1)-fp(1,1)) * n; % Total number of samples
fpsz2 = (fp2(2,1)-fp2(1,1)) * n; % Total number of samples
if max(fpsz,fpsz2) > CHUNKLIM
% The required memory exceed the max memory threshold.
nfr = size(fp,2); % Total number of frames
frch = max(ceil(CHUNKLIM/(fp(2,1)-fp(1,1))),2); % Number of frames per chunk
frch = max(frch,frov*2);
nch = ceil((nfr-frch)/(frch-frov))+1; % Number of chunks
chbeg = (frch-frov)*(0:nch-1)+1; % First frame in the chunk
chend = (frch-frov)*(0:nch-1)+frch; % Last frame in the chunk
chend = min(chend,nfr);
if chend(end) == chbeg(end)
lszend = fp(2,end)-fp(1,end)+1; % Size of last chunk
nend = floor((lszend-fl)/h)+1; % Number of frames in the last chunk
if nend < 2 % Last chunk is too short (only one frame),
chend(end-1) = chend(end); % concatenated to previous one.
chbeg(end) = [];
chend(end) = [];
end
end
if frov > 1 % If case of overlap <<<< Check if OK or not? (Was commented out before)
chbeg = chend-frch+1;
end
chunks = [fp(1,chbeg) ; fp(2,chend)+1]; % After resampling, one sample may be missing, leading to a complete frame drop.
chunks(end) = min(chunks(end),fp(end)); % Last chunk should not extend beyond audio size.
else
chunks = [];
end
function res = combinechunk_frame(old,new,d2,fri)
if isa(new,'miraudio') && isempty(mirgetdata(new))
res = old;
return
end
if isstruct(old)
f = fields(old);
for i = 1:length(f)
res.(f{i}) = combinechunk_frame(old.(f{i}),new.(f{i}),d2,fri);
end
return
end
if fri == 1
res = new;
else
res = combineframes(old,new);
end
function res = combinechunk_noframe(old,new,sg,i,d2,chunks,single)
if isempty(new)
res = {};
return
end
if isempty(mirgetdata(new))
res = old;
return
end
if not(iscell(new))
new = {new};
end
if not(iscell(old))
old = {old};
end
if not(isempty(old)) && isstruct(old{1})
f = fields(old{1});
for j = 1:length(f)
index.type = '.';
index.subs = f{j};
res.(f{j}) = combinechunk_noframe(old{1}.(f{j}),new{1}.(f{j}),...
sg,i,subsref(d2,index),chunks,single);
end
return
end
if ischar(single) && not(isempty(old))
old = {old{1}};
end
if isempty(sg)
if iscombinemethod(d2.specif,'Average') || ...
iscombinemethod(d2.specif,'Sum')
% Measure total size for later averaging
if iscell(new)
new1 = new{1};
else
new1 = new;
end
dnew = get(new1,'Data');
if iscombinemethod(d2.specif,'Average')
dnew = mircompute(@multweight,dnew,chunks(2,i)-chunks(1,i)+1);
end
if iscell(new)
new{1} = set(new1,'Data',dnew);
else
new = set(new1,'Data',dnew);
end
end
%tmp = get(new{1},'InterChunk');
if not(isempty(d2.tmpfile)) && d2.tmpfile.fid > 0
% If temporary file is used, chunk results are written
% in the file
if i < size(chunks,2)
ds = get(new{1},'Data');
ps = get(new{1},'Pos');
%ftell(d2.tmpfile.fid)
count = fwrite(d2.tmpfile.fid,ds{1}{1},'double');
count = fwrite(d2.tmpfile.fid,ps{1}{1},'double');
%ftell(d2.tmpfile.fid)
clear ds ps
end
res = new;
else
% Else, chunk results are directly combined in active
% memory
if i == 1
res = new;
else
res = cell(1,length(old));
if isfield(d2.specif,'combinechunk')
if not(iscell(d2.specif.combinechunk))
method = {d2.specif.combinechunk};
else
method = d2.specif.combinechunk;
end
for z = 1:length(old)
if isframed(old{z})
res(z) = combineframes(old{z},new{z});
else
if ischar(method{z})
if strcmpi(method{z},'Concat')
doo = get(old{z},'Data');
dn = get(new{z},'Data');
fpo = get(old{z},'FramePos');
fpn = get(new{z},'FramePos');
if size(fpo{1}{1},2)>1
error('Fatal error. Please contact Olivier.');
end
if isa(old,'mirscalar')
res{z} = set(old{z},...
'Data',{{[doo{1}{1},dn{1}{1}]}},...
'FramePos',{{[fpo{1}{1}(1);fpn{1}{1}(2)]}});
else
to = get(old{z},'Pos');
tn = get(new{z},'Pos');
if d2.ascending
res{z} = set(old{z},...
'Data',{{[doo{1}{1};dn{1}{1}]}},...
'Pos',{{[to{1}{1};tn{1}{1}]}},...
'FramePos',{{[fpo{1}{1}(1);fpn{1}{1}(2)]}});
else
res{z} = set(old{z},...
'Data',{{[dn{1}{1};doo{1}{1}]}},...
'Pos',{{[tn{1}{1};to{1}{1}]}},...
'FramePos',{{[fpo{1}{1}(1);fpn{1}{1}(2)]}});
end
end
elseif strcmpi(method{z},'Average') || ...
strcmpi(method{z},'Sum')
doo = get(old{z},'Data');
dn = get(new{z},'Data');
res{z} = set(old{z},...
'ChunkData',doo{1}{1}+dn{1}{1});
else
error(['SYNTAX ERROR: ',method{z},...
' is not a known keyword for combinechunk.']);
end
else
res{z} = method{z}(old{z},new{z});
end
lo = get(old{z},'Length');
ln = get(new{z},'Length');
res{z} = set(res{z},'Length',{{lo{1}{1}+ln{1}{1}}});
end
end
else
for z = 1:length(old)
if isframed(old{z})
res(z) = combineframes(old{z},new{z});
else
mirerror('MIREVAL',...
'Chunk recombination in non-framed mode is not available for all features yet. Please turn off the chunk decomposition.');
end
end
end
end
end
else
if i == 1
res = new;
else
for z = 1:length(old)
res{z} = combinesegment(old{z},new{z});
end
end
end
function old = combineframes(old,new)
if not(iscell(old))
old = {old};
end
if not(iscell(new))
new = {new};
end
for var = 1:length(new)
ov = old{var};
nv = new{var};
if isa(ov,'mirscalar')
ov = combinedata(ov,nv,'Data');
ov = combinedata(ov,nv,'Mode');
if isa(ov,'mirpitch')
ov = combinedata(ov,nv,'Amplitude');
end
else
if isa(ov,'mirtemporal')
[ov omatch nmatch] = combinedata(ov,nv,'Time',[],[],@modiftime);
else
[ov omatch nmatch] = combinedata(ov,nv,'Pos',[],[]);
if isa(ov,'mirspectrum')
[ov omatch nmatch] = combinedata(ov,nv,'Phase',[],[]);
end
end
ov = combinedata(ov,nv,'Data',omatch,nmatch);
end
ov = combinedata(ov,nv,'FramePos');
ov = combinedata(ov,nv,'PeakPos');
ov = combinedata(ov,nv,'PeakVal');
ov = combinedata(ov,nv,'PeakMode');
old{var} = ov;
end
function [ov omatch nmatch] = combinedata(ov,nv,key,omatch,nmatch,modifdata)
if isstruct(ov)
omatch = [];
nmatch = [];
f = fields(ov);
for i = 1:length(f)
ov.(f{i}) = combinedata(ov.(f{i}),nv.(f{i}),key);
end
return
end
odata = get(ov,key);
if isempty(odata) || isempty(odata{1})
return
end
odata = odata{1};
if iscell(odata)
if ischar(odata{1})
return
else
odata = odata{1};
end
end
ndata = get(nv,key);
ndata = ndata{1};
if iscell(ndata)
ndata = ndata{1};
end
if nargin>3
if isempty(omatch)
ol = size(odata,1);
nl = size(ndata,1);
unmatch = ol-nl;
if unmatch>0
[unused idx] = min(odata(1:1+unmatch,1,1)-ndata(1));
omatch = idx:idx+nl-1;
nmatch = 1:nl;
elseif unmatch<0
[unused idx] = min(ndata(1:1-unmatch,1,1)-odata(1));
nmatch = idx:idx+ol-1;
omatch = 1:ol;
else
nmatch = 1:nl;
omatch = 1:ol;
end
end
odata(omatch,end+1:end+size(ndata,2),:,:) = ndata(nmatch,:,:,:); %4.D for keysom
else
odata(:,end+1:end+size(ndata,2),:,:) = ndata;
end
ov = set(ov,key,{{odata}}); %{odata} for warped chromagram for instance....
function d = modiftime(d,p)
d = d + p;
function [y d] = evalnow(d)
% Go one step further in the top-down evaluation initialisation
argin = d.argin;
if not(iscell(argin))
argin = {argin};
end
for i = 1:length(argin)
a = argin{i};
if not(d.ascending)
a.ascending = 0;
end
if isa(a,'mirdata')
% Input already computed
tmpfile = get(a,'TmpFile');
if not(isempty(tmpfile)) && tmpfile.fid > 0
% The input can be read from the temporary file
ch = get(d,'Chunk');
a = tmpfile.data;
a = set(a,'InterChunk',get(d,'InterChunk'),'TmpFile',tmpfile);
channels = get(a,'Channels');
channels = length(channels{1});
if not(channels)
da = get(a,'Data');
channels = size(da{1}{1},3);
end
sz = (ch(2)-ch(1)+1);
current = ftell(tmpfile.fid);
origin = current-sz*(channels+1)*8;
if origin < 0
sz = sz + origin/(channels+1)/8;
origin = 0;
end
fseek(tmpfile.fid,origin,'bof');
%ftell(tmpfile.fid)
[data count] = fread(tmpfile.fid,[sz,channels],'double');
%count
data = reshape(data,[sz,1,channels]);
[pos count] = fread(tmpfile.fid,sz,'double');
%count
%ftell(tmpfile.fid)
fseek(tmpfile.fid,current-sz*(channels+1)*8,'bof');
a = set(a,'Data',{{data}},'Pos',{{pos}});
if ch(3)
fclose(tmpfile.fid);
delete([d.file '.mirtmp']);
end
argin{i} = a;
end
elseif isa(a,'mirdesign')
if isempty(a.stored)
% The design parameters are transfered to the previous component
% in the design process
a.size = d.size;
a.chunk = d.chunk;
a.file = d.file;
a.channel = d.channel;
a.scale = d.scale;
a.eval = 1;
a.interchunk = d.interchunk;
a.sampling = d.sampling;
if isstruct(d.frame) && isfield(d.frame,'decomposition') ...
&& not(isempty(d.frame.decomposition))
a.chunkdecomposed = 1;
else
a.chunkdecomposed = d.chunkdecomposed;
end
if not(isempty(d.frame)) && ...
not(strcmp(func2str(d.method),'mirframe'))
a.frame = d.frame;
end
a.ready = 1;
a.acrosschunks = d.acrosschunks;
a.index = d.index;
argin{i} = a;
else
% Variable already calculated
tmp = get(d,'Struct');
if not(isempty(tmp))
for j = 1:length(a.stored) % (if modified, modify also mirframe)
stored = a.stored{j};
if iscell(stored)
if length(stored)>1
tmp = tmp{stored{1},stored{2}};
else
tmp = tmp{stored{1}};
end
else
tmp = getfield(tmp,stored);
end
end
if iscell(tmp)
tmp = tmp{1};
end
else
mirerror('evaleach','THERE is a problem..');
end
argin{i} = tmp;
end
end
end
if not(iscell(d.argin))
argin = argin{1};
end
d.option.struct = get(d,'Struct');
if iscell(d.postoption)
[y argin] = d.method(argin,d.option,d.postoption{:});
else
[y argin] = d.method(argin,d.option,d.postoption);
end
d = set(d,'Argin',argin);
if isa(d,'mirstruct') && not(isfield(d.frame,'dontchunk')) && isempty(get(d,'Chunk'))
y = evalbranches(d,y);
end
function z = evalbranches(d,y)
% For complex flowcharts, now that the first temporary variable (y) has been
% computed, the dependent features (d) should be evaluated as well.
branch = get(d,'Data');
for i = 1:length(branch)
if isa(branch{i},'mirdesign') && get(branch{i},'NoChunk') == 1
% if the value is 2, it is OK.
%mirerror('mireval','Flowchart badly designed: mirstruct should not be used if one or several final variables do not accept chunk decomposition.');
end
end
fields = get(d,'Fields');
z = struct;
tmp = get(d,'Tmp');
for i = 1:length(branch)
z.(fields{i}) = evalbranch(branch{i},tmp,y);
end
if get(d,'Stat') && isempty(get(d,'Chunk'))
z = mirstat(z,'FileNames',0);
end
function b = evalbranch(b,d,y)
% We need to evaluate the branch reaching the current node (b) from the parent
% corresponding to the temporary variable (d),
if iscell(b)
mirerror('MIREVAL','Sorry, forked branching of temporary variable cannnot be evaluated in current version of MIRtoolbox.');
end
if isstruct(b)
% Subtrees are evaluated branch after branch.
f = fields(b);
for i = 1:length(f)
b.(f{i}) = evalbranch(b.(f{i}),d,y);
end
return
end
if isequal(b,d)
%% Does it happen ever??
b = y;
return
end
if not(isa(b,'mirdesign'))
mirerror('MIRSTRUCT','In the mirstruct object you defined, the final output should only depend on ''tmp'' variables, and should not therefore reuse the ''Design'' keyword.');
end
v = get(b,'Stored');
if length(v)>1 && ischar(v{2})
%
f = fields(d);
for i = 1:length(f)
if strcmpi(v{2},f)
b = y; % OK, now the temporary variable has been found.
% End of recursion.
return
end
end
end
argin = evalbranch(get(b,'Argin'),d,y); % Recursion one parent up
% The operation corresponding to the branch from the parent to the node
% is finally evaluated.
if iscell(b.postoption)
b = b.method(argin,b.option,b.postoption{:});
else
b = b.method(argin,b.option,b.postoption);
end
function res = iscombinemethod(specif,method)
res = isfield(specif,'combinechunk') && ...
((ischar(specif.combinechunk) && ...
strcmpi(specif.combinechunk,method)) || ...
(iscell(specif.combinechunk) && ...
ischar(specif.combinechunk{1}) && ...
strcmpi(specif.combinechunk{1},method)));
function d0 = callbeforechunk(d0,d,w,lsz)
% If necessary, the chunk decomposition is performed a first time for
% initialisation purposes.
% Currently used only for miraudio(...,'Normal')
if not(ischar(d)) && not(iscell(d))
specif = d.specif;
CHUNKLIM = mirchunklim;
nch = ceil(lsz/CHUNKLIM);
if isfield(specif,'beforechunk') ...
&& ((isfield(d.option,specif.beforechunk{2}) ...
&& d.option.(specif.beforechunk{2})) ...
|| (isfield(d.postoption,specif.beforechunk{2}) ...
&& d.postoption.(specif.beforechunk{2})) )
if mirwaitbar
h = waitbar(0,['Preparing ' func2str(d.method)]);
else
h = 0;
end
for i = 1:nch
disp(['Chunk ',num2str(i),'/',num2str(nch),'...'])
chbeg = CHUNKLIM*(i-1);
chend = CHUNKLIM*i-1;
d2 = set(d,'Size',d0.size,'File',d0.file,...
'Chunk',[chbeg+w(1) min(chend,lsz-1)+w(1)]);
d2.method = specif.beforechunk{1};
d2.postoption = {chend-lsz};
d2.chunkdecomposed = 1;
[tmp d] = evalnow(d2);
d0 = set(d0,'AcrossChunks',tmp);
if isa(h,'matlab.ui.Figure')
waitbar(chend/lsz,h)
end
end
if isa(h,'matlab.ui.Figure')
close(h);
end
drawnow
else
d0 = callbeforechunk(d0,d.argin,w,lsz);
end
end
function y = combinesegment(old,new)
doo = get(old,'Data');
to = get(old,'Pos');
fpo = get(old,'FramePos');
ppo = get(old,'PeakPos');
pppo = get(old,'PeakPrecisePos');
pvo = get(old,'PeakVal');
ppvo = get(old,'PeakPreciseVal');
pmo = get(old,'PeakMode');
apo = get(old,'AttackPos');
rpo = get(old,'ReleasePos');
tpo = get(old,'TrackPos');
tvo = get(old,'TrackVal');
dn = get(new,'Data');
tn = get(new,'Pos');
fpn = get(new,'FramePos');
ppn = get(new,'PeakPos');
pppn = get(new,'PeakPrecisePos');
pvn = get(new,'PeakVal');
ppvn = get(new,'PeakPreciseVal');
pmn = get(new,'PeakMode');
apn = get(new,'AttackPos');
rpn = get(new,'ReleasePos');
tpn = get(new,'TrackPos');
tvn = get(new,'TrackVal');
y = old;
if not(isempty(doo))
y = set(y,'Data',{{doo{1}{:},dn{1}{:}}});
end
y = set(y,'FramePos',{{fpo{1}{:},fpn{1}{:}}});
if not(isempty(to)) && size(doo{1},2) == size(to{1},2)
y = set(y,'Pos',{{to{1}{:},tn{1}{:}}});
end
if not(isempty(ppo))
y = set(y,'PeakPos',{{ppo{1}{:},ppn{1}{:}}},...
'PeakVal',{{pvo{1}{:},pvn{1}{:}}},...
'PeakMode',{{pmo{1}{:},pmn{1}{:}}});
end
if not(isempty(pppn))
y = set(y,'PeakPrecisePos',{[pppo{1},pppn{1}{1}]},...
'PeakPreciseVal',{[ppvo{1},ppvn{1}{1}]});
end
if not(isempty(apn))
y = set(y,'AttackPos',{[apo{1},apn{1}{1}]});
end
if not(isempty(rpn))
y = set(y,'ReleasePos',{[rpo{1},rpn{1}{1}]});
end
if not(isempty(tpn))
y = set(y,'TrackPos',{[tpo{1},tpn{1}{1}]});
end
if not(isempty(tvn))
y = set(y,'TrackVal',{[tvo{1},tvn{1}{1}]});
end
if isa(old,'mirchromagram')
clo = get(old,'ChromaClass');
cln = get(new,'ChromaClass');
y = set(y,'ChromaClass',{[clo{1},cln{1}{1}]});
end
if isa(old,'miremotion')
deo = get(old,'DimData');
ceo = get(old,'ClassData');
den = get(new,'DimData');
cen = get(new,'ClassData');
afo = get(old,'ActivityFactors');
vfo = get(old,'ValenceFactors');
tfo = get(old,'TensionFactors');
hfo = get(old,'HappyFactors');
sfo = get(old,'SadFactors');
tdo = get(old,'TenderFactors');
ago = get(old,'AngerFactors');
ffo = get(old,'FearFactors');
afn = get(new,'ActivityFactors');
vfn = get(new,'ValenceFactors');
tfn = get(new,'TensionFactors');
hfn = get(new,'HappyFactors');
sfn = get(new,'SadFactors');
tdn = get(new,'TenderFactors');
agn = get(new,'AngerFactors');
ffn = get(new,'FearFactors');
y = set(y,'DimData',{[deo{1},den{1}{1}]},'ClassData',{[ceo{1},cen{1}{1}]});
% Code improved by Ming-Hsu Chang
if iscell(afo)
y = set(y, 'ActivityFactors',{[afo{1},afn{1}{1}]});
end
if iscell(vfo)
y = set(y, 'ValenceFactors',{[vfo{1},vfn{1}{1}]});
end
if iscell(tfo)
y = set(y, 'TensionFactors',{[tfo{1},tfn{1}{1}]});
end
if iscell(hfo)
y = set(y, 'HappyFactors',{[hfo{1},hfn{1}{1}]});
end
if iscell(sfo)
y = set(y, 'SadFactors',{[sfo{1},sfn{1}{1}]});
end
if iscell(tdo)
y = set(y, 'TenderFactors',{[tdo{1},tdn{1}{1}]});
end
if iscell(ago)
y = set(y, 'AngerFactors',{[ago{1},agn{1}{1}]});
end
if iscell(ffo)
y = set(y, 'FearFactors',{[ffo{1},ffn{1}{1}]});
end
end
function y = divideweightchunk(orig,length)
d = get(orig,'Data');
if isempty(d)
y = orig;
else
v = mircompute(@divideweight,d,length);
y = set(orig,'Data',v);
end
function e = multweight(d,length)
e = d*length;
function e = divideweight(d,length)
e = d/length;
|
github
|
martinarielhartmann/mirtooloct-master
|
plus.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirdesign/plus.m
| 159 |
utf_8
|
4c83543fc53f3b9d77b3dea756c818b3
|
function varargout = plus(a,b)
varargout = mirfunction(@pluscell,{a,b},{},1,struct,@init,@plus);
function [x type] = init(x,option)
type = get(x{1},'Type');
|
github
|
martinarielhartmann/mirtooloct-master
|
max.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirdesign/max.m
| 156 |
utf_8
|
152a5a361e15225fc399aaf072410341
|
function varargout = max(a,b)
varargout = mirfunction(@maxcell,{a,b},{},1,struct,@init,@max);
function [x type] = init(x,option)
type = get(x{1},'Type');
|
github
|
martinarielhartmann/mirtooloct-master
|
mtimes.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirdesign/mtimes.m
| 169 |
utf_8
|
91a0abb2d0c449aeac1669a109b4cc06
|
function varargout = mtimes(a,b)
varargout = mirfunction(@mtimescell,{a,b},{},1,struct,@init,@mtimescell);
function [x type] = init(x,option)
type = get(x{1},'Type');
|
github
|
martinarielhartmann/mirtooloct-master
|
mirspectrum.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirspectrum/mirspectrum.m
| 35,371 |
utf_8
|
fcf94ab6871881278d4ed96df6962384
|
function varargout = mirspectrum(orig,varargin)
% s = mirspectrum(x) computes the spectrum of the audio signal x, showing
% the distribution of the energy along the frequencies.
% (x can be the name of an audio file as well.)
% Optional argument:
% mirspectrum(...,'Frame',l,h) computes spectrogram, using frames of
% length l seconds and a hop rate h.
% Default values: l = .05 s, h = .5.
% mirspectrum(...,'Min',mi) indicates the lowest frequency taken into
% consideration, expressed in Hz.
% Default value: 0 Hz.
% mirspectrum(...,'Max',ma) indicates the highest frequency taken into
% consideration, expressed in Hz.
% Default value: the maximal possible frequency, corresponding to
% the sampling rate of x divided by 2.
% mirspectrum(...,'Window',w): windowing method:
% either w = 0 (no windowing) or any windowing function proposed
% in the Signal Processing Toolbox (help window).
% default value: w = 'hamming'
%
% mirspectrum(...,'Cents'): decomposes the energy in cents.
% mirspectrum(...,'Collapsed'): collapses the spectrum into one
% octave divided into 1200 cents.
% Redistribution of the frequencies into bands:
% mirspectrum(...,'Mel'): Mel bands.
% (Requires the Auditory Toolbox.)
% mirspectrum(...,'Bark'): Bark bands.
% (Code based on Pampalk's MA toolbox).
% If the audio signal was frame decomposed, the output s is a
% band-decomposed spectrogram. It is then possible to compute
% the spectrum of the temporal signal in each band,
% using the following syntax:
% mirspectrum(s,'AlongBands')
% This corresponds to fluctuation (cf. mirfluctuation).
% mirspectrum(...,'Mask'): Models masking phenomena in each band.
% (Code based on Pampalk's MA toolbox).
% mirspectrum(...,'Normal'): normalizes with respect to energy.
% mirspectrum(...,'NormalLength'): normalizes with respect to input length.
% mirspectrum(...,'NormalInput'): input signal is normalized from 0 to 1.
% mirspectrum(...,'Power'): squares the energy.
% mirspectrum(...,'dB'): represents the spectrum energy in decibel scale.
% mirspectrum(...,'dB',th): keeps only the highest energy over a
% range of th dB.
% mirspectrum(...,'Terhardt'): modulates the energy following
% (Terhardt, 1979) outer ear model. (Code based on Pampalk's MA
% toolbox).
% mirspectrum(...,'Resonance',r): multiplies the spectrum with a
% resonance curve. Possible value for r:
% 'ToiviainenSnyder': highlights best perceived meter
% (Toiviainen & Snyder 2003)
% (default value for spectrum of envelopes)
% 'Fluctuation': fluctuation strength (Fastl 1982)
% (default value for spectrum of band channels)
% mirspectrum(...,'Prod',m): Enhances components that have harmonics
% located at multiples of range(s) m of the signal's fundamental
% frequency. Computed by compressing the signal by the list of
% factors m, and by multiplying all the results with the original
% signa (Alonso et al, 2003).
% mirspectrum(...,'Sum',s): Similar option using summation instead of
% product.
%
% mirspectrum(...,'MinRes',mr): Indicates a minimal accepted
% frequency resolution, in Hz. The audio signal is zero-padded in
% order to reach the desired resolution.
% If the 'Mel' option is toggled on, 'MinRes' is set by default
% to 66 Hz.
% mirspectrum(...,'MinRes',mr,'OctaveRatio',tol): Indicates the
% minimal accepted resolution in terms of number of divisions of
% the octave. Low frequencies are ignored in order to reach the
% desired resolution.
% The corresponding required frequency resolution is equal to
% the difference between the first frequency bins, multiplied
% by the constraining multiplicative factor tol (set by
% default to .75).
% mirspectrum(...,'Res',r): Indicates the required precise frequency
% resolution, in Hz. The audio signal is zero-padded in order to
% reach the desired resolution.
% mirspectrum(...,'Length',l): Specifies the length of the FFT,
% overriding the FFT length initially planned.
% mirspectrum(...,'ZeroPad',s): Zero-padding of s samples.
% mirspectrum(...,'WarningRes',s): Indicates a required frequency
% resolution, in Hz, for the input signal. If the resolution does
% not reach that prerequisite, a warning is displayed.
% mirspectrum(...,'ConstantQ',nb): Carries out a Constant Q Transform
% instead of a FFT, with a number of bins per octave fixed to nb.
% Default value for nb: 12 bins per octave.
%
% mirspectrum(...,'Smooth',o): smooths the envelope using a moving
% average of order o.
% Default value when the option is toggled on: o=10
% mirspectrum(...,'Gauss',o): smooths the envelope using a gaussian
% of standard deviation o samples.
% Default value when the option is toggled on: o=10
% mirspectrum(...,'TimeSmooth',o): smooths each frequency channel of
% a spectrogram using a moving average of order o.
% Default value when the option is toggled on: o=10
% mirspectrum(...,'Phase',0): do not compute the FFT phase.
win.key = 'Window';
win.type = 'String';
win.default = 'hamming';
option.win = win;
min.key = 'Min';
min.type = 'Integer';
min.default = 0;
option.min = min;
max.key = 'Max';
max.type = 'Integer';
max.default = Inf;
option.max = max;
mr.key = 'MinRes';
mr.type = 'Integer';
mr.default = 0;
option.mr = mr;
res.key = 'Res';
res.type = 'Integer';
res.default = NaN;
option.res = res;
length.key = 'Length';
length.type = 'Integer';
length.default = NaN;
option.length = length;
zp.key = 'ZeroPad';
zp.type = 'Integer';
zp.default = 0;
zp.keydefault = Inf;
option.zp = zp;
wr.key = 'WarningRes';
wr.type = 'Integer';
wr.default = 0;
option.wr = wr;
octave.key = 'OctaveRatio';
octave.type = 'Boolean';
octave.default = 0;
option.octave = octave;
constq.key = 'ConstantQ';
constq.type = 'Integer';
constq.default = 0;
constq.keydefault = 12;
option.constq = constq;
alongbands.key = 'AlongBands';
alongbands.type = 'Boolean';
alongbands.default = 0;
option.alongbands = alongbands;
ni.key = 'NormalInput';
ni.type = 'Boolean';
ni.default = 0;
option.ni = ni;
nl.key = 'NormalLength';
nl.type = 'Boolean';
nl.default = 0;
nl.when = 'After';
option.nl = nl;
norm.key = 'Normal';
norm.type = 'Integer';
norm.default = 0;
norm.keydefault = 1;
norm.when = 'After';
option.norm = norm;
band.type = 'String';
band.choice = {'Freq','Mel','Bark','Cents'};
band.default = 'Freq';
band.when = 'Both';
option.band = band;
nbbands.key = 'Bands';
nbbands.type = 'Integer';
nbbands.default = 0;
nbbands.when = 'After';
option.nbbands = nbbands;
mprod.key = 'Prod';
mprod.type = 'Integers';
mprod.default = [];
mprod.keydefault = 2:6;
mprod.when = 'After';
option.mprod = mprod;
msum.key = 'Sum';
msum.type = 'Integers';
msum.default = [];
msum.keydefault = 2:6;
msum.when = 'After';
option.msum = msum;
reso.key = 'Resonance';
reso.type = 'String';
reso.choice = {'ToiviainenSnyder','Fluctuation','Meter',0,'no','off'};
reso.default = 0;
reso.when = 'After';
option.reso = reso;
log.key = 'log';
log.type = 'Boolean';
log.default = 0;
log.when = 'After';
option.log = log;
db.key = 'dB';
db.type = 'Integer';
db.default = 0;
db.keydefault = Inf;
db.when = 'After';
option.db = db;
pow.key = 'Power';
pow.type = 'Boolean';
pow.default = 0;
pow.when = 'After';
option.pow = pow;
terhardt.key = 'Terhardt';
terhardt.type = 'Boolean';
terhardt.default = 0;
terhardt.when = 'After';
option.terhardt = terhardt;
mask.key = 'Mask';
mask.type = 'Boolean';
mask.default = 0;
mask.when = 'After';
option.mask = mask;
% e.key = 'Enhanced';
% e.type = 'Integer';
% e.default = [];
% e.keydefault = 2:10;
% e.when = 'After';
%option.e = e;
collapsed.key = 'Collapsed';
collapsed.type = 'Boolean';
collapsed.default = 0;
collapsed.when = 'Both';
option.collapsed = collapsed;
aver.key = 'Smooth';
aver.type = 'Integer';
aver.default = 0;
aver.keydefault = 10;
aver.when = 'After';
option.aver = aver;
gauss.key = 'Gauss';
gauss.type = 'Integer';
gauss.default = 0;
gauss.keydefault = 10;
gauss.when = 'After';
option.gauss = gauss;
timesmooth.key = 'TimeSmooth';
timesmooth.type = 'Integer';
timesmooth.default = 0;
timesmooth.keydefault = 10;
timesmooth.when = 'After';
option.timesmooth = timesmooth;
rapid.key = 'Rapid';
rapid.type = 'Boolean';
rapid.default = 0;
option.rapid = rapid;
phase.key = 'Phase';
phase.type = 'Boolean';
phase.default = 1;
option.phase = phase;
specif.option = option;
specif.defaultframelength = 0.05;
specif.defaultframehop = 0.5;
specif.eachchunk = @eachchunk;
specif.combinechunk = @combinechunk;
if isamir(orig,'mirscalar') || isamir(orig,'mirenvelope')
specif.nochunk = 1;
end
varargout = mirfunction(@mirspectrum,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type = 'mirspectrum';
function s = main(orig,option,postoption)
if isstruct(option)
if option.collapsed
option.band = 'Cents';
end
if isnan(option.res) && strcmpi(option.band,'Cents') && option.min
option.res = option.min *(2^(1/1200)-1)*.9;
end
end
if not(isempty(postoption))
if not(strcmpi(postoption.band,'Freq') && isempty(postoption.msum) ...
&& isempty(postoption.mprod)) ...
|| postoption.log || postoption.db ...
|| postoption.pow || postoption.mask || postoption.collapsed ...
|| postoption.aver || postoption.gauss
option.phase = 0;
end
end
if iscell(orig)
orig = orig{1};
end
if isa(orig,'mirspectrum') && ...
not(isfield(option,'alongbands') && option.alongbands)
s = orig;
if isfield(option,'min') && ...
(option.min || iscell(option.max) || option.max < Inf)
magn = get(s,'Magnitude');
freq = get(s,'Frequency');
for k = 1:length(magn)
m = magn{k};
f = freq{k};
if iscell(option.max)
mi = option.min{k};
ma = option.max{k};
else
mi = option.min;
ma = option.max;
end
if not(iscell(m))
m = {m};
f = {f};
end
for l = 1:length(m)
range = find(and(f{l}(:,1) >= mi,f{l}(:,1) <= ma));
m{l} = m{l}(range,:,:);
f{l} = f{l}(range,:,:);
end
magn{k} = m;
freq{k} = f;
end
s = set(s,'Magnitude',magn,'Frequency',freq);
end
if not(isempty(postoption)) && not(isequal(postoption,0))
s = post(s,postoption);
end
elseif ischar(orig)
s = mirspectrum(miraudio(orig),option,postoption);
else
if nargin == 0
orig = [];
end
s.phase = [];
s.log = 0;
s.xscale = 'Freq';
s.pow = 1;
s = class(s,'mirspectrum',mirdata(orig));
s = purgedata(s);
s = set(s,'Title','Spectrum','Abs','frequency (Hz)','Ord','magnitude');
%disp('Computing spectrum...')
sig = get(orig,'Data');
t = get(orig,'Pos');
if isempty(t)
t = get(orig,'FramePos');
for k = 1:length(sig)
for l = 1:length(sig{k})
sig{k}{l} = sig{k}{l}';
t{k}{l} = t{k}{l}(1,:,:)';
end
end
end
fs = get(orig,'Sampling');
fp = get(orig,'FramePos');
fr = get(orig,'FrameRate');
lg = get(orig,'Length');
m = cell(1,length(sig));
p = cell(1,length(sig));
f = cell(1,length(sig));
for i = 1:length(sig)
d = sig{i};
fpi = fp{i};
if not(iscell(d))
d = {d};
end
if option.alongbands
fsi = fr{i};
else
fsi = fs{i};
end
mi = cell(1,length(d));
phi = cell(1,length(d));
fi = cell(1,length(d));
for J = 1:length(d)
dj = d{J};
if option.ni
mxdj = repmat(max(dj),[size(dj,1),1,1]);
mndj = repmat(min(dj),[size(dj,1),1,1]);
dj = (dj-mndj)./(mxdj-mndj);
end
if option.alongbands
if size(dj,1)>1
error('ERROR IN MIRSPECTRUM: ''AlongBands'' is restricted to spectrum decomposed into bands, such as ''Mel'' and ''Bark''.')
end
dj = reshape(dj,[size(dj,2),1,size(dj,3)]);
fp{i}{J} = fp{i}{J}([1;end]);
lg{i}{J} = diff(fp{i}{J}) * fs{i};
end
if option.constq
% Constant Q Transform
r = 2^(1/option.constq);
Q = 1 / (r - 1);
f_max = min(fsi/2,option.max);
f_min = option.min;
if not(f_min)
f_min = 16.3516;
end
B = floor(log(f_max/f_min) / log(r)); % number of bins
N0 = round(Q*fsi/f_min); % maximum Nkcq
j2piQn = -1i*2*pi*Q*(0:N0-1)';
fj = f_min * r.^(0:B-1)';
transf = NaN(B,size(dj,2),size(dj,3));
for kcq = 1:B
Nkcq = round(Q*fsi/fj(kcq));
win = mirwindow(dj,option.win,Nkcq);
exq = repmat(exp(j2piQn(1:Nkcq)/Nkcq),...
[1,size(win,2),size(win,3)]);
transf(kcq,:) = sum(win.* exq) / Nkcq;
end
else
% FFT
dj = mirwindow(dj,option.win);
if option.zp
if option.zp < Inf
dj = [dj;zeros(option.zp,size(dj,2),size(dj,3))];
else
dj = [dj;zeros(size(dj))];
end
end
if isstruct(postoption)
if strcmpi(postoption.band,'Mel') && ...
(not(option.mr) || option.mr > 66)
option.mr = 66;
end
else
%warning('WARNING in MIRSPECTRUM (for debugging purposes): By default, minimum resolution specified.')
if not(option.mr)
option.mr = 66;
end
end
if option.octave
N = size(dj,1);
res = (2.^(1/option.mr)-1)*option.octave;
% Minimal freq ratio between 2 first bins.
% freq resolution should be > option.min * res
Nrec = fsi/(option.min*res);
% Recommended minimal sample length.
if Nrec > N
% If actual sample length is too small.
option.min = fsi/N / res;
warning('WARNING IN MIRSPECTRUM: The input signal is too short to obtain the desired octave resolution. Lowest frequencies will be ignored.');
display(['(The recommended minimal input signal length would be ' num2str(Nrec/fsi) ' s.)']);
display(['New low frequency range: ' num2str(option.min) ' Hz.']);
end
N = 2^nextpow2(N);
elseif isnan(option.length)
if isnan(option.res)
N = size(dj,1);
if option.mr && N < fsi/option.mr
if option.wr && N < fsi/option.wr
warning('WARNING IN MIRSPECTRUM: The input signal is too short to obtain the desired frequency resolution. Performed zero-padding will not guarantee the quality of the results.');
end
N = max(N,fsi/option.mr);
end
N = 2^nextpow2(N);
else
N = ceil(fsi/option.res);
end
else
N = option.length;
end
% Here is the spectrum computation itself
transf = fft(dj,N); %/(length(dj));
len = floor(N/2+1);
fj = fsi/2 * linspace(0,1,len)';
if option.max
maxf = find(fj>=option.max,1);
if isempty(maxf)
maxf = len;
end
else
maxf = len;
end
if option.min
minf = find(fj>=option.min,1);
if isempty(minf)
maxf = len;
end
else
minf = 1;
end
transf = transf(minf:maxf,:,:);
fj = fj(minf:maxf);
end
mi{J} = abs(transf);
if option.phase
phi{J} = angle(transf);
end
fi{J} = repmat(fj,[1,size(transf,2),size(transf,3)]);
end
if iscell(sig{i})
m{i} = mi;
p{i} = phi;
f{i} = fi;
else
m{i} = mi{1};
p{i} = phi{1};
f{i} = fi{1};
end
end
s = set(s,'Frequency',f,'Magnitude',m,'Phase',p,...
'FramePos',fp,'Length',lg);
if not(isempty(postoption)) && isstruct(postoption)
s = post(s,postoption,orig);
end
end
function s = post(s,option,orig)
if option.collapsed
option.band = 'Cents';
end
m = get(s,'Magnitude');
f = get(s,'Frequency');
sr = get(s,'Sampling');
for k = 1:length(m)
if not(iscell(m{k}))
m{k} = {m{k}};
f{k} = {f{k}};
end
end
if option.timesmooth
[state s] = gettmp(s);
B = ones(1,option.timesmooth)/option.timesmooth;
for h = 1:length(m)
for l = 1:length(m{k})
[m{h}{l} state] = filter(B,1,m{h}{l},state,2);
%mhl = m{h}{l};
%for i = 1:size(m{h}{l},2)
% m{h}{l}(:,i) = min(mhl(:,max(1,i-option.timesmooth+1):i),...
% [],2);
%end
end
end
s = settmp(s,state);
end
if get(s,'Power') == 1 && ...
(option.pow || any(option.mprod) || any(option.msum))
% mprod could be tried without power?
for h = 1:length(m)
for l = 1:length(m{k})
m{h}{l} = m{h}{l}.^2;
end
end
s = set(s,'Power',2,'Title',['Power ',get(s,'Title')],'Phase',[]);
end
if any(option.mprod)
for h = 1:length(m)
for l = 1:length(m{k})
z0 = m{h}{l};
z1 = z0;
for k = 1:length(option.mprod)
mpr = option.mprod(k);
if mpr
zi = ones(size(z0));
zi(1:floor(end/mpr),:,:) = z0(mpr:mpr:end,:,:);
z1 = z1.*zi;
end
end
m{h}{l} = z1;
end
end
s = set(s,'Title','Spectral product');
end
if any(option.msum)
for h = 1:length(m)
for l = 1:length(m{k})
z0 = m{h}{l};
z1 = z0;
for k = 1:length(option.msum)
mpr = option.msum(k);
if mpr
zi = ones(size(z0));
zi(1:floor(end/mpr),:,:) = z0(mpr:mpr:end,:,:);
z1 = z1+zi;
end
end
m{h}{l} = z1;
end
end
s = set(s,'Title','Spectral sum');
end
if option.norm
for k = 1:length(m)
for l = 1:length(m{k})
mkl = m{k}{l};
nkl = zeros(1,size(mkl,2),size(mkl,3));
for kk = 1:size(mkl,2)
for ll = 1:size(mkl,3)
nkl(1,kk,l) = norm(mkl(:,kk,ll));
end
end
m{k}{l} = mkl./repmat(nkl,[size(m{k}{k},1),1,1]);
end
end
end
if option.nl
lg = get(s,'Length');
for k = 1:length(m)
for l = 1:length(m{k})
m{k}{l} = m{k}{l}/(lg{k}{l}/sr{k});
end
end
end
if option.terhardt && not(isempty(find(f{1}{1}))) % This excludes the case where spectrum already along bands
% Code taken from Pampalk's MA Toolbox
for k = 1:length(m)
for l = 1:length(m{k})
W_Adb = zeros(size(f{k}{l}));
W_Adb(2:size(f{k}{l},1),:,:) = ...
+ 10.^((-3.64*(f{k}{l}(2:end,:,:)/1000).^-0.8 ...
+ 6.5 * exp(-0.6 * (f{k}{l}(2:end,:,:)/1000 - 3.3).^2) ...
- 0.001*(f{k}{l}(2:end,:,:)/1000).^4)/20);
W_Adb = W_Adb.^2;
m{k}{l} = m{k}{l}.*W_Adb;
end
end
end
if option.reso
if not(ischar(option.reso))
if strcmp(get(orig,'XScale'),'Mel')
option.reso = 'Fluctuation';
else
option.reso = 'ToiviainenSnyder';
end
end
for k = 1:length(m)
for l = 1:length(m{k})
if strcmpi(option.reso,'ToiviainenSnyder') || strcmpi(option.reso,'Meter')
w = max(0,...
1 - 0.25*(log2(max(1./max(f{k}{l},1e-12),1e-12)/0.5)).^2);
elseif strcmpi(option.reso,'Fluctuation')
w1 = f{k}{l} / 4; % ascending part of the fluctuation curve;
w2 = 1 - 0.3 * (f{k}{l} - 4)/6; % descending part; %%% Negative!
w = min(w1,w2);
w = max(0,w);
end
if max(w) == 0
warning('The resonance curve, not defined for this range of delays, will not be applied.')
else
m{k}{l} = m{k}{l}.* w;
end
end
end
end
if strcmp(s.xscale,'Freq')
if strcmpi(option.band,'Mel')
% The following is largely based on the source code from Auditory Toolbox
% (A part that I could not call directly from MIRtoolbox)
% (Malcolm Slaney, August 1993, (c) 1998 Interval Research Corporation)
try
MakeERBFilters(1,1,1); % Just to be sure that the Auditory Toolbox is installed
catch
error('ERROR IN MIRFILTERBANK: Auditory Toolbox needs to be installed.');
end
%disp('Computing Mel-frequency spectral representation...')
lowestFrequency = 133.3333;
if not(option.nbbands)
option.nbbands = 40;
end
linearFilters = min(13,option.nbbands);
linearSpacing = 66.66666666;
logFilters = option.nbbands - linearFilters;
logSpacing = 1.0711703;
totalFilters = option.nbbands;
% Figure the band edges. Interesting frequencies are spaced
% by linearSpacing for a while, then go logarithmic. First figure
% all the interesting frequencies. Lower, center, and upper band
% edges are all consequtive interesting frequencies.
freqs = lowestFrequency + (0:linearFilters-1)*linearSpacing;
freqs(linearFilters+1:totalFilters+2) = ...
freqs(linearFilters) * logSpacing.^(1:logFilters+2);
lower = freqs(1:totalFilters);
center = freqs(2:totalFilters+1);
upper = freqs(3:totalFilters+2);
% Figure out the height of the triangle so that each filter has
% unit weight, assuming a triangular weighting function.
triangleHeight = 2./(upper-lower);
e = cell(1,length(m));
nch = cell(1,length(m));
for h = 1:length(m)
e{h} = cell(1,length(m{h}));
for i = 1:length(m{h})
mi = m{h}{i};
fi = f{h}{i}(:,1,1);
fftSize = size(fi,1);
% We now want to combine FFT bins and figure out
% each frequencies contribution
mfccFilterWeights = zeros(totalFilters,fftSize);
for chan=1:totalFilters
mfccFilterWeights(chan,:) = triangleHeight(chan).*...
((fi > lower(chan) & fi <= center(chan)).* ...
(fi-lower(chan))/(center(chan)-lower(chan)) + ...
(fi > center(chan) & fi < upper(chan)).* ...
(upper(chan)-fi)/(upper(chan)-center(chan)));
end
if find(diff(not(sum(mfccFilterWeights,2)))==-1)
% If one channel has no weight whereas the higher one
% has a positive weight.
warning('WARNING in MIRSPECTRUM: The frequency resolution of the spectrum is too low for the Mel transformation. Some Mel components are undefined.')
display('Recommended frequency resolution: at least 66 Hz.')
end
e{h}{i} = zeros(1,size(mi,2),totalFilters);
for J = 1:size(mi,2)
if max(mi(:,J)) > 0
fftData = zeros(fftSize,1); % Zero-padding ?
fftData(1:size(mi,1)) = mi(:,J);
p = mfccFilterWeights * fftData + 1e-16;
e{h}{i}(1,J,:) = reshape(p,[1,1,length(p)]);
end
end
f{h}{i} = zeros(1,size(mi,2),totalFilters);
end
nch{h} = (1:totalFilters)';
end
m = e;
s = set(s,'XScale','Mel','Title','Mel-Spectrum',...
'Abs','Mel bands','Channels',nch,'Phase',[]);
elseif strcmpi(option.band,'Bark')
sr = get(s,'Sampling');
% Code taken from Pampalk's MA Toolbox.
%% zwicker & fastl: psychoacoustics 1999, page 159
bark_upper = [10 20 30 40 51 63 77 92 108 127 148 172 200 232 270 315 370 440 530 640 770 950 1200 1550]*10; %% Hz
e = cell(1,length(m));
nch = cell(1,length(m));
for h = 1:length(m)
%% ignore critical bands outside of sampling rate range
cb = min(min([find(bark_upper>sr{h}/2),length(bark_upper)]),length(bark_upper));
e{h} = cell(1,length(m{h}));
for i = 1:length(m{h})
mi = sum(m{h}{i},3);
e{h}{i} = zeros(1,size(mi,2),cb);
k = 1;
for J=1:cb, %% group into bark bands
idx = find(f{h}{i}(k:end,1,1)<=bark_upper(J));
idx = idx + k-1;
e{h}{i}(1,:,J) = sum(mi(idx,:,:),1);
k = max(idx)+1;
end
f{h}{i} = zeros(1,size(mi,2),cb);
end
nch{h} = (1:cb)';
end
m = e;
s = set(s,'XScale','Bark','Title','Bark-Spectrum',...
'Abs','Bark bands','Channels',nch,'Phase',[]);
elseif strcmpi(option.band,'Cents') || option.collapsed
for h = 1:length(m)
for i = 1:length(m{h})
mi = m{h}{i};
fi = f{h}{i};
isgood = fi(:,1,1)*(2^(1/1200)-1) >= fi(2,1,1)-fi(1,1,1);
good = find(isgood);
if isempty(good)
mirerror('mirspectrum',...
'The frequency resolution of the spectrum is too low to be decomposed into cents.');
display('Hint: if you specify a minimum value for the frequency range (''Min'' option)');
display(' and if you do not specify any frequency resolution (''Res'' option), ');
display(' the correct frequency resolution will be automatically chosen.');
end
if good>1
warning(['MIRSPECTRUM: Frequency resolution in cent is achieved for frequencies over ',...
num2str(floor(fi(good(1)))),' Hz. Lower frequencies are ignored.'])
display('Hint: if you specify a minimum value for the frequency range (''Min'' option)');
display(' and if you do not specify any frequency resolution (''Res'' option), ');
display(' the correct frequency resolution will be automatically chosen.');
end
fi = fi(good,:,:);
mi = mi(good,:,:);
f2cents = 440*2.^(1/1200*(-1200*10:1200*10-1)');
% The frequencies corresponding to the cent range
cents = repmat((0:1199)',[20,size(fi,2),size(fi,3)]);
% The cent range is for the moment centered to A440
octaves = ones(1200,1)*(-10:10);
octaves = repmat(octaves(:),[1,size(fi,2),size(fi,3)]);
select = find(f2cents>fi(1) & f2cents<fi(end));
% Cent range actually used in the current spectrum
f2cents = repmat(f2cents(select),[1,size(fi,2),size(fi,3)]);
cents = repmat(cents(select),[1,size(fi,2),size(fi,3)]);
octaves = repmat(octaves(select),[1,size(fi,2),size(fi,3)]);
m{h}{i} = interp1(fi(:,1,1),mi,f2cents(:,1,1));
f{h}{i} = octaves*1200+cents + 6900;
% Now the cent range is expressed in midicent
end
end
s = set(s,'Abs','pitch (in midicents)','XScale','Cents','Phase',[]);
end
end
if option.mask
if strcmp(s.xscale,'Freq')
warning('WARNING IN MIRSPECTRUM: ''Mask'' option available only for Mel-spectrum and Bark-spectrum.');
disp('''Mask'' option ignored');
else
nch = get(s,'Channels');
for h = 1:length(m)
% Code taken from Pampalk's MA Toolbox.
%% spreading function: schroeder et al., 1979, JASA, optimizing
%% digital speech coders by exploiting masking properties of the human ear
cb = length(nch{h}); % Number of bands.
for i = 1:cb,
spread(i,:) = 10.^((15.81+7.5*((i-(1:cb))+0.474)-17.5*(1+((i-(1:cb))+0.474).^2).^0.5)/10);
end
for i = 1:length(m{h})
for J = 1:size(m{h}{i},2)
mj = m{h}{i}(1,J,:);
mj = spread(1:length(mj),1:length(mj))*mj(:);
m{h}{i}(1,J,:) = reshape(mj,1,1,length(mj));
end
end
end
end
end
if option.collapsed
for h = 1:length(m)
for i = 1:length(m{h})
mi = m{h}{i};
fi = f{h}{i};
centclass = rem(fi(:,1,1),1200);
%neg = find(centclass<0);
%centclass(neg) = 1200 + centclass(neg);
m{h}{i} = NaN(1200,size(mi,2),size(mi,3));
for k = 0:1199
m{h}{i}(k+1,:,:) = sum(mi(find(centclass==k),:,:),1);
end
f{h}{i} = repmat((0:1199)',[1,size(fi,2),size(fi,3)]);
end
end
s = set(s,'Abs','Cents','XScale','Cents(Collapsed)');
end
if option.log || option.db
if not(isa(s,'mirspectrum') && s.log)
for k = 1:length(m)
if not(iscell(m{k}))
m{k} = {m{k}};
f{k} = {f{k}};
end
for l = 1:length(m{k})
m{k}{l} = log10(m{k}{l}+1e-16);
if option.db
m{k}{l} = 10*m{k}{l};
if get(s,'Power') == 1
m{k}{l} = m{k}{l}*2;
end
end
end
end
elseif isa(s,'mirspectrum') && option.db && s.log<10
for k = 1:length(m)
for l = 1:length(m{k})
m{k}{l} = 10*m{k}{l};
if get(s,'Power') == 1
m{k}{l} = m{k}{l}*2;
end
end
end
end
if option.db
s = set(s,'log',10);
s = set(s,'Power',2);
else
s = set(s,'log',1);
end
if option.db>0 && option.db < Inf
for k = 1:length(m)
for l = 1:length(m{k})
m{k}{l} = m{k}{l}-repmat(max(m{k}{l}),[size(m{k}{l},1) 1 1]);
m{k}{l} = option.db + max(-option.db,m{k}{l});
end
end
end
s = set(s,'Phase',[]);
end
if option.aver
for k = 1:length(m)
for i = 1:length(m{k})
m{k}{i} = filter(ones(1,option.aver)/option.aver,1,m{k}{i});
end
end
s = set(s,'Phase',[]);
end
if option.gauss
for k = 1:length(m)
for i = 1:length(m{k})
sigma = option.gauss;
gauss = 1/sigma/2/pi...
*exp(- (-4*sigma:4*sigma).^2 /2/sigma^2);
y = filter(gauss,1,[m{k}{i};zeros(4*sigma,size(m{k}{1},2))]);
y = y(4*sigma:end,:,:);
m{k}{i} = y(1:size(m{k}{i},1),:,:);
end
end
s = set(s,'Phase',[]);
end
s = set(s,'Magnitude',m,'Frequency',f);
function dj = mirwindow(dj,win,N)
if nargin<3
N = size(dj,1);
elseif size(dj,1)<N
dj(N,1,1) = 0;
end
if not(win == 0)
winf = str2func(win);
try
w = window(winf,N);
catch
if strcmpi(win,'hamming')
disp('Signal Processing Toolbox does not seem to be installed. Recompute the hamming window manually.');
w = 0.54 - 0.46 * cos(2*pi*(0:N-1)'/(N-1));
else
error(['ERROR in MIRSPECTRUM: Unknown windowing function ',win,' (maybe Signal Processing Toolbox is not installed).']);
end
end
kw = repmat(w,[1,size(dj,2),size(dj,3)]);
dj = dj(1:N,:,:).* kw;
end
function [y orig] = eachchunk(orig,option,missing,postchunk)
option.zp = option.zp+missing;
y = mirspectrum(orig,option);
function y = combinechunk(old,new)
doo = get(old,'Data');
doo = doo{1}{1};
dn = get(new,'Data');
dn = dn{1}{1};
y = set(old,'ChunkData',doo+dn);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirhisto.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirhisto/mirhisto.m
| 3,357 |
utf_8
|
6f716ac28bcb19a4355603f708ace933
|
function varargout = mirhisto(x,varargin)
% h = mirhisto(x) constructs the histogram from x. The elements of x are
% binned into equally spaced containers.
% Optional argument:
% mirhisto(...,'Number',n): specifies the number of containers.
% Default value : n = 10.
% mirhisto(...,'Ampli'): adds the amplitude of the elements,instead of
% simply counting then.
n.key = 'Number';
n.type = 'Integer';
n.default = 10;
option.n = n;
a.key = 'Ampli';
a.type = 'Boolean';
a.default = 0;
option.a = a;
specif.option = option;
varargout = mirfunction(@mirhisto,x,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type = 'mirhisto';
function h = main(x,option,postoption)
if iscell(x)
x = x{1};
end
d = get(x,'Data');
fp = get(x,'FramePos');
%disp('Computing histogram...')
ddd = cell(1,length(d));
bbb = cell(1,length(d));
for i = 1:length(d)
di = d{i}{1}; % To be generalized for segmented data
if iscell(di)
mx = -Inf;
mn = Inf;
nc = size(di,2);
for k = 1:nc
dk = di{k};
if size(dk,4) == 2
dk(end+1:end*2,:,:,1) = dk(:,:,:,2);
dk(:,:,:,2) = [];
end
mxk = max(dk);
mnk = min(dk);
if mxk > mx
mx = mxk;
end
if mnk < mn
mn = mnk;
end
end
if isinf(mx) || isinf(mx)
b = [];
dd = [];
else
dd = zeros(1,option.n);
if mn == mx
b(1,:) = mn-ceil(option.n/2) : mn+floor(option.n/2);
else
b(1,:) = mn : (mx-mn)/option.n : mx;
end
for k = 1:nc
dk = di{k};
for j = 1:option.n
found = find(and(dk>=b(1,j),dk<=b(1,j+1)));
if option.a
dd(1,j) = dd(1,j) + sum(dk(found));
else
dd(1,j) = dd(1,j) + length(found);
end
end
end
end
else
if isa(x,'mirscalar')
di = permute(di,[3 2 1]);
end
if size(di,4) == 2
di(end+1:end*2,:,:,1) = di(:,:,:,2);
di(:,:,:,2) = [];
end
nl = size(di,1);
nc = size(di,2);
np = size(di,3);
dd = zeros(1,option.n,np);
for l = 1:np
mx = max(max(di(:,:,l),[],1),[],2);
mn = min(min(di(:,:,l),[],1),[],2);
b(l,:) = mn:(mx-mn)/option.n:mx;
for k = 1:nc
dk = di(:,k,l);
for j = 1:option.n
found = (find(and(dk>=b(l,j),dk<=b(l,j+1))));
if option.a
dd(1,j,l) = dd(1,j,l) + sum(dk(found));
else
dd(1,j,l) = dd(1,j,l) + length(found);
end
end
end
end
end
ddd{i} = ipermute(dd,[3 2 1]);
bbb{i}(:,:,1) = b(:,1:end-1);
bbb{i}(:,:,2) = b(:,2:end);
fp{i} = {fp{i}{1}([1 end])'};
end
x = set(x,'FramePos',fp);
h = class(struct,'mirhisto',mirdata(x));
h = purgedata(h);
h = set(h,'Bins',bbb,'Weight',ddd);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirclassify.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirclassify/mirclassify.m
| 8,794 |
utf_8
|
2defa2037b7f54ef59ab70750bf85223
|
function c = mirclassify(x,varargin)
% Optional argument:
% mirclassify(...,'Nearest') uses the minimum distance strategy.
% (by default)
% mirclassify(...,'Nearest',k) uses the k-nearest-neighbour strategy.
% Default value: k = 1, corresponding to the minimum distance
% strategy.
% mirclassify(...,'GMM',ng) uses a gaussian mixture model. Each class is
% modeled by at most ng gaussians.
% Default value: ng = 1.
% Additionnally, the type of mixture model can be specified,
% using the set of value proposed in the gmm function: i.e.,
% 'spherical','diag','full' (default value) and 'ppca'.
% (cf. help gmm)
% Requires the Netlab toolbox.
if not(iscell(x))
x = {x};
end
n = get(x{1},'Name');
l = length(n); % Number of training samples
lab = cell(1,l);
allabs = struct;
for i = 1:l
sl = strfind(n{i},'/');
if isempty(sl)
mirerror('mirclassify','There should not be audio files in the main folder. They should be all in subfolders corresponding to the different classes.');
end
lab{i} = n{i}(1:sl(end)-1);
if i == 1
allabs.name = lab{1};
allabs.idx = 1;
continue
end
[test li] = ismember(lab{i},{allabs.name});
if test
allabs(li).idx(end+1) = i;
else
allabs(end+1).name = lab{i};
allabs(end).idx = i;
end
end
[k,ncentres,covartype,kmiter,emiter,d,mahl] = scanargin(varargin);
nfolds = length(allabs(1).idx);
v = []; % Preprocessed training vectors
mn = cell(1,length(x));
sd = cell(1,length(x));
for i = 1:length(x)
if isnumeric(x{i})
d = cell(1,size(x{i},2));
for j = 1:size(x{i},2)
d{j} = x{i}(:,j);
end
else
d = get(x{i},'Data');
end
[v mn{i} sd{i}] = integrate(v,d,l);
if 0 %isa(t{i},'scalar')
m = mode(x{i});
if not(isempty(m))
v = integrate(v,m,l);
end
end
end
mahl = cov(v');
for i = 1:length(allabs)
s = RandStream('mt19937ar','Seed','shuffle');
p = randperm(s,length(allabs(i).idx));
allabs(i).idx = allabs(i).idx(p);
end
for h = 1:nfolds
va = [];
vt = [];
idxh = zeros(1,length(allabs));
rlab = cell(1,length(allabs));
rlabt = cell(1,length(allabs)*(nfolds-1));
for i = 1:length(allabs)
idxi = allabs(i).idx;
va = [va v(:,allabs(i).idx(h))];
for j = (nfolds-1)*(i-1)+(1:nfolds-1)
rlabt{j} = allabs(i).name;
end
idxi(h) = [];
vt = [vt v(:,idxi)];
rlab{i} = allabs(i).name;
end
if k % k-Nearest Neighbour
c.nbparam = size(vt,2);
for l = 1:size(va,2)
[sv,idx] = sort(distance(va(:,l),vt,mahl));
labs = cell(0); % Class labels
founds = []; % Number of found elements in each class
for i = idx(1:k)
labi = rlabt{i};
found = 0;
for j = 1:length(labs)
if isequal(labi,labs{j})
found = j;
end
end
if found
founds(found) = founds(found)+1;
else
labs{end+1} = labi;
founds(end+1) = 1;
end
end
[b ib] = max(founds);
c.classes{h,l} = labs{ib};
end
elseif ncentres % Gaussian Mixture Model
labs = cell(0); % Class labels
founds = cell(0); % Elements associated to each label.
for i = 1:size(vt,2)
labi = rlabt{i};
found = 0;
for j = 1:length(labs)
if isequal(labi,labs{j})
founds{j}(end+1) = i;
found = 1;
end
end
if not(found)
labs{end+1} = labi;
founds{end+1} = i;
end
end
options = zeros(1, 18);
options(2:3) = 1e-4;
options(4) = 1e-6;
options(16) = 1e-8;
options(17) = 0.1;
options(1) = 0; %Prints out error values, -1 else
c.nbparam = 0;
OK = 0;
while not(OK)
OK = 1;
for i = 1:length(labs)
options(14) = kmiter;
try
mix{i} = gmm(size(vt,1),ncentres,covartype);
catch
error('ERROR IN CLASSIFY: Netlab toolbox not installed.');
end
mix{i} = netlabgmminit(mix{i},vt(:,founds{i})',options);
options(5) = 1;
options(14) = emiter;
try
mix{i} = gmmem(mix{i},vt(:,founds{i})',options);
c.nbparam = c.nbparam + ...
length(mix{i}.centres(:)) + length(mix{i}.covars(:));
catch
%err = lasterr;
%warning('WARNING IN CLASSIFY: Problem when calling GMMEM:');
%disp(err);
%disp('Let us try again...');
OK = 0;
end
end
end
pr = zeros(size(va,2),length(labs));
for i = 1:length(labs)
prior = length(founds{i})/size(vt,2);
pr(:,i) = prior * gmmprob(mix{i},va');
%c.post{i} = gmmpost(mix{i},va');
end
[mm ib] = max(pr');
for i = 1:size(va,2)
c.classes{h,i} = labs{ib(i)};
end
end
if isempty(rlab)
c.correct = NaN;
else
correct = 0;
for i = 1:length(rlab)
if isequal(c.classes{h,i},rlab{i})
correct = correct + 1;
end
end
c.correct(h) = correct / length(rlab);
end
end
c = class(c,'mirclassify');
disp('Expected classes:')
rlab
disp('Classification results:')
c.classes
if isnan(c.correct)
disp('No label has been associated to the test set. Correct classification rate cannot be computed.');
else
disp(['Correct classification rate: ',num2str(mean(c.correct),2)]);
end
figure
if k
v0 = zeros(size(v,1),0);
for i = 1:length(allabs)
v0 = [v0 v(:,allabs(i).idx)];
end
ltick = nfolds;
elseif ncentres
v0 = zeros(size(v,1),length(mix)*ncentres);
for i = 1:length(mix)
for j = 1:ncentres
v0(:,ncentres*(i-1)+j) = mix{i}.centres(j,:)';
end
end
ltick = ncentres;
end
imagesc(v0)
set(gca,'XTick',ltick+.5:ltick:size(v,2)+.5)
set(gca,'XTickLabel','')
set(gca,'YTick',[])
grid on
for i = 1:length(allabs)
text(ltick*(i-1)+1,1,allabs(i).name);
end
function [vt m s] = integrate(vt,v,lvt,m,s)
% lvt is the number of samples
vtl = [];
for l = 1:lvt
vl = v{l};
if iscell(vl)
vl = vl{1};
end
if iscell(vl)
vl = vl{1};
end
if size(vl,2) > 1
mirerror('MIRCLASSIFY','The analytic features guiding the classification should not be frame-decomposed.');
end
vtl(:,l) = vl;
end
if nargin<4
m = mean(vtl,2);
s = std(vtl,0,2);
end
dnom = repmat(s,[1 size(vtl,2)]);
dnom = dnom + (dnom == 0); % In order to avoid division by 0
vtl = (vtl - repmat(m,[1 size(vtl,2)])) ./ dnom;
vt(end+1:end+size(vtl,1),:) = vtl;
function [k,ncentres,covartype,kmiter,emiter,d,mahl] = scanargin(v)
k = 1;
d = 0;
i = 1;
ncentres = 0;
covartype = 'full';
kmiter = 10;
emiter = 100;
mahl = 1;
while i <= length(v)
arg = v{i};
if ischar(arg) && strcmpi(arg,'Nearest')
k = 1;
if length(v)>i && isnumeric(v{i+1})
i = i+1;
k = v{i};
end
elseif ischar(arg) && strcmpi(arg,'GMM')
k = 0;
ncentres = 1;
if length(v)>i
if isnumeric(v{i+1})
i = i+1;
ncentres = v{i};
if length(v)>i && ischar(v{i+1})
i = i+1;
covartype = v{i};
end
elseif ischar(v{i+1})
i = i+1;
covartype = v{i};
if length(v)>i && isnumeric(v{i+1})
i = i+1;
ncentres = v{i};
end
end
end
elseif isnumeric(arg)
k = v{i};
else
error('ERROR IN MIRCLASSIFY: Syntax error. See help mirclassify.');
end
i = i+1;
end
function y = distance(a,t,mahl)
for i = 1:size(t,2)
if det(mahl) > 0 % more generally, uses cond
lham = inv(mahl);
else
lham = pinv(mahl);
end
y(i) = sqrt((a - t(:,i))'*lham*(a - t(:,i)));
end
%y = sqrt(sum(repmat(a,[1,size(t,2)])-t,1).^2);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirkeystrength.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirkeystrength/mirkeystrength.m
| 3,445 |
utf_8
|
25ad1c532626e1a1e817061b825a234b
|
function varargout = mirkeystrength(orig,varargin)
% ks = mirkeystrength(x) computes the key strength, i.e., the probability
% associated with each possible key candidate.
% Optional parameters:
% mirkeystrength(...,'Frame',l,h) orders a frame decomposition of window
% length l (in seconds) and hop factor h, expressed relatively to
% the window length. For instance h = 1 indicates no overlap.
% Default values: l = 1 seconds and h = .5
% The mirchromagram options 'Weight' and 'Triangle' can be specified.
% [ks,c] = mirkeystrength(...) also displays the chromagram used for the key
% strength estimation.
%
% Krumhansl, Cognitive foundations of musical pitch. Oxford UP, 1990.
% Gomez, Tonal description of polyphonic audio for music content processing,
% INFORMS Journal on Computing, 18-3, pp. 294-304, 2006.
wth.key = 'Weight';
wth.type = 'Integer';
wth.default = .5;
option.wth = wth;
tri.key = 'Triangle';
tri.type = 'Boolean';
tri.default = 0;
option.tri = tri;
transp.key = 'Transpose';
transp.type = 'Integer';
transp.default = 0;
transp.when = 'After';
option.transp = transp;
specif.option = option;
specif.defaultframelength = .1;
specif.defaultframehop = .125;
varargout = mirfunction(@mirkeystrength,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
if not(isamir(x,'mirkeystrength'))
if not(isamir(x,'mirchromagram'))
x = mirchromagram(x,'Weight',option.wth,'Triangle',option.tri,'Normal');
else
x = mirchromagram(x,'Wrap','Normal');
end
end
type = 'mirkeystrength';
function k = main(orig,option,postoption)
if iscell(orig)
orig = orig{1};
end
if isa(orig,'mirkeystrength')
c = [];
k = orig;
else
c = orig;
load gomezprofs;
m = get(c,'Magnitude');
st = cell(1,length(m));
kk = cell(1,length(m));
%disp('Computing key strengths...')
for i = 1:length(m)
mi = m{i};
if not(iscell(mi))
mi = {mi};
end
si = cell(1,length(mi));
ki = cell(1,length(mi));
for j = 1:length(mi)
mj = mi{j};
sj = zeros(12,size(mj,2),size(mj,3),2);
kj = cell(12,size(mj,2),size(mj,3));
for k = 1:size(mj,2)
for l = 1:size(mj,3)
if ~max(abs(mj(:,k,l)))
sj(:,k,l,:) = 0;
else
tmp = corrcoef([mj(:,k,l) gomezprofs']);
sj(:,k,l,1) = tmp(1,2:13);
sj(:,k,l,2) = tmp(1,14:25);
end
kj(:,k,l) = {'C','C#','D','D#','E','F','F#','G','G#','A','A#','B'};
end
end
si{j} = sj;
ki{j} = kj;
end
st{i} = si;
kk{i} = ki;
end
k = class(struct,'mirkeystrength',mirdata(c));
k = purgedata(k);
k = set(k,'Title','Key strength','Abs','tonal center','Ord','strength',...
'Tonic',kk,'Strength',st,'MultiData',{'maj','min'},'Interpolable',0);
end
k = after(k,postoption);
k = {k c};
function k = after(k,postoption)
if postoption.transp
transp = mod(postoption.transp,12);
k = purgedata(k);
d = mirgetdata(k);
d = [d(13-transp:end,:,:,:);d(1:12-transp,:,:,:)];
k = set(k,'Data',{{d}});
end
|
github
|
martinarielhartmann/mirtooloct-master
|
mircepstrum.m
|
.m
|
mirtooloct-master/MIRToolbox/@mircepstrum/mircepstrum.m
| 4,674 |
utf_8
|
ce23ede2483821abfd1017cf8cdf0265
|
function varargout = mircepstrum(orig,varargin)
% s = mircepstrum(x) computes the cepstrum, which indicates
% periodicities, and is used for instance for pitch detection.
% x can be either a spectrum, an audio signal, or the name of an audio file.
% Optional parameter:
% mircepstrum(...,'Min',min) specifies the lowest delay taken into
% consideration, in seconds.
% Default value: 0.0002 s (corresponding to a maximum frequency of
% 5 kHz).
% mircepstrum(...,'Max',max) specifies the highest delay taken into
% consideration, in seconds.
% Default value: 0.05 s (corresponding to a minimum frequency of
% 20 Hz).
% mircepstrum(...,'Freq') represents the cepstrum in the frequency
% domain.
mi.key = 'Min';
mi.type = 'Integer';
mi.default = 0.0002;
mi.unit = {'s','Hz'};
mi.defaultunit = 's';
mi.opposite = 'ma';
option.mi = mi;
ma.key = 'Max';
ma.type = 'Integer';
ma.default = .05;
ma.unit = {'s','Hz'};
ma.defaultunit = 's';
ma.opposite = 'mi';
option.ma = ma;
fr.key = 'Freq';
fr.type = 'Boolean';
fr.default = 0;
option.fr = fr;
complex.key = 'Complex';
complex.type = 'Boolean';
complex.default = 0;
option.complex = complex;
specif.option = option;
specif.defaultframelength = 0.05;
specif.defaultframehop = 0.5;
varargout = mirfunction(@mircepstrum,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
if not(isamir(x,'mircepstrum'))
x = mirspectrum(x);
end
type = 'mircepstrum';
function c = main(orig,option,postoption)
if iscell(orig)
orig = orig{1};
end
c.phase = [];
if isa(orig,'mircepstrum')
c.freq = orig.freq;
else
c.freq = 0;
end
c = class(c,'mircepstrum',mirdata(orig));
c = purgedata(c);
c = set(c,'Title','Cepstrum','Abs','quefrency (s)','Ord','magnitude');
if isa(orig,'mircepstrum')
if option.ma < Inf || option.mi > 0 || get(orig,'FreqDomain')
mag = get(orig,'Magnitude');
pha = get(orig,'Phase');
que = get(orig,'Quefrency');
for h = 1:length(mag)
for k = 1:length(mag{h})
if get(orig,'FreqDomain')
mag{h}{k} = flipud(mag{h}{k});
que{h}{k} = flipud(1./que{h}{k});
pha{h}{k} = flipud(pha{h}{k});
end
range = find(que{h}{k}(:,1,1) <= option.ma & ...
que{h}{k}(:,1,1) >= option.mi);
mag{h}{k} = mag{h}{k}(range,:,:);
pha{h}{k} = pha{h}{k}(range,:,:);
que{h}{k} = que{h}{k}(range,:,:);
end
end
c = set(c,'Magnitude',mag,'Phase',pha,'Quefrency',que,'FreqDomain',0);
end
c = modif(c,option);
elseif isa(orig,'mirspectrum')
mag = get(orig,'Magnitude');
pha = get(orig,'Phase');
f = get(orig,'Sampling');
q = cell(1,length(mag));
for h = 1:length(mag)
len = ceil(option.ma*f{h});
start = ceil(option.mi*f{h})+1;
q{h} = cell(1,length(mag{h}));
for k = 1:length(mag{h})
m = mag{h}{k}.*exp(1i*pha{h}{k});
m = [m(1:end-1,:) ; conj(flipud(m))]; % Reconstitution of the complete abs(FFT)
if not(option.complex)
m = abs(m);
end
m = log(m + 1e-12);
c0=fft(m);
q0=repmat((0:(size(c0,1)-1))'/f{k},[1,size(m,2),size(m,3)]);
len = min(len,floor(size(c0,1)/2));
mag{h}{k} = abs(c0(start:len,:,:));
if option.complex
pha{h}{k} = unwrap(angle(c0(start:len,:,:)));
else
pha{h}{k} = nan(size(c0(start:len,:,:)));
end
q{h}{k} = q0(start:len,:,:);
end
end
c = set(c,'Magnitude',mag,'Phase',pha,'Quefrency',q);
c = modif(c,option);
end
function c = modif(c,option)
mag = get(c,'Magnitude');
que = get(c,'Quefrency');
if option.fr && not(get(c,'FreqDomain'))
for k = 1:length(mag)
for l = 1:length(mag{k})
m = mag{k}{l};
q = que{k}{l};
if not(isempty(m))
if q(1,1) == 0
m = m(2:end,:,:);
q = q(2:end,:,:);
end
m = flipud(m);
q = flipud(1./q);
end
mag{k}{l} = m;
que{k}{l} = q;
end
end
c = set(c,'FreqDomain',1,'Abs','frequency (Hz)');
end
c = set(c,'Magnitude',mag,'Quefrency',que,'Freq');
|
github
|
martinarielhartmann/mirtooloct-master
|
mirpartial.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirpartial/mirpartial.m
| 1,009 |
utf_8
|
2a4f8c2bb5ed2f391f6fe7eab0719176
|
function varargout = mirpartial(orig,varargin)
max.key = 'Max';
max.type = 'Integer';
max.default = Inf;
option.max = max;
specif.option = option;
varargout = mirfunction(@mirpartial,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type = 'mirpartial';
function p = main(orig,option,postoption)
a = get(orig,'Amplitude');
p = class(struct,'mirpartial',mirdata(orig));
p = purgedata(p);
%fp = get(orig,'FramePos');
pos = cell(1,length(a));
dat = cell(1,length(a));
tp = cell(1,length(a));
for i = 1:length(a)
pos{i} = cell(1,length(a{i}));
dat{i} = cell(1,length(a{i}));
for j = 1:length(a{i})
sizj = min(size(a{i}{j}{1},1),option.max);
dat{i}{j} = a{i}{j}{1}(1:sizj,:);
pos{i}{j} = repmat((1:sizj)',[1 size(dat{i}{j},2)]);
end
end
p = set(p,'Title','Energy along partials',...
'Abs','partials','Ord','energy','Data',dat,'Pos',pos,...
'TrackPos',tp,'TrackVal',tp);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirplay.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirmidi/mirplay.m
| 523 |
utf_8
|
278bd0f7f6429d6298d1a5e2583f7110
|
function varargout = mirplay(a,varargin)
% mirplay method for mirmidi objects.
specif.option = struct;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,a,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = main(a,option,postoption)
if iscell(a)
a = a{1};
end
d = get(a,'Data');
n = get(a,'Name');
for k = 1:length(d)
display(['Playing analysis of file: ' n{k}])
playmidi(d{k});
end
noargout = {};
|
github
|
martinarielhartmann/mirtooloct-master
|
mirmidi.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirmidi/mirmidi.m
| 3,166 |
utf_8
|
0a67555741f7166d9cdf9af228766d2d
|
function varargout = mirmidi(orig,varargin)
% m = mirmidi(x) converts into a MIDI sequence.
% Option associated to mirpitch function can be specified:
% 'Contrast' with default value c = .3
thr.key = 'Contrast';
thr.type = 'Integer';
thr.default = .3;
option.thr = thr;
mono.key = 'Mono';
mono.type = 'Boolean';
mono.default = 1;
option.mono = mono;
release.key = {'Release','Releases'};
release.type = 'String';
release.choice = {'Olivier','Valeri',0,'no','off'};
release.default = 'Valeri';
option.release = release;
specif.option = option;
varargout = mirfunction(@mirmidi,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
try
hz2midi(440);
catch
mirerror('MIRMIDI','MIDItoolbox does not seem to be installed.');
end
if not(isamir(x,'mirmidi')) && not(isamir(x,'mirpitch'))
if isa(x,'mirdesign') && not(option.mono)
x = set(x,'SeparateChannels',1);
end
o = mironsets(x,'Attacks','Releases',option.release);
x = {o x};
end
type = 'mirmidi';
function m = main(x,option,postoption)
transcript = 0;
if iscell(x)
o = x{1};
doo = get(o,'PeakVal');
da = get(o,'AttackPosUnit');
dr = get(o,'ReleasePosUnit');
a = x{2};
s = mirsegment(a,o);
x = mirpitch(s,'Contrast',option.thr,'Sum',0);
% x = mircentroid(s);
dp = get(x,'Data');
else
doo = [];
if isa(x,'mirpitch')
da = get(x,'Start');
dr = get(x,'End');
dp = get(x,'Degrees');
if isempty(da)
dp = get(x,'Data');
else
transcript = 1;
end
else
da = get(x,'AttackPosUnit');
dr = get(x,'ReleasePosUnit');
end
end
df = get(x,'FramePos');
nmat = cell(1,length(dp));
if transcript
for i = 1:length(dp)
nmat{i} = zeros(length(dp{i}{1}{1}),7);
for j = 1:length(dp{i}{1}{1})
t = df{i}{1}(1,da{i}{1}{1}(j));
d = df{i}{1}(2,dr{i}{1}{1}(j) )- t;
v = 120;
p = dp{i}{1}{1}(j) + 62;
nmat{i}(j,:) = [t d 1 p v t d];
end
end
else
for i = 1:length(dp)
nmat{i} = [];
if isempty(doo)
first = 1;
else
first = 2;
end
for j = first:length(dp{i})
if isempty(doo)
tij = df{i}{j}(1);
dij = df{i}{j}(2)- tij;
vij = 120;
else
tij = da{i}{1}{1}(j-1);
if isempty(dr{i})
dij = 0;
else
dij = dr{i}{1}{1}(j-1) - tij;
end
vij = round(doo{i}{1}{1}(j-1)/max(doo{i}{1}{1})*120);
end
for k = 1:size(dp{i}{j},3)
for l = 1:size(dp{i}{j},2)
for n = 1:length(dp{i}{j}{1,l,k})
f = dp{i}{j}{1,l,k}(n);
p = round(hz2midi(f));
nmat{i} = [nmat{i}; tij dij 1 p vij tij dij];
end
end
end
end
end
end
m = class(struct,'mirmidi',mirdata(x));
m = set(m,'Data',nmat);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirsave.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirmidi/mirsave.m
| 2,242 |
utf_8
|
058b8881d0014a70aa350bbd21fcb9fe
|
function mirsave(m,f)
ext = 0; % Specified new extension
if nargin == 1
f = '.mir';
elseif length(f)>3 && strcmpi(f(end-3:end),'.mid')
ext = '.mid';
if length(f)==4
f = '.mir';
end
elseif length(f)>2 && strcmpi(f(end-2:end),'.ly')
ext = '.ly';
if length(f)==3
f = '.mir';
end
end
nmat = get(m,'Data');
n = get(m,'Name');
nf = length(nmat);
for k = 1:nf
nk = n{k};
if nf>1 || strcmp(f(1),'.')
nk = [nk f];
else
nk = f;
end
if not(ischar(ext)) || strcmp(ext,'.mid')
if length(n)<4 || not(strcmpi(n(end-3:end),'.mid'))
n = [n '.mid'];
end
%writemidi(nmat{k},nk,120,60);
nmat2midi(nmat{k},nk);
elseif strcmp(ext,'.ly')
if length(n)<3 || not(strcmpi(n(end-2:end),'.ly '))
n = [n '.ly'];
end
lywrite(nmat{k},nk);
end
disp([nk,' saved.']);
end
function lywrite(nmat,filename)
fid = fopen(filename,'wt');
v = ver('MIRtoolbox');
fprintf(fid,['% LilyPond score automatically generated using MIRtoolbox version ' v.Version]);
fprintf(fid,'\\new Score \\with {\\override TimeSignature #''transparent = ##t} \n');
fprintf(fid,'\\relative c'' { \n');
fprintf(fid,'\\cadenzaOn \n');
for i = 1:size(nmat,1)
switch mod(nmat(i,4)+2,12)
case 0
p = 'c';
case 1
p = 'cis';
case 2
p = 'd';
case 3
p = 'dis';
case 4
p = 'e';
case 5
p = 'f';
case 6
p = 'fis';
case 7
p = 'g';
case 8
p = 'gis';
case 9
p = 'a';
case 10
p = 'ais';
case 11
p = 'b';
end
if ~mod(i,15)
fprintf(fid,'\\bar ""\n');
end
if i>1
doo = round((nmat(i,4)-nmat(i-1,4))/12);
if doo>0
for j = 1:doo
p = [p ''''];
end
elseif doo<0
for j = 1:-doo
p = [p ','];
end
end
end
du = nmat(i,2);
if du < .2
fprintf(fid,[p '16 ']);
else
fprintf(fid,[p '8 ']);
end
end
fprintf(fid,'\n } \n \n \\version "2.14.2"');
|
github
|
martinarielhartmann/mirtooloct-master
|
mirtonalcentroid.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirtonalcentroid/mirtonalcentroid.m
| 2,827 |
utf_8
|
ea99af0926744591d9cf85297a10c9d2
|
function varargout = mirtonalcentroid(orig,varargin)
% c = mirtonalcentroid(x) calculates the 6-dimensional tonal centroid
% vector from the chromagram.
% It corresponds to a projection of the chords along circles of fifths,
% of minor thirds, and of major thirds.
% [c ch] = mirtonalcentroid(x) also returns the intermediate chromagram.
%
% C. A. Harte and M. B. Sandler, Detecting harmonic change in musical
% audio, in Proceedings of Audio and Music Computing for Multimedia
% Workshop, Santa Barbara, CA, 2006.
frame.key = 'Frame';
frame.type = 'Integer';
frame.number = 2;
frame.default = [0 0];
frame.keydefault = [.743 .1];
option.frame = frame;
specif.option = option;
varargout = mirfunction(@mirtonalcentroid,orig,varargin,nargout,specif,@init,@main);
function [c type] = init(orig,option)
if option.frame.length.val
c = mirchromagram(orig,'Frame',option.frame.length.val,...
option.frame.length.unit,...
option.frame.hop.val,...
option.frame.hop.unit,...
option.frame.phase.val,...
option.frame.phase.unit,...
option.frame.phase.atend);
else
c = mirchromagram(orig);
end
type = 'mirtonalcentroid';
function tc = main(ch,option,postoption)
if iscell(ch)
ch = ch{1};
end
if isa(ch,'mirtonalcentroid')
tc = orig;
ch = [];
else
x1 = sin(pi*7*(0:11)/6)';
y1 = cos(pi*7*(0:11)/6)';
% minor thirds circle
x2 = sin(pi*3*(0:11)/2)';
y2 = cos(pi*3*(0:11)/2)';
% major thirds circle
x3 = 0.5 * sin(pi*2*(0:11)/3)';
y3 = 0.5 * cos(pi*2*(0:11)/3)';
c = [x1 y1 x2 y2 x3 y3];
c = c';
tc = class(struct,'mirtonalcentroid',mirdata(ch));
tc = purgedata(tc);
tc = set(tc,'Title','Tonal centroid','Abs','dimensions','Ord','position');
m = get(ch,'Magnitude');
%disp('Computing tonal centroid...')
n = cell(1,length(m)); % The final structured list of magnitudes.
d = cell(1,length(m)); % The final structured list of centroid dimensions.
for i = 1:length(m)
mi = m{i};
if not(iscell(mi))
mi = {mi};
end
ni = cell(1,length(mi)); % The list of magnitudes.
di = cell(1,length(mi)); % The list of centroid dimensions.
for j = 1:length(mi)
mj = mi{j};
ni{j} = zeros(6,size(mj,2),size(mi,3));
for k = 1:size(mj,3)
ni{j}(:,:,k) = c * mj(:,:,k);
end
di{j} = repmat((1:6)',[1,size(mj,2),size(mi,3)]);
end
n{i} = ni;
d{i} = di;
end
tc = set(tc,'Positions',n,'Dimensions',d);
end
tc = {tc,ch};
|
github
|
martinarielhartmann/mirtooloct-master
|
mirplay.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirsimatrix/mirplay.m
| 2,286 |
utf_8
|
50e71f123737e04a5b795409d5de3af3
|
function mirplay(e,varargin)
% mirplay method for mirsimatrix objects.
specif.option = struct;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,e,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = main(m,option,postoption)
d = get(m,'Data');
n = get(m,'Name');
fp = get(m,'FramePos');
w = get(m,'Warp');
figure
fp1 = cell2mat(fp{1});
fp2 = cell2mat(fp{2});
fp1m = (fp1(1,:,:,:)+fp1(2,:,:,:))/2;
fp2m = (fp2(1,:,:,:)+fp2(2,:,:,:))/2;
imagesc(fp2m,fp1m,d{1}{1});
hold on
paths = w{1};
bests = w{2};
bestsindex = w{3};
[i j] = find(bests);
mirverbose(0)
for k = 1:length(i)
best = bests(i(k),j(k));
bestindex = bestsindex(i(k),j(k));
path = paths{real(best)+1,imag(best)+1}{bestindex};
if path(2,end) - path(2,1) > 10 && path(1,end) - path(1,1) > 10
if 1
for h = 1:size(path,2)
plot(fp2m(path(2,h)),fp1m(path(1,h)),'k+')
end
drawnow
pause
for h = 1:size(path,2)
plot(fp2m(path(2,h)),fp1m(path(1,h)),'w+')
end
%a = miraudio(n{1},'Extract',fp1(1,path(1,1)),...
% fp1(2,path(1,end)));
%b = miraudio(n{1},'Extract',fp2(1,path(2,1)),...
% fp2(2,path(2,end)));
%mirplay(a);
%mirplay(b);
else
d = 1;
h = 1;
while h < size(path,2)
chge = find(path(d,h+1:end) > path(d,h),1);
if isempty(chge)
hh = h:size(path,2);
else
hh = h:h+chge-1;
end
plot(fp2m(path(2,hh)),fp1m(path(1,hh)),'k+');
drawnow
a = miraudio(n{1},'Extract',fp1(1,path(1,hh(1))),...
fp1(2,path(1,hh(end))));
b = miraudio(n{1},'Extract',fp2(1,path(2,hh(1))),...
fp2(2,path(2,hh(end))));
mirplay(a);
mirplay(b);
h = hh(end)+1;
d = 3-d;
end
end
end
end
noargout = {};
|
github
|
martinarielhartmann/mirtooloct-master
|
mirsimatrix.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirsimatrix/mirsimatrix.m
| 25,398 |
utf_8
|
ed638b58e2097bd95838bdda1b1b83c5
|
function varargout = mirsimatrix(orig,varargin)
% m = mirsimatrix(x) computes the similarity matrix resulting from the
% mutual comparison between each possible frame analysis in x.
% By default, x is the spectrum of the frame decomposition.
% But it can be any other frame analysis.
% Optional argument:
% mirsimatrix(...,'Distance',f) specifies the name of a distance
% function, from those proposed in the Statistics Toolbox
% (help pdist).
% default value: f = 'cosine'
% mirsimatrix(...,'Dissimilarity') return the dissimilarity matrix,
% which is the intermediary result before the computation of the
% actual similarity matrix. It shows the distance between each
% possible frame analysis in x.
% mirsimatrix(...,'Similarity',f) indicates the function f specifying
% the way the distance values in the dissimilarity matrix are
% transformed into similarity values.
% Possible values:
% f = 'oneminus' (default value)
% corresponding to f(x) = 1-x
% f = 'exponential'
% corresponding to f(x)= exp(-x)
% mirsimatrix(...,'Width',w) or more simply dissimatrix(...,w)
% specifies the size of the diagonal bandwidth, in samples,
% outside which the dissimilarity will not be computed.
% if w = inf (default value), all the matrix will be computed.
% mirsimatrix(...,'Horizontal') rotates the matrix 45 degrees in order to
% make the first diagonal horizontal, and to restrict on the
% diagonal bandwidth only.
% mirsimatrix(...,'TimeLag') transforms the (non-rotated) matrix into
% a time-lag matrix, making the first diagonal horizontal as well
% (corresponding to the zero-lag line).
%
% Foote, J. & Cooper, M. (2003). Media Segmentation using Self-Similarity
% Decomposition,. In Proc. SPIE Storage and Retrieval for Multimedia
% Databases, Vol. 5021, pp. 167-75.
% mirsimatrix(...,'Filter',10) filter along the diagonal of the matrix,
% using a uniform moving average filter of size f. The result is
% represented in a time-lag matrix.
distance.key = 'Distance';
distance.type = 'String';
distance.default = 'cosine';
option.distance = distance;
simf.key = 'Similarity';
simf.type = 'String';
simf.default = 'oneminus';
simf.keydefault = 'Similarity';
simf.when = 'After';
option.simf = simf;
dissim.key = 'Dissimilarity';
dissim.type = 'Boolean';
dissim.default = 0;
option.dissim = dissim;
K.key = 'Width';
K.type = 'Integer';
K.default = Inf;
option.K = K;
filt.key = 'Filter';
filt.type = 'Integer';
filt.default = 0;
filt.when = 'After';
option.filt = filt;
view.type = 'String';
view.default = 'Standard';
view.choice = {'Standard','Horizontal','TimeLag'};
view.when = 'After';
option.view = view;
half.key = 'Half';
half.type = 'Boolean';
half.default = 0;
half.when = 'Both';
option.half = half;
warp.key = 'Warp';
warp.type = 'Integer';
warp.default = 0;
warp.keydefault = .5;
warp.when = 'After';
option.warp = warp;
cluster.key = 'Cluster';
cluster.type = 'Boolean';
cluster.default = 0;
cluster.when = 'After';
option.cluster = cluster;
arg2.position = 2;
arg2.default = [];
option.arg2 = arg2;
frame.key = 'Frame';
frame.type = 'Integer';
frame.number = 2;
frame.default = [.05 1];
option.frame = frame;
rate.type = 'Integer';
rate.position = 2;
rate.default = 20;
option.rate = rate;
specif.option = option;
specif.nochunk = 1;
varargout = mirfunction(@mirsimatrix,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
if isnumeric(x)
m.diagwidth = Inf;
m.view = 's';
m.half = 0;
m.similarity = NaN;
m.graph = {};
m.branch = {};
m.warp = [];
m.clusters = [];
m = class(m,'mirsimatrix',mirdata);
m = set(m,'Title','Dissimilarity matrix');
fp = repmat(((1:size(x,1))-.5)/option.rate,[2,1]);
x = set(m,'Data',{{x}},'Pos',[],...
'FramePos',{{fp}},'Name',{inputname(1)});
else
if not(isamir(x,'mirsimatrix'))
if isamir(x,'miraudio')
if isframed(x)
x = mirspectrum(x);
else
x = mirspectrum(x,'Frame',option.frame.length.val,option.frame.length.unit,...
option.frame.hop.val,option.frame.hop.unit,...
option.frame.phase.val,option.frame.phase.unit);
end
end
end
end
type = 'mirsimatrix';
function m = main(orig,option,postoption)
if postoption.filt
postoption.view = 'TimeLag';
end
if iscell(orig)
orig = orig{1};
end
if ~isframed(orig)
mirerror('mirsimatrix','The input should be frame decomposed.');
end
if isa(orig,'mirsimatrix')
d = get(orig,'Data');
for k = 1:length(d)
if iscell(option) && isfield(option,'K') && option.K < orig.diagwidth
nl = size(d{k},1);
if strcmp(orig.view,'h')
dl = floor((nl - option.K)/2);
dk = d{k}(dl:nl-dl,:);
else
[spA,spd] = spdiags(d{k},-floor(option.K/2):floor(option.K/2));
dk = full(spdiags(spA,spd,nl,size(d{k},2)));
end
d{k} = dk;
orig.diagwidth = 2*floor(option.K/2)+1;
end
end
m = set(orig,'Data',d);
elseif isempty(option.arg2)
v = get(orig,'Data');
d = cell(1,length(v));
lK = 2*floor(option.K/2)+1;
for k = 1:length(v)
vk = v{k};
if mirwaitbar
handle = waitbar(0,'Computing dissimilarity matrix...');
else
handle = [];
end
if not(iscell(vk))
vk = {vk};
end
for z = 1:length(vk)
vz = vk{z};
ll = size(vz,1);
l = size(vz,2);
nc = size(vz,3);
if ll==1 && nc>1
vz = squeeze(vz)';
ll = nc;
nc = 1;
end
nd = size(vz,4);
if not(isempty(postoption)) && ...
strcmpi(postoption.view,'TimeLag')
if isinf(lK)
if option.half
lK = l;
else
lK = l*2-1;
end
end
dk{z} = NaN(lK,l,nc);
else
dk{z} = zeros(l,l,nc);
end
for g = 1:nc
if nd == 1
vv = vz;
else
vv = zeros(ll*nd,l);
for h = 1:nd
if iscell(vz)
for i = 1:ll
for j = 1:l
vj = vz{i,j,g,h};
if isempty(vj)
vv((h-1)*ll+i,j) = NaN;
else
vv((h-1)*ll+i,j) = vj;
end
end
end
else
vv((h-1)*ll+1:h*ll,:) = vz(:,:,g,h);
end
end
end
if isinf(option.K) && not(strcmpi(postoption.view,'TimeLag'))
try
manually = 0;
dk{z}(:,:,g) = squareform(pdist(vv',option.distance));
catch
manually = 1;
end
else
manually = 1;
end
if manually
if strcmpi(option.distance,'cosine')
for i = 1:l
nm = norm(vv(:,i));
if ~isnan(nm) && nm
vv(:,i) = vv(:,i)/nm;
end
end
end
hK = ceil(lK/2);
if not(isempty(postoption)) && ...
strcmpi(postoption.view,'TimeLag')
for i = 1:l
if mirwaitbar && (mod(i,100) == 1 || i == l)
waitbar(i/l,handle);
end
ij = min(i+lK-1,l); % Frame region i:ij
if ij==i
continue
end
if strcmpi(option.distance,'cosine')
dkij = cosine(vv(:,i),vv(:,i:ij));
else
mm = squareform(pdist(vv(:,i:ij)',...
option.distance));
dkij = mm(:,1);
end
if option.half
for j = 1:length(dkij)
dk{z}(j,i+j-1,g) = dkij(j);
end
else
for j = 0:ij-i
if hK-j>0
dk{z}(hK-j,i,g) = dkij(j+1);
end
if hK+j<=lK
dk{z}(hK+j,i+j,g) = dkij(j+1);
end
end
end
end
else
win = window(@hanning,lK);
win = win(ceil(length(win)/2):end);
for i = 1:l
if mirwaitbar && (mod(i,100) == 1 || i == l)
waitbar(i/l,handle);
end
j = min(i+hK-1,l);
if j==i
continue
end
if strcmpi(option.distance,'cosine')
dkij = cosine(vv(:,i),vv(:,i:j))';
else
mm = squareform(pdist(vv(:,i:j)',...
option.distance));
dkij = mm(:,1);
end
dkij = dkij.*win(1:length(dkij));
dk{z}(i,i:j,g) = dkij';
if ~option.half
dk{z}(i:j,i,g) = dkij;
end
end
end
elseif option.half
for i = 1:l
dk{z}(i+1:end,i,g) = 0;
end
end
end
end
d{k} = dk;
if ~isempty(handle)
delete(handle)
end
end
m.diagwidth = lK;
if not(isempty(postoption)) && strcmpi(postoption.view,'TimeLag')
m.view = 'l';
else
m.view = 's';
end
m.half = option.half;
m.similarity = 0;
m.graph = {};
m.branch = {};
m.warp = [];
m.clusters = [];
m = class(m,'mirsimatrix',mirdata(orig));
m = purgedata(m);
m = set(m,'Title','Dissimilarity matrix');
m = set(m,'Data',d,'Pos',[]);
else
v1 = get(orig,'Data');
v2 = get(option.arg2,'Data');
n1 = get(orig,'Name');
n2 = get(option.arg2,'Name');
fp1 = get(orig,'FramePos');
fp2 = get(option.arg2,'FramePos');
v1 = v1{1}{1};
v2 = v2{1}{1};
nf1 = size(v1,2);
nf2 = size(v2,2);
nd = size(v1,4);
if nd>1
l1 = size(v1,1);
vv = zeros(l1*nd,nf1);
for h = 1:nd
vv((h-1)*l1+1:h*l1,:) = v1(:,:,1,h);
end
v1 = vv;
l2 = size(v2,1);
vv = zeros(l2*nd,nf2);
for h = 1:nd
vv((h-1)*l2+1:h*l2,:) = v2(:,:,1,h);
end
v2 = vv;
clear vv
end
d = zeros(nf1,nf2);
disf = str2func(option.distance);
if strcmpi(option.distance,'cosine')
for i = 1:nf1
v1(:,i) = v1(:,i)/norm(v1(:,i));
end
for i = 1:nf2
v2(:,i) = v2(:,i)/norm(v2(:,i));
end
end
for i = 1:nf1
%if mirwaitbar && (mod(i,100) == 1 || i == nf1)
% waitbar(i/nf1,handle);
%end
d(i,:) = disf(v1(:,i),v2);
end
d = {{d}};
m.diagwidth = NaN;
m.view = 's';
m.half = 0;
m.similarity = 0;
m.graph = {};
m.branch = {};
m.warp = [];
m.clusters = [];
m = class(m,'mirsimatrix',mirdata(orig));
m = purgedata(m);
m = set(m,'Title','Dissimilarity matrix','Data',d,'Pos',[],...
'Name',{n1{1},n2{1}},'FramePos',{fp1{1},fp2{1}});
end
lK = option.K;
if not(isempty(postoption))
if strcmpi(m.view,'s') && isempty(option.arg2)
if strcmpi(postoption.view,'Horizontal')
for k = 1:length(d)
for z = 1:length(d{k})
d{k}{z} = rotatesim(d{k}{z},m.diagwidth);
if lK < m.diagwidth
W = size(d{k}{z},1);
hW = ceil(W/2);
hK = floor(lK/2);
d{k}{z} = d{k}{z}(hW-hK:hW+hK,:);
m.diagwidth = lK;
end
end
end
m = set(m,'Data',d);
m.view = 'h';
elseif strcmpi(postoption.view,'TimeLag')
for k = 1:length(d)
for z = 1:length(d{k})
if isinf(m.diagwidth)
if option.half
nlines = size(d{k}{z},1);
half = floor(size(d{k}{z},1)/2);
else
nlines = 2*size(d{k}{z},1)-1;
half = size(d{k}{z},1);
end
else
nlines = m.diagwidth;
half = (m.diagwidth+1)/2;
end
dz = NaN(nlines,size(d{k}{z},2));
if option.half
for l = 1:nlines
dz(l,l:end) = diag(d{k}{z},l-1)';
end
else
for l = 1:nlines
dia = abs(half-l);
if l<half
dz(l,1:end-dia) = diag(d{k}{z},dia)';
else
dz(l,dia+1:end) = diag(d{k}{z},dia)';
end
end
if lK < m.diagwidth
nlines2 = floor(lK/2);
if size(dz,1)>lK
dz = dz(half-nlines2:half+nlines2,:);
end
m.diagwidth = lK;
end
end
d{k}{z}= dz;
end
end
m = set(m,'Data',d);
m.view = 'l';
end
end
if strcmpi(m.view,'l') && ~m.half && option.half
for k = 1:length(d)
for z = 1:length(d{k})
for l = 1:size(d{k}{z},1)
dz(l,1:l-1) = NaN;
end
d{k}{z}= dz;
end
end
end
if ischar(postoption.simf)
if strcmpi(postoption.simf,'Similarity')
if not(isequal(m.similarity,NaN))
option.dissim = 0;
end
postoption.simf = 'oneminus';
end
if isequal(m.similarity,0) && isstruct(option) ...
&& isfield(option,'dissim') && not(option.dissim)
simf = str2func(postoption.simf);
for k = 1:length(d)
for z = 1:length(d{k})
d{k}{z} = simf(d{k}{z});
end
end
m.similarity = postoption.simf;
m = set(m,'Title','Similarity matrix','Data',d);
elseif length(m.similarity) == 1 && isnan(m.similarity) ...
&& option.dissim
m.similarity = 0;
end
end
if postoption.filt
fp = get(m,'FramePos');
for k = 1:length(d)
for z = 1:length(d{k})
dz = filter(ones(postoption.filt,1),1,d{k}{z}')';
if option.half
d{k}{z} = dz(1:end-postoption.filt,...
postoption.filt+1:end);
fp{k}{z} = [fp{k}{z}(1,1:end-postoption.filt);...
fp{k}{z}(2,postoption.filt+1:end)];
else
d{k}{z} = dz(1+ceil(postoption.filt/2):...
end-floor(postoption.filt/2),...
postoption.filt+1:end);
fp{k}{z} = [fp{k}{z}(1,1:end-postoption.filt);...
fp{k}{z}(2,postoption.filt+1:end)];
end
end
end
m = set(m,'Data',d,'FramePos',fp);
end
if postoption.warp
dz = 1 - (d{1}{1} - min(min(d{1}{1}))) ...
/ (max(max(d{1}{1})) - min(min(d{1}{1})));
paths = cell(size(dz)+1);
bests = sparse(size(dz,1),size(dz,2));
bestsindex = sparse(size(dz,1),size(dz,2));
for i = 1:size(dz,1)
if ~mod(i,100)
i/size(dz,1)
end
for j = 1:size(dz,2)
if dz(i,j) > postoption.warp
continue
end
ending = [i; j];
[newpaths bests bestsindex] = ...
addpaths({},paths{i,j},ending,dz(i,j),...
bests,bestsindex,paths,1);
[newpaths bests bestsindex] = ...
addpaths(newpaths,paths{i+1,j},ending,dz(i,j),...
bests,bestsindex,paths,0);
[newpaths bests bestsindex] = ...
addpaths(newpaths,paths{i,j+1},ending,dz(i,j),...
bests,bestsindex,paths,0);
if isempty(newpaths) && (i == 1 || j == 1 || ...
(dz(i-1,j-1)> postoption.warp && ...
dz(i-1,j)> postoption.warp && ...
dz(i,j-1)> postoption.warp))
newpaths = {[ending; 0]};
end
paths{i+1,j+1} = newpaths;
end
end
m = set(m,'Warp',{paths,bests,bestsindex});
end
if postoption.cluster % && isempty(get(orig,'Clusters'))
clus = cell(1,length(d));
for k = 1:length(d)
clus{k} = cell(1,length(d{k}));
for z = 1:length(d{k})
dz = d{k}{z};
l = size(dz,1);
sim = NaN(l);
for i = 2:l-1
for j = 1:l-i-1
in = dz(i:i+j,i:i+j);
homg = mean(in(:))-std(in(:));
out = [dz(i:i+j,i-1),dz(i:i+j,i+j+1)];
halo = mean(out(:)) - std(out(:));
if homg > .7 && homg-halo>.01
sim(i,j) = homg;
if j>1 && ~isnan(sim(i,j-1)) && ...
sim(i,j-1)-sim(i,j) < .01
sim(i,j-1) = NaN;
end
if i>1 && j<l-i-1 && ...
~isnan(sim(i-1,j+1)) && ...
sim(i,j)-sim(i-1,j+1) < .01
sim(i-1,j+1) = NaN;
end
if i>1 && ~isnan(sim(i-1,j))
if abs(sim(i-1,j)-sim(i,j)) < .01
sim(i-1,j) = NaN;
sim(i,j) = NaN;
elseif sim(i-1,j) > sim(i,j)
sim(i,j) = NaN;
else
sim(i-1,j) = NaN;
end
end
end
end
end
clus{k}{z} = sim;
end
end
m = set(m,'Clusters',clus);
end
end
function [newpaths bests bestsindex] = addpaths(newpaths,oldpaths,ending,d,...
bests,bestsindex,paths,diag)
for k = 1:length(oldpaths)
% For each path
if oldpaths{k}(3,end)<10 && d>.33
continue
end
found_redundant = 0;
for l = 1:length(newpaths)
if (newpaths{l}(1,1) - oldpaths{k}(1,1)) * ...
(newpaths{l}(2,1) - oldpaths{k}(2,1)) >= 0
% Redundant paths. One is removed.
found_redundant = 1;
if oldpaths{k}(1,1) < newpaths{l}(1,1)
% The new candidate is actually better.
% The path stored in newpaths is modified.
newpaths{l} = [oldpaths{k} ...
[ending; oldpaths{k}(3,end)+diag]];
if bests(newpaths{l}(1,1),newpaths{l}(2,1)) ...
== ending(1)+1i*ending(2)
bests(newpaths{l}(1,1),newpaths{l}(2,1)) = 0;
end
[bests bestsindex] = update(bests,bestsindex,...
oldpaths{k}(1:2,1),...
ending(1:2),l,...
paths,newpaths);
end
else
% Each of the 2 paths explores more one particular dimension
% upfront. No path should be removed.
% +
% --+
% - |
% |
% |
if oldpaths{k}(1,1) < newpaths{l}(1,1)
if bests(oldpaths{k}(1,1),oldpaths{k}(2,1))...
== ending(1)+1i*ending(2)
bests(oldpaths{k}(1,1),oldpaths{k}(2,1)) = 0;
end
else
if bests(newpaths{l}(1,1),newpaths{l}(2,1))...
== ending(1)+1i*ending(2)
bests(newpaths{l}(1,1),newpaths{l}(2,1)) = 0;
end
end
end
end
if ~found_redundant
newpaths{end+1} = [oldpaths{k} ...
[ending; oldpaths{k}(3,end)+diag]];
[bests bestsindex] = update(bests,bestsindex,...
oldpaths{k}(1:2,1),...
ending(1:2),length(newpaths),...
paths,newpaths);
end
end
function [bests bestsindex] = update(bests,bestsindex,starts,ends,...
pathindex,paths,newpaths)
key = ends(1)+1i*ends(2);
[i,j,v] = find(bests);
k = find(v == key);
if ~isempty(k)
bests(i(k),j(k)) = 0;
end
previous = bests(starts(1),starts(2));
previndex = bestsindex(starts(1),starts(2));
replace = 0;
if previous == 0
replace = 1;
else
newscore = newpaths{pathindex}(3,end-1);
oldscore = paths{real(previous)+1,imag(previous)+1}{previndex}(3,end);
if newscore > oldscore
replace = 1;
elseif newscore == oldscore
paths{real(previous)+1,imag(previous)+1}{previndex};
newpaths{pathindex};
end
end
if replace
bests(starts(1),starts(2)) = key;
bestsindex(starts(1),starts(2)) = pathindex;
end
function S = rotatesim(d,K)
if length(d) == 1;
S = d;
else
K = min(K,size(d,1)*2+1);
lK = floor(K/2);
S = NaN(K,size(d,2),size(d,3));
for k = 1:size(d,3)
for j = -lK:lK
S(lK+j+1,:,k) = [NaN(1,floor(abs(j)/2)) diag(d(:,:,k),j)' ...
NaN(1,ceil(abs(j)/2))];
end
end
end
function d = cosine(r,s)
d = 1-r'*s;
function d = KL(x,y)
% Kullback-Leibler distance
if size(x,4)>1
x(end+1:2*end,:,:,1) = x(:,:,:,2);
x(:,:,:,2) = [];
end
if size(y,4)>1
y(end+1:2*end,:,:,1) = y(:,:,:,2);
y(:,:,:,2) = [];
end
m1 = mean(x);
m2 = mean(y);
S1 = cov(x);
S2 = cov(y);
d = (trace(S1/S2)+trace(S2/S1)+(m1-m2)'*inv(S1+S2)*(m1-m2))/2 - size(S1,1);
function s = exponential(d)
s = (exp(-d) - exp(-1)) / (1-exp(-1));
function s = oneminus(d)
s = 1-d;
|
github
|
martinarielhartmann/mirtooloct-master
|
mirautocor.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirautocor/mirautocor.m
| 25,732 |
utf_8
|
738d1a46a0baaa7a8a3825ebbe1bda84
|
function varargout = mirautocor(orig,varargin)
% a = mirautocor(x) computes the autocorrelation function related to x.
% Optional parameters:
% mirautocor(...,'Min',mi) indicates the lowest delay taken into
% consideration. The unit can be precised:
% mirautocor(...,'Min',mi,'s') (default unit)
% mirautocor(...,'Min',mi,'Hz')
% Default value: 0 s.
% mirautocor(...,'Max',ma) indicates the highest delay taken into
% consideration. The unit can be specified as for 'Min'.
% Default value:
% if x is a signal, the highest delay is 0.05 s
% (corresponding to a minimum frequency of 20 Hz).
% if x is an envelope, the highest delay is 2 s.
% mirautocor(...,'Resonance',r) multiplies the autocorrelation function
% with a resonance curve:
% Possible values:
% 'Toiviainen' from (Toiviainen & Snyder, 2003)
% 'vanNoorden' from (van Noorden & Moelants, 2001)
% mirautocor(...,'Center',c) assigns the center value of the
% resonance curve, in seconds.
% Works mainly with 'Toiviainen' option.
% Default value: c = 0.5
% mirautocor(...,'Enhanced',a) reduces the effect of subharmonics.
% The original autocorrelation function is half-wave rectified,
% time-scaled by the factor a (which can be a factor list as
% well), and substracted from the original clipped function.
% (Tolonen & Karjalainen)
% If the 'Enhanced' option is not followed by any value,
% default value is a = 2:10
% mirautocor(...,'Halfwave') performs a half-wave rectification on the
% result.
% mirautocor(...,'Freq') represents the autocorrelation function in the
% frequency domain.
% mirautocor(...,'NormalWindow',w): applies a window to the input
% signal and divides the autocorrelation by the autocorrelation of
% that window (Boersma 1993).
% Possible values: any windowing function proposed in the Signal
% Processing Toolbox (help window) plus 'rectangle' (no
% windowing)
% Default value: w = 'hanning'
% mirautocor(...,'NormalWindow',0): toggles off this normalization
% (which is on by default).
% All the parameters described previously can be applied to an
% autocorrelation function itself, in order to arrange the results
% after the actual computation of the autocorrelation computations.
% For instance: a = mirautocor(a,'Resonance','Enhanced')
% Other optional parameter:
% mirautocor(...,'Compres',k) computes the autocorrelation in the
% frequency domain and includes a magnitude compression of the
% spectral representation. A normal autocorrelation corresponds
% to the value k=2, but values lower than 2 are suggested by
% (Tolonen & Karjalainen, 2000).
% Default value: k = 0.67
% mirautocor(...,'Normal',n) or simply mirautocor(...,n) specifies
% the normalization strategy. Accepted values are 'biased',
% 'unbiased', 'coeff' (default value) and 'none'.
% See help xcorr for an explanation.
min.key = 'Min';
min.type = 'Integer';
min.unit = {'s','Hz'};
if isamir(orig,'mirspectrum')
min.defaultunit = 'Hz';
else
min.defaultunit = 's';
end
min.default = 0;
min.opposite = 'max';
option.min = min;
max.key = 'Max';
max.type = 'Integer';
max.unit = {'s','Hz'};
if isamir(orig,'mirspectrum')
max.defaultunit = 'Hz';
else
max.defaultunit = 's';
end
if isamir(orig,'mirenvelope') || isamir(orig,'mirdiffenvelope')
max.default = 2; % for envelopes, longest period: 2 seconds.
elseif isamir(orig,'miraudio') || ischar(orig) % for audio signal,lowest frequency: 20 Hz.
max.default = 1/20;
else
max.default = Inf;
end
max.opposite = 'min';
option.max = max;
xtend.key = {'Extend','Extended'};
xtend.type = 'Boolean';
xtend.default = 0;
option.xtend = xtend;
scaleoptbw.key = 'Normal'; %'Normal' keyword optional
scaleoptbw.key = 'Boolean';
option.scaleoptbw = scaleoptbw;
scaleopt.type = 'String';
scaleopt.choice = {'biased','unbiased','coeff','none'};
scaleopt.default = 'coeff';
option.scaleopt = scaleopt;
gener.key = {'Generalized','Compres'};
gener.type = 'Integer';
gener.default = 2;
gener.keydefault = .67;
option.gener = gener;
ni.key = 'NormalInput'; %% Normalize before frame or chunk??
ni.type = 'Boolean';
ni.default = 0;
option.ni = ni;
reso.key = 'Resonance';
reso.type = 'String';
reso.choice = {'ToiviainenSnyder','Toiviainen','vanNoorden','no','off',0};
reso.keydefault = 'Toiviainen';
reso.when = 'After';
reso.default = 0;
option.reso = reso;
resocenter.key = {'Center','Centre'};
resocenter.type = 'Integer';
resocenter.when = 'After';
option.resocenter = resocenter;
h.key = 'Halfwave';
h.type = 'Boolean';
h.when = 'After';
h.default = 0;
option.h = h;
e.key = 'Enhanced';
e.type = 'Integers';
e.default = [];
e.keydefault = 2:10;
e.when = 'After';
option.e = e;
fr.key = 'Freq';
fr.type = 'Boolean';
fr.default = 0;
fr.when = 'After';
option.fr = fr;
nw.key = 'NormalWindow';
nw.when = 'Both';
if isamir(orig,'mirspectrum')
nw.default = 0;
elseif isamir(orig,'mirenvelope')
nw.default = 'rectangular';
else
nw.default = 'hanning';
end
option.nw = nw;
win.key = 'Window';
win.type = 'String';
win.default = NaN;
option.win = win;
phase.key = 'Phase';
phase.type = 'Boolean';
phase.when = 'Both';
phase.default = 0;
option.phase = phase;
specif.option = option;
specif.defaultframelength = 0.05;
specif.defaultframehop = 0.5;
specif.eachchunk = @eachchunk;
specif.combinechunk = @combinechunk;
if isamir(orig,'mirscalar') || isamir(orig,'mirenvelope')
specif.nochunk = 1;
end
varargout = mirfunction(@mirautocor,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type = 'mirautocor';
function a = main(orig,option,postoption)
if iscell(orig)
orig = orig{1};
end
if isa(orig,'mirautocor')
a = orig;
if not(isempty(option)) && ...
(option.min || iscell(option.max) || option.max < Inf)
coeff = get(a,'Coeff');
delay = get(a,'Delay');
for h = 1:length(coeff)
if a.freq
mi = 1/option.max;
ma = 1/option.min;
else
mi = option.min;
ma = option.max;
end
for k = 1:length(coeff{h})
range = find(and(delay{h}{k}(:,1,1) >= mi,...
delay{h}{k}(:,1,1) <= ma));
coeff{h}{k} = coeff{h}{k}(range,:,:);
delay{h}{k} = delay{h}{k}(range,:,:);
end
end
a = set(a,'Coeff',coeff,'Delay',delay);
end
if not(isempty(postoption)) && not(isequal(postoption,0))
a = post(a,postoption);
end
elseif ischar(orig)
a = mirautocor(miraudio(orig),option,postoption);
else
if nargin == 0
orig = [];
end
a.freq = 0;
a.ofspectrum = 0;
a.window = {};
a.normalwindow = 0;
a.resonance = '';
a.input = [];
a.phase = {};
a = class(a,'mirautocor',mirdata(orig));
a = purgedata(a);
a = set(a,'Ord','coefficients');
sig = get(orig,'Data');
if isa(orig,'mirspectrum')
a = set(a,'Title','Spectrum autocorrelation','OfSpectrum',1,...
'Abs','frequency (Hz)');
pos = get(orig,'Pos');
else
if isa(orig,'mirscalar')
a = set(a,'Title',[get(orig,'Title') ' autocorrelation']);
pos = get(orig,'FramePos');
for k = 1:length(sig)
for l = 1:length(sig{k})
sig{k}{l} = sig{k}{l}';
pos{k}{l} = pos{k}{l}(1,:,:)';
end
end
else
if isa(orig,'mirenvelope')
a = set(a,'Title','Envelope autocorrelation');
elseif not(isa(orig,'mirautocor'))
a = set(a,'Title','Waveform autocorrelation');
end
pos = get(orig,'Pos');
end
a = set(a,'Abs','lag (s)');
end
f = get(orig,'Sampling');
if isnan(option.win)
if isequal(option.nw,0) || ...
strcmpi(option.nw,'Off') || strcmpi(option.nw,'No')
option.win = 0;
elseif isequal(option.nw,1) || strcmpi(option.nw,'On') || ...
strcmpi(option.nw,'Yes')
option.win = 'hanning';
else
option.win = option.nw;
end
end
coeff = cell(1,length(sig));
lags = cell(1,length(sig));
wind = cell(1,length(sig));
for k = 1:length(sig)
s = sig{k};
p = pos{k};
fk = f{k};
if iscell(option.max)
mi = option.min{k};
ma = option.max{k};
else
mi = option.min;
ma = option.max;
end
coeffk = cell(1,length(s));
lagsk = cell(1,length(s));
windk = cell(1,length(s));
for l = 1:length(s)
sl = s{l};
sl(isnan(sl)) = 0;
if option.ni
mxsl = repmat(max(sl),[size(sl,1),1,1]);
mnsl = repmat(min(sl),[size(sl,1),1,1]);
sl = (sl-mnsl)./(mxsl-mnsl);
end
pl = p{l};
pl = pl-repmat(pl(1,:,:),[size(pl,1),1,1]);
ls = size(sl,1);
if mi
misp = find(pl(:,1,1)>=mi,1);
if isempty(misp)
warning('WARNING IN MIRAUTOCOR: The specified range of delays exceeds the temporal length of the signal.');
disp('Minimum delay set to zero.')
misp = 1; % misp is the lowest index of the lag range
mi = 0;
end
else
misp = 1;
end
if ma
masp = find(pl(:,1,1)>=ma,1);
if isempty(masp)
masp = Inf;
end
else
masp = Inf;
end
if option.xtend
masp = min(masp,ls);
else
masp = min(masp,ceil(ls/2));
end
if masp <= misp
if size(sl,2) > 1
warning('WARNING IN MIRAUTOCOR: Frame length is too small.');
else
warning('WARNING IN MIRAUTOCOR: The audio sequence is too small.');
end
display('The autocorrelation is not defined for this range of delays.');
end
sl = center(sl);
if not(ischar(option.win)) || strcmpi(option.win,'Rectangular')
kw = ones(size(sl));
else
N = size(sl,1);
winf = str2func(option.win);
try
w = window(winf,N);
catch
if strcmpi(option.win,'hamming')
disp('Signal Processing Toolbox does not seem to be installed. Recompute the hamming window manually.');
w = 0.54 - 0.46 * cos(2*pi*(0:N-1)'/(N-1));
else
warning(['WARNING in MIRAUTOCOR: Unknown windowing function ',option.win,' (maybe Signal Processing Toolbox is not installed).']);
disp('No windowing performed.')
w = ones(size(sl,1),1);
end
end
kw = repmat(w,[1,size(sl,2),size(sl,3)]);
sl = sl.* kw;
end
if strcmpi(option.scaleopt,'coeff')
scaleopt = 'none';
else
scaleopt = option.scaleopt;
end
c = zeros(masp,size(sl,2),size(sl,3));
for i = 1:size(sl,2)
for j = 1:size(sl,3)
if option.gener == 2
cc = xcorr(sl(:,i,j),masp-1,scaleopt);
c(:,i,j) = cc(masp:end);
else
ss = abs(fft(sl(:,i,j)));
ss = ss.^option.gener;
cc = ifft(ss);
ll = (0:masp-1);
c(:,i,j) = cc(ll+1);
end
end
if strcmpi(option.scaleopt,'coeff') && option.gener == 2
% to be adapted to generalized autocor
xc = xcorr(sum(sl(:,i,:),3),0);
if xc
c(:,i,:) = c(:,i,:)/xc;
% This is a kind of generalization of the 'coeff'
% normalization for multi-channels signals. In the
% original 'coeff' option, the autocorrelation at zero
% lag is identically 1.0. In this multi-channels
% version, the autocorrelation at zero lag is such that
% the sum over channels becomes identically 1.0.
end
end
end
coeffk{l} = c(misp:end,:,:);
pl = pl(misp:end,:,:);
lagsk{l} = pl(1:min(size(coeffk{l},1),size(pl,1)),:,:);
windk{l} = kw;
end
coeff{k} = coeffk;
lags{k} = lagsk;
wind{k} = windk;
end
a = set(a,'Coeff',coeff,'Delay',lags,'Window',wind);
if option.phase
a = set(a,'Input',orig);
end
if not(isempty(postoption))
a = post(a,postoption);
end
end
function a = post(a,option)
debug = 0;
coeff = get(a,'Coeff');
lags = get(a,'Delay');
wind = get(a,'Window');
freq = option.fr && not(get(a,'FreqDomain'));
if isequal(option.e,1)
option.e = 2:10;
end
if max(option.e) > 1
pa = mirpeaks(a,'NoBegin','NoEnd','Contrast',.01,'Normalize','Local');
va = mirpeaks(a,'Valleys','Contrast',.01,'Normalize','Local');
pv = get(pa,'PeakVal');
vv = get(va,'PeakVal');
end
if option.phase
x = get(a,'Input');
d = get(x,'Data');
pk = get(a,'PeakPos');
sr = get(a,'Sampling');
phas = cell(1,length(coeff));
end
for k = 1:length(coeff)
if option.phase
phask = cell(1,length(coeff{k}));
end
for l = 1:length(coeff{k})
c = coeff{k}{l}; % Coefficients of autocorrelation
t = lags{k}{l}; % Delays of autocorrelation
if not(isempty(c))
if not(isequal(option.nw,0) || strcmpi(option.nw,'No') || ...
strcmpi(option.nw,'Off') || a.normalwindow) % 'NormalWindow' option
xw = zeros(size(c));
lc = size(c,1);
for j = 1:size(c,3)
for i = 1:size(c,2)
xwij = xcorr(wind{k}{l}(:,i,j),lc,'coeff');
xw(:,i,j) = xwij(lc+2:end);
end
end
c = c./ xw;
a.normalwindow = 1;
end
if ischar(option.reso) && isempty(a.resonance) && ...
(strcmpi(option.reso,'ToiviainenSnyder') || ...
strcmpi(option.reso,'Toiviainen') || ...
strcmpi(option.reso,'vanNoorden'))
if isa(a,'mirautocor') && get(a,'FreqDomain')
ll = 1./t;
else
ll = t;
end
if not(option.resocenter)
option.resocenter = .5;
end
if strcmpi(option.reso,'ToiviainenSnyder') || ...
strcmpi(option.reso,'Toiviainen')
w = max(1 - 0.25*(log2(max(ll,1e-12)/option.resocenter)).^2, 0);
elseif strcmpi(option.reso,'vanNoorden')
f0=2.193; b=option.resocenter;
f=1./ll; a1=(f0*f0-f.*f).^2+b*f.^2; a2=f0^4+f.^4;
w=(1./sqrt(a1))-(1./sqrt(a2));
end
if max(w) == 0
warning('The resonance curve, not defined for this range of delays, will not be applied.')
else
w = w/max(w);
c = c.* repmat(w,[1,size(c,2),size(c,3)]);
end
a.resonance = option.reso;
end
if option.h
c = hwr(c);
end
if max(option.e) > 1
if a.freq
freq = 1;
for i = 1:size(c,3)
c(:,:,i) = flipud(c(:,:,i));
end
t = flipud(1./t);
end
for g = 1:size(c,2)
for h = 1:size(c,3)
cgh = c(:,g,h);
if length(cgh)>1
pvk = pv{k}{l}{1,g,h};
mv = [];
if not(isempty(pvk))
mp = min(pvk); %Lowest peak
vvv = vv{k}{l}{1,g,h}; %Valleys
mv = vvv(find(vvv<mp,1,'last'));
%Highest valley below the lowest peak
if not(isempty(mv))
cgh = cgh-mv;
end
end
cgh2 = cgh;
tgh2 = t(:,g,1);
coef = cgh(2)-cgh(1); % initial slope of the autocor curve
tcoef = tgh2(2)-tgh2(1);
deter = 0;
inter = 0;
repet = find(not(diff(tgh2))); % Avoid bug if repeated x-values
if repet
warning('WARNING in MIRAUTOCOR: Two successive samples have exactly same temporal position.');
tgh2(repet+1) = tgh2(repet)+1e-12;
end
if coef < 0
% initial descending slope removed
deter = find(diff(cgh2)>0,1)-1;
% number of removed points
if isempty(deter)
deter = 0;
end
cgh2(1:deter) = [];
tgh2(1:deter) = [];
coef = cgh2(2)-cgh2(1);
end
if coef > 0
% initial ascending slope prolonged to the left
% until it reaches the x-axis
while cgh2(1) > 0
coef = coef*1.1;
% the further to the left, ...
% the more ascending is the slope
% (not sure it always works, though...)
inter = inter+1;
% number of added points
cgh2 = [cgh2(1)-coef; cgh2];
tgh2 = [tgh2(1)-tcoef; tgh2];
end
cgh2(1) = 0;
end
for i = option.e % Enhancing procedure
% option.e is the list of scaling factors
% i is the scaling factor
if i
be = find(tgh2 & tgh2/i >= tgh2(1),1);
% starting point of the substraction
% on the X-axis
if not(isempty(be))
ic = interp1(tgh2,cgh2,tgh2/i);
% The scaled autocorrelation
ic(1:be-1) = 0;
ic(find(isnan(ic))) = Inf;
% All the NaN values are changed
% into 0 in the resulting curve
ic = max(ic,0);
if debug
hold off,plot(tgh2,cgh2)
end
cgh2 = cgh2 - ic;
% The scaled autocorrelation
% is substracted to the initial one
cgh2 = max(cgh2,0);
% Half-wave rectification
if debug
hold on,plot(tgh2,ic,'r')
hold on,plot(tgh2,cgh2,'g')
drawnow
figure
end
end
end
end
if 0 %~isempty(mv)
cgh2 = max(cgh2 + mv,0);
end
% The temporary modifications are
% removed from the final curve
if inter>=deter
c(:,g,h) = cgh2(inter-deter+1:end);
if not(isempty(mv))
c(:,g,h) = c(:,g,h) + mv;
end
else
c(:,g,h) = [zeros(deter-inter,1);cgh2];
end
end
end
end
end
if option.phase
ph = cell(1,size(d{k}{l},2),size(d{k}{l},3));
for i = 1:size(ph,2)
for j = 1:size(ph,3)
if ~isempty(pk{k}{l})
option.phase = 2;
pkj = pk{k}{l}{1,i,j};
phj = zeros(1,length(pkj));
for h = 1:length(pkj)
lag = round(t(pkj(h))*sr{k});
if ~lag
continue
end
pha = zeros(1,lag);
for g = 1:lag
pha(g) = sum(d{k}{l}(g:lag:end,i,j));
end
[unused phj(h)] = max(pha);
end
ph{1,i,j} = phj;
end
end
end
phask{l} = ph;
end
if freq
if t(1,1) == 0
c = c(2:end,:,:);
t = t(2:end,:,:);
end
for i = 1:size(c,3)
c(:,:,i) = flipud(c(:,:,i));
end
t = flipud(1./t);
end
coeff{k}{l} = c;
lags{k}{l} = t;
if 0 %option.ph
for g = 1:size(p{k}{l},2)
for i = 1:length(p{k}{l}{1,g})
if t(1)
error('check here');
end
indx = p{k}{l}{1,g}(i);
end
end
end
end
end
if option.phase
phas{k} = phask;
end
end
a = set(a,'Coeff',coeff,'Delay',lags,'Freq');
if freq
a = set(a,'FreqDomain',1,'Abs','frequency (Hz)');
end
if option.phase == 2
a = set(a,'Phase',phas);
end
function [y orig] = eachchunk(orig,option,missing,postchunk)
option.scaleopt = 'none';
y = mirautocor(orig,option);
function y = combinechunk(old,new)
doo = get(old,'Data');
doo = doo{1}{1};
dn = get(new,'Data');
dn = dn{1}{1};
if abs(size(dn,1)-size(doo,1)) <= 2 % Probleme of border fluctuation
mi = min(size(dn,1),size(doo,1));
dn = dn(1:mi,:,:);
doo = doo(1:mi,:,:);
po = get(old,'Pos');
po{1}{1} = po{1}{1}(1:mi,:,:);
old = set(old,'Pos',po);
elseif length(dn) < length(doo)
dn(length(doo),:,:) = 0; % Zero-padding
end
y = set(old,'ChunkData',doo+dn);
|
github
|
martinarielhartmann/mirtooloct-master
|
combine.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirdata/combine.m
| 3,357 |
utf_8
|
501ac7884a5a69e4c2961e5522b546a1
|
function c = combine(varargin)
c = varargin{1};
l = length(varargin);
p = cell(1,l);
ch = cell(1,l);
d = cell(1,l);
fp = cell(1,l);
fr = cell(1,l);
sr = cell(1,l);
n = cell(1,l);
la = cell(1,l);
le = cell(1,l);
cl = cell(1,l);
pp = cell(1,l);
pm = cell(1,l);
pv = cell(1,l);
ppp = cell(1,l);
ppv = cell(1,l);
tp = cell(1,l);
tv = cell(1,l);
tpp = cell(1,l);
tpv = cell(1,l);
ap = cell(1,l);
rp = cell(1,l);
if isa(c,'temporal')
nb = cell(1,l);
end
if isa(c,'mirscalar')
m = cell(1,l);
if isa(c,'mirpitch')
pa = cell(1,l);
ps = cell(1,l);
pe = cell(1,l);
pi = cell(1,l);
pd = cell(1,l);
end
end
if isa(c,'miremotion')
dd = cell(1,l);
cd = cell(1,l);
end
if isa(c,'mirmetre')
ac = cell(1,l);
g = cell(1,l);
end
for i = 1:l
argin = varargin{i};
p{i} = getargin(argin,'Pos');
ch{i} = getargin(argin,'Channels');
d{i} = getargin(argin,'Data');
fp{i} = getargin(argin,'FramePos');
fr{i} = getargin(argin,'FrameRate');
sr{i} = getargin(argin,'Sampling');
nb{i} = getargin(argin,'NBits');
n{i} = getargin(argin,'Name');
la{i} = getargin(argin,'Label');
le{i} = getargin(argin,'Length');
cl{i} = getargin(argin,'Clusters');
pp{i} = getargin(argin,'PeakPos');
pm{i} = getargin(argin,'PeakMode');
pv{i} = getargin(argin,'PeakVal');
ppp{i} = getargin(argin,'PeakPrecisePos');
ppv{i} = getargin(argin,'PeakPreciseVal');
tp{i} = getargin(argin,'TrackPos');
tv{i} = getargin(argin,'TrackVal');
tpp{i} = getargin(argin,'TrackPrecisePos');
tpv{i} = getargin(argin,'TrackPreciseVal');
ap{i} = getargin(argin,'AttackPos');
rp{i} = getargin(argin,'ReleasePos');
if isa(c,'temporal')
ct = getargin(argin,'Centered');
nb{i} = getargin(argin,'NBits');
end
if isa(c,'mirscalar')
m{i} = getargin(argin,'Mode');
if isa(c,'mirpitch')
pa{i} = getargin(argin,'Amplitude');
ps{i} = getargin(argin,'Start');
pe{i} = getargin(argin,'End');
pi{i} = getargin(argin,'Mean');
pd{i} = getargin(argin,'Degrees');
end
end
if isa(c,'miremotion')
dd{i} = getargin(argin,'DimData');
cd{i} = getargin(argin,'ClassData');
end
if isa(c,'mirmetre')
ac{i} = getargin(argin,'Autocor');
g{i} = getargin(argin,'Globpm');
end
end
c = set(c,'Pos',p,'Data',d,'FramePos',fp,'FrameRate',fr,'Channels',ch,...
'Sampling',sr,'NBits',nb,'Name',n,'Label',la,'Length',le,...
'Clusters',cl,'PeakPos',pp,'PeakMode',pm,'PeakVal',pv,...
'PeakPrecisePos',ppp,'PeakPreciseVal',ppv,...
'TrackPos',tp,'TrackVal',tv,...
'TrackPrecisePos',tpp,'TrackPreciseVal',tpv,...
'AttackPos',ap,'ReleasePos',rp);
if isa(c,'temporal')
c = set(c,'Centered',ct,'NBits',nb);
end
if isa(c,'mirscalar')
c = set(c,'Mode',m);
if isa(c,'mirpitch')
c = set(c,'Amplitude',pa,'Start',ps,'End',pe,'Mean',pi,...
'Degrees',pd);
end
end
if isa(c,'miremotion')
c = set(c,'DimData',dd,'ClassData',cd);
end
if isa(c,'mirmetre')
c = set(c,'Autocor',ac,'Globpm',g);
end
function y = getargin(argin,field)
yi = get(argin,field);
if isempty(yi) || ischar(yi) || ~iscell(yi)
y = yi;
else
y = yi{1};
end
|
github
|
martinarielhartmann/mirtooloct-master
|
miraudio.m
|
.m
|
mirtooloct-master/MIRToolbox/@miraudio/miraudio.m
| 13,358 |
utf_8
|
6e9885c23f30543534bc72a8250a76cb
|
function varargout = miraudio(orig,varargin)
% a = miraudio('filename') loads the sound file 'filename' (in WAV or AU
% format) into a miraudio object.
% a = miraudio('Folder') loads all the sound files in the CURRENT folder
% into a miraudio object.
% a = miraudio(v,sr), where v is a column vector, translates the vector v
% into a miraudio object. The sampling frequency is set to sr Hertz.
% Default value for sr: 44100 Hz.
% a = miraudio(b, ...), where b is already a miraudio object, performs
% operations on b specified by the optional arguments (see below).
%
% Transformation options:
% miraudio(...,'Mono',0) does not perform the default summing of
% channels into one single mono track, but instead stores each
% channel of the initial soundfile separately.
% miraudio(...,'Center') centers the signals.
% miraudio(...,'Sampling',r) resamples at sampling rate r (in Hz).
% (Requires the Signal Processing Toolbox.)
% miraudio(...,'Normal') normalizes with respect to RMS energy.
% Extraction options:
% miraudio(...,'Extract',t1,t2,u,f) extracts the signal between dates
% t1 and t2, expressed in the unit u.
% Possible values for u:
% 's' (seconds, by default),
% 'sp' (sample index, starting from 1).
% The additional optional argument f indicates the referential
% origin of the temporal positions. Possible values for f:
% 'Start' (by default)
% 'Middle' (of the sequence)
% 'End' of the sequence
% When using 'Middle' or 'End', negative values for t1 or t2
% indicate values before the middle or the end of the audio
% sequence.
% miraudio(...,'Trim') trims the pseudo-silence beginning and end off
% the audio file. Silent frames are frames with RMS below t times
% the medium RMS of the whole audio file.
% Default value: t = 0.06
% instead of 'Trim':
% 'TrimStart' only trims the beginning of the audio file,
% 'TrimEnd' only trims the end.
% miraudio(...,'TrimThreshold',t) specifies the trimming threshold t.
% miraudio(...,'Channel',c) or miraudio(...,'Channels',c) selects the
% channels indicated by the (array of) integer(s) c.
% Labeling option:
% miraudio(...,'Label',l) labels the audio signal(s) following the
% label(s) l.
% If l is a (series of) number(s), the audio signal(s) are
% labelled using the substring of their respective file name of
% index l. If l=0, the audio signal(s) are labelled using the
% whole file name.
% MP3 behave badly!
if isempty(orig)
varargout = {{}};
return
end
if isnumeric(orig)
if size(orig,2) > 1 || size(orig,3) > 1
mirerror('MIRAUDIO','Only column vectors can be imported into mirtoolbox.');
end
if nargin == 1
f = 44100;
else
f = varargin{1};
end
b = 32;
if size(orig,1) == 1
orig = orig';
end
tp = (0:size(orig,1)-1)'/f;
l = (size(orig,1)-1); %/f;
t = mirtemporal([],'Time',{{tp}},'Data',{{orig}},'Length',{{l}},...
'FramePos',{{tp([1 end])}},'Sampling',{f},...
'Name',{inputname(1)},'Label',{{}},'Clusters',{{}},...
'Channels',[],'Centered',0,'NBits',{b},...
'Title','Audio signal',...
'PeakPos',{{{}}},'PeakVal',{{{}}},'PeakMode',{{{}}});
aa.fresh = 1;
aa.extracted = 0;
varargout = {class(aa,'miraudio',t)};
return
end
center.key = 'Center';
center.type = 'Boolean';
center.default = 0;
center.when = 'After';
option.center = center;
normal.key = 'Normal';
normal.type = 'Boolean';
normal.default = 0;
normal.when = 'After';
option.normal = normal;
extract.key = {'Extract','Excerpt'};
extract.type = 'Integer';
extract.number = 2;
extract.default = [];
extract.unit = {'s','sp'};
extract.defaultunit = 's';
extract.from = {'Start','Middle','End'};
extract.defaultfrom = 'Start';
option.extract = extract;
trim.type = 'String';
trim.choice = {'NoTrim','Trim','TrimBegin','TrimStart','TrimEnd'};
trim.default = 'NoTrim';
trim.when = 'After';
option.trim = trim;
trimthreshold.key = 'TrimThreshold';
trimthreshold.type = 'Integer';
trimthreshold.default = .06;
trimthreshold.when = 'After';
option.trimthreshold = trimthreshold;
label.key = 'Label';
label.default = '';
label.when = 'After';
option.label = label;
sampling.key = 'Sampling';
sampling.type = 'Integer';
sampling.default = 0;
sampling.when = 'Both';
option.sampling = sampling;
% segment.key = 'Segment';
% segment.type = 'Integer';
% segment.default = [];
% segment.when = 'After';
% option.segment = segment;
reverse.key = 'Reverse';
reverse.type = 'Boolean';
reverse.default = 0;
reverse.when = 'After';
option.reverse = reverse;
mono.key = 'Mono';
mono.type = 'Boolean';
mono.default = NaN;
mono.when = 'After';
option.mono = mono;
separate.key = 'SeparateChannels';
separate.type = 'Boolean';
separate.default = 0;
option.separate = separate;
Ch.key = {'Channel','Channels'};
Ch.type = 'Integer';
Ch.default = [];
Ch.when = 'After';
option.Ch = Ch;
specif.option = option;
specif.beforechunk = {@beforechunk,'normal'};
specif.eachchunk = @eachchunk;
specif.combinechunk = @combinechunk;
if nargin > 1 && ischar(varargin{1}) && strcmp(varargin{1},'Now')
if nargin > 2
extract = varargin{2};
else
extract = [];
end
para = [];
varargout = {main(orig,[],para,[],extract)};
else
varargout = mirfunction(@miraudio,orig,varargin,nargout,specif,@init,@main);
end
if isempty(varargout)
varargout = {{}};
end
function [x type] = init(x,option)
if isa(x,'mirdesign')
if option.sampling
x = setresampling(x,option.sampling);
end
end
type = 'miraudio';
function a = main(orig,option,after,index,extract)
if iscell(orig)
orig = orig{1};
end
if ischar(orig)
if nargin < 5
extract = [];
end
[d{1},tp{1},fp{1},f{1},l{1},b{1},n{1},ch{1}] = mirread(extract,orig,1,0);
l{1}{1} = l{1}{1}*f{1};
t = mirtemporal([],'Time',tp,'Data',d,'FramePos',fp,'Sampling',f,...
'Name',n,'Label',cell(1,length(d)),...
'Clusters',cell(1,length(d)),'Length',l,...
'Channels',ch,'Centered',0,'NBits',b);
t = set(t,'Title','Audio waveform');
a.fresh = 1;
a.extracted = 1;
a = class(a,'miraudio',t);
else
if not(isempty(option)) && not(isempty(option.extract))
if not(isstruct(after))
after = struct;
end
after.extract = option.extract;
end
if isa(orig,'miraudio')
a = orig;
else
a.fresh = 1;
a.extracted = 0;
a = class(a,'miraudio',orig);
end
end
if not(isempty(after))
a = post(a,after);
end
function a = post(a,para)
if a.fresh && isfield(para,'mono')
a.fresh = 0;
if isnan(para.mono)
para.mono = 1;
end
end
if isfield(para,'mono') && para.mono == 1
a = mirsum(a,'Mean');
end
d = get(a,'Data');
t = get(a,'Time');
ac = get(a,'AcrossChunks');
f = get(a,'Sampling');
cl = get(a,'Clusters');
for h = 1:length(d)
for k = 1:length(d{h})
tk = t{h}{k};
dk = d{h}{k};
if isfield(para,'extract') && not(isempty(para.extract)) ...
&& ~a.extracted
t1 = para.extract(1);
t2 = para.extract(2);
if para.extract(4)
if para.extract(4) == 1
shift = round(size(tk,1)/2);
elseif para.extract(4) == 2
shift = size(tk,1);
end
if para.extract(3)
shift = tk(shift,1,1);
end
t1 = t1+shift;
t2 = t2+shift;
end
if para.extract(3) % in seconds
ft = find(tk>=t1 & tk<=t2);
else % in samples
if not(t1)
warning('WARNING IN MIRAUDIO: Extract sample positions should be real positive integers.')
display('Positions incremented by one.');
t1 = t1+1;
t2 = t2+1;
end
ft = t1:t2;
end
tk = tk(ft,:,:);
dk = dk(ft,:,:);
end
if isfield(para,'Ch') && not(isempty(para.Ch))
dk = dk(:,:,para.Ch);
end
if isfield(para,'center') && para.center
dk = center_(dk);
a = set(a,'Centered',1);
end
if isfield(para,'normal') && para.normal
nl = size(dk,1);
nc = size(dk,3);
if isempty(ac)
ee = 0;
for j = 1:nc
ee = ee+sum(dk(:,:,j).^2);
end
ee = sqrt(ee/nl/nc);
else
ee = sqrt(sum(ac.sqrsum.^2)/ac.samples);
end
if ee
dk = dk./repmat(ee,[nl,1,nc]);
end
end
if isfield(para,'trim') && not(isequal(para.trim,0)) ... %%%% NOT A POST OPERATION!!
&& not(strcmpi(para.trim,'NoTrim'))
if not(para.trimthreshold)
para.trimthreshold = 0.06;
end
trimframe = 100;
trimhop = 10;
nframes = floor((length(tk)-trimframe)/trimhop)+1;
rms = zeros(1,nframes);
ss = sum(dk,3);
for j = 1:nframes
st = floor((j-1)*trimhop)+1;
rms(j) = norm(ss(st:st+trimframe-1))/sqrt(trimframe);
end
rms = (rms-min(rms))./(max(rms)-min(rms));
nosil = find(rms>para.trimthreshold);
if strcmpi(para.trim,'Trim') || strcmpi(para.trim,'TrimStart') ...
|| strcmpi(para.trim,'TrimBegin')
nosil1 = min(nosil);
if nosil1 > 1
nosil1 = nosil1-1;
end
n1 = floor((nosil1-1)*trimhop)+1;
else
n1 = 1;
end
if strcmpi(para.trim,'Trim') || strcmpi(para.trim,'TrimEnd')
nosil2 = max(nosil);
if nosil2 < length(rms)
nosil2 = nosil2+1;
end
n2 = floor((nosil2-1)*trimhop)+1;
else
n2 = length(tk);
end
tk = tk(n1:n2);
dk = dk(n1:n2,1,:);
end
if isfield(para,'sampling') && para.sampling
if and(f{k}, not(f{k} == para.sampling))
for j = 1:size(dk,3)
rk(:,:,j) = resample(dk(:,:,j),para.sampling,f{k});
end
dk = rk;
tk = repmat((0:size(dk,1)-1)',[1 1 size(tk,3)])...
/para.sampling + tk(1,:,:);
end
f{k} = para.sampling;
end
d{h}{k} = dk;
t{h}{k} = tk;
%if isfield(para,'reverse') && para.reverse
% d{h}{k} = flipdim(d{h}{k},1);
%end
end
end
a = set(a,'Data',d,'Time',t,'Sampling',f,'Clusters',cl);
a = set(a,'Extracted',0);
if isfield(para,'label')
if isnumeric(para.label)
n = get(a,'Name');
l = cell(1,length(d));
for k = 1:length(d)
if para.label
l{k} = n{k}(para.label);
else
l{k} = n{k};
end
end
a = set(a,'Label',l);
elseif iscell(para.label)
idx = mod(get(a,'Index'),length(para.label));
if not(idx)
idx = length(para.label);
end
a = set(a,'Label',para.label{idx});
elseif ischar(para.label) && ~isempty(para.label)
l = cell(1,length(d));
for k = 1:length(d)
l{k} = para.label;
end
a = set(a,'Label',l);
end
end
function [new orig] = beforechunk(orig,option,missing)
option.normal = 0;
a = miraudio(orig,option);
d = get(a,'Data');
old = get(orig,'AcrossChunks');
if isempty(old)
old.sqrsum = 0;
old.samples = 0;
end
new = mircompute(@crossum,d);
new = new{1}{1};
new.sqrsum = old.sqrsum + new.sqrsum;
new.samples = old.samples + new.samples;
function s = crossum(d)
s.sqrsum = sum(d.^2);
s.samples = length(d);
function [y orig] = eachchunk(orig,option,missing)
y = miraudio(orig,option);
function y = combinechunk(old,new)
doo = get(old,'Data');
to = get(old,'Time');
dn = get(new,'Data');
tn = get(new,'Time');
y = set(old,'Data',{{[doo{1}{1};dn{1}{1}]}},...
'Time',{{[to{1}{1};tn{1}{1}]}});
|
github
|
martinarielhartmann/mirtooloct-master
|
miremotion.m
|
.m
|
mirtooloct-master/MIRToolbox/@miremotion/miremotion.m
| 14,268 |
utf_8
|
e1e8b3cb2dcd7d9172b5b27dcc2240b7
|
function varargout = miremotion(orig,varargin)
% Predicts emotion along three dimensions and five basic concepts.
% Optional parameters:
% miremotion(...,'Dimensions',0) excludes all three dimensions.
% miremotion(...,'Dimensions',3) includes all three dimensions (default).
% miremotion(...,'Activity') includes the 'Activity' dimension.
% miremotion(...,'Valence') includes the 'Valence' dimension.
% miremotion(...,'Tension') includes the 'Tension' dimension.
% miremotion(...,'Dimensions',2) includes 'Activity' and 'Valence'.
% miremotion(...,'Arousal') includes 'Activity' and 'Tension'.
% miremotion(...,'Concepts',0) excludes all five concepts.
% miremotion(...,'Concepts') includes all five concepts (default).
% miremotion(...,'Happy') includes the 'Happy' concept.
% miremotion(...,'Sad') includes the 'Sad' concept.
% miremotion(...,'Tender') includes the 'Tender' concept.
% miremotion(...,'Anger') includes the 'Anger' concept.
% miremotion(...,'Fear') includes the 'Fear' concept.
% miremotion(...,'Frame',...) predict emotion frame by frame.
%
% Selection of features and coefficients are taken from a study:
% Eerola, T., Lartillot, O., and Toiviainen, P.
% (2009). Prediction of multidimensional emotional ratings in
% music from audio using multivariate regression models.
% In Proceedings of 10th International Conference on Music Information Retrieval
% (ISMIR 2009), pages 621-626.
%
% The implemented models are based on multiple linear regression with 5 best
% predictors (MLR option in the paper). The box-cox transformations have now been
% removed until the normalization values have been established with a large sample of music.
%
% TODO: Revision of coefficients to (a) force the output range between 0 - 1 and
% (b) to be based on alternative models and materials (training sets).
%
% Updated 03.05.2010 TE
%
frame.key = 'Frame';
frame.type = 'Integer';
frame.number = 2;
frame.default = [0 0];
frame.keydefault = [2 .5];
option.frame = frame;
dim.key = 'Dimensions';
dim.type = 'Integer';
dim.default = NaN;
dim.keydefault = 3;
option.dim = dim;
activity.key = 'Activity';
activity.type = 'Boolean';
activity.default = NaN;
option.activity = activity;
valence.key = 'Valence';
valence.type = 'Boolean';
valence.default = NaN;
option.valence = valence;
tension.key = 'Tension';
tension.type = 'Boolean';
tension.default = NaN;
option.tension = tension;
arousal.key = 'Arousal';
arousal.type = 'Boolean';
arousal.default = NaN;
option.arousal = arousal;
concepts.key = 'Concepts';
concepts.type = 'Boolean';
concepts.default = NaN;
option.concepts = concepts;
happy.key = 'Happy';
happy.type = 'Boolean';
happy.default = NaN;
option.happy = happy;
sad.key = 'Sad';
sad.type = 'Boolean';
sad.default = NaN;
option.sad = sad;
tender.key = 'Tender';
tender.type = 'Boolean';
tender.default = NaN;
option.tender = tender;
anger.key = 'Anger';
anger.type = 'Boolean';
anger.default = NaN;
option.anger = anger;
fear.key = 'Fear';
fear.type = 'Boolean';
fear.default = NaN;
option.fear = fear;
specif.option = option;
specif.defaultframelength = 2;
%specif.defaultframehop = .5;
specif.combinechunk = {'Average',@nothing};
specif.extensive = 1;
varargout = mirfunction(@miremotion,orig,varargin,nargout,specif,@init,@main);
%%
function [x type] = init(x,option)
option = process(option);
if option.frame.length.val
hop = option.frame.hop.val;
if strcmpi(option.frame.hop.unit,'Hz')
hop = 1/hop;
option.frame.hop.unit = 's';
end
if strcmpi(option.frame.hop.unit,'s')
hop = hop*get(x,'Sampling');
end
if strcmpi(option.frame.hop.unit,'%')
hop = hop/100;
option.frame.hop.unit = '/1';
end
if strcmpi(option.frame.hop.unit,'/1')
hop = hop*option.frame.length.val;
end
frames = 0:hop:1000000;
x = mirsegment(x,frames');
elseif isa(x,'mirdesign')
x = set(x,'NoChunk',1);
end
rm = mirrms(x,'Frame',.046,.5);
le = 0; %mirlowenergy(rm,'ASR');
o = mironsets(x,'Filterbank',15,'Contrast',0.1);
at = mirattacktime(o);
as = 0; %mirattackslope(o);
ed = 0; %mireventdensity(o,'Option1');
fl = mirfluctuation(x,'Summary');
fp = mirpeaks(fl,'Total',1);
fc = 0; %mircentroid(fl);
tp = 0; %mirtempo(x,'Frame',2,.5,'Autocor','Spectrum');
pc = mirpulseclarity(x,'Frame',2,.5); %%%%%%%%%%% Why 'Frame'??
s = mirspectrum(x,'Frame',.046,.5);
sc = mircentroid(s);
ss = mirspread(s);
sr = mirroughness(s);
%ps = mirpitch(x,'Frame',.046,.5,'Tolonen');
c = mirchromagram(x,'Frame','Wrap',0,'Pitch',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
cp = mirpeaks(c,'Total',1);
ps = 0;%cp;
ks = mirkeystrength(c);
[k kc] = mirkey(ks);
mo = mirmode(ks);
hc = mirhcdf(c);
se = mirentropy(mirspectrum(x,'Collapsed','Min',40,'Smooth',70,'Frame',1.5,.5)); %%%%%%%%% Why 'Frame'??
ns = mirnovelty(mirspectrum(x,'Frame',.1,.5,'Max',5000),'Normal',0);
nt = mirnovelty(mirchromagram(x,'Frame',.2,.25),'Normal',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
nr = mirnovelty(mirchromagram(x,'Frame',.2,.25,'Wrap',0),'Normal',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
x = {rm,le, at,as,ed, fp,fc, tp,pc, sc,ss,sr, ps, cp,kc,mo,hc, se, ns,nt,nr};
type = {'miremotion','mirscalar','mirscalar',...
'mirscalar','mirscalar','mirscalar',...
'mirspectrum','mirscalar',...
'mirscalar','mirscalar',...
'mirscalar','mirscalar','mirscalar',...
'mirscalar',...
'mirchromagram','mirscalar','mirscalar','mirscalar',...
'mirscalar',...
'mirscalar','mirscalar','mirscalar'};
%%
function e = main(x,option,postoption)
warning('WARNING IN MIREMOTION: The current model of miremotion is not correctly calibrated with this version of MIRtoolbox (but with version 1.3 only).');
option = process(option);
rm = get(x{1},'Data');
%le = get(x{2},'Data');
at = get(x{3},'Data');
%as = get(x{4},'Data');
%ed = get(x{5},'Data');
%fpp = get(x{6},'PeakPosUnit');
fpv = get(x{6},'PeakVal');
%fc = get(x{7},'Data');
%tp = get(x{8},'Data');
pc = get(x{9},'Data');
sc = get(x{10},'Data');
ss = get(x{11},'Data');
rg = get(x{12},'Data');
%ps = get(x{13},'PeakPosUnit');
cp = get(x{14},'PeakPosUnit');
kc = get(x{15},'Data');
mo = get(x{16},'Data');
hc = get(x{17},'Data');
se = get(x{18},'Data');
ns = get(x{19},'Data');
nt = get(x{20},'Data');
nr = get(x{21},'Data');
e.dim = {};
e.dimdata = mircompute(@initialise,rm);
if option.activity == 1
[e.dimdata e.activity_fact] = mircompute(@activity,e.dimdata,rm,fpv,sc,ss,se);
e.dim = [e.dim,'Activity'];
else
e.activity_fact = NaN;
end
if option.valence == 1
[e.dimdata e.valence_fact] = mircompute(@valence,e.dimdata,rm,fpv,kc,mo,ns);
e.dim = [e.dim,'Valence'];
else
e.valence_fact = NaN;
end
if option.tension == 1
[e.dimdata e.tension_fact] = mircompute(@tension,e.dimdata,rm,fpv,kc,hc,nr);
e.dim = [e.dim,'Tension'];
else
e.tension_fact = NaN;
end
e.class = {};
e.classdata = mircompute(@initialise,rm);
if option.happy == 1
[e.classdata e.happy_fact] = mircompute(@happy,e.classdata,fpv,ss,cp,kc,mo);
e.class = [e.class,'Happy'];
else
e.happy_fact = NaN;
end
if option.sad == 1
[e.classdata e.sad_fact] = mircompute(@sad,e.classdata,ss,cp,mo,hc,nt);
e.class = [e.class,'Sad'];
else
e.sad_fact = NaN;
end
if option.tender == 1
[e.classdata e.tender_fact] = mircompute(@tender,e.classdata,sc,rg,kc,hc,ns);
e.class = [e.class,'Tender'];
else
e.tender_fact = NaN;
end
if option.anger == 1
[e.classdata e.anger_fact] = mircompute(@anger,e.classdata,rg,kc,se,nr);
e.class = [e.class,'Anger'];
else
e.anger_fact = NaN;
end
if option.fear == 1
[e.classdata e.fear_fact] = mircompute(@fear,e.classdata,rm,at,fpv,kc,mo);
e.class = [e.class,'Fear'];
else
e.fear_fact = NaN;
end
e = class(e,'miremotion',mirdata(x{1}));
e = purgedata(e);
fp = mircompute(@noframe,get(x{1},'FramePos'));
e = set(e,'Title','Emotion','Abs','emotions','Ord','magnitude','FramePos',fp);
%%
function option = process(option)
if option.arousal==1
option.activity = 1;
option.tension = 1;
if isnan(option.dim)
option.dim = 0;
end
end
if option.activity==1 || option.valence==1 || option.tension==1
if isnan(option.activity)
option.activity = 0;
end
if isnan(option.valence)
option.valence = 0;
end
if isnan(option.tension)
option.tension = 0;
end
if isnan(option.concepts)
option.concepts = 0;
end
end
if not(isnan(option.dim)) && option.dim
if isnan(option.concepts)
option.concepts = 0;
end
end
if not(isnan(option.concepts)) && option.concepts
if isnan(option.dim)
option.dim = 0;
end
end
if not(isnan(option.dim))
switch option.dim
case 0
if isnan(option.activity)
option.activity = 0;
end
if isnan(option.valence)
option.valence = 0;
end
if isnan(option.tension)
option.tension = 0;
end
case 2
option.activity = 1;
option.valence = 1;
if isnan(option.tension)
option.tension = 0;
end
case 3
option.activity = 1;
option.valence = 1;
option.tension = 1;
end
end
if isnan(option.activity)
option.activity = 1;
end
if isnan(option.valence)
option.valence = 1;
end
if isnan(option.tension)
option.tension = 1;
end
if isnan(option.concepts)
option.concepts = 1;
end
if option.concepts
option.happy = 1;
option.sad = 1;
option.tender = 1;
option.anger = 1;
option.fear = 1;
end
if option.happy==1 || option.sad==1 || option.tender==1 ...
|| option.anger==1 || option.fear==1
if isnan(option.happy)
option.happy = 0;
end
if isnan(option.sad)
option.sad = 0;
end
if isnan(option.tender)
option.tender = 0;
end
if isnan(option.anger)
option.anger = 0;
end
if isnan(option.fear)
option.fear = 0;
end
end
%%
function e = initialise(rm)
e = [];
function [e af] = activity(e,rm,fpv,sc,ss,se) % without the box-cox transformation, revised coefficients
af = zeros(5,1);
% In the code below, removal of nan values added by Ming-Hsu Chang
af(1) = 0.6664* ((mean(rm(~isnan(rm))) - 0.0559)/0.0337);
tmp = fpv{1};
af(2) = 0.6099 * ((mean(tmp(~isnan(tmp))) - 13270.1836)/10790.655);
tmp = cell2mat(sc);
af(3) = 0.4486*((mean(tmp(~isnan(tmp))) - 1677.7)./570.34);
tmp = cell2mat(ss);
af(4) = -0.4639*((mean(tmp(~isnan(tmp))) - (250.5574*22.88))./(205.3147*22.88)); % New normalisation proposed by Ming-Hsu Chang
af(5) = 0.7056*((mean(se(~isnan(se))) - 0.954)./0.0258);
af(isnan(af)) = [];
e(end+1,:) = sum(af)+5.4861;
function [e vf] = valence(e,rm,fpv,kc,mo,ns) % without the box-cox transformation, revised coefficients
vf = zeros(5,1);
vf(1) = -0.3161 * ((std(rm) - 0.024254)./0.015667);
vf(2) = 0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655);
vf(3) = 0.8802 * ((mean(kc) - 0.5123)./0.091953);
vf(4) = 0.4565 * ((mean(mo) - -0.0019958)./0.048664);
ns(isnan(ns)) = [];
vf(5) = 0.4015 * ((mean(ns) - 131.9503)./47.6463);
vf(isnan(vf)) = [];
e(end+1,:) = sum(vf)+5.2749;
function [e tf] = tension(e,rm,fpv,kc,hc,nr)
tf = zeros(5,1);
tf(1) = 0.5382 * ((std(rm) - 0.024254)./0.015667);
tf(2) = -0.5406 * ((mean(fpv{1}) - 13270.1836)/10790.655);
tf(3) = -0.6808 * ((mean(kc) - 0.5124)./0.092);
tf(4) = 0.8629 * ((mean(hc) - 0.2962)./0.0459);
tf(5) = -0.5958 * ((mean(nr) - 71.8426)./46.9246);
tf(isnan(tf)) = [];
e(end+1,:) = sum(tf)+5.4679;
% BASIC EMOTION PREDICTORS
function [e ha_f] = happy(e,fpv,ss,cp,kc,mo)
ha_f = zeros(5,1);
ha_f(1) = 0.7438*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
ha_f(2) = -0.3965*((mean(cell2mat(ss)) - 250.5574)./205.3147);
ha_f(3) = 0.4047*((std(cell2mat(cp)) - 8.5321)./2.5899);
ha_f(4) = 0.7780*((mean(kc) - 0.5124)./0.092);
ha_f(5) = 0.6220*((mean(mo) - -0.002)./0.0487);
ha_f(isnan(ha_f)) = [];
e(end+1,:) = sum(ha_f)+2.6166;
function [e sa_f] = sad(e,ss,cp,mo,hc,nt)
sa_f = zeros(5,1);
sa_f(1) = 0.4324*((mean(cell2mat(ss)) - 250.5574)./205.3147);
sa_f(2) = -0.3137*((std(cell2mat(cp)) - 8.5321)./2.5899);
sa_f(3) = -0.5201*((mean(mo) - -0.0020)./0.0487);
sa_f(4) = -0.6017*((mean(hc) - 0.2962)./0.0459);
sa_f(5) = 0.4493*((mean(nt) - 42.2022)./36.7782);
sa_f(isnan(sa_f)) = [];
e(end+1,:) = sum(sa_f)+2.9756;
function [e te_f] = tender(e,sc,rg,kc,hc,ns)
te_f = zeros(5,1);
te_f(1) = -0.2709*((mean(cell2mat(sc)) - 1677.7106)./570.3432);
te_f(2) = -0.4904*((std(rg) - 85.9387)./106.0767);
te_f(3) = 0.5192*((mean(kc) - 0.5124)./0.0920);
te_f(4) = -0.3995*((mean(hc) - 0.2962)./0.0459);
te_f(5) = 0.3391*((mean(ns) - 131.9503)./47.6463);
te_f(isnan(te_f)) = [];
e(end+1,:) = sum(te_f)+2.9756;
function [e an_f] = anger(e,rg,kc,se,nr) %
an_f = zeros(5,1);
%an_f(1) = -0.2353*((mean(pc) - 0.1462)./.1113);
an_f(2) = 0.5517*((mean(rg) - 85.9387)./106.0767);
an_f(3) = -.5802*((mean(kc) - 0.5124)./0.092);
an_f(4) = .2821*((mean(se) - 0.954)./0.0258);
an_f(5) = -.2971*((mean(nr) - 71.8426)./46.9246);
an_f(isnan(an_f)) = [];
e(end+1,:) = sum(an_f)+1.9767;
function [e fe_f] = fear(e,rm,at,fpv,kc,mo)
fe_f = zeros(5,1);
fe_f(1) = 0.4069*((std(rm) - 0.0243)./0.0157);
fe_f(2) = -0.6388*((mean(at) - 0.0707)./0.015689218536423);
fe_f(3) = -0.2538*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
fe_f(4) = -0.9860*((mean(kc) - 0.5124)./0.0920);
fe_f(5) = -0.3144*((mean(mo) - -0.0019958)./0.048663550639094);
fe_f(isnan(fe_f)) = [];
e(end+1,:) = sum(fe_f)+2.7847;
function fp = noframe(fp)
fp = [fp(1);fp(end)];
|
github
|
martinarielhartmann/mirtooloct-master
|
mirplay.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirpattern/mirplay.m
| 899 |
utf_8
|
ea782c3f96091cb9b147f96756704a55
|
function varargout = mirplay(p,varargin)
pat.key = 'Pattern';
pat.type = 'Integer';
pat.default = 0;
option.pat = pat;
specif.option = option;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,p,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = main(p,option,postoption)
if not(option.pat)
option.pat = 1:length(p.pattern);
end
n = get(p,'Name');
for h = 1:length(n)
for i = option.pat
display(['Pattern # ',num2str(i)])
for j = 1:length(p.pattern{i}.occurrence)
display(['Occurrence # ',num2str(j)])
a = miraudio(n{h},'Extract',p.pattern{i}.occurrence{j}.start,...
p.pattern{i}.occurrence{j}.end);
mirplay(a)
end
end
end
noargout = {};
|
github
|
martinarielhartmann/mirtooloct-master
|
mirpattern.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirpattern/mirpattern.m
| 1,867 |
utf_8
|
1def40f8b242e54a318195d5ecc0c60f
|
function varargout = mirpattern(orig,varargin)
% p = mirpattern(a)
period.key = 'Period';
period.type = 'Boolean';
period.when = 'After';
period.default = 0;
option.period = period;
specif.option = option;
varargout = mirfunction(@mirpattern,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
if not(isamir(x,'mirpattern'))
x = mirsimatrix(x);
end
type = 'mirpattern';
function p = main(orig,option,postoption)
if not(isamir(orig,'mirpattern'))
b = get(orig,'Branch');
fp = get(orig,'FramePos');
pp = get(orig,'PeakPos');
for i = 1:length(b)
for j = 1:length(b{i}{1})
bi = b{i}{1}{j};
pi1 = sort(pp{i}{1}{bi(1,1)});
pi2 = sort(pp{i}{1}{bi(end,1)});
p.pattern{j}.occurrence{1}.start = ...
fp{i}{1}(1,bi(1,1)) - mean(fp{i}{1}(1:2,pi1(bi(1,2))));
p.pattern{j}.occurrence{2}.start = ...
fp{i}{1}(1,bi(1,1));
p.pattern{j}.occurrence{1}.end = ...
fp{i}{1}(1,bi(end,1)) - mean(fp{i}{1}(1:2,pi2(bi(end,2))));
p.pattern{j}.occurrence{2}.end = ...
fp{i}{1}(1,bi(end,1));
end
end
p = class(p,'mirpattern',mirdata(orig));
end
if postoption.period
for i = 1:length(p.pattern)
poi = p.pattern{i}.occurrence;
if poi{1}.end > poi{2}.start
poi{1}.end = poi{2}.start;
cycle = poi{1}.end - poi{1}.start;
ncycles = floor((poi{2}.end-poi{2}.start)/cycle)+2;
poi{ncycles}.end = poi{2}.end;
poi{2}.end = poi{2}.start + cycle;
for j = 2:ncycles-1
poi{j}.end = poi{j}.start + cycle;
poi{j+1}.start = poi{j}.end;
end
end
p.pattern{i}.occurrence = poi;
end
end
|
github
|
martinarielhartmann/mirtooloct-master
|
mirpitch.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirpitch/mirpitch.m
| 34,447 |
utf_8
|
60e5f74d92ec86e6551b87495a9a6792
|
function varargout = mirpitch(orig,varargin)
% p = mirpitch(x) evaluates the pitch frequencies (in Hz).
% Specification of the method(s) for pitch estimation (these methods can
% be combined):
% mirpitch(...,'Autocor') computes an autocorrelation function
% (Default method)
% mirpitch(...'Enhanced',a) computes enhanced autocorrelation
% (see help mirautocor)
% toggled on by default
% mirpitch(...,'Compress',k) performs magnitude compression
% (see help mirautocor)
% mirpitch(...,fb) specifies a type of filterbank.
% Possible values:
% fb = 'NoFilterBank': no filterbank decomposition
% fb = '2Channels' (default value)
% fb = 'Gammatone'
% mirpitch(...,'Spectrum') computes the FFT spectrum
% mirpitch(...,'AutocorSpectrum') computes the autocorrelation of
% the FFT spectrum
% mirpitch(...,'Cepstrum') computes the cepstrum
% Alternatively, an autocorrelation or a cepstrum can be directly
% given as first argument of the mirpitch function.
% Peak picking options:
% mirpitch(...,'Total',m) selects the m best pitches.
% Default value: m = Inf, no limit is set concerning the number
% of pitches to be detected.
% mirpitch(...,'Mono') corresponds to mirpitch(...,'Total',1)
% mirpitch(...,'Min',mi) indicates the lowest frequency taken into
% consideration.
% Default value: 75 Hz. (Praat)
% mirpitch(...,'Max',ma) indicates the highest frequency taken into
% consideration.
% Default value: 2400 Hz. Because there seems to be some problems
% with higher frequency, due probably to the absence of
% pre-whitening in our implementation of Tolonen and Karjalainen
% approach (used by default, cf. below).
% mirpitch(...,'Contrast',thr) specifies a threshold value.
% (see help peaks)
% Default value: thr = .1
% mirpitch(...,'Order',o) specifies the ordering for the peak picking.
% Default value: o = 'Amplitude'.
% Alternatively, the result of a mirpeaks computation can be directly
% given as first argument of the mirpitch function.
% Post-processing options:
% mirpitch(..., 'Cent') convert the pitch axis from Hz to cent scale.
% One octave corresponds to 1200 cents, so that 100 cents
% correspond to a semitone in equal temperament.
% mirpitch(..., 'Segment') segments the obtained monodic pitch curve
% in cents as a succession of notes with stable frequencies.
% Additional parameters available: 'SegMinLength', 'SegPitchGap',
% 'SegTimeGap'.
% mirpitch(...,'Sum','no') does not sum back the channels at the end
% of the computation. The resulting pitch information remains
% therefore decomposed into several channels.
% mirpitch(...,'Median') performs a median filtering of the pitch
% curve. When several pitches are extracted in each frame, the
% pitch curve contains the best peak of each successive frame.
% mirpitch(...,'Stable',th,n) remove pitch values when the difference
% (or more precisely absolute logarithmic quotient) with the
% n precedent frames exceeds the threshold th.
% if th is not specified, the default value .1 is used
% if n is not specified, the default value 3 is used
% mirpitch(...'Reso',r) removes peaks whose distance to one or
% several higher peaks is lower than a given threshold.
% Possible value for the threshold r:
% 'SemiTone': ratio between the two peak positions equal to
% 2^(1/12)
% mirpitch(...,'Frame',l,h) orders a frame decomposition of window
% length l (in seconds) and hop factor h, expressed relatively to
% the window length. For instance h = 1 indicates no overlap.
% Default values: l = 46.4 ms and h = 10 ms (Tolonen and
% Karjalainen, 2000)
% Preset model:
% mirpitch(...,'Tolonen') implements (part of) the model proposed in
% (Tolonen & Karjalainen, 2000). It is equivalent to
% mirpitch(...,'Enhanced',2:10,'Generalized',.67,'2Channels')
% [p,a] = mirpitch(...) also displays the result of the method chosen for
% pitch estimation, and shows in particular the peaks corresponding
% to the pitch values.
% p = mirpitch(f,a,<r>) creates a mirpitch object based on the frequencies
% specified in f and the related amplitudes specified in a, using a
% frame sampling rate of r Hz (set by default to 100 Hz).
%
% T. Tolonen, M. Karjalainen, "A Computationally Efficient Multipitch
% Analysis Model", IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING,
% VOL. 8, NO. 6, NOVEMBER 2000
ac.key = 'Autocor';
ac.type = 'Boolean';
ac.default = 0;
option.ac = ac;
enh.key = 'Enhanced';
enh.type = 'Integer';
enh.default = 2:10;
option.enh = enh;
filtertype.type = 'String';
filtertype.choice = {'NoFilterBank','2Channels','Gammatone'};
filtertype.default = '2Channels';
option.filtertype = filtertype;
sum.key = 'Sum';
sum.type = 'Boolean';
sum.default = 1;
option.sum = sum;
gener.key = {'Generalized','Compress'};
gener.type = 'Integer';
gener.default = .5;
option.gener = gener;
as.key = 'AutocorSpectrum';
as.type = 'Boolean';
as.default = 0;
option.as = as;
s.key = 'Spectrum';
s.type = 'Boolean';
s.default = 0;
option.s = s;
res.key = 'Res';
res.type = 'Integer';
res.default = NaN;
option.res = res;
db.key = 'dB';
db.type = 'Integer';
db.default = 0;
db.keydefault = Inf;
option.db = db;
ce.key = 'Cepstrum';
ce.type = 'Boolean';
ce.default = 0;
option.ce = ce;
comb.key = 'Comb';
comb.type = 'Boolean';
comb.default = 0;
option.comb = comb;
%% peak picking options
m.key = 'Total';
m.type = 'Integer';
m.default = Inf;
option.m = m;
multi.key = 'Multi';
multi.type = 'Boolean';
multi.default = 0;
option.multi = multi;
mono.key = 'Mono';
mono.type = 'Boolean';
mono.default = 0;
option.mono = mono;
mi.key = 'Min';
mi.type = 'Integer';
mi.default = 75;
option.mi = mi;
ma.key = 'Max';
ma.type = 'Integer';
ma.default = 2400;
option.ma = ma;
cthr.key = 'Contrast';
cthr.type = 'Integer';
cthr.default = .1;
option.cthr = cthr;
thr.key = 'Threshold';
thr.type = 'Integer';
thr.default = .4;
option.thr = thr;
order.key = 'Order';
order.type = 'String';
order.choice = {'Amplitude','Abscissa'};
order.default = 'Amplitude';
option.order = order;
reso.key = 'Reso';
reso.type = 'String';
reso.choice = {0,'SemiTone'};
reso.default = 0;
option.reso = reso;
track.key = 'Track'; % Not used yet
track.type = 'Boolean';
track.default = 0;
option.track = track;
%% post-processing options
cent.key = 'Cent';
cent.type = 'Boolean';
cent.default = 0;
option.cent = cent;
segm.key = 'Segment';
segm.type = 'Boolean';
segm.when = 'Both';
segm.default = 0;
option.segm = segm;
segmin.key = 'SegMinLength';
segmin.type = 'Integer';
segmin.when = 'Both';
segmin.default = 2;
option.segmin = segmin;
segpitch.key = 'SegPitchGap';
segpitch.type = 'Integer';
segpitch.when = 'Both';
segpitch.default = 45;
option.segpitch = segpitch;
segtime.key = 'SegTimeGap';
segtime.type = 'Integer';
segtime.when = 'Both';
segtime.default = 20;
option.segtime = segtime;
octgap.key = 'OctaveGap';
octgap.type = 'Boolean';
octgap.when = 'Both';
octgap.default = 0;
option.octgap = octgap;
ref.key = 'Ref';
ref.type = 'Integer';
ref.default = 0;
option.ref = ref;
stable.key = 'Stable';
stable.type = 'Integer';
stable.number = 2;
stable.default = [Inf 0];
stable.keydefault = [.1 3];
option.stable = stable;
median.key = 'Median';
median.type = 'Integer';
median.default = 0;
median.keydefault = .1;
option.median = median;
frame.key = 'Frame';
frame.type = 'Integer';
frame.number = 2;
frame.default = [0 0];
frame.keydefault = [NaN NaN];
option.frame = frame;
%% preset model
tolo.key = 'Tolonen';
tolo.type = 'Boolean';
tolo.default = 0;
option.tolo = tolo;
harmonic.key = 'Harmonic';
harmonic.type = 'Boolean';
harmonic.default = 0;
option.harmonic = harmonic;
specif.option = option;
specif.chunkframebefore = 1;
if isnumeric(orig)
if nargin<3
f = 100;
else
f = varargin{2};
end
fp = (0:size(orig,1)-1)/f;
fp = [fp;fp+1/f];
p.amplitude = {{varargin{1}'}};
s = mirscalar([],'Data',{{orig'}},'Title','Pitch','Unit','Hz',...
'FramePos',{{fp}},'Sampling',f,'Name',{inputname(1)});
p = class(p,'mirpitch',s);
varargout = {p};
else
varargout = mirfunction(@mirpitch,orig,varargin,nargout,specif,@init,@main);
end
function [y type] = init(orig,option)
if option.tolo
option.enh = 2:10;
option.gener = .67;
option.filtertype = '2Channels';
elseif option.harmonic
option.s = 1;
option.frame.hop.val = .1;
option.res = 1;
option.db = Inf;
end
if not(option.ac) && not(option.as) && not(option.ce) && not(option.s)
option.ac = 1;
end
if option.segm && option.frame.length.val==0
option.frame.length.val = NaN;
option.frame.hop.val = NaN;
end
if isnan(option.frame.length.val)
option.frame.length.val = .0464;
end
if isnan(option.frame.hop.val)
option.frame.hop.val = .01;
option.frame.hop.unit = 's';
end
if isamir(orig,'mirmidi') || isamir(orig,'mirscalar') || haspeaks(orig)
y = orig;
else
if isamir(orig,'mirautocor')
y = mirautocor(orig,'Min',option.mi,'Hz','Max',option.ma,'Hz','Freq');
elseif isamir(orig,'mircepstrum')
y = orig;
elseif isamir(orig,'mirspectrum')
if not(option.as) && not(option.ce) && not(option.s)
option.ce = 1;
end
if option.as
y = mirautocor(orig,...
'Min',option.mi,'Hz','Max',option.ma,'Hz');
end
if option.ce
ce = mircepstrum(orig,'freq',...
'Min',option.mi,'Hz','Max',option.ma,'Hz');
if option.as
y = y*ce;
else
y = ce;
end
end
if option.s
y = orig;
end
else
if option.ac
x = orig;
if not(strcmpi(option.filtertype,'NoFilterBank'))
x = mirfilterbank(x,option.filtertype);
end
x = mirframenow(x,option);
y = mirautocor(x,'Generalized',option.gener);%,...
% 'Min',option.mi,'Hz','Max',option.ma,'Hz');
if option.sum
y = mirsummary(y);
end
y = mirautocor(y,'Enhanced',option.enh,'Freq');
y = mirautocor(y,'Min',option.mi,'Hz','Max',option.ma,'Hz');
end
if option.as || option.ce || option.s
x = mirframenow(orig,option);
if option.comb
y = mirspectrum(x,'Min',option.mi,'Max',2000,'Res',1);%,'Sum');
elseif option.s
s = mirspectrum(x,'Min',option.mi,'Max',option.ma,...
'Res',option.res,'dB',option.db);
if option.ac
y = y*s;
else
y = s;
end
end
if option.as || option.ce
s = mirspectrum(x);
if option.as
as = mirautocor(s,'Min',option.mi,'Hz',...
'Max',option.ma,'Hz');
if option.ac || option.s
y = y*as;
else
y = as;
end
end
if option.ce
ce = mircepstrum(s,'freq','Min',option.mi,'Hz',...
'Max',option.ma,'Hz');
if option.ac || option.s || option.as
y = y*ce;
else
y = ce;
end
end
end
end
end
end
type = {'mirpitch',mirtype(y)};
function o = main(x,option,postoption)
if option.comb == 2
option.m = Inf;
option.order = 'Abscissa';
elseif option.multi && option.m == 1
option.m = Inf;
elseif (option.mono && option.m == Inf) %|| option.segm
option.m = 1;
elseif option.harmonic
option.cthr = .01;
option.thr = .5;
end
if iscell(x)
if length(x)>1
x2 = get(x{2},'Data');
f2 = get(x{2},'Pos');
end
x = x{1};
else
x2 = [];
end
if option.comb == 1
d = get(x,'Data');
pos = get(x,'Pos');
cb = cell(1,length(d));
for i = 1:length(d)
cb{i} = cell(1,length(d{i}));
for j = 1:length(d{i})
cb{i}{j} = zeros(size(d{i}{j},1),...
size(d{i}{j},2),...
size(d{i}{j},3));
dij = d{i}{j}/max(max(max(d{i}{j})));
for h = 1:size(d{i}{j},1)
ph = pos{i}{j}(h,1,1);
ip = h;
for k = 2:size(d{i}{j},1)
[unused mp] = min(abs(pos{i}{j}(ip(end)+1:end,1,1) ...
- ph * k));
if isempty(mp)
break
end
ip(end+1) = ip(end) + mp;
end
if length(ip) == 1
break
end
cbh = sum(dij(ip,:,:));
for k = 1:length(ip)
cbh = cbh .* ...
(.5 * (2 - ...
exp(-(max(dij(ip(1:k),:,:),[],1).^2 * 5000))));
end
cb{i}{j}(h,:,:) = cbh;
end
cb{i}{j}(h+1:end,:,:) = [];
pos{i}{j}(h+1:end,:,:) = [];
end
end
x = set(x,'Data',cb,'Pos',pos,'Title','Spectral Comb');
end
if isa(x,'mirpitch')
pf = get(x,'Data');
pa = get(x,'Amplitude');
if option.m < Inf
for i = 1:length(pf)
for j = 1:length(pf{i})
for h = 1:length(pf{i}{j})
pf{i}{j}{h} = pf{i}{j}{h}(1:option.m,:);
pa{i}{j}{h} = pa{i}{j}{h}(1:option.m,:);
end
end
end
end
else
if not(isa(x,'mirpitch') || isa(x,'mirmidi'))
x = mirpeaks(x,'Total',option.m,'Track',option.track,...
'Contrast',option.cthr,'Threshold',option.thr,...
'Reso',option.reso,'NoBegin','NoEnd',...
'Order',option.order,'Harmonic',option.harmonic);
end
if isa(x,'mirscalar')
pf = get(x,'Data');
elseif option.harmonic
pf = get(x,'TrackPos');
pa = get(x,'TrackVal');
else
pf = get(x,'PeakPrecisePos');
pa = get(x,'PeakPreciseVal');
end
end
fp = get(x,'FramePos');
punit = 'Hz';
if option.comb == 2
pp = get(x,'PeakPos');
pv = get(x,'PeakVal');
pm = get(x,'PeakMode');
f = get(x,'Pos');
for i = 1:length(pf)
for j = 1:length(pf{i})
maxf = f{i}{j}(end,1);
for h = 1:length(pf{i}{j})
sco = zeros(length(pf{i}{j}{h}),1);
for k = 1:length(pf{i}{j}{h})
fk = pf{i}{j}{h}(k);
if fk > option.ma
break
end
ws = zeros(round(maxf / fk) ,1);
%err = mod(pf{i}{j}{h}/fk,1);
%err = min(err,1-err);
ws(1) = pa{i}{j}{h}(k);
for l = k+1:length(pf{i}{j}{h})
r = round(pf{i}{j}{h}(l) / fk);
if r == 1
continue
end
err = mod(pf{i}{j}{h}(l) / fk ,1);
err = min(err,1-err);
ws(r) = max(ws(r),pa{i}{j}{h}(l).*exp(-err^2*50));
end
sco(k) = sum(ws);
if length(ws)>3 && ws(3)<.5
sco(k) = sco(k)/2;
end
%if length(ws)>5 && ws(5)<.5
% sco(k) = sco(k)/2;
%end
%/(1+length(find(ws(2:end-1)<.01)));
%sco(k) = sum(pa{i}{j}{h}.*exp(-err));
end
%pa{i}{j}{h} = sco;
[unused b] = max(sco);
pf{i}{j}{h} = pf{i}{j}{h}(b);
pa{i}{j}{h} = pa{i}{j}{h}(b);
pp{i}{j}{h} = pp{i}{j}{h}(b);
pv{i}{j}{h} = pv{i}{j}{h}(b);
pm{i}{j}{h} = pm{i}{j}{h}(b);
end
end
end
x = set(x,'PeakPrecisePos',pf,'PeakPreciseVal',pa,...
'PeakPos',pp,'PeakVal',pv,'PeakMode',pm);
end
if (option.cent || option.segm) && ...
(~isa(x,'mirpitch') || strcmp(get(x,'Unit'),'Hz'))
punit = 'cents';
for i = 1:length(pf)
for j = 1:length(pf{i})
for k = 1:size(pf{i}{j},3)
for l = 1:size(pf{i}{j},2)
pf{i}{j}{1,l,k} = 1200*log2(pf{i}{j}{1,l,k});
end
end
end
end
end
if option.segm
scale = [];
for i = 1:length(pf)
for j = 1:length(pf{i})
if size(pf{i}{j},2) == 1 && size(pf{i}{j}{1},2) > 1
pfj = cell(1,size(pf{i}{j}{1},2));
paj = cell(1,size(pa{i}{j}{1},2));
for l = 1:size(pf{i}{j}{1},2)
if isnan(pf{i}{j}{1}(l))
pfj{l} = [];
paj{l} = 0;
else
pfj{l} = pf{i}{j}{1}(l);
paj{l} = pa{i}{j}{1}(l);
end
end
pf{i}{j} = pfj;
pa{i}{j} = paj;
end
for k = 1:size(pf{i}{j},3)
startp = [];
meanp = [];
endp = [];
deg = [];
stabl = [];
buffer = [];
breaks = [];
currentp = [];
maxp = 0;
reson = [];
attack = [];
if ~isempty(pf{i}{j}{1,1,k})
pf{i}{j}{1,1,k} = pf{i}{j}{1,1,k}(1);
pa{i}{j}{1,1,k} = pa{i}{j}{1,1,k}(1);
end
if ~isempty(pf{i}{j}{1,end,k})
pf{i}{j}{1,end,k} = pf{i}{j}{1,end,k}(1);
pa{i}{j}{1,end,k} = pa{i}{j}{1,end,k}(1);
end
for l = 2:size(pf{i}{j},2)-1
if ~isempty(pa{i}{j}{1,l,k}) && ...
pa{i}{j}{1,l,k}(1) > maxp
maxp = pa{i}{j}{1,l,k}(1);
end
if ~isempty(reson) && l-reson(1).end>50
reson(1) = [];
end
if ~isempty(pf{i}{j}{1,l,k})
if 1 %isempty(pf{i}{j}{1,l-1,k})
pf{i}{j}{1,l,k} = pf{i}{j}{1,l,k}(1);
pa{i}{j}{1,l,k} = pa{i}{j}{1,l,k}(1);
else
[dpf idx] = min(abs(pf{i}{j}{1,l,k} - ...
pf{i}{j}{1,l-1,k}));
if idx > 1 && ...
pa{i}{j}{1,l,k}(1) - pa{i}{j}{1,l,k}(idx) > .02
pf{i}{j}{1,l,k} = pf{i}{j}{1,l,k}(1);
pa{i}{j}{1,l,k} = pa{i}{j}{1,l,k}(1);
else
pf{i}{j}{1,l,k} = pf{i}{j}{1,l,k}(idx);
pa{i}{j}{1,l,k} = pa{i}{j}{1,l,k}(idx);
end
end
end
interrupt = 0;
if l == size(pf{i}{j},2)-1 || ...
isempty(pf{i}{j}{1,l,k}) || ...
(~isempty(buffer) && ...
abs(pf{i}{j}{1,l,k} - pf{i}{j}{1,l-1,k})...
> option.segpitch) || ...
(~isempty(currentp) && ...
abs(pf{i}{j}{1,l,k} - currentp) > ...
option.segpitch)
interrupt = 1;
elseif (~isempty(pa{i}{j}{1,l-1,k}) && ...
pa{i}{j}{1,l,k} - pa{i}{j}{1,l-1,k} > .01)
interrupt = 2;
end
if ~interrupt
for h = 1:length(reson)
if abs(pf{i}{j}{1,l,k}-reson(h).pitch) < 50 && ...
pa{i}{j}{1,l,k} < reson(h).amp/5
pa{i}{j}{1,l,k} = [];
pf{i}{j}{1,l,k} = [];
interrupt = 1;
break
end
end
end
if interrupt
% Segment interrupted
if isempty(buffer) || ...
...%length(buffer.pitch) < option.segmin || ...
0 %std(buffer.pitch) > 25
if length(startp) > length(endp)
startp(end) = [];
end
else
if isempty(currentp)
strong = find(buffer.amp > max(buffer.amp)*.75);
meanp(end+1) = mean(buffer.pitch(strong));
else
meanp(end+1) = currentp;
end
endp(end+1) = l-1;
hp = hist(buffer.pitch,5);
hp = hp/sum(hp);
entrp = -sum(hp.*log(hp+1e-12))./log(length(hp));
stabl(end+1) = entrp>.7;
deg(end+1) = cent2deg(meanp(end),scale);
reson(end+1).pitch = meanp(end);
reson(end).amp = mean(buffer.amp);
reson(end).end = l-1;
attack(end+1) = max(buffer.amp) > .05;
end
if isempty(pf{i}{j}{1,l,k})
buffer = [];
else
buffer.pitch = pf{i}{j}{1,l,k};
buffer.amp = pa{i}{j}{1,l,k};
startp(end+1) = l;
end
currentp = [];
breaks(end+1) = l;
elseif isempty(buffer)
% New segment starting
startp(end+1) = l;
buffer.pitch = pf{i}{j}{1,l,k};
buffer.amp = pa{i}{j}{1,l,k};
else
if length(pf{i}{j}{1,l,k})>1
mirerror('mirpitch','''Segment'' option only for monodies (use also ''Mono'')');
end
buffer.pitch(end+1) = pf{i}{j}{1,l,k};
buffer.amp(end+1) = pa{i}{j}{1,l,k};
if length(buffer.pitch) > 4 && ...
std(buffer.pitch(1:end)) < 5 && ...
buffer.amp(end) > max(buffer.amp)*.5
currentp = mean(buffer.pitch(1:end));
%else
% l
end
end
end
if length(startp) > length(meanp)
startp(end) = [];
end
l = 1;
while l <= length(endp)
if 1 %~isempty(intersect(startp(l)-(1:5),breaks)) && ...
% ~isempty(intersect(endp(l)+(1:5),breaks))
if 1 %attack(l)
minlength = option.segmin;
else
minlength = 6;
end
else
minlength = 2;
end
if endp(l)-startp(l) > minlength
% Segment sufficiently long
if l>1 && ~attack(l) && ...
startp(l) <= endp(l-1)+option.segtime && ...
abs(meanp(l)-meanp(l-1)) < 50
% Segment fused with previous one
startp(l) = [];
%meanp(l-1) = mean(meanp(l-1:l));
meanp(l) = [];
deg(l-1) = cent2deg(meanp(l-1),scale);
deg(l) = [];
attack(l-1) = max(attack(l),attack(l-1));
attack(l) = [];
endp(l-1) = [];
found = 1;
else
l = l+1;
end
% Other cases: Segment too short
elseif l>1 && ...
startp(l) <= endp(l-1)+option.segtime && ...
abs(meanp(l)-meanp(l-1)) < 50
% Segment fused with previous one
startp(l) = [];
%meanp(l-1) = mean(meanp(l-1:l));
meanp(l) = [];
deg(l) = [];
attack(l-1) = max(attack(l),attack(l-1));
attack(l) = [];
endp(l-1) = [];
elseif 0 && l < length(meanp) && ...
startp(l+1) <= endp(l)+option.segtime && ...
abs(meanp(l+1)-meanp(l)) < 50
% Segment fused with next one
startp(l+1) = [];
meanp(l) = meanp(l+1); %mean(meanp(l:l+1));
meanp(l+1) = [];
deg(l) = deg(l+1);
deg(l+1) = [];
attack(l) = max(attack(l),attack(l+1));
attack(l+1) = [];
endp(l) = [];
else
% Segment removed
startp(l) = [];
meanp(l) = [];
deg(l) = [];
attack(l) = [];
endp(l) = [];
end
end
l = 1;
while l <= length(endp)
if (max([pa{i}{j}{1,startp(l):endp(l),k}]) < maxp/20 ...
&& isempty(pa{i}{j}{1,startp(l)-1,k}) ...
&& isempty(pa{i}{j}{1,endp(l)+1,k})) ...
|| endp(l) - startp(l) < option.segmin
% Segment removed
fusetest = endp(l) - startp(l) < option.segmin;
startp(l) = [];
meanp(l) = [];
deg(l) = [];
endp(l) = [];
stabl(l) = [];
attack(l) = [];
if fusetest && ...
l > 1 && l <= length(meanp) && ...
abs(meanp(l-1)-meanp(l)) < 50
% Preceding segment fused with next one
startp(l) = [];
meanp(l-1) = meanp(l); %mean(meanp(l:l+1));
meanp(l) = [];
deg(l-1) = deg(l);
deg(l) = [];
attack(l-1) = max(attack(l),attack(l-1));
attack(l) = [];
endp(l-1) = [];
end
else
l = l+1;
end
end
if option.octgap
l = 2;
while l <= length(endp)
if abs(meanp(l-1) - meanp(l) - 1200) < 50
% Segment removed
startp(l) = [];
meanp(l-1) = meanp(l);
meanp(l) = [];
deg(l-1) = deg(l);
deg(l) = [];
attack(l) = [];
endp(l-1) = [];
stabl(l) = [];
elseif abs(meanp(l) - meanp(l-1) - 1200) < 50
% Segment removed
startp(l) = [];
meanp(l) = meanp(l-1);
meanp(l) = [];
deg(l) = deg(l-1);
deg(l) = [];
attack(l) = [];
endp(l-1) = [];
stabl(l) = [];
else
l = l+1;
end
end
end
ps{i}{j}{k} = startp;
pe{i}{j}{k} = endp;
pm{i}{j}{k} = meanp;
stb{i}{j}{k} = stabl;
dg = {}; %{i}{j}{k} = deg;
end
end
end
elseif isa(x,'mirpitch')
ps = get(x,'Start');
pe = get(x,'End');
pm = get(x,'Mean');
dg = get(x,'Degrees');
stb = get(x,'Stable');
elseif isa(x,'mirmidi')
nm = get(x,'Data');
for i = 1:length(nm)
startp = nm{i}(:,1);
endp = startp + nm{i}(:,2);
fp{i} = [startp endp]';
ps{i} = {{1:length(startp)}};
pe{i} = {{1:length(endp)}};
pm{i} = {{nm{i}(:,4)'-68}};
dg{i} = pm{i};
stb{i} = [];
pf{i} = {NaN(size(startp'))};
end
x = set(x,'FramePos',{fp});
else
ps = {};
pe = {};
pm = {};
dg = {};
stb = {};
end
if option.stable(1) < Inf
for i = 1:length(pf)
for j = 1:length(pf{i})
for k = 1:size(pf{i}{j},3)
for l = size(pf{i}{j},2):-1:option.stable(2)+1
for m = length(pf{i}{j}{1,l,k}):-1:1
found = 0;
for h = 1:option.stable(2)
for n = 1:length(pf{i}{j}{1,l-h,k})
if abs(log10(pf{i}{j}{1,l,k}(m) ...
/pf{i}{j}{1,l-h,k}(n))) ...
< option.stable(1)
found = 1;
end
end
end
if not(found)
pf{i}{j}{1,l,k}(m) = [];
end
end
pf{i}{j}{1,1,k} = zeros(1,0);
end
end
end
end
end
if option.median
for i = 1:length(pf)
for j = 1:length(pf{i})
if size(fp{i}{j},2) > 1
npf = zeros(size(pf{i}{j}));
for k = 1:size(pf{i}{j},3)
for l = 1:size(pf{i}{j},2)
if isempty(pf{i}{j}{1,l,k})
npf(1,l,k) = NaN;
else
npf(1,l,k) = pf{i}{j}{1,l,k}(1);
end
end
end
pf{i}{j} = medfilt1(npf,...
round(option.median/(fp{i}{j}(1,2)-fp{i}{j}(1,1))));
end
end
end
end
if 0 %isa(x,'mirscalar')
p.amplitude = 0;
else
p.amplitude = pa;
end
p.start = ps;
p.end = pe;
p.mean = pm;
p.degrees = dg;
p.stable = stb;
s = mirscalar(x,'Data',pf,'Title','Pitch','Unit',punit);
p = class(p,'mirpitch',s);
o = {p,x};
function [deg ref] = cent2deg(cent,ref)
deg = round((cent-ref)/100);
if isempty(deg)
deg = 0;
end
%ref = cent - deg*100
|
github
|
martinarielhartmann/mirtooloct-master
|
mirplay.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirenvelope/mirplay.m
| 3,662 |
utf_8
|
2e8e380750d648bcb991781e813423fd
|
function mirplay(e,varargin)
% mirplay method for mirenvelope objects. Help displayed in ../mirplay.m
ch.key = 'Channel';
ch.type = 'Integer';
ch.default = 0;
option.ch = ch;
sg.key = 'Segment';
sg.type = 'Integer';
sg.default = 0;
option.sg = sg;
se.key = 'Sequence';
se.type = 'Integer';
se.default = 0;
option.se = se;
inc.key = 'Increasing';
inc.type = 'MIRtb';
option.inc = inc;
dec.key = 'Decreasing';
dec.type = 'MIRtb';
option.dec = dec;
every.key = 'Every';
every.type = 'Integer';
option.every = every;
burst.key = 'Burst';
burst.type = 'Boolean';
burst.default = 1;
option.burst = burst;
specif.option = option;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,e,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = main(a,option,postoption)
if iscell(a)
a = a{1};
end
d = get(a,'Data');
f = get(a,'Sampling');
n = get(a,'Name');
c = get(a,'Channels');
pp = get(a,'PeakPosUnit');
if not(option.se)
if length(d)>1
if isfield(option,'inc')
[unused order] = sort(mirgetdata(option.inc));
elseif isfield(option,'dec')
[unused order] = sort(mirgetdata(option.dec),'descend');
else
order = 1:length(d);
end
if isfield(option,'every')
order = order(1:option.every:end);
end
else
order = 1;
end
else
order = option.se;
end
if not(isempty(order))
for k = order(:)'
display(['Playing envelope of file: ' n{k}])
dk = d{k};
if not(iscell(dk))
dk = {dk};
end
if option.ch
if isempty(c{k})
chk = option.ch;
else
[unused unused chk] = intersect(option.ch,c{k});
end
else
chk = 1:size(dk{1},3);
end
if isempty(chk)
display('No channel to play.');
end
for l = chk
if chk(end)>1
display([' Playing channel #' num2str(l)]);
end
if option.sg
sgk = option.sg(find(option.sg<=length(dk)));
else
sgk = 1:length(dk);
end
for i = sgk
if sgk(end)>1
display([' Playing segment #' num2str(i)])
end
di = dk{i};
for j = 1:size(di,2)
djl = resample(di(:,j,l),11025,round(f{k}));
djl = djl/max(djl);
djl = rand(length(djl),1).*djl; %djl(:)?;
if ~isempty(pp) && ~isempty(pp{k}{i})
pjl = pp{k}{i}{j,l};
d2jl = zeros(length(djl),1);
for h = 1:length(pjl)
d2jl(round(pjl(h)*11025)) = 1;
end
djl = djl/10 + d2jl;
end
sound(djl,11025);
idealtime = length(djl)/11025;
practime = toc;
if practime < idealtime
pause(idealtime-practime)
end
end
if option.burst && sgk(end)>1
sound(rand(1,10))
end
end
end
end
end
noargout = {};
|
github
|
martinarielhartmann/mirtooloct-master
|
mirenvelope.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirenvelope/mirenvelope.m
| 26,047 |
utf_8
|
b2e8c24762ea7e5c9b41c1db89666a70
|
function varargout = mirenvelope(orig,varargin)
% e = mirenvelope(x) extracts the envelope of x, showing the global shape
% of the waveform.
% mirenvelope(...,m) specifies envelope extraction method.
% Possible values:
% m = 'Filter' uses a low-pass filtering. (Default strategy)
% m = 'Spectro' uses a spectrogram.
%
% Options related to the 'Filter' method:
% mirenvelope(...,'Hilbert'): performs a preliminary Hilbert
% transform.
% mirenvelope(...,'PreDecim',N) downsamples by a factor N>1, where
% N is an integer, before the low-pass filtering (Klapuri, 1999).
% Default value: N = 1.
% mirenvelope(...,'Filtertype',f) specifies the filter type.
% Possible values are:
% f = 'IIR': filter with one autoregressive coefficient
% (default)
% f = 'HalfHann': half-Hanning (raised cosine) filter
% (Scheirer, 1998)
% Option related to the 'IIR' option:
% mirenvelope(...,'Tau',t): time constant of low-pass filter in
% seconds.
% Default value: t = 0.02 s.
% mirenvelope(...,'PostDecim',N) downsamples by a factor N>1, where
% N is an integer, after the low-pass filtering.
% Default value: N = 16 if 'PreDecim' is not used, else N = 1.
% mirenvelope(...,'Trim'): trims the initial ascending phase of the
% curves related to the transitory state.
%
% Options related to the 'Spectro' method:
% mirenvelope(...,b) specifies whether the frequency range is further
% decomposed into bands. Possible values:
% b = 'Freq': no band decomposition (default value)
% b = 'Mel': Mel-band decomposition
% b = 'Bark': Bark-band decomposition
% b = 'Cents': decompositions into cents
% mirenvelope(...,'Frame',...) specifies the frame configuration.
% Default value: length: .1 s, hop factor: 10 %.
% mirenvelope(...,'UpSample',N) upsamples by a factor N>1, where
% N is an integer.
% Default value if 'UpSample' called: N = 2
% mirenvelope(...,'Complex') toggles on the 'Complex' method for the
% spectral flux computation.
%
% Other available for all methods:
% mirenvelope(...,'Sampling',r): resamples to rate r (in Hz).
% 'Down' and 'Sampling' options cannot therefore be combined.
% mirenvelope(...,'Halfwave'): performs a half-wave rectification.
% mirenvelope(...,'Center'): centers the extracted envelope.
% mirenvelope(...,'HalfwaveCenter'): performs a half-wave
% rectification on the centered envelope.
% mirenvelope(...,'Log'): computes the common logarithm (base 10) of
% the envelope.
% mirenvelope(...,'Mu',mu): computes the logarithm of the
% envelope, before the eventual differentiation, using a mu-law
% compression (Klapuri, 2006).
% Default value for mu: 100
% mirenvelope(...,'Log'): computes the logarithm of the envelope.
% mirenvelope(...,'Power'): computes the power (square) of the
% envelope.
% mirenvelope(...,'Diff'): computes the differentation of the
% envelope, i.e., the differences between successive samples.
% mirenvelope(...,'HalfwaveDiff'): performs a half-wave
% rectification on the differentiated envelope.
% mirenvelope(...,'Normal'): normalizes the values of the envelope by
% fixing the maximum value to 1.
% mirenvelope(...,'Lambda',l): sums the half-wave rectified envelope
% with the non-differentiated envelope, using the respective
% weight 0<l<1 and (1-l). (Klapuri et al., 2006)
% mirenvelope(...,'Smooth',o): smooths the envelope using a moving
% average of order o.
% Default value when the option is toggled on: o=30
% mirenvelope(...,'Gauss',o): smooths the envelope using a gaussian
% of standard deviation o samples.
% Default value when the option is toggled on: o=30
% mirenvelope(...,'Klapuri06'): follows the model proposed in
% (Klapuri et al., 2006).
method.type = 'String';
method.choice = {'Filter','Spectro'};
method.default = 'Filter';
option.method = method;
%% options related to 'Filter':
hilb.key = 'Hilbert';
hilb.type = 'Boolean';
hilb.default = 0;
option.hilb = hilb;
decim.key = {'Decim','PreDecim'};
decim.type = 'Integer';
decim.default = 0;
option.decim = decim;
filter.key = 'FilterType';
filter.type = 'String';
filter.choice = {'IIR','HalfHann','Butter',0};
if isamir(orig,'mirenvelope')
filter.default = 0; % no more envelope extraction, already done
else
filter.default = 'IIR';
end
option.filter = filter;
%% options related to 'IIR':
tau.key = 'Tau';
tau.type = 'Integer';
tau.default = .02;
option.tau = tau;
zp.key = 'ZeroPhase'; % internal use: for manual filtfilt
zp.type = 'Boolean';
if isamir(orig,'mirenvelope')
zp.default = 0;
else
zp.default = NaN;
end
option.zp = zp;
ds.key = {'Down','PostDecim'};
ds.type = 'Integer';
if isamir(orig,'mirenvelope')
ds.default = 1;
else
ds.default = NaN; % 0 if 'PreDecim' is used, else 16
end
ds.when = 'After';
ds.chunkcombine = 'During';
option.ds = ds;
trim.key = 'Trim';
trim.type = 'Boolean';
trim.default = 0;
trim.when = 'After';
option.trim = trim;
%% Options related to 'Spectro':
band.type = 'String';
band.choice = {'Freq','Mel','Bark','Cents'};
band.default = 'Freq';
option.band = band;
up.key = {'UpSample'};
up.type = 'Integer';
up.default = 0;
up.keydefault = 2;
up.when = 'After';
option.up = up;
complex.key = 'Complex';
complex.type = 'Boolean';
complex.default = 0;
complex.when = 'After';
option.complex = complex;
powerspectrum.key = 'PowerSpectrum';
powerspectrum.type = 'Boolean';
powerspectrum.default = 1;
option.powerspectrum = powerspectrum;
timesmooth.key = 'TimeSmooth';
timesmooth.type = 'Boolean';
timesmooth.default = 0;
timesmooth.keydefault = 10;
option.timesmooth = timesmooth;
terhardt.key = 'Terhardt';
terhardt.type = 'Boolean';
terhardt.default = 0;
option.terhardt = terhardt;
frame.key = 'Frame';
frame.type = 'Integer';
frame.number = 2;
frame.default = [.1 .1];
option.frame = frame;
%% Options related to all methods:
sampling.key = 'Sampling';
sampling.type = 'Integer';
sampling.default = 0;
sampling.when = 'After';
option.sampling = sampling;
hwr.key = 'Halfwave';
hwr.type = 'Boolean';
hwr.default = 0;
hwr.when = 'After';
option.hwr = hwr;
c.key = 'Center';
c.type = 'Boolean';
c.default = 0;
c.when = 'After';
option.c = c;
chwr.key = 'HalfwaveCenter';
chwr.type = 'Boolean';
chwr.default = 0;
chwr.when = 'After';
option.chwr = chwr;
mu.key = 'Mu';
mu.type = 'Integer';
mu.default = 0;
mu.keydefault = 100;
mu.when = 'After';
option.mu = mu;
oplog.key = 'Log';
oplog.type = 'Boolean';
oplog.default = 0;
oplog.when = 'After';
option.log = oplog;
minlog.key = 'MinLog';
minlog.type = 'Integer';
minlog.default = 0;
minlog.when = 'After';
option.minlog = minlog;
oppow.key = 'Power';
oppow.type = 'Boolean';
oppow.default = 0;
oppow.when = 'After';
option.power = oppow;
diff.key = 'Diff';
diff.type = 'Integer';
diff.default = 0;
diff.keydefault = 1;
diff.when = 'After';
option.diff = diff;
diffhwr.key = 'HalfwaveDiff';
diffhwr.type = 'Integer';
diffhwr.default = 0;
diffhwr.keydefault = 1;
diffhwr.when = 'After';
option.diffhwr = diffhwr;
lambda.key = 'Lambda';
lambda.type = 'Integer';
lambda.default = 1;
lambda.when = 'After';
option.lambda = lambda;
aver.key = 'Smooth';
aver.type = 'Integer';
aver.default = 0;
aver.keydefault = 30;
aver.when = 'After';
option.aver = aver;
gauss.key = 'Gauss';
gauss.type = 'Integer';
gauss.default = 0;
gauss.keydefault = 30;
gauss.when = 'After';
option.gauss = gauss;
% iir.key = 'IIR';
% iir.type = 'Boolean';
% iir.default = 0;
% iir.when = 'After';
% option.iir = iir;
norm.key = 'Normal';
norm.type = 'String';
norm.choice = {0,1,'AcrossSegments'};
norm.default = 0;
norm.keydefault = 1;
norm.when = 'After';
option.norm = norm;
presel.type = 'String';
presel.choice = {'Klapuri06'};
presel.default = 0;
option.presel = presel;
specif.option = option;
specif.eachchunk = 'Normal';
specif.combinechunk = 'Concat';
specif.extensive = 1;
varargout = mirfunction(@mirenvelope,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type = 'mirenvelope';
if isamir(x,'mirscalar') %% Should return in other cases as well?
return
end
if ischar(option.presel) && strcmpi(option.presel,'Klapuri06')
option.method = 'Spectro';
end
if not(isamir(x,'mirenvelope'))
if strcmpi(option.method,'Filter')
if isnan(option.zp)
if strcmpi(option.filter,'IIR')
option.zp = 1;
else
option.zp = 0;
end
end
if option.zp == 1
x = mirenvelope(x,'ZeroPhase',2,'Down',1,...
'Tau',option.tau,'PreDecim',option.decim);
end
elseif strcmpi(option.method,'Spectro')
x = mirspectrum(x,'Frame',option.frame.length.val,...
option.frame.length.unit,...
option.frame.hop.val,...
option.frame.hop.unit,...
option.frame.phase.val,...
option.frame.phase.unit,...
option.frame.phase.atend,...
'Window','hanning',option.band,...
...'dB',
'Power',option.powerspectrum,...
'TimeSmooth',option.timesmooth,...
'Terhardt',option.terhardt);%,'Mel');
end
end
function e = main(orig,option,postoption)
if iscell(orig)
orig = orig{1};
end
if isamir(orig,'mirscalar')
d = get(orig,'Data');
fp = get(orig,'FramePos');
for i = 1:length(d)
for j = 1:length(d{i})
d{i}{j} = reshape(d{i}{j},size(d{i}{j},2),1,size(d{i}{j},3));
p{i}{j} = mean(fp{i}{j})';
end
end
e.downsampl = 0;
e.hwr = 0;
e.diff = 0;
e.log = 0;
e.method = 'Scalar';
e.phase = {{}};
e = class(e,'mirenvelope',mirtemporal(orig));
e = set(e,'Title','Envelope','Data',d,'Pos',p,...
'FramePos',{{p{1}{1}([1 end])}},...
'Sampling',{1/diff(p{1}{1}([1 2]))});
postoption.trim = 0;
postoption.ds = 0;
e = post(e,postoption);
return
end
if isfield(option,'presel') && ischar(option.presel) && ...
strcmpi(option.presel,'Klapuri06')
option.method = 'Spectro';
postoption.up = 2;
postoption.mu = 100;
postoption.diffhwr = 1;
postoption.lambda = .8;
end
if isfield(postoption,'ds') && isnan(postoption.ds)
if option.decim
postoption.ds = 0;
else
postoption.ds = 16;
end
end
if not(isfield(option,'filter')) || not(ischar(option.filter))
e = post(orig,postoption);
elseif strcmpi(option.method,'Spectro')
d = get(orig,'Data');
fp = get(orig,'FramePos');
sr = get(orig,'Sampling');
ch = get(orig,'Channels');
ph = get(orig,'Phase');
for h = 1:length(d)
sr{h} = 0;
for i = 1:length(d{h})
if size(d{h}{i},3)>1 % Already in bands (channels in 3d dim)
d{h}{i} = permute(sum(d{h}{i}),[2 1 3]);
if ~isempty(ph)
ph{h}{i} = permute(ph{h}{i},[2 1 3]);
end
else % Simple spectrogram, frequency range sent to 3d dim
d{h}{i} = permute(d{h}{i},[2 3 1]);
if ~isempty(ph)
ph{h}{i} = permute(ph{h}{i},[2 3 1]);
end
end
p{h}{i} = mean(fp{h}{i})';
if not(sr{h}) && size(fp{h}{i},2)>1
sr{h} = 1/(fp{h}{i}(1,2)-fp{h}{i}(1,1));
end
end
if not(sr{h})
warning('WARNING IN MIRENVELOPE: The frame decomposition did not succeed. Either the input is of too short duration, or the chunk size is too low.');
end
ch{h} = (1:size(d{h}{1},3))';
end
e.downsampl = 0;
e.hwr = 0;
e.diff = 0;
e.log = 0;
e.method = 'Spectro';
e.phase = ph;
e = class(e,'mirenvelope',mirtemporal(orig));
e = set(e,'Title','Envelope','Data',d,'Pos',p,...
'Sampling',sr,'Channels',ch,'FramePos',{{fp{1}{1}([1 end])'}});
postoption.trim = 0;
postoption.ds = 0;
e = post(e,postoption);
else
if isnan(option.zp)
if strcmpi(option.filter,'IIR')
option.zp = 1;
else
option.zp = 0;
end
end
if option.zp == 1
option.decim = 0;
end
e.downsampl = 1;
e.hwr = 0;
e.diff = 0;
e.log = 0;
e.method = option.filter;
e.phase = {};
e = class(e,'mirenvelope',mirtemporal(orig));
e = purgedata(e);
e = set(e,'Title','Envelope');
sig = get(e,'Data');
x = get(e,'Pos');
sr = get(e,'Sampling');
%disp('Extracting envelope...')
d = cell(1,length(sig));
for k = 1:length(sig)
if length(sig)==1
[state e] = gettmp(orig,e);
else
state = [];
end
if option.decim
sr{k} = sr{k}/option.decim;
end
if strcmpi(option.filter,'IIR')
a2 = exp(-1/(option.tau*sr{k})); % filter coefficient
a = [1 -a2];
b = 1-a2;
elseif strcmpi(option.filter,'HalfHann')
a = 1;
b = hann(sr{k}*.4);
b = b(ceil(length(b)/2):end);
elseif strcmpi(option.filter,'Butter')
% From Timbre Toolbox
w = 5 / ( sr{k}/2 );
[b,a] = butter(3, w);
end
d{k} = cell(1,length(sig{k}));
for i = 1:length(sig{k})
sigi = sig{k}{i};
if option.zp == 2
sigi = flipdim(sigi,1);
end
if option.hilb
try
for h = 1:size(sigi,2)
for j = 1:size(sigi,3)
sigi(:,h,j) = hilbert(sigi(:,h,j));
end
end
catch
disp('Signal Processing Toolbox does not seem to be installed. No Hilbert transform.');
end
end
sigi = abs(sigi);
if option.decim
dsigi = zeros(ceil(size(sigi,1)/option.decim),...
size(sigi,2),size(sigi,3));
for f = 1:size(sigi,2)
for c = 1:size(sigi,3)
dsigi(:,f,c) = decimate(sigi(:,f,c),option.decim);
end
end
sigi = dsigi;
clear dsigi
x{k}{i} = x{k}{i}(1:option.decim:end,:,:);
end
% tmp = filtfilt(1-a,[1 -a],sigi); % zero-phase IIR filter for smoothing the envelope
% Manual filtfilt
emptystate = isempty(state);
tmp = zeros(size(sigi));
for c = 1:size(sigi,3)
if emptystate
[tmp(:,:,c) state(:,c,1)] = filter(b,a,sigi(:,:,c));
else
[tmp(:,:,c) state(:,c,1)] = filter(b,a,sigi(:,:,c),...
state(:,c,1));
end
end
tmp = max(tmp,0); % For security reason...
if option.zp == 2
tmp = flipdim(tmp,1);
end
d{k}{i} = tmp;
%td{k} = round(option.tau*sr{k}*1.5);
end
end
e = set(e,'Data',d,'Pos',x,'Sampling',sr);
if length(sig)==1
e = settmp(e,state);
end
if not(option.zp == 2)
e = post(e,postoption);
end
end
if isfield(option,'presel') && ischar(option.presel) && ...
strcmpi(option.presel,'Klapuri06')
e = mirsum(e,'Adjacent',10);
end
function e = post(e,postoption)
if isempty(postoption)
return
end
if isfield(postoption,'lambda') && not(postoption.lambda)
postoption.lambda = 1;
end
d = get(e,'Data');
tp = get(e,'Time');
sr = get(e,'Sampling');
ds = get(e,'DownSampling');
ph = get(e,'Phase');
for k = 1:length(d)
if isfield(postoption,'sampling')
if postoption.sampling
newsr = postoption.sampling;
elseif isfield(postoption,'ds') && postoption.ds>1
newsr = sr{k}/postoption.ds;
else
newsr = sr{k};
end
end
if isfield(postoption,'up') && postoption.up
[z,p,gain] = butter(6,10/newsr/postoption.up*2,'low');
[sos,g] = zp2sos(z,p,gain);
Hd = dfilt.df2tsos(sos,g);
end
if isfield(postoption,'norm') && ...
ischar(postoption.norm) && ...
strcmpi(postoption.norm,'AcrossSegments')
mdk = 0;
for i = 1:length(d{k})
mdk = max(mdk,max(abs(d{k}{i})));
end
end
for i = 1:length(d{k})
if isfield(postoption,'sampling') && postoption.sampling
if and(sr{k}, not(sr{k} == postoption.sampling))
dk = d{k}{i};
for j = 1:size(dk,3)
if not(sr{k} == round(sr{k}))
mirerror('mirenvelope','The ''Sampling'' postoption cannot be used after using the ''Down'' postoption.');
end
rk(:,:,j) = resample(dk(:,:,j),postoption.sampling,sr{k});
end
d{k}{i} = rk;
tp{k}{i} = repmat((0:size(d{k}{i},1)-1)',...
[1 1 size(tp{k}{i},3)])...
/postoption.sampling + tp{k}{i}(1,:,:);
if not(iscell(ds))
ds = cell(length(d));
end
ds{k} = round(sr{k}/postoption.sampling);
end
elseif isfield(postoption,'ds') && postoption.ds>1
if not(postoption.ds == round(postoption.ds))
mirerror('mirenvelope','The ''Down'' sampling rate should be an integer.');
end
ds = postoption.ds;
tp{k}{i} = tp{k}{i}(1:ds:end,:,:); % Downsampling...
d{k}{i} = d{k}{i}(1:ds:end,:,:);
end
if isfield(postoption,'sampling')
if not(strcmpi(e.method,'Spectro')) && postoption.trim
tdk = round(newsr*.1);
d{k}{i}(1:tdk,:,:) = repmat(d{k}{i}(tdk,:,:),[tdk,1,1]);
d{k}{i}(end-tdk+1:end,:,:) = repmat(d{k}{i}(end-tdk,:,:),[tdk,1,1]);
end
if postoption.log && ~get(e,'Log')
d{k}{i} = log10(d{k}{i});
end
if postoption.mu
dki = max(0,d{k}{i});
mu = postoption.mu;
dki = log(1+mu*dki)/log(1+mu);
dki(~isfinite(d{k}{i})) = NaN;
d{k}{i} = dki;
end
if postoption.power
d{k}{i} = d{k}{i}.^2;
end
if postoption.up
dki = zeros(size(d{k}{i},1).*postoption.up,...
size(d{k}{i},2),size(d{k}{i},3));
dki(1:postoption.up:end,:,:) = d{k}{i};
dki = filter(Hd,[dki;...
zeros(6,size(d{k}{i},2),size(d{k}{i},3))]);
d{k}{i} = dki(1+ceil(6/2):end-floor(6/2),:,:);
tki = zeros(size(tp{k}{i},1).*postoption.up,...
size(tp{k}{i},2),...
size(tp{k}{i},3));
dt = repmat((tp{k}{i}(2)-tp{k}{i}(1))...
/postoption.up,...
[size(tp{k}{i},1),1,1]);
for j = 1:postoption.up
tki(j:postoption.up:end,:,:) = tp{k}{i}+dt*(j-1);
end
tp{k}{i} = tki;
newsr = sr{k}*postoption.up;
end
if (postoption.diffhwr || postoption.diff) && ...
not(get(e,'Diff'))
tp{k}{i} = tp{k}{i}(1:end-1,:,:);
order = max(postoption.diffhwr,postoption.diff);
if postoption.complex
dph = diff(ph{k}{i},2);
dph = dph/(2*pi);% - round(dph/(2*pi));
ddki = sqrt(d{k}{i}(3:end,:,:).^2 + d{k}{i}(2:end-1,:,:).^2 ...
- 2.*d{k}{i}(3:end,:,:)...
.*d{k}{i}(2:end-1,:,:)...
.*cos(dph));
d{k}{i} = d{k}{i}(2:end,:,:);
tp{k}{i} = tp{k}{i}(2:end,:,:);
elseif order == 1
ddki = diff(d{k}{i},1,1);
else
b = firls(order,[0 0.9],[0 0.9*pi],'differentiator');
ddki = filter(b,1,...
[repmat(d{k}{i}(1,:,:),[order,1,1]);...
d{k}{i};...
repmat(d{k}{i}(end,:,:),[order,1,1])]);
ddki = ddki(order+1:end-order-1,:,:);
end
if postoption.diffhwr
ddki = hwr(ddki);
end
d{k}{i} = (1-postoption.lambda)*d{k}{i}(1:end-1,:,:)...
+ postoption.lambda*sr{k}/10*ddki;
end
if postoption.aver
y = filter(ones(1,postoption.aver),1,...
[d{k}{i};zeros(postoption.aver,...
size(d{k}{i},2),...
size(d{k}{i},3))]);
d{k}{i} = y(1+ceil(postoption.aver/2):...
end-floor(postoption.aver/2),:,:);
end
if postoption.gauss
sigma = postoption.gauss;
gauss = 1/sigma/2/pi...
*exp(- (-4*sigma:4*sigma).^2 /2/sigma^2);
y = filter(gauss,1,[d{k}{i};zeros(4*sigma,1,size(d{k}{i},3))]);
y = y(4*sigma:end,:,:);
d{k}{i} = y(1:size(d{k}{i},1),:,:);
end
%if postoption.iir
% a2 = exp(-1/(.4*sr{k}));
% d{k}{i} = filter(1-a2,[1 -a2],d{k}{i});
% % [d{k}{i};zeros(postoption.filter,...
% % size(d{k}{i},2),...
% % size(d{k}{i},3))]);
% %d{k}{i} = y(1+ceil(postoption.filter/2):...
% % end-floor(postoption.filter/2),:,:);
%end
if postoption.chwr
d{k}{i} = center(d{k}{i});
d{k}{i} = hwr(d{k}{i});
end
if postoption.hwr
d{k}{i} = hwr(d{k}{i});
end
if postoption.c
d{k}{i} = center(d{k}{i});
end
if get(e,'Log')
if postoption.minlog
d{k}{i}(d{k}{i} < -postoption.minlog) = NaN;
end
else
if postoption.norm == 1
d{k}{i} = d{k}{i}./repmat(max(abs(d{k}{i})),...
[size(d{k}{i},1),1,1]);
elseif ischar(postoption.norm) && ...
strcmpi(postoption.norm,'AcrossSegments')
d{k}{i} = d{k}{i}./repmat(mdk,[size(d{k}{i},1),1,1]);
end
end
end
end
if isfield(postoption,'sampling')
sr{k} = newsr;
end
end
if isfield(postoption,'ds') && postoption.ds>1
e = set(e,'DownSampling',postoption.ds,'Sampling',sr);
elseif isfield(postoption,'sampling') && postoption.sampling
e = set(e,'DownSampling',ds,'Sampling',sr);
elseif isfield(postoption,'up') && postoption.up
e = set(e,'Sampling',sr);
end
if isfield(postoption,'sampling')
if postoption.hwr
e = set(e,'Halfwave',1);
end
if postoption.diff
e = set(e,'Diff',1,'Halfwave',0,'Title','Differentiated envelope');
end
if postoption.diffhwr
e = set(e,'Diff',1,'Halfwave',1,'Centered',0);
end
if postoption.c
e = set(e,'Centered',1);
end
if postoption.chwr
e = set(e,'Halfwave',1,'Centered',1);
end
if postoption.log
e = set(e,'Log',1);
end
end
e = set(e,'Data',d,'Time',tp);
|
github
|
martinarielhartmann/mirtooloct-master
|
mirquery.m
|
.m
|
mirtooloct-master/MIRToolbox/@mirquery/mirquery.m
| 2,122 |
utf_8
|
69ba1787084c74f96a14a9faf74ff367
|
function res = mirquery(varargin)
% r = mirquery(q,b), where
% q is the analysis of one audio file and
% b is the analysis of a folder of audio files,
% according to the same mirtoolbox feature,
% returns the name of the audio files in the database b in an
% increasing distance to q with respect to the chosen feature.
% r = mirquery(d), where
% d is the distance between one audio file and a folder of audio
% file, according to a mirtoolbox feature,
% returns the name of the audio files in an increasing distance d.
%
% Optional argument:
% mirquery(...,'Best',n) returns the name of the n closest audio
% files.
% mirquery(..,'Distance',d) specifies the distance to use.
% Default value: d = 'Cosine' (cf. mirdist)
[distfunc,nbout] = scanargin(varargin);
if nargin<2 || not(isa(varargin{2},'mirdata'))
returnval=0;
dist = varargin{1};
name = get(dist,'Name2');
res.query.val = [];
res.query.name = get(dist,'Name');
else
returnval=1;
query = varargin{1};
base = varargin{2};
name = get(base,'Name');
res.query.val = mirgetdata(query);
res.query.name = get(query,'Name');
database = mirgetdata(base);
dist = mirdist(query,base,distfunc);
end
datadist = mirgetdata(dist);
[ordist order] = sort(datadist);
order(isnan(ordist)) = [];
nbout = min(nbout,length(order));
res.dist = ordist(1:nbout);
if returnval
res.val = database(order);
else
res.val = [];
end
res.name = name(order);
res = class(res,'mirquery');
function [distfunc,nbout] = scanargin(v)
distfunc = 'Cosine';
nbout=Inf;
i = 1;
while i <= length(v)
arg = v{i};
if ischar(arg) && strcmpi(arg,'Distance')
if length(v)>i && ischar(v{i+1})
i = i+1;
distfunc = v{i};
end
elseif ischar(arg) && strcmpi(arg,'Best')
if length(v)>i && isnumeric(v{i+1})
i = i+1;
nbout = v{i};
end
%else
% error('ERROR IN MIRQUERY: Syntax error. See help mirquery.');
end
i = i+1;
end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_probability_gmm.m
|
.m
|
mirtooloct-master/somtoolbox/som_probability_gmm.m
| 2,782 |
utf_8
|
1d0b944d5fda0f9051e055d366e40be7
|
function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
%SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model.
%
% [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
%
% [K,P] = som_estimate_gmm(sM,D);
% [pd,Pdm,pmd] = som_probability_gmm(D,sM,K,P);
% som_show(sM,'color',pmd(:,1),'color',Pdm(:,1))
%
% Input and output arguments:
% D (matrix) size dlen x dim, the data for which the
% (struct) data struct, probabilities are calculated
% sM (struct) map struct
% (matrix) size munits x dim, the kernel centers
% K (matrix) size munits x dim, kernel width parameters
% computed by SOM_ESTIMATE_GMM
% P (matrix) size 1 x munits, a priori probabilities for each
% kernel computed by SOM_ESTIMATE_GMM
%
% pd (vector) size dlen x 1, probability of each data vector in
% terms of the whole gaussian mixture model
% Pdm (matrix) size munits x dlen, probability of each vector in
% terms of each kernel
% pmd (matrix) size munits x dlen, probability of each vector to
% have been generated by each kernel
%
% See also SOM_ESTIMATE_GMM.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
% Copyright (c) by Esa Alhoniemi
% http://www.cis.hut.fi/projects/somtoolbox/
% ecco 180298 juuso 050100
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% input arguments
if isstruct(sM), M = sM.codebook; else M = sM; end
[c dim] = size(M);
if isstruct(D), D = D.data; end
dlen = size(D,1);
% reserve space for output variables
pd = zeros(dlen,1);
if nargout>=2, Pdm = zeros(c,dlen); end
if nargout==3, pmd = zeros(c,dlen); end
% the parameters of each kernel
cCoeff = cell(c,1);
cCoinv = cell(c,1);
for m=1:c,
co = diag(K(m,:));
cCoinv{m} = inv(co);
cCoeff{m} = 1 / ((2*pi)^(dim/2)*det(co)^.5);
end
% go through the vectors one by one
for i=1:dlen,
x = D(i,:);
% compute p(x|m)
pxm = zeros(c,1);
for m = 1:c,
dx = M(m,:) - x;
pxm(m) = cCoeff{m} * exp(-.5 * dx * cCoinv{m} * dx');
%pxm(m) = normal(dx, zeros(1,dim), diag(K(m,:)));
end
pxm(isnan(pxm(:))) = 0;
% p(x|m)
if nargin>=2, Pdm(:,i) = pxm; end
% P(x) = P(x|M) = sum( P(m) * p(x|m) )
pd(i) = P*pxm;
% p(m|x) = p(x|m) * P(m) / P(x)
if nargout==3, pmd(:,i) = (P' .* pxm) / pd(i); end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% subfunction normal
%
% computes probability of x when mean and covariance matrix
% of a distribution are known
function result = normal(x, mu, co)
[l dim] = size(x);
coinv = inv(co);
coeff = 1 / ((2*pi)^(dim/2)*det(co)^.5);
diff = x - mu;
result = coeff * exp(-.5 * diff * coinv * diff');
|
github
|
martinarielhartmann/mirtooloct-master
|
som_clget.m
|
.m
|
mirtooloct-master/somtoolbox/som_clget.m
| 3,420 |
utf_8
|
34bca7118530f042e1b9d90718cf688a
|
function a = som_clget(sC, mode, ind)
%SOM_CLGET Get properties of specified clusters.
%
% a = som_clget(sC, mode, ind)
%
% inds = som_clget(sC,'dinds',20);
% col = som_clget(sC,'depth',[1 2 3 20 54]);
%
% Input and output arguments:
% sC (struct) clustering struct
% mode (string) what kind of property is requested
% 'binds' (a union over) indeces of base clusters
% belonging to the specified cluster(s)
% 'dinds' (a union over) indeces of the data vectors
% belonging to the specified cluster(s)
% 'dlen' number of data vectors belonging
% to each of the specified cluster(s)
% 'depth' depths of the specified clusters
% (depth of the root cluster is 0,
% depth of its children are 1, etc.)
% 'child' (a union over) children clusters
% of specified cluster(s), including
% the clusters themselves
% 'base' base partitioning based on given
% clusters
% ind (vector) indeces of the clusters
%
% a (vector) the answer
%
% See also SOM_CLSTRUCT, SOM_CLPLOT.
% Copyright (c) 2000 by the SOM toolbox programming team.
% Contributed to SOM Toolbox on XXX by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 180800
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clen = size(sC.tree,1)+1;
switch mode,
case 'binds',
a = [];
for i=1:length(ind), a = [a, getbaseinds(sC.tree,ind(i))]; end
a = unique(a);
case 'dinds',
b = [];
for i=1:length(ind), b = [b, getbaseinds(sC.tree,ind(i))]; end
b = unique(b);
a = zeros(length(sC.base),1);
for i=1:length(b), a(find(sC.base==b(i)))=1; end
a = find(a);
case 'dlen',
a = zeros(length(ind),1);
for i=1:length(ind),
b = getbaseinds(sC.tree,ind(i));
for j=1:length(b), a(i) = a(i) + sum(sC.base==b(j)); end
end
case 'depth',
a = getdepth(sC.tree);
a = a(ind);
case 'child',
a = getchildren(sC.tree,ind);
case 'base',
a = sC.base*0;
ind = -sort(-ind);
for i=1:length(ind), a(som_clget(sC,'dinds',ind(i))) = ind(i); end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function ch = getchildren(Z,ind)
clen = size(Z,1)+1;
ch = ind; cho = ind;
while any(cho),
i = cho(1); cho = cho(2:end);
j = Z(i-clen,1); k = Z(i-clen,2);
if j>clen, cho(end+1) = j; end
if k>clen, cho(end+1) = k; end
ch(end+1) = j; ch(end+1) = k;
end
return;
function binds = getbaseinds(Z,ind)
clen = size(Z,1)+1;
binds = ind;
while binds(1)>clen,
i = binds(1);
binds = binds(2:end);
j = Z(i-clen,1); k = Z(i-clen,2);
if j>clen, binds = [j binds]; else binds(end+1) = j; end
if k>clen, binds = [k binds]; else binds(end+1) = k; end
end
return;
function depth = getdepth(Z)
clen = size(Z,1)+1;
depth = zeros(2*clen-1,1);
ch = 2*clen-1; % active nodes
while any(ch),
c = ch(1); ch = ch(2:end);
if c>clen & isfinite(Z(c-clen,3)),
chc = Z(c-clen,1:2); % children of c
depth(chc) = depth(c) + 1; % or +(ind==chc(1))
ch = [ch, chc];
end
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
lvq3.m
|
.m
|
mirtooloct-master/somtoolbox/lvq3.m
| 5,951 |
utf_8
|
3d1d8a994701991b148ab22ae8bceb1a
|
function codebook = lvq3(codebook,data,rlen,alpha,win,epsilon)
%LVQ3 trains codebook with LVQ3 -algorithm
%
% sM = lvq3(sM,D,rlen,alpha,win,epsilon)
%
% sM = lvq3(sM,sD,50*length(sM.codebook),0.05,0.2,0.3);
%
% Input and output arguments:
% sM (struct) map struct, the class information must be
% present on the first column of .labels field
% D (struct) data struct, the class information must
% be present on the first column of .labels field
% rlen (scalar) running length
% alpha (scalar) learning parameter, e.g. 0.05
% win (scalar) window width parameter, e.g. 0.25
% epsilon (scalar) relative learning parameter, e.g. 0.3
%
% sM (struct) map struct, the trained codebook
%
% NOTE: does not take mask into account.
%
% For more help, try 'type lvq3', or check out online documentation.
% See also LVQ1, SOM_SUPERVISED, SOM_SEQTRAIN.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% lvq3
%
% PURPOSE
%
% Trains codebook with the LVQ3 -algorithm (described below).
%
% SYNTAX
%
% sM = lvq3(sM, data, rlen, alpha, win, epsilon)
%
% DESCRIPTION
%
% Trains codebook with the LVQ3 -algorithm. Codebook contains a number
% of vectors (mi, i=1,2,...,n) and so does data (vectors xj, j=1,2,...k).
% Both vector sets are classified: vectors may have a class (classes are
% set to data- or map -structure's 'labels' -field. For each xj the two
% closest codebookvectors mc1 and mc2 are searched (euclidean distances
% d1 and d2). xj must fall into the zone of window. That happens if:
%
% min(d1/d2, d2/d1) > s, where s = (1-win) / (1+win).
%
% If xj belongs to the same class of one of the mc1 and mc1, codebook
% is updated as follows (let mc1 belong to the same class as xj):
% mc1(t+1) = mc1(t) + alpha * (xj(t) - mc1(t))
% mc2(t+1) = mc2(t) - alpha * (xj(t) - mc2(t))
% If both mc1 and mc2 belong to the same class as xj, codebook is
% updated as follows:
% mc1(t+1) = mc1(t) + epsilon * alpha * (xj(t) - mc1(t))
% mc2(t+1) = mc2(t) + epsilon * alpha * (xj(t) - mc2(t))
% Otherwise updating is not performed.
%
% Argument 'rlen' tells how many times training -sequence is performed.
%
% Argument 'alpha' is recommended to be smaller than 0.1 and argument
% 'epsilon' should be between 0.1 and 0.5.
%
% NOTE: does not take mask into account.
%
% REFERENCES
%
% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
% Berlin, 1995, pp. 181-182.
%
% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml
%
% REQUIRED INPUT ARGUMENTS
%
% sM The data to be trained.
% (struct) A map struct.
%
% data The data to use in training.
% (struct) A data struct.
%
% rlen (integer) Running length of LVQ3 -algorithm.
%
% alpha (float) Learning rate used in training, e.g. 0.05
%
% win (float) Window length, e.g. 0.25
%
% epsilon (float) Relative learning parameter, e.g. 0.3
%
% OUTPUT ARGUMENTS
%
% sM Trained data.
% (struct) A map struct.
%
% EXAMPLE
%
% lab = unique(sD.labels(:,1)); % different classes
% mu = length(lab)*5; % 5 prototypes for each
% sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes
% sM.labels = [lab;lab;lab;lab;lab]; % their classes
% sM = lvq1(sM,sD,50*mu,0.05); % use LVQ1 to adjust
% % the prototypes
% sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3); % then use LVQ3
%
% SEE ALSO
%
% lvq1 Use LVQ1 algorithm for training.
% som_supervised Train SOM using supervised training.
% som_seqtrain Train SOM with sequential algorithm.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 310100 juuso 020200
NOTFOUND = 1;
cod = codebook.codebook;
dat = data.data;
c_class = codebook.labels(:,1);
d_class = data.labels(:,1);
s = (1-win)/(1+win);
x = size(dat,1);
y = size(cod,2);
c_class=class2num(c_class);
d_class=class2num(d_class);
ONES=ones(size(cod,1),1);
for t=1:rlen
fprintf('\rTraining round: %d/%d',t,rlen);
tmp = NaN*ones(x,y);
for j=1:x
flag = 0;
mj = 0;
mi = 0;
no_NaN=find(~isnan(dat(j,:)));
di=sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2));
[foo, ind1] = min(di);
di(ind1)=Inf;
[foo,ind2] = min(di);
%ind2=ind2+1;
if d_class(j) & d_class(j)==c_class(ind1)
mj = ind1;
mi = ind2;
if d_class(j)==c_class(ind2)
flag = 1;
end
elseif d_class(j) & d_class(j)==c_class(ind2)
mj = ind2;
mi = ind1;
if d_class(j)==c_class(ind1)
flag = 1;
end
end
if mj & mi
if flag
tmp([mj mi],:) = cod([mj mi],:) + epsilon*alpha*...
(dat([j j],:) - cod([mj mi],:));
else
tmp(mj,:) = cod(mj,:) + alpha * (dat(j,:)-cod(mj,:));
tmp(mi,:) = cod(mi,:) - alpha * (dat(j,:)-cod(mj,:));
end
end
end
inds = find(~isnan(sum(tmp,2)));
cod(inds,:) = tmp(inds,:);
end
fprintf(1,'\n');
sTrain = som_set('som_train','algorithm','lvq3',...
'data_name',data.name,...
'neigh','',...
'mask',ones(y,1),...
'radius_ini',NaN,...
'radius_fin',NaN,...
'alpha_ini',alpha,...
'alpha_type','constant',...
'trainlen',rlen,...
'time',datestr(now,0));
codebook.trainhist(end+1) = sTrain;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function nos = class2num(class)
names = {};
nos = zeros(length(class),1);
for i=1:length(class)
if ~isempty(class{i}) & ~any(strcmp(class{i},names))
names=cat(1,names,class(i));
end
end
tmp_nos = (1:length(names))';
for i=1:length(class)
if ~isempty(class{i})
nos(i,1) = find(strcmp(class{i},names));
end
end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_select.m
|
.m
|
mirtooloct-master/somtoolbox/som_select.m
| 20,295 |
utf_8
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8d0b3f1b93252ad6250273831b30b5ad
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function varargout=som_select(c_vect,plane_h,arg)
%SOM_SELECT Manual selection of map units from a visualization.
%
% som_select(c_vect,[plane_h])
%
% som_select(3)
% som_select(sM.labels(:,1))
%
% Input arguments ([]'s are optional):
% c_vect (scalar) number of classes
% (vector) initial class identifiers
% (cell array) of strings, class names
% (matrix) size * x 3, the color of each class
% [plane_h] (scalar) handle of the plane (axes) to be marked.
% By default, the current axes is used (GCA).
% For the function to work, the plot in the
% axes must have been created with the
% SOM_CPLANE function (or SOM_SHOW).
%
% Launches a GUI which allows user to select nodes from plane by
% clicking them or by choosing a region (a polygon).
%
% Middle mouse button: selects (or clears selection of) a single node
% Left mouse button: lets user draw a polygon
% Right mouse button: selects (or clears selection of) the units
% inside the polygon
%
% From the GUI, the color (class) is selected as well as whether
% but buttons select or clear the selection from the units. The
% buttons on the bottom have the following actions:
%
% 'OK' Assigns the class identifiers to the 'ans' variable in
% workspace. The value is an array of class identifiers:
% strings (cellstr) if the c_vect was an array of
% strings, a vector otherwise.
% 'Clear' Removes marks from the plane.
% 'Close' Closes the application.
%
% See also SOM_SHOW, SOM_CPLANE.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100, juuso 010200
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
if nargin < 2, plane_h = gca; end
if(isempty(gcbo)), arg='start'; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
switch arg
case 'start'
patch_h=find_patch(plane_h);
lattice=getfield(size(get(patch_h,'XData')),{1});
msize(1)=floor(getfield(get(plane_h,'YLim'),{2}));
msize(2)=floor(getfield(get(plane_h,'XLim'),{2})-0.5);
if lattice==6
lattice='hexa';
else
lattice='rect';
end
if any(strcmp(get(patch_h,'Tag'),{'planeBar','planePie'}))
tmp_dim=size(get(patch_h,'XData'),2)/prod(msize);
tmp_xdata=get(patch_h,'XData');
tmp_x=tmp_xdata(:,(msize(1)*(msize(2)-1)+2)*tmp_dim);
if floor(tmp_x(1)) ~= round(tmp_x(1))
lattice = 'hexa';
else
lattice = 'rect';
end
elseif strcmp(get(patch_h,'Tag'),'planePlot')
tmp_lines_h=get(gca,'Children');
test_x=mean(get(tmp_lines_h(2),'XData'));
if round(test_x) ~= floor(test_x)
lattice = 'hexa';
else
lattice = 'rect';
end
form=0.5*vis_patch('hexa');
l = size(form,1);
nx = repmat(form(:,1),1,prod(msize));
ny = repmat(form(:,2),1,prod(msize));
x=reshape(repmat(1:msize(2),l*msize(1),1),l,prod(msize));
y=repmat(repmat(1:msize(1),l,1),1,msize(2));
if strcmp(lattice,'hexa')
t = find(~rem(y(1,:),2));
x(:,t)=x(:,t)+.5;
end
x=x+nx;
y=y+ny;
colors=reshape(ones(prod(msize),1)*[NaN NaN NaN],...
[1 prod(msize) 3]);
v=caxis;
patch_h=patch(x,y,colors,...
'EdgeColor','none',...
'ButtonDownFcn',...
'som_select([],[],''click'')',...
'Tag','planePlot');
set([gca gcf],'ButtonDownFcn','som_select([],[],''click'')');
caxis(v)
end
c_colors = [];
if iscell(c_vect)
[c_vect,c_names,c_classes]=class2num(c_vect);
if length(c_classes)<prod(msize),
c_classes = zeros(prod(msize),1);
end
else
if all(size(c_vect)>1),
c_colors = c_vect;
c_names = 1:size(c_vect,1);
c_vect = size(c_vect,1);
c_classes = zeros(prod(msize),1);
elseif length(c_vect)==prod(msize),
c_classes = c_vect;
u = unique(c_classes(isfinite(c_classes) & c_classes>0));
c_names = u;
c_vect = length(u);
elseif length(c_vect)>1,
c_names = c_vect;
c_vect = length(c_vect);
c_classes = zeros(prod(msize),1);
elseif length(c_vect)==1,
c_names = 1:c_vect;
c_classes = zeros(prod(msize),1);
end
end
udata.lattice=lattice;
udata.patch_h=patch_h;
udata.plane_h=plane_h;
udata.type=get(udata.patch_h,'Tag');
udata.msize=msize;
set(patch_h,'UserData',udata);
if strcmp(udata.type,'planePlot')
set([gca gcf],'UserData',udata);
end
str=cat(2,'som_select([],[],''click'')');
set(patch_h,'ButtonDownFcn',str);
draw_colorselection(c_names,c_colors);
tmp_data=findobj(get(0,'Children'),'Tag','SELECT_GUI');
tmp_data=get(tmp_data,'UserData');
tmp_data.c_names=c_names;
tmp_data.mat=reshape(c_classes,msize);
tmp_data.patch_h=patch_h;
tmp_data.plane_h=plane_h;
tmp_data.type=get(udata.patch_h,'Tag');
tmp_data.lattice=lattice;
tmp_data.coords=[];
tmp_data.poly_h=[];
tmp_data.msize=msize;
tmp_data.mode='select';
set(tmp_data.fig_h,'UserData',tmp_data);
draw_classes;
case 'click'
switch get(gcf,'SelectionType')
case 'open'
return;
case {'normal','alt'}
draw_poly;
case 'extend'
click;
end
case 'choose'
draw_colorselection(0,0,'choose');
case 'close'
close_gui;
case 'clear'
clear_plane;
case 'rb'
rb_control;
case 'ret_mat'
gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
gui=get(gui,'UserData');
mat=reshape(gui.mat,prod(size(gui.mat)),1);
if ~isempty(gui.c_names)
if isnumeric(gui.c_names), tmp=zeros(length(mat),1);
else tmp=cell(length(mat),1);
end
for i=1:length(gui.c_names)
inds=find(mat==i);
tmp(inds)=gui.c_names(i);
end
mat=tmp;
end
varargout{1}=mat;
%gui.mat=zeros(size(gui.mat));
%set(gui.fig_h,'UserData',gui);
%h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH');
%delete(h);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function rb_control;
h=findobj(get(gcf,'Children'),'Style','radiobutton');
set(h,'Value',0);
set(gcbo,'Value',1);
udata=get(gcf,'UserData');
if strcmp(get(gcbo,'Tag'),'Radiobutton1')
udata.mode='select';
else
udata.mode='clear';
end
set(gcf,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clear_plane
h=findobj(get(0,'Children'),'Tag','SELECT_GUI');
gui=get(h,'UserData');
if strcmp(get(gui.patch_h,'Tag'),'planePlot')
colors=reshape(get(gui.patch_h,'FaceVertexCData'),[prod(gui.msize) 3]);
colors(:,:)=NaN;
set(gui.patch_h,'FaceVertexCData',colors);
end
h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH');
gui.mat=zeros(gui.msize);
set(gui.fig_h,'UserData',gui);
delete(h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function click
udata=get(gcbo,'UserData');
udata=get(udata.patch_h,'UserData');
coords=get(gca,'CurrentPoint');
row=round(coords(1,2));
if row > udata.msize(1), row = udata.msize(1); end
if row < 1, row = 1; end
if any(strcmp(udata.lattice,{'hexa','hexaU'})) & ~mod(row,2),
col=floor(coords(1,1))+0.5;
if col > udata.msize(2)+0.5, col=udata.msize(2)+0.5; end
else
col=round(coords(1,1));
if col > udata.msize(2), col=udata.msize(2); end
end
if col < 1, col = 1; end
if strcmp(udata.type,'planePlot')
if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end
ind=sub2ind(udata.msize,row,col);
colors=reshape(get(udata.patch_h,'FaceVertexCData'),[prod(udata.msize) 3]);
gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
gui=get(gui,'UserData');
if ~isempty(gui.curr_col) & all(~isnan(colors(ind,1,:))),
if ~strcmp(gui.mode,'clear') & ~all(gui.curr_col == colors(ind,:))
colors(ind,:)=gui.curr_col;
gui.mat(row,col)=gui.class;
else
colors(ind,:)=[NaN NaN NaN];
gui.mat(row,col)=0;
end
elseif strcmp(gui.mode,'clear')
colors(ind,:)=[NaN NaN NaN];
gui.mat(row,col)=0;
elseif isempty(gui.curr_col)
return;
else
gui.mat(row,col)=gui.class;
colors(ind,:)=gui.curr_col;
end
set(udata.patch_h,'FaceVertexCData',colors);
set(gui.fig_h,'UserData',gui);
return;
end
if any(strcmp(udata.type,{'planePie','planeBar'}))
[x,y]=pol2cart(0:0.1:2*pi,0.5);
coords=[x';0.5]*0.7;
coords(:,2)=[y';0]*0.7;
elseif strcmp(udata.lattice,'hexa');
coords=0.7*vis_patch('hexa');
else
coords=0.7*vis_patch('rect');
end
coords(:,1)=coords(:,1)+col;
coords(:,2)=coords(:,2)+row;
if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end
hold on;
if gco == udata.patch_h
gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
gui=get(gui,'UserData');
if isnan(gui.curr_col) | strcmp(gui.mode,'clear'), return; end
h=fill(coords(:,1),coords(:,2),gui.curr_col);
str=cat(2,'som_select([],[],''click'')');
set(h,'ButtonDownFcn',str,'Tag','SEL_PATCH');
tmp.patch_h=udata.patch_h;
set(h,'UserData',tmp);
gui.mat(row,col)=gui.class;
set(gui.fig_h,'UserData',gui);
else
gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
gui=get(gui,'UserData');
if ~all(get(gcbo,'FaceColor') == gui.curr_col) & ~strcmp(gui.mode,'clear'),
if ~isnan(gui.curr_col),
set(gcbo,'FaceColor',gui.curr_col);
gui.mat(row,col) = gui.class;
end
else
gui.mat(row,col)=0;
delete(gco);
end
set(gui.fig_h,'UserData',gui);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_colorselection(varargin)
if length(varargin)==2,
if length(varargin{1})==1,
n = varargin{1};
names = 1:n;
else
n = length(varargin{1});
names = varargin{1};
end
colors = varargin{2};
shape=[0.5 -0.5;0.5 0.5;1.5 0.5;1.5 -0.5];
rep_x=repmat(shape(:,1),1,n);
rep_y=repmat(shape(:,2),1,n);
for i=0:getfield(size(rep_y,2))-1, rep_x(:,i+1)=rep_x(:,i+1)+i; end
if isempty(colors), colors=jet(n); end
data=som_select_gui;
data.colors=colors;
data.curr_col=NaN;
data.class=0;
set(0,'CurrentFigure',data.fig_h);
hold on;
tmp=fill(rep_x,rep_y,0.8);
for i=1:n
set(tmp(i),...
'EdgeColor',[0 0 0],...
'FaceColor',colors(i,:),...
'ButtonDownFcn','som_select([],0,''choose'');');
end
axis('equal');
axis('on');
set(gca,'XTick',1:n,'XTickLabel',names,'XAxisLocation','top');
set(data.a_h,'YLim',[-0.5,0.5],...
'XLim',[0.5 n+0.5],...
'YTickLabel','');
set(data.fig_h,'UserData',data);
elseif strcmp(varargin{3},'choose')
udata=get(gcf,'UserData');
if strcmp(get(gcbo,'Selected'),'off')
old=findobj(get(gca,'Children'),'Type','patch');
set(old,'Selected','off');
set(gcbo,'Selected','on');
udata.curr_col=udata.colors(round(mean(get(gcbo,'XData'))),:);
udata.class=mean(get(gcbo,'XData'));
else
set(gcbo,'Selected','off');
udata.curr_col=NaN;
udata.class=0;
end
set(gcf,'UserData',udata);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data=som_select_gui()
a = figure('Color',[0.8 0.8 0.8], ...
'PaperType','a4letter', ...
'Position',[586 584 560 210], ...
'Tag','SELECT_GUI');
data.fig_h=a;
b = axes('Parent',a, ...
'Box','on', ...
'CameraUpVector',[0 1 0], ...
'Color',[1 1 1], ...
'DataAspectRatioMode','manual', ...
'PlotBoxAspectRatio',[20 1 2], ...
'PlotBoxAspectRatioMode','manual', ...
'Position',[0.13 0.11 0.775 0.815], ...
'Tag','Axes1', ...
'WarpToFill','off', ...
'XColor',[0 0 0], ...
'XLimMode','manual', ...
'YColor',[0 0 0], ...
'YLimMode','manual', ...
'YTickLabelMode','manual', ...
'ZColor',[0 0 0]);
data.a_h=b;
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.701961 0.701961 0.701961], ...
'Callback','som_select([],[],''close'')', ...
'FontWeight','demi', ...
'Position',[150 12 50 20], ...
'String','CLOSE', ...
'Tag','Pushbutton1');
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.701961 0.701961 0.701961], ...
'Callback','som_select([],0,''ret_mat'')',...
'FontWeight','demi', ...
'Position',[365 12 50 20], ...
'String','OK', ...
'Tag','Pushbutton2');
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.701961 0.701961 0.701961], ...
'Callback','som_select([],0,''clear'')',...
'FontWeight','demi', ...
'Position',[257.5 12 50 20], ...
'String','CLEAR', ...
'Tag','Pushbutton3');
b = uicontrol('Parent',a, ...
'Units','points', ...
'Position',[50 27 17 16], ...
'Callback','som_select([],[],''rb'')',...
'Style','radiobutton', ...
'Tag','Radiobutton1', ...
'Value',1);
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.701961 0.701961 0.701961], ...
'Callback','som_select([],[],''rb'')',...
'Position',[50 7 17 16], ...
'Style','radiobutton', ...
'Tag','Radiobutton2');
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',9, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[72 25 28 15], ...
'String','Select', ...
'Style','text', ...
'Tag','StaticText1');
b = uicontrol('Parent',a, ...
'Units','points', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',9, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[72 7 25 13.6], ...
'String','Clear', ...
'Style','text', ...
'Tag','StaticText2');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function close_gui
udata=get(get(gcbo,'Parent'),'UserData');
if strcmp(udata.type,'planePlot');
set(udata.plane_h,'ButtonDownFcn','','UserData',[]);
set(get(udata.plane_h,'Parent'),'ButtonDownFcn','');
delete(udata.patch_h);
return;
end
h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
set(udata.patch_h,'ButtonDownFcn','','UserData',[]);
delete(h);
close(udata.fig_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_poly
udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData');
if isempty(udata.coords) & strcmp(get(gcf,'SelectionType'),'alt')
return;
end
coords(1,1) = getfield(get(gca,'CurrentPoint'),{3});
coords(1,2) = getfield(get(gca,'CurrentPoint'),{1});
udata.coords = cat(1,udata.coords,coords);
delete(udata.poly_h);
subplot(udata.plane_h);
hold on;
switch get(gcf,'SelectionType');
case 'normal'
udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',...
'ButtonDownFcn','som_select([],[],''click'')',...
'LineWidth',2);
set(udata.fig_h,'UserData',udata);
case 'alt'
udata.coords=cat(1,udata.coords,udata.coords(1,:));
udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',...
'LineWidth',2);
delete(udata.poly_h);
if ~isnan(udata.curr_col)
tmp=sort(repmat((1:udata.msize(1))',udata.msize(2),1));
tmp(:,2)=repmat((1:udata.msize(2))',udata.msize(1),1);
tmp2=tmp;
if strcmp(udata.type,'planePlot')
in=find(inpolygon(tmp(:,2),tmp(:,1),...
udata.coords(:,2),udata.coords(:,1)));
row=tmp2(in,1);
col=tmp2(in,2);
in=sub2ind(udata.msize,row,col);
colors=reshape(get(udata.patch_h,'FaceVertexCData'),...
[prod(udata.msize) 3]);
if ~isnan(udata.curr_col) & ~strcmp(udata.mode,'clear')
colors(in,:)=ones(length(in),1)*udata.curr_col;
udata.mat(row,col)=udata.class;
elseif strcmp(udata.mode,'clear')
colors(in,:)=[NaN NaN NaN];
udata.mat(row,col)=0;
end
udata.poly_h=[];
udata.coords=[];
set(udata.patch_h,'FaceVertexCData',colors);
set(udata.fig_h,'UserData',udata);
return;
end
if strcmp(udata.lattice,'hexa');
t=find(~rem(tmp(:,1),2));
tmp(t,2)=tmp(t,2)+0.5;
if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
p=0.7*vis_patch('hexa');
else
[x,y]=pol2cart(0:0.1:2*pi,0.5);
p=[x';0.5]*0.7;
p(:,2)=[y';0]*0.7;
end
else
if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
p=0.7*vis_patch('rect');
else
[x,y]=pol2cart(0:0.1:2*pi,0.5);
p=[x';0.5]*0.7;
p(:,2)=[y';0]*0.7;
end
end
in=find(inpolygon(tmp(:,2),tmp(:,1),udata.coords(:,2),udata.coords(:,1)));
set(udata.fig_h,'UserData',udata);
if strcmp(udata.mode,'select')
remove_selpatches;
udata=get(udata.fig_h,'UserData');
for i=1:length(in)
udat.patch_h=udata.patch_h;
h=patch(p(:,1)+tmp(in(i),2),p(:,2)+tmp(in(i),1),...
udata.curr_col,...
'EdgeColor','black',...
'ButtonDownFcn','som_select([],[],''click'')', ...
'Tag','SEL_PATCH',...
'UserData',udat);
udata.mat(tmp2(in(i),1),tmp2(in(i),2))=udata.class;
end
else
remove_selpatches;
udata=get(udata.fig_h,'UserData');
%h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
%for i=1:length(h)
% if all(get(h(i),'FaceColor')==udata.curr_col) & ...
% inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),...
% udata.coords(:,2),udata.coords(:,1))
% coords=[floor(mean(get(h(i),'YData')))...
% floor(mean(get(h(i),'XData')))];
% udata.mat(coords(1),coords(2))=0;
% delete(h(i));
% end
%end
end
end
udata.poly_h=[];
udata.coords=[];
set(udata.fig_h,'UserData',udata);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function remove_selpatches
udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData');
h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
for i=1:length(h)
if inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),...
udata.coords(:,2),udata.coords(:,1));
coords=[floor(mean(get(h(i),'YData')))...
floor(mean(get(h(i),'XData')))];
udata.mat(coords(1),coords(2))=0;
delete(h(i));
end
end
set(udata.fig_h,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [n,names,classes]=class2num(class)
names = {};
classes = zeros(length(class),1);
for i=1:length(class)
if ~isempty(class{i}),
a = find(strcmp(class{i},names));
if isempty(a),
names=cat(1,names,class(i));
classes(i) = length(names);
else
classes(i) = a;
end
end
end
n=length(names);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h=find_patch(a_h)
h=[];
tags={'planeC','planeU','planePie','planeBar','planePlot'};
for i=1:5
if ~isempty(findobj(get(a_h,'Children'),'Tag',tags{i}))
h=findobj(get(gca,'Children'),'Tag',tags{i});
if length(h) > 1
h=h(1);
end
return;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_classes
udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'), ...
'UserData');
figure(get(udata.plane_h,'Parent'))
subplot(udata.plane_h);
colors=zeros(prod(udata.msize),3)+NaN;
c_map=jet(length(udata.c_names));
inds = find(udata.mat);
for i=1:length(inds),
colors(inds(i),:) = c_map(udata.mat(inds(i)),:);
end
if strcmp(udata.type,'planePlot'),
set(udata.patch_h,'FaceVertexCData',colors);
set(udata.fig_h,'UserData',udata);
else
hold on
co = som_vis_coords(udata.lattice,udata.msize);
if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
p=0.7*vis_patch(udata.lattice);
else
[x,y]=pol2cart(0:0.1:2*pi,0.5);
p=[x';0.5]*0.7;
p(:,2)=[y';0]*0.7;
end
for i=1:length(inds),
udat.patch_h=udata.patch_h;
h=patch(p(:,1)+co(inds(i),1),p(:,2)+co(inds(i),2),...
colors(inds(i),:),...
'EdgeColor','black',...
'ButtonDownFcn','som_select([],[],''click'')', ...
'Tag','SEL_PATCH',...
'UserData',udat);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_unit_coords.m
|
.m
|
mirtooloct-master/somtoolbox/som_unit_coords.m
| 8,082 |
utf_8
|
1656dc53e5cdea92d6870107451337dd
|
function Coords = som_unit_coords(topol,lattice,shape)
%SOM_UNIT_COORDS Locations of units on the SOM grid.
%
% Co = som_unit_coords(topol, [lattice], [shape])
%
% Co = som_unit_coords(sMap);
% Co = som_unit_coords(sMap.topol);
% Co = som_unit_coords(msize, 'hexa', 'cyl');
% Co = som_unit_coords([10 4 4], 'rect', 'toroid');
%
% Input and output arguments ([]'s are optional):
% topol topology of the SOM grid
% (struct) topology or map struct
% (vector) the 'msize' field of topology struct
% [lattice] (string) map lattice, 'rect' by default
% [shape] (string) map shape, 'sheet' by default
%
% Co (matrix, size [munits k]) coordinates for each map unit
%
% For more help, try 'type som_unit_coords' or check out online documentation.
% See also SOM_UNIT_DISTS, SOM_UNIT_NEIGHS.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_unit_coords
%
% PURPOSE
%
% Returns map grid coordinates for the units of a Self-Organizing Map.
%
% SYNTAX
%
% Co = som_unit_coords(sTopol);
% Co = som_unit_coords(sM.topol);
% Co = som_unit_coords(msize);
% Co = som_unit_coords(msize,'hexa');
% Co = som_unit_coords(msize,'rect','toroid');
%
% DESCRIPTION
%
% Calculates the map grid coordinates of the units of a SOM based on
% the given topology. The coordinates are such that they can be used to
% position map units in space. In case of 'sheet' shape they can be
% (and are) used to measure interunit distances.
%
% NOTE: for 'hexa' lattice, the x-coordinates of every other row are shifted
% by +0.5, and the y-coordinates are multiplied by sqrt(0.75). This is done
% to make distances of a unit to all its six neighbors equal. It is not
% possible to use 'hexa' lattice with higher than 2-dimensional map grids.
%
% 'cyl' and 'toroid' shapes: the coordinates are initially determined as
% in case of 'sheet' shape, but are then bended around the x- or the
% x- and then y-axes to get the desired shape.
%
% POSSIBLE BUGS
%
% I don't know if the bending operation works ok for high-dimensional
% map grids. Anyway, if anyone wants to make a 4-dimensional
% toroid map, (s)he deserves it.
%
% REQUIRED INPUT ARGUMENTS
%
% topol Map grid dimensions.
% (struct) topology struct or map struct, the topology
% (msize, lattice, shape) of the map is taken from
% the appropriate fields (see e.g. SOM_SET)
% (vector) the vector which gives the size of the map grid
% (msize-field of the topology struct).
%
% OPTIONAL INPUT ARGUMENTS
%
% lattice (string) The map lattice, either 'rect' or 'hexa'. Default
% is 'rect'. 'hexa' can only be used with 1- or
% 2-dimensional map grids.
% shape (string) The map shape, either 'sheet', 'cyl' or 'toroid'.
% Default is 'sheet'.
%
% OUTPUT ARGUMENTS
%
% Co (matrix) coordinates for each map units, size is [munits k]
% where k is 2, or more if the map grid is higher
% dimensional or the shape is 'cyl' or 'toroid'
%
% EXAMPLES
%
% Simplest case:
% Co = som_unit_coords(sTopol);
% Co = som_unit_coords(sMap.topol);
% Co = som_unit_coords(msize);
% Co = som_unit_coords([10 10]);
%
% If topology is given as vector, lattice is 'rect' and shape is 'sheet'
% by default. To change these, you can use the optional arguments:
% Co = som_unit_coords(msize, 'hexa', 'toroid');
%
% The coordinates can also be calculated for high-dimensional grids:
% Co = som_unit_coords([4 4 4 4 4 4]);
%
% SEE ALSO
%
% som_unit_dists Calculate interunit distance along the map grid.
% som_unit_neighs Calculate neighborhoods of map units.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 1.0beta juuso 110997
% Version 2.0beta juuso 101199 070600
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check arguments
error(nargchk(1, 3, nargin));
% default values
sTopol = som_set('som_topol','lattice','rect');
% topol
if isstruct(topol),
switch topol.type,
case 'som_map', sTopol = topol.topol;
case 'som_topol', sTopol = topol;
end
elseif iscell(topol),
for i=1:length(topol),
if isnumeric(topol{i}), sTopol.msize = topol{i};
elseif ischar(topol{i}),
switch topol{i},
case {'rect','hexa'}, sTopol.lattice = topol{i};
case {'sheet','cyl','toroid'}, sTopol.shape = topol{i};
end
end
end
else
sTopol.msize = topol;
end
if prod(sTopol.msize)==0, error('Map size is 0.'); end
% lattice
if nargin>1 & ~isempty(lattice) & ~isnan(lattice), sTopol.lattice = lattice; end
% shape
if nargin>2 & ~isempty(shape) & ~isnan(shape), sTopol.shape = shape; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Action
msize = sTopol.msize;
lattice = sTopol.lattice;
shape = sTopol.shape;
% init variables
if length(msize)==1, msize = [msize 1]; end
munits = prod(msize);
mdim = length(msize);
Coords = zeros(munits,mdim);
% initial coordinates for each map unit ('rect' lattice, 'sheet' shape)
k = [1 cumprod(msize(1:end-1))];
inds = [0:(munits-1)]';
for i = mdim:-1:1,
Coords(:,i) = floor(inds/k(i)); % these are subscripts in matrix-notation
inds = rem(inds,k(i));
end
% change subscripts to coordinates (move from (ij)-notation to (xy)-notation)
Coords(:,[1 2]) = fliplr(Coords(:,[1 2]));
% 'hexa' lattice
if strcmp(lattice,'hexa'),
% check
if mdim > 2,
error('You can only use hexa lattice with 1- or 2-dimensional maps.');
end
% offset x-coordinates of every other row
inds_for_row = (cumsum(ones(msize(2),1))-1)*msize(1);
for i=2:2:msize(1),
Coords(i+inds_for_row,1) = Coords(i+inds_for_row,1) + 0.5;
end
end
% shapes
switch shape,
case 'sheet',
if strcmp(lattice,'hexa'),
% this correction is made to make distances to all
% neighboring units equal
Coords(:,2) = Coords(:,2)*sqrt(0.75);
end
case 'cyl',
% to make cylinder the coordinates must lie in 3D space, at least
if mdim<3, Coords = [Coords ones(munits,1)]; mdim = 3; end
% Bend the coordinates to a circle in the plane formed by x- and
% and z-axis. Notice that the angle to which the last coordinates
% are bended is _not_ 360 degrees, because that would be equal to
% the angle of the first coordinates (0 degrees).
Coords(:,1) = Coords(:,1)/max(Coords(:,1));
Coords(:,1) = 2*pi * Coords(:,1) * msize(2)/(msize(2)+1);
Coords(:,[1 3]) = [cos(Coords(:,1)) sin(Coords(:,1))];
case 'toroid',
% NOTE: if lattice is 'hexa', the msize(1) should be even, otherwise
% the bending the upper and lower edges of the map do not match
% to each other
if strcmp(lattice,'hexa') & rem(msize(1),2)==1,
warning('Map size along y-coordinate is not even.');
end
% to make toroid the coordinates must lie in 3D space, at least
if mdim<3, Coords = [Coords ones(munits,1)]; mdim = 3; end
% First bend the coordinates to a circle in the plane formed
% by x- and z-axis. Then bend in the plane formed by y- and
% z-axis. (See also the notes in 'cyl').
Coords(:,1) = Coords(:,1)/max(Coords(:,1));
Coords(:,1) = 2*pi * Coords(:,1) * msize(2)/(msize(2)+1);
Coords(:,[1 3]) = [cos(Coords(:,1)) sin(Coords(:,1))];
Coords(:,2) = Coords(:,2)/max(Coords(:,2));
Coords(:,2) = 2*pi * Coords(:,2) * msize(1)/(msize(1)+1);
Coords(:,3) = Coords(:,3) - min(Coords(:,3)) + 1;
Coords(:,[2 3]) = Coords(:,[3 3]) .* [cos(Coords(:,2)) sin(Coords(:,2))];
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function C = bend(cx,cy,angle,xishexa)
dx = max(cx) - min(cx);
if dx ~= 0,
% in case of hexagonal lattice it must be taken into account that
% coordinates of every second row are +0.5 off to the right
if xishexa, dx = dx-0.5; end
cx = angle*(cx - min(cx))/dx;
end
C(:,1) = (cy - min(cy)+1) .* cos(cx);
C(:,2) = (cy - min(cy)+1) .* sin(cx);
% end of bend
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
vis_footnote.m
|
.m
|
mirtooloct-master/somtoolbox/vis_footnote.m
| 3,091 |
utf_8
|
bdff65a1392daa41414831644ccc8235
|
function h=vis_footnote(txt)
% VIS_FOOTNOTE Adds a movable text to the current figure
%
% h = vis_footnote(T)
%
% Input and output arguments ([]'s are optional)
% [T] (string) text to be written
% (scalar) font size to use in all strings
%
% h (vector) handles to axis objects created by this function
%
% This function sets a text to the current figure. If T is a string,
% it's written as it is to the same place. If T is a scalar, the font
% size of all text objects created by this function are changed to the
% pointsize T. If no input argument is given the function only returns
% the handles to all objects created by this function. The texts may
% be dragged to a new location at any time using mouse. Note that the
% current axis will be the parent of the text object after dragging.
%
% String 'Info' is set to the Tag property field of the objects.
%
% EXAMPLES
%
% % add movable texts to the current figure and change their
% % fontsize to 20 points
% vis_footnote('Faa'); vis_footnote('Foo'); vis_footnote(20);
%
% % delete all objects created by this function from the current figure
% delete(vis_footnote);
%
% See also SOM_SHOW.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 080698
%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error(nargchk(0, 1, nargin)) % check no. of input args
%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Get the handles to the existing Info-axes objects
h_infotxt=findobj(gcf,'tag','Info','type','text');
h_infoax=findobj(gcf,'tag','Info','type','axes');
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If no arguments are given, return the old axes handles
if nargin == 0 | isempty(txt),
;
elseif ischar(txt) % text: set new text
[t,h_]=movetext(txt);
h_infoax=[h_; h_infoax];
elseif vis_valuetype(txt,{'1x1'}) % scalar: change font size
set(h_infotxt,'fontunits','points');
set(h_infotxt,'fontsize',txt);
else
error('Input argument should be a string or a scalar.');
end
%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>0 % output only if necessary
h=h_infoax;
end
%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [t,h]=movetext(txt)
% Moves the text. See also VIS_FOOTNOTEBUTTONDOWNFCN
%
%
initpos=[0.05 0.05 0.01 0.01];
memaxes = gca; % Memorize the gca
%% Create new axis on the lower left corner.
%% This will be the parent for the text object
h = axes('position',initpos,'units','normalized');
set(h,'visible','off'); % hide axis
t = text(0,0,txt); % write text
set(t,'tag','Info'); % set tag
set(h,'tag','Info'); % set tag
set(t,'verticalalignment','bottom'); % set text alignment
set(t,'horizontalalignment','left');
%% Set ButtonDownFcn
set(t,'buttondownfcn','vis_footnoteButtonDownFcn')
axes(memaxes); % Reset original gca
|
github
|
martinarielhartmann/mirtooloct-master
|
vis_trajgui.m
|
.m
|
mirtooloct-master/somtoolbox/vis_trajgui.m
| 41,530 |
utf_8
|
7afe711e9155c89b97c444b1d7e39710
|
function vis_trajgui(trajStruct,arg)
% VIS_TRAJGUI subfuntion for SOM_TRAJECTORY
%
% This function is the actual GUI called by SOM_TRAJECTORY
% function.
%
% See also SOM_TRAJECTORY.
% Contributed code to SOM Toolbox 2.0, February 11th, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juha 180699
if nargin == 1
sM_h=trajStruct.figure;
if size(trajStruct.bmus,1) ~= 1 & size(trajStruct.bmus,2) ~= 1
fuzzy_traj(trajStruct,[]);
return;
end
if size(trajStruct.bmus,1) == 1 | size(trajStruct.bmus,2) == 1
udata.bmus = trajStruct.bmus;
udata.a_h=[findobj(get(sM_h,'Children'),'Tag','Uplane');...
findobj(get(sM_h,'Children'),'Tag','Cplane')];
udata.sM_h=trajStruct.figure;
udata.traj=[];
data1 = trajStruct.primary_data;
if ~isempty(trajStruct.primary_names)
names=trajStruct.primary_names;
else
for i=1:size(data1,2)
names{i,1}=sprintf('Var%d',i);
end
end
udata.lattice=trajStruct.lattice;
form = 0.7*vis_patch(udata.lattice);
udata.msize = trajStruct.msize;
%%%%%%%%%%%%%%%%%%%%%%%%
%
% forming a patch object, which is placed above every component plane
%
l = size(form,1);
nx = repmat(form(:,1),1,prod(udata.msize));
ny = repmat(form(:,2),1,prod(udata.msize));
x=reshape(repmat(1:udata.msize(2),l*udata.msize(1),1),l,prod(udata.msize));
y=repmat(repmat(1:udata.msize(1),l,1),1,udata.msize(2));
if strcmp(udata.lattice,'hexa')
t = find(~rem(y(1,:),2));
x(:,t)=x(:,t)+.5;
end
x=x+nx;
y=y+ny;
colors=reshape(ones(prod(udata.msize),1)*[NaN NaN NaN],...
[1 prod(udata.msize) 3]);
set(0,'CurrentFigure',udata.sM_h);
%%%%%%%%%%%%%%%%%%%%%%
%
% drawing patch
%
% caxis -commands keep the colormap of the original patch unchanged.
%
for i=1:length(udata.a_h)
udata.real_patch(i)=get(udata.a_h(i),'Children');
set(udata.real_patch(i),'ButtonDownFcn',...
'vis_trajgui([],''click'')');
subplot(udata.a_h(i));
v=caxis;
udata.tmp_patch(i)=patch(x,y,colors,'EdgeColor','none',...
'ButtonDownFcn',...
'vis_trajgui([],''click'')',...
'Tag','TmpPatch');
caxis(v);
end
%%%%%%%%%%%%%%%%%%%%
udata.length_of_traj=length(trajStruct.size);
udata.size=trajStruct.size;
udata.color=trajStruct.color;
udata.poly.x=[];
udata.poly.y=[];
udata.poly.h=[];
udata.new_marks=[];
udata.all_marks=[];
udata.d_mark2=[];
udata.fig1 = figure;
set(udata.fig1,'KeyPressFcn','vis_trajgui([],''key'')',...
'Name','Primary Data');
%%%%%%%%%%%%%%%%%%%%
%
% making the 'Tools' -menu
%
udata.m_i=uimenu(udata.fig1,'Label','Trajectoy T&ools');
udata.m_i(2)=uimenu(udata.m_i,'Label','&Remove Trajectory',...
'Callback',...
'vis_trajgui([],''remove_traj'')');
udata.m_i(3)=uimenu(udata.m_i(1),'Label','&Dye Nodes',...
'Callback',...
'vis_trajgui([],''dye_gui'')');
udata.m_i(4)=uimenu(udata.m_i(1),'Label','&Clear Markers',...
'Callback',...
'vis_trajgui([],''clear'')');
udata.m_i(5)=uimenu(udata.m_i(1),'Label','&Save',...
'Callback',...
'vis_trajgui([],''save'')');
udata.m_i(6)=uimenu(udata.m_i(1),'Label','&Load',...
'Callback',...
'vis_trajgui([],''load'')');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% drawing data components to the figure ....
%
%
if nargin < 5 | isempty(comps) | (isstr(comps) & strcmp(comps,'all'))
comps = 1:size(data1,2);
end
x=1:size(data1,1);
for i=1:length(comps)
subplot(length(comps),1,i);
udata.h(i)=gca;
udata.d_mark(i).h=[];
udata.d(i)=plot(x,data1(:,comps(i)),...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')');
set(gca,'XLim',[1 size(data1,1)],...
'XTick',[],...
'ButtonDownFcn','vis_trajgui([],''line_down'')'); %,...
%'YLim',[min(data1(:,comps(i))) max(data1(:,comps(i)))]);
ylabel(names{comps(i)});
hold on;
ymin=get(udata.h(i),'YLim');
pos=mean(get(udata.h(i),'XLim'));
udata.l(i) = line([pos pos],[ymin(1) ymin(2)],...
'Color','red',...
'ButtonDownFcn',...
'vis_trajgui([],''down'')');
end
udata.text1=[];
udata.fig2=[];
udata.h2=[];
udata.l2=[];
udata.text2=[];
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% ... and to the figure 2.
%
if ~isempty(trajStruct.secondary_data)
data2=trajStruct.secondary_data;
if isempty(trajStruct.secondary_names)
for i=1:size(data1,2)
names2{i,1}=sprintf('Var%d',i);
end
else
names2=trajStruct.secondary_names;
end
udata.fig2 = figure;
set(udata.fig2,'Name','Secondary Data');
set(udata.fig2,'KeyPressFcn',...
'vis_trajgui([],''key'')');
for i=1:size(data2,2)
subplot(size(data2,2),1,i);
udata.h2(i) = gca;
udata.d_mark2(i).h=[];
udata.d2(i) = plot(x,data2(:,i),...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')');
set(gca,'XLim',[1 size(data1,1)],'XTick',[],'ButtonDownFcn',...
'vis_trajgui([],[],[],[],[],[],''line_down'')');
ylabel(names2{i});
hold on;
ymin = get(udata.h2(i),'YLim');
pos = mean(get(udata.h2(i),'XLim'));
udata.l2(i) = line([pos pos],ymin,'Color','red',...
'ButtonDownFcn','vis_trajgui([],''down'')');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%
set(udata.fig1,'UserData',udata);
if ~isempty(udata.fig2);
tmp.fig1=udata.fig1;
set(udata.fig2,'UserData',tmp);
end
tmp=get(udata.sM_h,'UserData');
tmp.fig1=udata.fig1;
set(udata.sM_h,'UserData',tmp);
set_numbers(round(pos));
return;
end
end
%%%%%%%%%%%%%%%%%
%
% if figures have been drawn, the only function calls that may exist
% are the ones that change the state of the application.
%
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
switch arg
case 'fuzzy'
fuzzy_traj(tS,[]);
return;
case 'move_fuzzy'
fuzzy_traj([],'move');
return;
case 'remove_traj'
remove_traj;
return;
case 'line_down'
line_bdf('down');
return;
case 'line_drag'
line_bdf('drag');
return;
case 'line_up'
line_bdf('up');
return;
case 'dye_gui';
color_gui(udata.fig1);
return;
case {'dye','cyan','magenta','yellow','red','green','blue','white','grey'}
dye_nodes(arg);
return;
case 'clear'
clear_markers;
return;
case 'key'
key_bdf;
return;
case 'click'
click;
return;
case 'save'
save_data;
return;
case 'load'
load_data;
return;
end
%%%%%%%%%%
%
% lines in the data figure(s) are dragged ...
%
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
lims=get(gca,'XLim');
x = getfield(get(gca,'CurrentPoint'),{1}); % the location of the line
if x < lims(1)
x=lims(1);
elseif x > lims(2)
x=lims(2);
end
old = gcf;
switch arg
case 'down',...
% mouse button is pressed down above the line
set(gcf,'WindowButtonMotionFcn','vis_trajgui([],''drag'')');
set(gcf,'WindowButtonUpFcn','vis_trajgui([],''up'')');
set(udata.l,'EraseMode','xor');
delete(udata.text1);
if ~isempty(udata.l2);
set(udata.l2,'EraseMode','xor');
delete(udata.text2);
end
set(gcf,'Pointer','crosshair');
case 'drag'
% change the location of the lines
set(0,'CurrentFigure',udata.fig1);
set(udata.l,'XData',[x x]);
if ~isempty(udata.fig2)
set(0,'CurrentFigure',udata.fig2);
set(udata.l2,'XData',[x x]);
end
draw_traj(round(x));
set(0,'CurrentFigure',old);
case 'up'
% draw trajectory and set figure to the normal state.
set(udata.l,'EraseMode','normal');
set(gcf,'Pointer','arrow','WindowButtonMotionFcn','',...
'WindowButtonUpFcn','');
draw_traj(round(x));
set_numbers(round(x));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_traj(point)
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
color=udata.color;
eMode='normal';
if isstr(udata.color) & strcmp(udata.color,'xor')
eMode='xor';
color='black';
end
ind=udata.bmus(point);
[i j] = ind2sub(udata.msize,ind);
if ~mod(i,2)
j=j+0.5;
end
old = gcf;
set(0,'CurrentFigure',udata.sM_h);
hold on;
if isempty(udata.traj) | length(udata.traj.h) ~= length(udata.a_h)
% trajectory does not exist
for i=1:length(udata.a_h)
subplot(udata.a_h(i));
hold on;
new.h = plot(j,i,'Color',color,'EraseMode',eMode,'LineStyle','none');
udata.traj.h(i)=new;
udata.traj.j=j;
udata.traj.i=i;
end
else
if length(udata.traj.j) == udata.length_of_traj
% if the length of trajectory == ...,
udata.traj.j(1) = []; % the first (the oldest) coordinate pair
udata.traj.i(1) = []; % is removed.
end
udata.traj.j=[udata.traj.j;j]; % the new point is added to the
udata.traj.i=[udata.traj.i;i]; % end of coordinate vectors (i and j)
for i=1:length(udata.a_h)
subplot(udata.a_h(i)); % remove the existing trajectory
delete(udata.traj.h(i).h); % and plot the new one.
for j=1:length(udata.traj.j)
udata.traj.h(i).h(j)=plot(udata.traj.j(j),udata.traj.i(j),...
'Color',color,...
'EraseMode',eMode,'Marker','o','LineWidth',2,...
'MarkerSize',udata.size(udata.length_of_traj-j+1),...
'LineStyle','none');
end
end
end
set(0,'CurrentFigure',udata.fig1);
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function set_numbers(x);
% This function writes the numbers beside of the pointer lines
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
xlim = get(gca,'XLim');
ylim = get(gca,'YLim');
p = ylim(1) + 0.9*(ylim(2)-ylim(1));
old = gcf;
set(0,'CurrentFigure',udata.fig1);
for i=1:length(udata.h)
subplot(udata.h(i));
% check if the text is placed to the left side of the line...
if abs(x-xlim(1)) > (abs(x-xlim(2)))
udata.text1(i)=text(x-1,p,sprintf('%d ->',x),...
'VerticalAlignment','top',...
'HorizontalAlignment','right',...
'FontWeight','demi');
else
% or to the right side.
udata.text1(i)=text(x+1,p,sprintf('<- %d',x),...
'VerticalAlignment','top',...
'FontWeight','demi');
end
end
if ~isempty(udata.fig2)
set(0,'CurrentFigure',udata.fig2);
for i=1:length(udata.h2)
subplot(udata.h2(i));
if abs(x-xlim(1)) > (abs(x-xlim(2)))
udata.text2(i)=text(x-1,p,sprintf('%d ->',x),...
'VerticalAlignment','top',...
'HorizontalAlignment','right',...
'FontWeight','demi');
else
udata.text2(i)=text(x+1,p,sprintf('<- %d',x),...
'VerticalAlignment','top',...
'FontWeight','demi');
end
end
end
set(0,'CurrentFigure',old);
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function remove_traj()
% delete trajectory -object from every component plane.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
if isempty(udata.traj)
return;
end
for i=1:length(udata.traj.h)
delete(udata.traj.h(i).h);
end
udata.traj=[];
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function line_bdf(arg)
% this function takes care of action when region is selected in the
% data figure.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
xlim=get(gca,'XLim');
if ~(any(strcmp('THIS',fieldnames(udata))))
p = getfield(get(gca,'CurrentPoint'),{1});
else
p = getfield(get(udata.THIS,'CurrentPoint'),{1});
end
if p < xlim(1)
p = xlim(1);
elseif p > xlim(2)
p = xlim(2);
end
switch arg
case 'down'
% the mouse button is pressed down, the pointer lines are drawn.
% and the state of the figure is changed.
udata.THIS=gca;
set(gcf,'WindowButtonMotionFcn',...
'vis_trajgui([],''line_drag'')',...
'WindowButtonUpFcn','vis_trajgui([],''line_up'')');
udata.start_p=p;
old = gcf;
set(0,'CurrentFigure',udata.fig1);
for i=1:length(udata.h)
subplot(udata.h(i));
udata.t_line.h(i)=line([p p],get(gca,'YLim'),'Color','red');
udata.t_line.h2(i)=line([p p],get(gca,'YLim'),'Color','red',...
'EraseMode','xor');
end
if ~isempty(udata.h2)
set(0,'CurrentFigure',udata.fig2);
for i=1:length(udata.h2)
subplot(udata.h2(i));
udata.t_line2.h(i)=line([p p],get(gca,'YLim'),'Color','red');
udata.t_line2.h2(i)=line([p p],get(gca,'YLim'),'Color','red',...
'EraseMode','xor');
end
end
case 'drag'
% change the position of the pointer lines
old = gcf;
set(0,'CurrentFigure',udata.fig1);
set(udata.t_line.h2,'XData',[p p]);
if ~isempty(udata.fig2)
set(0,'CurrentFigure',udata.fig2);
set(udata.t_line2.h2,'XData',[p p]);
end
set(0,'CurrentFigure',old);
case 'up'
% sort the 'points' -vector and draw the markers to the data and nodes
points=sort([round(udata.start_p) round(p)]);
draw_markers(points(1):points(2));
udata=get(udata.fig1,'UserData');
udata.new_marks=unique([udata.new_marks ;(points(1):points(2))']);
delete([udata.t_line.h2 udata.t_line.h]);
if ~isempty(udata.fig2)
delete([udata.t_line2.h2 udata.t_line2.h]);
end
set(get(udata.THIS,'Parent'),'WindowButtonMotionFcn','',...
'WindowButtonUpFcn','');
udata=rmfield(udata,'THIS');
end
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_markers(x);
plot2data(x);
plot2plane(x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plot2data(x);
% plot black markers to the data figure(s)
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
old = gcf;
set(0,'CurrentFigure',udata.fig1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if there already exist points in the positions that are members
% of the set x, then the old points are removed from data figures...
for i=1:length(udata.d_mark(1).h)
tmp1 = get(udata.d_mark(1).h(i),'XData');
tmp2 = setdiff(tmp1,x);
if length(tmp1) ~= length(tmp2)
inds=[];
for j=1:length(tmp2);
inds=[inds find(tmp2(j)==tmp1)];
end
for j=1:length(udata.d_mark)
ydata=getfield(get(udata.d_mark(j).h(i),'YData'),{inds});
set(udata.d_mark(j).h(i),'XData',tmp2,'YData',ydata);
end
if ~isempty(udata.fig2)
for j=1:length(udata.d_mark2)
ydata=getfield(get(udata.d_mark2(j).h(i),'YData'),{inds});
set(udata.d_mark2(j).h(i),'XData',tmp2,'YData',ydata);
end
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ... and the new ones are plotted.
for i=1:length(udata.h)
subplot(udata.h(i));
h=plot(x,getfield(get(udata.d(i),'YData'),{x}),'oblack',...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')');
udata.d_mark(i).h=[udata.d_mark(i).h;h];
end
if ~isempty(udata.h2)
set(0,'CurrentFigure',udata.fig2);
for i=1:length(udata.h2)
subplot(udata.h2(i));
h=plot(x,getfield(get(udata.d2(i),'YData'),{x}),'oblack',...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')');
udata.d_mark2(i).h=[udata.d_mark2(i).h;h];
end
end
set(0,'CurrentFigure',old);
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plot2plane(x);
% sets markers to the component planes.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% actually new markers are never plotted, but the color of the patch
% lying above the original component plane patch is changed black in
% the right positions.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
udata.new_marks=unique([udata.new_marks ;x']);
for i=1:length(udata.a_h)
col=get(udata.tmp_patch(i),'FaceVertexCData');
if length(size(col)) == 3
col = reshape(col,[size(col,1) 3]);
end
for j=1:length(udata.new_marks)
col(udata.bmus(udata.new_marks(j)),:)=[0 0 0];
end
set(udata.tmp_patch(i),'FaceVertexCData',col);
end
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function color_gui(fig1)
% construct the graphical user interface for changing the color of the
% black (marked) nodes.
udata=get(fig1,'UserData');
a = figure('Color',[0.8 0.8 0.8], ...
'Name','Colors', ...
'PaperType','a4letter', ...
'Position',[518 456 120 311], ...
'Tag','Fig1');
udata.c_struct.fig=a;
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.701961 0.701961 0.701961], ...
'Position',[0.0700415 0.28956 0.830492 0.594566], ...
'Style','frame', ...
'Tag','Frame1');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Position',[0.100059 0.301143 0.770456 0.571399], ...
'Style','frame', ...
'Tag','Frame2');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''cyan'')', ...
'Position',[0.130077 0.795326 0.170101 0.0617729], ...
'Style','radiobutton', ...
'Tag','cyan', ...
'Value',1);
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''magenta'')', ...
'Position',[0.130077 0.733553 0.170101 0.057912], ...
'Style','radiobutton', ...
'Tag','magenta');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''yellow'')', ...
'Position',[0.130077 0.664059 0.170101 0.0617729], ...
'Style','radiobutton', ...
'Tag','yellow');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''red'')', ...
'Position',[0.130077 0.590703 0.170101 0.0617729], ...
'Style','radiobutton', ...
'Tag','red');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''green'')', ...
'Position',[0.130077 0.525068 0.170101 0.057912], ...
'Style','radiobutton', ...
'Tag','green');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''blue'')', ...
'Position',[0.130077 0.455575 0.170101 0.0617729], ...
'Style','radiobutton', ...
'Tag','blue');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''white'')', ...
'Position',[0.130077 0.38608 0.170101 0.0617729], ...
'Style','radiobutton', ...
'Tag','white');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''grey'')', ...
'Position',[0.130077 0.320447 0.170101 0.057912], ...
'Style','radiobutton', ...
'Tag','grey');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.795326 0.470278 0.0501905], ...
'String','Cyan', ...
'Style','text', ...
'Tag','StaticText1');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.733553 0.520308 0.0463296], ...
'String','Magenta', ...
'Style','text', ...
'Tag','StaticText2');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.664059 0.470278 0.0501905], ...
'String','Yellow', ...
'Style','text', ...
'Tag','StaticText3');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.590703 0.470278 0.0501905], ...
'String','Red', ...
'Style','text', ...
'Tag','StaticText4');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.525068 0.470278 0.0463296], ...
'String','Green', ...
'Style','text', ...
'Tag','StaticText5');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.455575 0.470278 0.0463296], ...
'String','Blue', ...
'Style','text', ...
'Tag','StaticText6');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.38608 0.470278 0.0501905], ...
'String','White', ...
'Style','text', ...
'Tag','StaticText7');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.32019 0.320447 0.470278 0.0463296], ...
'String','Grey', ...
'Style','text', ...
'Tag','StaticText8');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Position',[0.0700415 0.146711 0.830492 0.135128], ...
'Style','frame', ...
'Tag','Frame3');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Position',[0.100059 0.158293 0.770456 0.111963], ...
'Style','frame', ...
'Tag','Frame4');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.130077 0.177597 0.270833 0.0617729], ...
'String','RGB', ...
'Style','text', ...
'Tag','StaticText9');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Position',[0.410243 0.173736 0.420249 0.0810768], ...
'Style','edit', ...
'Tag','EditText1');
udata.c_struct.RGB=b;
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','vis_trajgui([],''dye'')', ...
'FontWeight','demi', ...
'Position',[0.0700415 0.0270256 0.360214 0.0772162], ...
'String','OK', ...
'Tag','Pushbutton1');
b = uicontrol('Parent',a, ...
'Units','normalized', ...
'Callback','close gcf', ...
'FontWeight','demi', ...
'Position',[0.54032 0.0270256 0.360214 0.0772162], ...
'String','Close', ...
'Tag','Pushbutton2');
udata.c_struct.color=[0 1 1];
tmp.fig1=fig1;
set(a,'UserData',tmp);
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function dye_nodes(arg)
% takes care of the action, when radiobuttons are pressed
% (or the RGB value is set) in the color_gui -figure.
% It also handles the starting of dying nodes and plots.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
switch arg
case {'cyan','magenta','yellow','red','green','blue','white','grey'}
h=findobj(get(gcf,'Children'),'Style','radiobutton');
set(h,'Value',0);
set(gcbo,'Value',1);
end
switch arg
case 'cyan'
RGB = [0 1 1];
case 'magenta'
RGB = [1 0 1];
case 'yellow'
RGB = [1 1 0];
case 'red'
RGB = [1 0 0];
case 'green'
RGB = [0 1 0];
case 'blue'
RGB = [0 0 1];
case 'white'
RGB = [1 1 1];
case 'grey'
RGB = [0.4 0.4 0.4];
case 'dye'
RGB = get(udata.c_struct.RGB,'String');
if isempty(RGB)
dye;
return;
else
str1='The value of RGB must be vector containing three scalars';
str2='between 0 and 1.';
color = str2num(RGB);
set(udata.c_struct.RGB,'String','');
if isempty(color)
close gcf;
udata=rmfield(udata,'c_struct');
set(udata.fig1,'UserData',udata);
errordlg([{str1};{str2}]);
return;
end
if ~all([1 3] == size(color)) & ~all([3 1] == size(color))
close gcf;
errordlg([{str1};{str2}]);
udata=rmfield(udata,'c_struct',udata);
set(udata.fig1,'UserData',udata);
return;
end
if ~isempty(cat(2,find(color>1),find(color<0)))
close gcf
errordlg([{str1};{str2}]);
udata=rmfield(udata,'c_struct',udata);
set(udata.fig1,'UserData',udata);
return;
end
udata.c_struct.color=color;
set(udata.fig1,'UserData',udata);
dye;
return;
end
end
udata.c_struct.color=RGB;
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function dye()
% dyes black markers in the component planes and in the data figures
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
inds=unique([udata.all_marks ; udata.new_marks]);
for i=1:length(udata.d_mark);
for j=1:length(udata.d_mark(i).h)
if all(get(udata.d_mark(i).h(j),'Color') == [0 0 0])
set(udata.d_mark(i).h(j),'Color',udata.c_struct.color);
end
end
end
if ~isempty(udata.fig2);
for i=1:length(udata.d_mark2)
for j=1:length(udata.d_mark2(i).h)
if all(get(udata.d_mark2(i).h(j),'Color') == [0 0 0])
set(udata.d_mark2(i).h(j),'Color',udata.c_struct.color);
end
end
end
end
for i=1:length(udata.a_h)
col=get(udata.tmp_patch(i),'FaceVertexCData');
for j=1:length(udata.new_marks)
col(udata.bmus(udata.new_marks(j)),:)=udata.c_struct.color;
end
set(udata.tmp_patch(i),'FaceVertexCData',col);
end
udata.all_marks=unique([udata.all_marks;udata.new_marks]);
udata.new_marks=[];
close gcf;
udata=rmfield(udata,'c_struct');
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clear_markers()
% removes markers from the componentplanes and the data figure(s).
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
for i=1:length(udata.d_mark)
delete(udata.d_mark(i).h);
udata.d_mark(i).h=[];
end
for i=1:length(udata.d_mark2)
delete(udata.d_mark2(i).h);
udata.d_mark2(i).h=[];
end
col=NaN*get(udata.tmp_patch(1),'FaceVertexCData');
col=reshape(col,[size(col,1) 3]);
for i=1:length(udata.tmp_patch)
set(udata.tmp_patch(i),'FaceVertexCData',col);
end
udata.new_marks=[];
udata.all_marks=[];
if any(strcmp('c_struct',fieldnames(udata)))
udata=rmfield(udata,'c_struct');
end
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function key_bdf
% moves trajectory and pointer lines, when either of
% the keys '>' or '<' is pressed.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
key=get(gcbo,'CurrentCharacter');
% The easiest way to get a new coordinates is to get them from the texts...
% The texts are either '<- x' or 'x ->'
x=get(udata.text1(1),'String');
x=str2num(x(4:length(x)));
if isempty(x)
x=get(udata.text1(1),'String');
x=str2num(x(1:length(x)-3));
end
switch(key)
case '<'
if x ~= 1
x= x-1;
end
case '>'
if x ~= getfield(get(get(udata.text1(1),'Parent'),'XLim'),{2})
x = x+1;
end
otherwise
return;
end
set(udata.l,'XData',[x x]);
if ~isempty(udata.fig2)
set(udata.l2,'XData',[x x]);
end
delete(udata.text1);
delete(udata.text2);
set_numbers(x);
draw_traj(x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function click()
switch get(gcf,'SelectionType')
case 'open'
return;
case {'normal','alt'}
draw_poly;
case 'extend'
click_node;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function click_node()
% takes care of the action, when the middle mouse button is
% pressed (mouse pointer is above some component plane).
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
new_marks=[];
old=gcf;
NEW=0;
AGAIN = 0;
coords=get(gca,'CurrentPoint');
row=round(coords(1,2));
if strcmp(udata.lattice,'hexa') & ~mod(row,2)
col = round(coords(1,1) - 0.5);
else
col = round(coords(1,1));
end
ind = sub2ind(udata.msize,row,col);
new_marks=find(udata.bmus==ind);
if strcmp(get(gcbo,'Tag'),'TmpPatch');
% if the callback is made via temporary patch object, node is marked
% (node is black) => the mark is to be removed
node_color = getfield(get(gcbo,'FaceVertexCData'),{ind,[1:3]});
AGAIN = 1;
end
for i=1:length(udata.tmp_patch)
color = get(udata.tmp_patch(i),'FaceVertexCData');
if length(size(color)) ~= 2
color = reshape(color,[size(color,1) 3]);
end
if all(isnan(color(ind,:)))
NEW=1;
color(ind,:)=[0 0 0];
else
color(ind,:)=[NaN NaN NaN];
end
set(udata.tmp_patch(i),'FaceVertexCData',color);
end
set(0,'CurrentFigure',udata.fig1);
for j=1:length(udata.h)
subplot(udata.h(j));
if NEW
y=getfield(get(udata.d(j),'YData'),{new_marks});
udata.d_mark(j).h=[udata.d_mark(j).h;plot(new_marks,y,'Color',[0 0 0],...
'LineStyle','none',...
'Marker','o')];
end
end
if ~isempty(udata.fig2)
set(0,'CurrentFigure',udata.fig2);
for j=1:length(udata.h2);
subplot(udata.h2(j));
if NEW
y=getfield(get(udata.d2(j),'YData'),{new_marks});
udata.d_mark2(j).h=[udata.d_mark2(j).h;plot(new_marks,y,...
'LineStyle','none',...
'Color','black',...
'Marker','o')];
end
end
end
if NEW
udata.new_marks=[udata.new_marks; new_marks];
end
if AGAIN
% find marks from the data that map to the clicked node. if the color
% of the mark(s) is the same as the node's color, remove mark(s), else
% let mark be unchanged.
for i=1:length(udata.d_mark(1).h)
if all(node_color==get(udata.d_mark(1).h(i),'Color'))
tmp1 = get(udata.d_mark(1).h(i),'XData');
tmp2 = setdiff(tmp1,new_marks);
if length(tmp1) ~= length(tmp2)
inds=[];
for j=1:length(tmp2);
inds=[inds find(tmp2(j)==tmp1)];
end
for j=1:length(udata.d_mark)
ydata=getfield(get(udata.d_mark(j).h(i),'YData'),{inds});
set(udata.d_mark(j).h(i),'XData',tmp2,'YData',ydata);
end
if ~isempty(udata.fig2)
for j=1:length(udata.d_mark2)
ydata=getfield(get(udata.d_mark2(j).h(i),'YData'),{inds});
set(udata.d_mark2(j).h(i),'XData',tmp2,'YData',ydata);
end
end
end
end
end
udata.new_marks=setdiff(udata.new_marks, new_marks);
udata.all_marks=setdiff(udata.all_marks,new_marks);
end
set(udata.fig1,'UserData',udata);
set(0,'CurrentFigure',old);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_poly()
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
if isempty(udata.poly.x)
if strcmp(get(gcf,'SelectionType'),'alt')
return;
end
udata.poly.THIS = gca;
end
% 'THIS' indicates what was the axes where the polygon was meant to
% drawn. It is not possible to add points, that lie in another axes, to the
% polygon.
if gca ~= udata.poly.THIS
return;
end
coords(1,1) = getfield(get(gca,'CurrentPoint'),{3});
coords(1,2) = getfield(get(gca,'CurrentPoint'),{1});
udata.poly.x=cat(1,udata.poly.x,coords(2));
udata.poly.y=cat(1,udata.poly.y,coords(1));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% remove old 'polygon' from axis
delete(udata.poly.h);
switch get(gcf,'SelectionType')
case 'normal'
% add point to the 'polygon' and draw it
for i=1:length(udata.a_h)
subplot(udata.a_h(i));
hold on;
udata.poly.h(i) = plot(udata.poly.x,udata.poly.y,'black',...
'EraseMode','xor',...
'ButtonDownFcn',...
'vis_trajgui([],''click'')',...
'LineWidth',2);
end
case 'alt'
% The polygon is ready.
udata.poly.x=cat(1,udata.poly.x,udata.poly.x(1));
udata.poly.y=cat(1,udata.poly.y,udata.poly.y(1));
for i=1:length(udata.a_h)
subplot(udata.a_h(i));
udata.poly.h(i) = plot(udata.poly.x,udata.poly.y,'black',...
'EraseMode','xor',...
'ButtonDownFcn',...
'vis_trajgui([],''click'')',...
'LineWidth',2);
end
tmp=sort(repmat((1:udata.msize(1))',udata.msize(2),1));
tmp(:,2)=repmat((1:udata.msize(2))',udata.msize(1),1);
tmp2=tmp;
if strcmp(udata.lattice,'hexa');
t=find(~rem(tmp(:,1),2));
tmp(t,2)=tmp(t,2)+0.5;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% find the nodes that lie inside polygon and change coordinates to
% linear indices.
in = find(inpolygon(tmp(:,2),tmp(:,1),udata.poly.x,udata.poly.y));
in = sub2ind(udata.msize,tmp2(in,1),tmp2(in,2));
colors=get(udata.tmp_patch(1),'FaceVertexCData');
colors=reshape(colors,[size(colors,1) 3]);
tmp=ones(length(in),1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set the color of the nodes just selected, black.
colors(in,:)=tmp*[0 0 0];
set(udata.tmp_patch,'FaceVertexCData',colors);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% find the points mapping to the nodes from data
inds = [];
for i=1:length(in)
inds=[inds;find(in(i) == udata.bmus)];
end
%%%%%%%%%%%%%%%%%%%
% plot marks to data
set(udata.fig1,'UserData',udata);
plot2data(inds);
udata=get(udata.fig1,'UserData');
udata.new_marks=union(udata.new_marks,inds);
delete(udata.poly.h);
udata.poly.h=[];
udata.poly.x=[];
udata.poly.y=[];
udata.poly.THIS=[];
end
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function save_data()
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
data.points=[];
data.nodes=[];
k=1;
for i=1:length(udata.d_mark(1).h)
data.points(i).inds=get(udata.d_mark(1).h(i),'XData');
data.points(i).color=get(udata.d_mark(1).h(i),'Color');
end
color=get(udata.tmp_patch(1),'FaceVertexCData');
color=reshape(color,[size(color,1) 3]);
for i=1:size(color,1)
if all(~isnan(color(i,:)))
tmp.ind=i;
tmp.color=color(i,:);
data.nodes(k)=tmp;
k=k+1;
end
end
answer=inputdlg('Enter the name of the output variable:','',1);
if isempty(answer) | isempty(answer{1})
msgbox('Output is not set to workspace.');
return;
else
assignin('base',answer{1},data);
disp(sprintf('Struct is set to the workspace as ''%s''.',answer{1}));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function load_data()
answer = inputdlg('Enter the name of the struct to be loaded:','',1);
if isempty(answer) | isempty(answer{1})
msgbox('Data is not loaded.');
return;
end
data=evalin('base',answer{1});
if ~isstruct(data)
errordlg('Input variable must be a struct.');
return;
end
tmp1 = fieldnames(data);
tmp2 = {'nodes','points'};
for i=1:length(tmp1)
for j=1:length(tmp2);
if ~any(strcmp(tmp2{j},tmp1))
errordlg('Wrong type of struct.');
return;
end
end
end
if ~isempty(data.points)
tmp1=fieldnames(data.points(1));
end
if ~isempty(data.nodes)
tmp2=fieldnames(data.nodes(1));
end
for i=1:length(tmp1)
if ~any(strcmp(tmp1{i},{'inds','color'}))
errordlg('Wrong type of struct.');
return;
end
end
for i=1:length(tmp2)
if ~any(strcmp(tmp2{i},{'ind','color'}))
errordlg('Wrong type of struct.');
return;
end
end
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
clear_markers;
remove_traj;
old = gcf;
for i=1:length(data.points)
for j=1:length(udata.h);
set(0,'CurrentFigure',udata.fig1);
subplot(udata.h(j));
ydata=getfield(get(udata.d(j),'YData'),{data.points(i).inds});
udata.d_mark(j).h=[udata.d_mark(j).h;...
plot(data.points(i).inds,ydata,...
'Color',data.points(i).color,...
'LineStyle','none',...
'Marker','o',...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')')];
if all(data.points(i).color == [0 0 0])
udata.new_marks=unique([udata.new_marks; (data.points(i).inds)']);
else
udata.all_marks=unique([udata.all_marks; (data.points(i).inds)']);
end
end
if ~isempty(udata.fig2)
set(0,'CurrentFigure',udata.fig2);
for j=1:length(udata.h2)
subplot(udata.h2(j));
ydata=getfield(get(udata.d2(j),'YData'),{data.points(i).inds});
udata.d_mark2(j).h=[udata.d_mark2(j).h;...
plot(data.points(i).inds,ydata,...
'Color',data.points(i).color,...
'LineStyle','none',...
'Marker','o',...
'ButtonDownFcn',...
'vis_trajgui([],''line_down'')')];
end
end
end
set(0,'CurrentFigure',udata.sM_h);
color=get(udata.tmp_patch(1),'FaceVertexCData');
color=reshape(color,[size(color,1) 3]);
for i=1:length(data.nodes)
color(data.nodes(i).ind,:)=data.nodes(i).color;
end
for i=1:length(udata.tmp_patch);
set(udata.tmp_patch(i),'FaceVertexCData',color);
end
set(0,'CurrentFigure',old);
set(udata.fig1,'UserData',udata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function fuzzy_traj(trajStruct,arg);
%function fuzzy_traj(sM_h,sM,sD,interval,arg)
if isempty(arg)
if strcmp(trajStruct.lattice,'hexa')
udata.lattice='hexa';
udata.form=vis_patch('hexa');
else
data.lattice='rect';
udata.form=vis_patch('rect');
end
% interval=[1 size(trajStruct.primary_data,1)];
l=size(udata.form,1);
dim = size(trajStruct.primary_data,2);
udata.a_h=[findobj(get(trajStruct.figure,'Children'),'Tag','Uplane');...
findobj(get(trajStruct.figure,'Children'),'Tag','Cplane')];
udata.sM_h=trajStruct.figure;
udata.msize=trajStruct.msize;
%%%%%%%%%%%%%%%
%
% constructing patch that is drawn above every plane in map
%
nx = repmat(udata.form(:,1),1,prod(udata.msize));
ny = repmat(udata.form(:,2),1,prod(udata.msize));
x_c=reshape(repmat(1:udata.msize(2),l*udata.msize(1),1),l,prod(udata.msize));
y_c=repmat(repmat(1:udata.msize(1),l,1),1,udata.msize(2));
if strcmp(udata.lattice,'hexa')
t = find(~rem(y_c(1,:),2));
x_c(:,t)=x_c(:,t)+.5;
end
x_c=x_c+nx;
y_c=y_c+ny;
udata.orig_c=ones(prod(udata.msize),1)*[NaN NaN NaN];
colors=reshape(udata.orig_c,[1 size(udata.orig_c,1) 3]);
set(0,'CurrentFigure',trajStruct.figure);
%%%%%%%%%
% drawing
for i=1:length(udata.a_h);
subplot(udata.a_h(i));
v=caxis;
udata.patch_h(i) =patch(x_c,y_c,colors,'EdgeColor','none');
caxis(v);
end
udata.orig_x=get(udata.patch_h(1),'XData');
udata.orig_y=get(udata.patch_h(1),'YData');
% if interval(1) < 1 | interval(2) > size(trajStruct.primary_data,1)
% error('Invalid argument ''interval''.');
% end
x=1:size(trajStruct.primary_data,1);
udata.fig1=figure;
set(udata.fig1,'KeyPressFcn',...
'vis_trajgui([],''move_fuzzy'')');
for i=1:size(trajStruct.primary_data,2)
subplot(size(trajStruct.primary_data,2),1,i);
udata.h(i)=gca;
set(udata.h(1),'XTick',[]);
udata.d(i)=plot(x,trajStruct.primary_data(:,i));
l_x=1;
lims(1) = round(min(trajStruct.primary_data(:,i)));
lims(2) = round(max(trajStruct.primary_data(:,i)));
udata.l(i) = line([l_x l_x],lims,'Color','red','EraseMode','xor');
end
udata.l_x = l_x;
udata.interval=[1 size(trajStruct.bmus,2)];
tmp.fig1=udata.fig1;
% [K,P] = estimate_kernels(sM,sD);
% udata.K=K;
% udata.P=P;
% udata.codebook=sM.codebook;
% udata.data=sD.data;
udata.bmus=trajStruct.bmus;
set(udata.fig1,'UserData',udata);
set(trajStruct.figure,'UserData',tmp);
draw_fuzzy(l_x);
return;
end
move_fuzzy;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_fuzzy(x)
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
inds = find(udata.bmus(:,x));
[row col] = ind2sub(udata.msize,inds);
if strcmp(udata.lattice,'hexa')
t=find(~mod(row,2));
col(t)=col(t)+0.5;
end
color=udata.orig_c;
color=reshape(color,[size(color,1) 3]);
xdata=udata.orig_x;
ydata=udata.orig_y;
tmp= ones(size(xdata(:,1),1),1)*udata.bmus(inds,x)';
color(inds,:) = ones(length(inds),1)*[0 0 0];
xdata(:,inds) = udata.form(:,1)*ones(1,length(inds)).*tmp+ones(6,1)*col';
ydata(:,inds) = udata.form(:,2)*ones(1,length(inds)).*tmp+ones(6,1)*row';
set(udata.patch_h,'FaceVertexCData',color,'XData',xdata,'YData',ydata);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function move_fuzzy
% moves pointer lines and draws fuzzy response.
udata=get(gcf,'UserData');
udata=get(udata.fig1,'UserData');
switch get(gcf,'CurrentCharacter');
case {'<','>'}
key = get(gcf,'CurrentCharacter');
if key == '>'
if udata.l_x + 1 > udata.interval(2)
return;
end
l_x = udata.l_x + 1;
else
if udata.l_x - 1 < udata.interval(1)
return;
end
l_x = udata.l_x - 1;
end
draw_fuzzy(l_x);
set(udata.l,'XData',[l_x l_x]);
udata.l_x=l_x;
set(udata.fig1,'UserData',udata);
otherwise
return;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_order_cplanes.m
|
.m
|
mirtooloct-master/somtoolbox/som_order_cplanes.m
| 8,524 |
utf_8
|
3b6f8da3cb8f17ae375f464280e0f4df
|
function P = som_order_cplanes(sM, varargin)
%SOM_ORDER_CPLANES Orders and shows the SOM component planes.
%
% P = som_order_cplanes(sM, [[argID,] value, ...])
%
% som_order_cplanes(sM);
% som_order_cplanes(sM,'comp',1:30,'simil',C,'pca');
% P = som_order_cplanes(sM);
%
% Input and output arguments ([]'s are optional):
% sM (struct) map or data struct
% (matrix) a data matrix, size * x dim
% [argID, (string) See below. The values which are unambiguous can
% value] (varies) be given without the preceeding argID.
%
% P (matrix) size n x * (typically n x 2), the projection coordinates
%
% Here are the valid argument IDs and corresponding values. The values
% which are unambiguous (marked with '*') can be given without the
% preceeding argID.
% 'comp' (vector) size 1 x n, which components to project, 1:dim by default
% 'simil' *(string) similarity measure to use
% 'corr' linear correlation between component planes
% 'abs(corr)' absolute value of correlation (default)
% 'umat' as 'abs(corr)' but calculated from U-matrices
% 'mutu' mutual information (not implemented yet)
% (matrix) size n x n, a similarity matrix to be used
% 'proj' *(string) projection method to use: 'SOM' (default),
% 'pca', 'sammon', 'cca', 'order', 'ring'
% 'msize' (vector) size of the SOM that is used for projection
% 'show' *(string) how visualization is done: 'planes' (default),
% 'names', or 'none'
% 'mask' (vector) dim x 1, the mask to use, ones(dim,1) by default
% 'comp_names' (cell array) of strings, size dim x 1, the component names
%
% The visualized objects have a callback associated with them: by
% clicking on the object, the index and name of the component are printed
% to the standard output.
%
% See also SOM_SHOW.
% Copyright (c) 2000 by the SOM toolbox programming team.
% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 120600 070601
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
% sM
if isstruct(sM),
switch sM.type
case 'som_map',
D = sM.codebook; dim = size(D,2); cnames = sM.comp_names; mask = sM.mask;
ismap = 1;
case 'som_data',
D = sM.data; dim = size(D,2); cnames = sM.comp_names; mask = ones(dim,1);
ismap = 0;
otherwise, error('Invalid first argument.');
end
else
D = sM;
dim = size(D,2); mask = ones(dim,1);
cnames = cell(dim,1);
for i = 1:dim, cnames{i} = sprintf('Variable%d',i); end
ismap = 0;
end
% defaults
comps = 1:dim;
simil = 'abs(corr)';
proj = 'SOM';
show = 'planes';
mapsize = NaN;
% varargin
i=1;
while i<=length(varargin),
argok = 1;
if ischar(varargin{i}),
switch varargin{i},
% argument IDs
case 'mask', i=i+1; mask = varargin{i};
case 'comp_names', i=i+1; cnames = varargin{i};
case 'comp', i=i+1; comps = varargin{i};
case 'proj', i=i+1; proj = varargin{i};
case 'show', i=i+1; show = varargin{i};
case 'simil', i=i+1; simil = varargin{i};
case 'msize', i=i+1; mapsize = varargin{i};
% unambiguous values
case {'corr','abs(corr)','umat','mutu'}, simil = varargin{i};
case {'SOM','pca','sammon','cca','order','ring'}, proj = varargin{i};
case {'planes','names','none'}, show = varargin{i};
otherwise argok=0;
end
else
argok = 0;
end
if ~argok,
disp(['(som_order_cplanes) Ignoring invalid argument #' num2str(i+1)]);
end
i = i+1;
end
if strcmp(show,'planes') & ~ismap,
warning('Given data is not a map: using ''names'' visualization.');
show = 'names';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% similarity matrix
fprintf(1,'Calculating similarity matrix\n');
% use U-matrix
if strcmp(simil,'umat'),
if ~ismap, error('Given data is not a map: cannot use U-matrix similarity.'); end
U = som_umat(sM);
D = zeros(prod(size(U)),dim);
m = zeros(dim,1);
for i=1:dim, m=m*0; m(i)=1; U = som_umat(sM,'mask',m); D(:,i) = U(:); end
end
% components
D = D(:,comps);
cnames = cnames(comps);
mask = mask(comps);
dim = length(comps);
% similarity matrix
if ischar(simil),
switch simil,
case {'corr','abs(corr)','umat'},
A = zeros(dim);
me = zeros(1,dim);
for i=1:dim,
me(i) = mean(D(isfinite(D(:,i)),i)); D(:,i) = D(:,i) - me(i);
end
for i=1:dim,
for j=i:dim,
c = D(:,i).*D(:,j); c = c(isfinite(c));
A(i,j) = sum(c)/length(c); A(j,i) = A(i,j);
end
end
s = diag(A);
A = A./sqrt(s*s');
switch simil,
case {'abs(corr)','umat'}, A = abs(A);
case 'corr', A = A + 1;
end
case 'mutu',
error('Mutual information not implemented yet.');
end
else
A = simil;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% projection
fprintf(1,'Projection\n');
mu = 2*dim;
switch proj,
case 'SOM',
if isnan(mapsize),
sMtmp = som_randinit(A,'munits',mu);
msize = sMtmp.topol.msize;
else
msize = mapsize;
end
sM2 = som_make(A,'msize',msize,'rect','tracking',0);
bm = assign_unique_bm(sM2,A);
Co = som_unit_coords(sM2);
P = Co(bm,:);
case 'ring',
if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end
sM2 = som_make(A,'msize',msize,'cyl','rect','tracking',0);
bm = assign_unique_bm(sM2,A);
Co = som_unit_coords(sM2);
P = Co(bm,[1 3]);
case 'order',
if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end
sM2 = som_make(A,'msize',msize,'tracking',0);
bm = assign_unique_bm(sM2,A);
[dummy,i] = sort(bm);
[dummy,P] = sort(i);
if size(P,2)>1, P = P'; end
if size(P,2)==1, P(:,2) = zeros(length(P),1); end
case {'pca','sammon','cca'},
P = pcaproj(A,2);
if strcmp(proj,'sammon'), P = sammon(A,P,50,'steps');
elseif strcmp(proj,'cca'), P = cca(A,P,50);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% visualization
if ~strcmp(show,'none'),
fprintf(1,'Visualization\n');
cla
hold on
if strcmp(show,'planes')
s = findscaling(sM.topol.msize,P);
for i=1:dim,
C = som_normcolor(D(:,i));
if strcmp(simil,'umat'),
h=som_cplane([sM.topol.lattice 'U'],sM.topol.msize,C,1,s*P(i,:));
else
h=som_cplane(sM,C,1,s*P(i,:));
end
set(h,'edgecolor','none','Userdata',sprintf('[%d] %s',i,cnames{i}));
set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))');
end
else
s=1;
a=[min(P(:,1))-1 max(P(:,1))+1 min(P(:,2))-1-3 max(P(:,2))+1-3];
axis(s*a);
end
h=text(s*P(:,1),s*P(:,2)-3,cnames);
for i=1:length(h), set(h(i),'Userdata',sprintf('[%d] %s',i,cnames{i})); end
set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))');
hold off
axis on; axis equal; axis tight; set(gca,'XTick',[],'YTick',[],'Box','on');
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% subfunctions
function bm = assign_unique_bm(sM,D)
munits = size(sM.codebook,1);
[dlen dim] = size(D);
margin = max(0,dlen-munits);
[bm,qers] = som_bmus(sM,D);
bmi=ones(dim,1);
hits = som_hits(sM,D);
mult = find(hits>1);
while any(mult) & sum(hits(mult))-length(mult)>margin,
choices = find(bm==mult(1));
while length(choices)>1,
[dummy,mv] = max(qers(choices)); mv = choices(mv);
[mv_to,q] = som_bmus(sM,D(mv,:),bmi(mv));
bmi(mv)=bmi(mv)+1; qers(mv) = q; bm(mv) = mv_to;
choices = find(bm==mv_to);
end
for i=1:length(hits), hits(i)=sum(bm==i); end
mult = find(hits>1);
end
return;
function s = findscaling(msize,P)
d1 = median(abs(diff(unique(sort(P(:,1))))));
d2 = median(abs(diff(unique(sort(P(:,2))))));
if d1>0, s1 = 1.5*msize(2)/d1; else s1 = 0; end
if d2>0, s2 = 1.5*msize(1)/d2; else s2 = 0; end
s = max(s1,s2);
if s==0, s=1; end
return;
function alternative_SOM_plane_vis(sT,bm,simil,D,cnames)
clf
for i=1:size(D,2),
subplot(sT.msize(2),sT.msize(1),bm(i));
if strcmp(simil,'umat'), h=som_cplane([sT.lattice 'U'],sT.msize,D(:,i));
else h=som_cplane(sT,D(:,i));
end
set(h,'edgecolor','none');
title(cnames{i});
axis off
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_batchtrain.m
|
.m
|
mirtooloct-master/somtoolbox/som_batchtrain.m
| 20,584 |
utf_8
|
55d753f2fe72fda2647ec34374600a86
|
function [sMap,sTrain] = som_batchtrain(sMap, D, varargin)
%SOM_BATCHTRAIN Use batch algorithm to train the Self-Organizing Map.
%
% [sM,sT] = som_batchtrain(sM, D, [argID, value, ...])
%
% sM = som_batchtrain(sM,D);
% sM = som_batchtrain(sM,sD,'radius',[10 3 2 1 0.1],'tracking',3);
% [M,sT] = som_batchtrain(M,D,'ep','msize',[10 3],'hexa');
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct, the trained and updated map is returned
% (matrix) codebook matrix of a self-organizing map
% size munits x dim or msize(1) x ... x msize(k) x dim
% The trained map codebook is returned.
% D (struct) training data; data struct
% (matrix) training data, size dlen x dim
% [argID, (string) See below. The values which are unambiguous can
% value] (varies) be given without the preceeding argID.
%
% sT (struct) learning parameters used during the training
%
% Here are the valid argument IDs and corresponding values. The values which
% are unambiguous (marked with '*') can be given without the preceeding argID.
% 'mask' (vector) BMU search mask, size dim x 1
% 'msize' (vector) map size
% 'radius' (vector) neighborhood radiuses, length 1, 2 or trainlen
% 'radius_ini' (scalar) initial training radius
% 'radius_fin' (scalar) final training radius
% 'tracking' (scalar) tracking level, 0-3
% 'trainlen' (scalar) training length in epochs
% 'train' *(struct) train struct, parameters for training
% 'sTrain','som_train' = 'train'
% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss',
% 'ep' or 'bubble'
% 'topol' *(struct) topology struct
% 'som_topol','sTopol' = 'topol'
% 'lattice' *(string) map lattice, 'hexa' or 'rect'
% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid'
% 'weights' (vector) sample weights: each sample is weighted
%
% For more help, try 'type som_batchtrain' or check out online documentation.
% See also SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_batchtrain
%
% PURPOSE
%
% Trains a Self-Organizing Map using the batch algorithm.
%
% SYNTAX
%
% sM = som_batchtrain(sM,D);
% sM = som_batchtrain(sM,sD);
% sM = som_batchtrain(...,'argID',value,...);
% sM = som_batchtrain(...,value,...);
% [sM,sT] = som_batchtrain(M,D,...);
%
% DESCRIPTION
%
% Trains the given SOM (sM or M above) with the given training data
% (sD or D) using batch training algorithm. If no optional arguments
% (argID, value) are given, a default training is done. Using optional
% arguments the training parameters can be specified. Returns the
% trained and updated SOM and a train struct which contains
% information on the training.
%
% REFERENCES
%
% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
% Berlin, 1995, pp. 127-128.
% Kohonen, T., "Things you haven't heard about the Self-Organizing
% Map", In proceedings of International Conference
% on Neural Networks (ICNN), San Francisco, 1993, pp. 1147-1156.
%
% KNOWN BUGS
%
% Batchtrain does not work correctly for a map with a single unit.
% This is because of the way 'min'-function works.
%
% REQUIRED INPUT ARGUMENTS
%
% sM The map to be trained.
% (struct) map struct
% (matrix) codebook matrix (field .data of map struct)
% Size is either [munits dim], in which case the map grid
% dimensions (msize) should be specified with optional arguments,
% or [msize(1) ... msize(k) dim] in which case the map
% grid dimensions are taken from the size of the matrix.
% Lattice, by default, is 'rect' and shape 'sheet'.
% D Training data.
% (struct) data struct
% (matrix) data matrix, size [dlen dim]
%
% OPTIONAL INPUT ARGUMENTS
%
% argID (string) Argument identifier string (see below).
% value (varies) Value for the argument (see below).
%
% The optional arguments can be given as 'argID',value -pairs. If an
% argument is given value multiple times, the last one is
% used. The valid IDs and corresponding values are listed below. The values
% which are unambiguous (marked with '*') can be given without the
% preceeding argID.
%
% Below is the list of valid arguments:
% 'mask' (vector) BMU search mask, size dim x 1. Default is
% the one in sM (field '.mask') or a vector of
% ones if only a codebook matrix was given.
% 'msize' (vector) map grid dimensions. Default is the one
% in sM (field sM.topol.msize) or
% 'si = size(sM); msize = si(1:end-1);'
% if only a codebook matrix was given.
% 'radius' (vector) neighborhood radius
% length = 1: radius_ini = radius
% length = 2: [radius_ini radius_fin] = radius
% length > 2: the vector given neighborhood
% radius for each step separately
% trainlen = length(radius)
% 'radius_ini' (scalar) initial training radius
% 'radius_fin' (scalar) final training radius
% 'tracking' (scalar) tracking level: 0, 1 (default), 2 or 3
% 0 - estimate time
% 1 - track time and quantization error
% 2 - plot quantization error
% 3 - plot quantization error and two first
% components
% 'trainlen' (scalar) training length in epochs
% 'train' *(struct) train struct, parameters for training.
% Default parameters, unless specified,
% are acquired using SOM_TRAIN_STRUCT (this
% also applies for 'trainlen', 'radius_ini'
% and 'radius_fin').
% 'sTrain', 'som_topol' (struct) = 'train'
% 'neigh' *(string) The used neighborhood function. Default is
% the one in sM (field '.neigh') or 'gaussian'
% if only a codebook matrix was given. Other
% possible values is 'cutgauss', 'ep' and 'bubble'.
% 'topol' *(struct) topology of the map. Default is the one
% in sM (field '.topol').
% 'sTopol', 'som_topol' (struct) = 'topol'
% 'lattice' *(string) map lattice. Default is the one in sM
% (field sM.topol.lattice) or 'rect'
% if only a codebook matrix was given.
% 'shape' *(string) map shape. Default is the one in sM
% (field sM.topol.shape) or 'sheet'
% if only a codebook matrix was given.
% 'weights' (vector) weight for each data vector: during training,
% each data sample is weighted with the corresponding
% value, for example giving weights = [1 1 2 1]
% would have the same result as having third sample
% appear 2 times in the data
%
% OUTPUT ARGUMENTS
%
% sM the trained map
% (struct) if a map struct was given as input argument, a
% map struct is also returned. The current training
% is added to the training history (sM.trainhist).
% The 'neigh' and 'mask' fields of the map struct
% are updated to match those of the training.
% (matrix) if a matrix was given as input argument, a matrix
% is also returned with the same size as the input
% argument.
% sT (struct) train struct; information of the accomplished training
%
% EXAMPLES
%
% Simplest case:
% sM = som_batchtrain(sM,D);
% sM = som_batchtrain(sM,sD);
%
% To change the tracking level, 'tracking' argument is specified:
% sM = som_batchtrain(sM,D,'tracking',3);
%
% The change training parameters, the optional arguments 'train','neigh',
% 'mask','trainlen','radius','radius_ini' and 'radius_fin' are used.
% sM = som_batchtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0);
%
% Another way to specify training parameters is to create a train struct:
% sTrain = som_train_struct(sM,'dlen',size(D,1));
% sTrain = som_set(sTrain,'neigh','cutgauss');
% sM = som_batchtrain(sM,D,sTrain);
%
% By default the neighborhood radius goes linearly from radius_ini to
% radius_fin. If you want to change this, you can use the 'radius' argument
% to specify the neighborhood radius for each step separately:
% sM = som_batchtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]);
%
% You don't necessarily have to use the map struct, but you can operate
% directly with codebook matrices. However, in this case you have to
% specify the topology of the map in the optional arguments. The
% following commads are identical (M is originally a 200 x dim sized matrix):
% M = som_batchtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl');
% or
% M = som_batchtrain(M,D,'msize',[20 10],'hexa','cyl');
% or
% sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl');
% M = som_batchtrain(M,D,sT);
% or
% M = reshape(M,[20 10 dim]);
% M = som_batchtrain(M,D,'hexa','cyl');
%
% The som_batchtrain also returns a train struct with information on the
% accomplished training. This struct is also added to the end of the
% trainhist field of map struct, in case a map struct was given.
% [M,sTrain] = som_batchtrain(M,D,'msize',[20 10]);
% [sM,sTrain] = som_batchtrain(sM,D); % sM.trainhist{end}==sTrain
%
% SEE ALSO
%
% som_make Initialize and train a SOM using default parameters.
% som_seqtrain Train SOM with sequential algorithm.
% som_train_struct Determine default training parameters.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 1.0beta juuso 071197 041297
% Version 2.0beta juuso 101199
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check arguments
error(nargchk(2, Inf, nargin)); % check the number of input arguments
% map
struct_mode = isstruct(sMap);
if struct_mode,
sTopol = sMap.topol;
else
orig_size = size(sMap);
if ndims(sMap) > 2,
si = size(sMap); dim = si(end); msize = si(1:end-1);
M = reshape(sMap,[prod(msize) dim]);
else
msize = [orig_size(1) 1];
dim = orig_size(2);
end
sMap = som_map_struct(dim,'msize',msize);
sTopol = sMap.topol;
end
[munits dim] = size(sMap.codebook);
% data
if isstruct(D),
data_name = D.name;
D = D.data;
else
data_name = inputname(2);
end
nonempty = find(sum(isnan(D),2) < dim);
D = D(nonempty,:); % remove empty vectors from the data
[dlen ddim] = size(D); % check input dimension
if dim ~= ddim,
error('Map and data input space dimensions disagree.');
end
% varargin
sTrain = som_set('som_train','algorithm','batch','neigh', ...
sMap.neigh,'mask',sMap.mask,'data_name',data_name);
radius = [];
tracking = 1;
weights = 1;
i=1;
while i<=length(varargin),
argok = 1;
if ischar(varargin{i}),
switch varargin{i},
% argument IDs
case 'msize', i=i+1; sTopol.msize = varargin{i};
case 'lattice', i=i+1; sTopol.lattice = varargin{i};
case 'shape', i=i+1; sTopol.shape = varargin{i};
case 'mask', i=i+1; sTrain.mask = varargin{i};
case 'neigh', i=i+1; sTrain.neigh = varargin{i};
case 'trainlen', i=i+1; sTrain.trainlen = varargin{i};
case 'tracking', i=i+1; tracking = varargin{i};
case 'weights', i=i+1; weights = varargin{i};
case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i};
case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i};
case 'radius',
i=i+1;
l = length(varargin{i});
if l==1,
sTrain.radius_ini = varargin{i};
else
sTrain.radius_ini = varargin{i}(1);
sTrain.radius_fin = varargin{i}(end);
if l>2, radius = varargin{i}; end
end
case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i};
case {'topol','sTopol','som_topol'},
i=i+1;
sTopol = varargin{i};
if prod(sTopol.msize) ~= munits,
error('Given map grid size does not match the codebook size.');
end
% unambiguous values
case {'hexa','rect'}, sTopol.lattice = varargin{i};
case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i};
otherwise argok=0;
end
elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
switch varargin{i}(1).type,
case 'som_topol',
sTopol = varargin{i};
if prod(sTopol.msize) ~= munits,
error('Given map grid size does not match the codebook size.');
end
case 'som_train', sTrain = varargin{i};
otherwise argok=0;
end
else
argok = 0;
end
if ~argok,
disp(['(som_batchtrain) Ignoring invalid argument #' num2str(i+2)]);
end
i = i+1;
end
% take only weights of non-empty vectors
if length(weights)>dlen, weights = weights(nonempty); end
% trainlen
if ~isempty(radius), sTrain.trainlen = length(radius); end
% check topology
if struct_mode,
if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ...
~strcmp(sTopol.shape,sMap.topol.shape) | ...
any(sTopol.msize ~= sMap.topol.msize),
warning('Changing the original map topology.');
end
end
sMap.topol = sTopol;
% complement the training struct
sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% initialize
M = sMap.codebook;
mask = sTrain.mask;
trainlen = sTrain.trainlen;
% neighborhood radius
if trainlen==1,
radius = sTrain.radius_ini;
elseif length(radius)<=2,
r0 = sTrain.radius_ini; r1 = sTrain.radius_fin;
radius = r1 + fliplr((0:(trainlen-1))/(trainlen-1)) * (r0 - r1);
else
% nil
end
% distance between map units in the output space
% Since in the case of gaussian and ep neighborhood functions, the
% equations utilize squares of the unit distances and in bubble case
% it doesn't matter which is used, the unitdistances and neighborhood
% radiuses are squared.
Ud = som_unit_dists(sTopol);
Ud = Ud.^2;
radius = radius.^2;
% zero neighborhood radius may cause div-by-zero error
radius(find(radius==0)) = eps;
% The training algorithm involves calculating weighted Euclidian distances
% to all map units for each data vector. Basically this is done as
% for i=1:dlen,
% for j=1:munits,
% for k=1:dim
% Dist(j,i) = Dist(j,i) + mask(k) * (D(i,k) - M(j,k))^2;
% end
% end
% end
% where mask is the weighting vector for distance calculation. However, taking
% into account that distance between vectors m and v can be expressed as
% |m - v|^2 = sum_i ((m_i - v_i)^2) = sum_i (m_i^2 + v_i^2 - 2*m_i*v_i)
% this can be made much faster by transforming it to a matrix operation:
% Dist = (M.^2)*mask*ones(1,d) + ones(m,1)*mask'*(D'.^2) - 2*M*diag(mask)*D'
% Of the involved matrices, several are constant, as the mask and data do
% not change during training. Therefore they are calculated beforehand.
% For the case where there are unknown components in the data, each data
% vector will have an individual mask vector so that for that unit, the
% unknown components are not taken into account in distance calculation.
% In addition all NaN's are changed to zeros so that they don't screw up
% the matrix multiplications and behave correctly in updating step.
Known = ~isnan(D);
W1 = (mask*ones(1,dlen)) .* Known';
D(find(~Known)) = 0;
% constant matrices
WD = 2*diag(mask)*D'; % constant matrix
dconst = ((D.^2)*mask)'; % constant in distance calculation for each data sample
% W2 = ones(munits,1)*mask'; D2 = (D'.^2);
% initialize tracking
start = clock;
qe = zeros(trainlen,1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Action
% With the 'blen' parameter you can control the memory consumption
% of the algorithm, which is in practive directly proportional
% to munits*blen. If you're having problems with memory, try to
% set the value of blen lower.
blen = min(munits,dlen);
% reserve some space
bmus = zeros(1,dlen);
ddists = zeros(1,dlen);
for t = 1:trainlen,
% batchy train - this is done a block of data (inds) at a time
% rather than in a single sweep to save memory consumption.
% The 'Dist' and 'Hw' matrices have size munits*blen
% which - if you have a lot of data - would be HUGE if you
% calculated it all at once. A single-sweep version would
% look like this:
% Dist = (M.^2)*W1 - M*WD; %+ W2*D2
% [ddists, bmus] = min(Dist);
% (notice that the W2*D2 term can be ignored since it is constant)
% This "batchy" version is the same as single-sweep if blen=dlen.
i0 = 0;
while i0+1<=dlen,
inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen;
Dist = (M.^2)*W1(:,inds) - M*WD(:,inds);
[ddists(inds), bmus(inds)] = min(Dist);
end
% tracking
if tracking > 0,
ddists = ddists+dconst; % add the constant term
ddists(ddists<0) = 0; % rounding errors...
qe(t) = mean(sqrt(ddists));
trackplot(M,D,tracking,start,t,qe);
end
% neighborhood
% notice that the elements Ud and radius have been squared!
% note: 'bubble' matches the original "Batch Map" algorithm
switch sTrain.neigh,
case 'bubble', H = (Ud<=radius(t));
case 'gaussian', H = exp(-Ud/(2*radius(t)));
case 'cutgauss', H = exp(-Ud/(2*radius(t))) .* (Ud<=radius(t));
case 'ep', H = (1-Ud/radius(t)) .* (Ud<=radius(t));
end
% update
% In principle the updating step goes like this: replace each map unit
% by the average of the data vectors that were in its neighborhood.
% The contribution, or activation, of data vectors in the mean can
% be varied with the neighborhood function. This activation is given
% by matrix H. So, for each map unit the new weight vector is
%
% m = sum_i (h_i * d_i) / sum_i (h_i),
%
% where i denotes the index of data vector. Since the values of
% neighborhood function h_i are the same for all data vectors belonging to
% the Voronoi set of the same map unit, the calculation is actually done
% by first calculating a partition matrix P with elements p_ij=1 if the
% BMU of data vector j is i.
P = sparse(bmus,[1:dlen],weights,munits,dlen);
% Then the sum of vectors in each Voronoi set are calculated (P*D) and the
% neighborhood is taken into account by calculating a weighted sum of the
% Voronoi sum (H*). The "activation" matrix A is the denominator of the
% equation above.
S = H*(P*D);
A = H*(P*Known);
% If you'd rather make this without using the Voronoi sets try the following:
% Hi = H(:,bmus);
% S = Hi * D; % "sum_i (h_i * d_i)"
% A = Hi * Known; % "sum_i (h_i)"
% The bad news is that the matrix Hi has size [munits x dlen]...
% only update units for which the "activation" is nonzero
nonzero = find(A > 0);
M(nonzero) = S(nonzero) ./ A(nonzero);
end; % for t = 1:trainlen
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Build / clean up the return arguments
% tracking
if tracking > 0, fprintf(1,'\n'); end
% update structures
sTrain = som_set(sTrain,'time',datestr(now,0));
if struct_mode,
sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh);
tl = length(sMap.trainhist);
sMap.trainhist(tl+1) = sTrain;
else
sMap = reshape(M,orig_size);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
%%%%%%%%
function [] = trackplot(M,D,tracking,start,n,qe)
l = length(qe);
elap_t = etime(clock,start);
tot_t = elap_t*l/n;
fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
switch tracking
case 1,
case 2,
plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
title('Quantization error after each epoch');
drawnow
otherwise,
subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
title('Quantization error after each epoch');
subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b+');
title('First two components of map units (o) and data vectors (+)');
drawnow
end
% end of trackplot
|
github
|
martinarielhartmann/mirtooloct-master
|
som_stats_report.m
|
.m
|
mirtooloct-master/somtoolbox/som_stats_report.m
| 3,633 |
utf_8
|
eca74b20e5ec9e82e9aeee118cd81303
|
function som_stats_report(csS,fname,fmt,texonly)
% SOM_STATS_REPORT Make report of the statistics.
%
% som_stats_report(csS, fname, fmt, [standalone])
%
% som_stats_report(csS, 'data_stats', 'ps')
%
% Input and output arguments ([]'s are optional):
% csS (cell array) of statistics structs
% (struct) a statistics struct
% fname (string) output file name (without extension)
% (cellstr) {direc, fname}
% fmt (string) report format: 'ps', 'pdf', 'html' or 'txt'
% [texonly] (any) for 'ps' and 'pdf' formats: if 4th argument
% is given, only the tex file is written
% (w/o document start/end), and it is not compiled
%
% See also SOM_STATS, SOM_STATS_PLOT, SOM_STATS_TABLE, SOM_TABLE_PRINT, REP_UTILS.
% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 311201
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
if isstruct(csS), csS = {csS}; end
dim = length(csS);
if iscell(fname), direc = fname{1}; fname = fname{2}; else direc = '.'; end
if nargin<4, texonly = 0; else texonly = 1; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
% additional analysis
continuity = zeros(dim,1);
for i=1:dim, continuity(i) = csS{i}.nunique / csS{i}.nvalid; end
entropy_rel = zeros(dim,1);
for i=1:dim,
c = csS{i}.hist.counts;
if length(c) < 2 | all(c==0), entropy(i) = 0;
else
maxent = log(length(c));
c = c(c>0)/sum(c);
entropy_rel(i) = -sum(c.*log(c)) / maxent;
end
end
% meta-statistics
values = {'Number of variables',dim; ...
'Number of samples',csS{1}.ntotal; ...
'Valid values',c_and_p_str(count_total(csS,'nvalid'),dim*csS{1}.ntotal); ...
'Mean(#unique / #valid)',mean(continuity); ...
'Mean relative entropy',mean(entropy_rel)};
%'Dataset name',sD.name; 'Report generated',datestr(now);
sTdset = som_table_struct(values);
% statistics tables
[sTstats,csThist] = som_stats_table(csS);
sTstats = som_table_modify(sTstats,'addcol',entropy_rel,{'entropy'});
% write report
if isempty(fname), fid = 1;
else
switch fmt,
case {'ps','pdf'}, ending = '.tex';
case 'html', ending = '.html';
case 'txt', ending = '.txt';
end
fid = fopen([direc '/' fname ending],'w');
end
if ~texonly, rep_utils('header',fmt,fid); end
rep_utils({'inserttable',sTdset,1,0},fmt,fid);
rep_utils({'insertbreak'},fmt,fid);
rep_utils({'inserttable',sTstats,1,0},fmt,fid);
rep_utils({'insertbreak'},fmt,fid);
som_stats_plot(csS,'stats');
rep_utils({'printfigure',[direc '/histograms']},fmt);
rep_utils({'insertfigure','histograms'},fmt,fid);
for i=1:dim,
rep_utils({'insertbreak'},fmt,fid);
rep_utils({'inserttable',csThist{i},1,0},fmt,fid);
end
if ~texonly, rep_utils('footer',fmt,fid); end
if fid~=1, fclose(fid); end
if ~texonly & any(strcmp(fmt,{'ps','pdf'})), rep_utils('compile',fmt); end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function a = count_total(csS,field)
% count total of the field values
a = 0; for i=1:length(csS), a = a + getfield(csS{i},field); end
return;
function str = c_and_p_str(n,m)
% return a string of form # (%), e.g. '23 (12%)'
if n==m, p = '100';
elseif n==0, p = '0';
else p = sprintf('%.2g',100*n/m);
end
str = sprintf('%d (%s%%)',round(n),p);
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_eucdist2.m
|
.m
|
mirtooloct-master/somtoolbox/som_eucdist2.m
| 2,270 |
utf_8
|
6f74c5daaf9a1667b8937a1ceb29ffa2
|
function d=som_eucdist2(Data, Proto)
%SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct
%
% d=som_eucdist2(D, P)
%
% d=som_eucdist(sMap, sData);
% d=som_eucdist(sData, sMap);
% d=som_eucdist(sMap1, sMap2);
% d=som_eucdist(datamatrix1, datamatrix2);
%
% Input and output arguments ([]'s are optional):
% D (matrix) size Nxd
% (struct) map or data struct
% P (matrix) size Pxd
% (struct) map or data struct
% d (matrix) distance matrix of size NxP
%
% IMPORTANT
%
% * Calculates _squared_ euclidean distances
% * Observe that the mask in the map struct is not taken into account while
% calculating the euclidean distance
%
% See also KNN, PDIST.
% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 291000
%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isstruct(Data);
if isfield(Data,'type') & ischar(Data.type),
;
else
error('Invalid map/data struct?');
end
switch Data.type
case 'som_map'
data=Data.codebook;
case 'som_data'
data=Data.data;
end
else
% is already a matrix
data=Data;
end
% Take prototype vectors from prototype struct
if isstruct(Proto),
if isfield(Proto,'type') & ischar(Proto.type),
;
else
error('Invalid map/data struct?');
end
switch Proto.type
case 'som_map'
proto=Proto.codebook;
case 'som_data'
proto=Proto.data;
end
else
% is already a matrix
proto=Proto;
end
% Check that inputs are matrices
if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
error('Prototype or data input not valid.')
end
% Record data&proto sizes and check their dims
[N_data dim_data]=size(data);
[N_proto dim_proto]=size(proto);
if dim_proto ~= dim_data,
error('Data and prototype vector dimension does not match.');
end
% Calculate euclidean distances between classifiees and prototypes
d=distance(data,proto);
%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function d=distance(X,Y);
% Euclidean distance matrix between row vectors in X and Y
U=~isnan(Y); Y(~U)=0;
V=~isnan(X); X(~V)=0;
d=abs(X.^2*U'+V*Y'.^2-2*X*Y');
|
github
|
martinarielhartmann/mirtooloct-master
|
som_norm_variable.m
|
.m
|
mirtooloct-master/somtoolbox/som_norm_variable.m
| 19,542 |
utf_8
|
9323ed0f31d148b4f88cacf4454fdb22
|
function [x,sNorm] = som_norm_variable(x, method, operation)
%SOM_NORM_VARIABLE Normalize or denormalize a scalar variable.
%
% [x,sNorm] = som_norm_variable(x, method, operation)
%
% xnew = som_norm_variable(x,'var','do');
% [dummy,sN] = som_norm_variable(x,'log','init');
% [xnew,sN] = som_norm_variable(x,sN,'do');
% xorig = som_norm_variable(xnew,sN,'undo');
%
% Input and output arguments:
% x (vector) a set of values of a scalar variable for
% which the (de)normalization is performed.
% The processed values are returned.
% method (string) identifier for a normalization method: 'var',
% 'range', 'log', 'logistic', 'histD', or 'histC'.
% A normalization struct with default values is created.
% (struct) normalization struct, or an array of such
% (cellstr) first string gives normalization operation, and the
% second gives denormalization operation, with x
% representing the variable, for example:
% {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}.
% Note that in the last case, no denorm operation is
% defined.
% operation (string) the operation to be performed: 'init', 'do' or 'undo'
%
% sNorm (struct) updated normalization struct/struct array
%
% For more help, try 'type som_norm_variable' or check out online documentation.
% See also SOM_NORMALIZE, SOM_DENORMALIZE.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_norm_variable
%
% PURPOSE
%
% Initialize, apply and undo normalizations on a given vector of
% scalar values.
%
% SYNTAX
%
% xnew = som_norm_variable(x,method,operation)
% xnew = som_norm_variable(x,sNorm,operation)
% [xnew,sNorm] = som_norm_variable(...)
%
% DESCRIPTION
%
% This function is used to initialize, apply and undo normalizations
% on scalar variables. It is the low-level function that upper-level
% functions SOM_NORMALIZE and SOM_DENORMALIZE utilize to actually (un)do
% the normalizations.
%
% Normalizations are typically performed to control the variance of
% vector components. If some vector components have variance which is
% significantly higher than the variance of other components, those
% components will dominate the map organization. Normalization of
% the variance of vector components (method 'var') is used to prevent
% that. In addition to variance normalization, other methods have
% been implemented as well (see list below).
%
% Usually normalizations convert the variable values so that they no
% longer make any sense: the values are still ordered, but their range
% may have changed so radically that interpreting the numbers in the
% original context is very hard. For this reason all implemented methods
% are (more or less) revertible. The normalizations are monotonic
% and information is saved so that they can be undone. Also, the saved
% information makes it possible to apply the EXACTLY SAME normalization
% to another set of values. The normalization information is determined
% with 'init' operation, while 'do' and 'undo' operations are used to
% apply or revert the normalization.
%
% The normalization information is saved in a normalization struct,
% which is returned as the second argument of this function. Note that
% normalization operations may be stacked. In this case, normalization
% structs are positioned in a struct array. When applied, the array is
% gone through from start to end, and when undone, in reverse order.
%
% method description
%
% 'var' Variance normalization. A linear transformation which
% scales the values such that their variance=1. This is
% convenient way to use Mahalanobis distance measure without
% actually changing the distance calculation procedure.
%
% 'range' Normalization of range of values. A linear transformation
% which scales the values between [0,1].
%
% 'log' Logarithmic normalization. In many cases the values of
% a vector component are exponentially distributed. This
% normalization is a good way to get more resolution to
% (the low end of) that vector component. What this
% actually does is a non-linear transformation:
% x_new = log(x_old - m + 1)
% where m=min(x_old) and log is the natural logarithm.
% Applying the transformation to a value which is lower
% than m-1 will give problems, as the result is then complex.
% If the minimum for values is known a priori,
% it might be a good idea to initialize the normalization with
% [dummy,sN] = som_norm_variable(minimum,'log','init');
% and normalize only after this:
% x_new = som_norm_variable(x,sN,'do');
%
% 'logistic' or softmax normalization. This normalization ensures
% that all values in the future, too, are within the range
% [0,1]. The transformation is more-or-less linear in the
% middle range (around mean value), and has a smooth
% nonlinearity at both ends which ensures that all values
% are within the range. The data is first scaled as in
% variance normalization:
% x_scaled = (x_old - mean(x_old))/std(x_old)
% and then transformed with the logistic function
% x_new = 1/(1+exp(-x_scaled))
%
% 'histD' Discrete histogram equalization. Non-linear. Orders the
% values and replaces each value by its ordinal number.
% Finally, scales the values such that they are between [0,1].
% Useful for both discrete and continuous variables, but as
% the saved normalization information consists of all
% unique values of the initialization data set, it may use
% considerable amounts of memory. If the variable can get
% more than a few values (say, 20), it might be better to
% use 'histC' method below. Another important note is that
% this method is not exactly revertible if it is applied
% to values which are not part of the original value set.
%
% 'histC' Continuous histogram equalization. Actually, a partially
% linear transformation which tries to do something like
% histogram equalization. The value range is divided to
% a number of bins such that the number of values in each
% bin is (almost) the same. The values are transformed
% linearly in each bin. For example, values in bin number 3
% are scaled between [3,4[. Finally, all values are scaled
% between [0,1]. The number of bins is the square root
% of the number of unique values in the initialization set,
% rounded up. The resulting histogram equalization is not
% as good as the one that 'histD' makes, but the benefit
% is that it is exactly revertible - even outside the
% original value range (although the results may be funny).
%
% 'eval' With this method, freeform normalization operations can be
% specified. The parameter field contains strings to be
% evaluated with 'eval' function, with variable name 'x'
% representing the variable itself. The first string is
% the normalization operation, and the second is a
% denormalization operation. If the denormalization operation
% is empty, it is ignored.
%
% INPUT ARGUMENTS
%
% x (vector) The scalar values to which the normalization
% operation is applied.
%
% method The normalization specification.
% (string) Identifier for a normalization method: 'var',
% 'range', 'log', 'logistic', 'histD' or 'histC'.
% Corresponding default normalization struct is created.
% (struct) normalization struct
% (struct array) of normalization structs, applied to
% x one after the other
% (cellstr) of length
% (cellstr array) first string gives normalization operation, and
% the second gives denormalization operation, with x
% representing the variable, for example:
% {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}.
% Note that in the last case, no denorm operation is
% defined.
%
% note: if the method is given as struct(s), it is
% applied (done or undone, as specified by operation)
% regardless of what the value of '.status' field
% is in the struct(s). Only if the status is
% 'uninit', the undoing operation is halted.
% Anyhow, the '.status' fields in the returned
% normalization struct(s) is set to approriate value.
%
% operation (string) The operation to perform: 'init' to initialize
% the normalization struct, 'do' to perform the
% normalization, 'undo' to undo the normalization,
% if possible. If operation 'do' is given, but the
% normalization struct has not yet been initialized,
% it is initialized using the given data (x).
%
% OUTPUT ARGUMENTS
%
% x (vector) Appropriately processed values.
%
% sNorm (struct) Updated normalization struct/struct array. If any,
% the '.status' and '.params' fields are updated.
%
% EXAMPLES
%
% To initialize and apply a normalization on a set of scalar values:
%
% [x_new,sN] = som_norm_variable(x_old,'var','do');
%
% To just initialize, use:
%
% [dummy,sN] = som_norm_variable(x_old,'var','init');
%
% To undo the normalization(s):
%
% x_orig = som_norm_variable(x_new,sN,'undo');
%
% Typically, normalizations of data structs/sets are handled using
% functions SOM_NORMALIZE and SOM_DENORMALIZE. However, when only the
% values of a single variable are of interest, SOM_NORM_VARIABLE may
% be useful. For example, assume one wants to apply the normalization
% done on a component (i) of a data struct (sD) to a new set of values
% (x) of that component. With SOM_NORM_VARIABLE this can be done with:
%
% x_new = som_norm_variable(x,sD.comp_norm{i},'do');
%
% Now, as the normalizations in sD.comp_norm{i} have already been
% initialized with the original data set (presumably sD.data),
% the EXACTLY SAME normalization(s) can be applied to the new values.
% The same thing can be done with SOM_NORMALIZE function, too:
%
% x_new = som_normalize(x,sD.comp_norm{i});
%
% Or, if the new data set were in variable D - a matrix of same
% dimension as the original data set:
%
% D_new = som_normalize(D,sD,i);
%
% SEE ALSO
%
% som_normalize Add/apply/redo normalizations for a data struct/set.
% som_denormalize Undo normalizations of a data struct/set.
% Copyright (c) 1998-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 151199 170400 150500
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
error(nargchk(3, 3, nargin)); % check no. of input arguments is correct
% method
sNorm = [];
if ischar(method)
if any(strcmp(method,{'var','range','log','logistic','histD','histC'})),
sNorm = som_set('som_norm','method',method);
else
method = cellstr(method);
end
end
if iscell(method),
if length(method)==1 & isstruct(method{1}), sNorm = method{1};
else
if length(method)==1 | isempty(method{2}), method{2} = 'x'; end
sNorm = som_set('som_norm','method','eval','params',method);
end
else
sNorm = method;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
order = [1:length(sNorm)];
if length(order)>1 & strcmp(operation,'undo'), order = order(end:-1:1); end
for i=order,
% initialize
if strcmp(operation,'init') | ...
(strcmp(operation,'do') & strcmp(sNorm(i).status,'uninit')),
% case method = 'hist'
if strcmp(sNorm(i).method,'hist'),
inds = find(~isnan(x) & ~isinf(x));
if length(unique(x(inds)))>20, sNorm(i).method = 'histC';
else sNorm{i}.method = 'histD'; end
end
switch(sNorm(i).method),
case 'var', params = norm_variance_init(x);
case 'range', params = norm_scale01_init(x);
case 'log', params = norm_log_init(x);
case 'logistic', params = norm_logistic_init(x);
case 'histD', params = norm_histeqD_init(x);
case 'histC', params = norm_histeqC_init(x);
case 'eval', params = sNorm(i).params;
otherwise,
error(['Unrecognized method: ' sNorm(i).method]);
end
sNorm(i).params = params;
sNorm(i).status = 'undone';
end
% do / undo
if strcmp(operation,'do'),
switch(sNorm(i).method),
case 'var', x = norm_scale_do(x,sNorm(i).params);
case 'range', x = norm_scale_do(x,sNorm(i).params);
case 'log', x = norm_log_do(x,sNorm(i).params);
case 'logistic', x = norm_logistic_do(x,sNorm(i).params);
case 'histD', x = norm_histeqD_do(x,sNorm(i).params);
case 'histC', x = norm_histeqC_do(x,sNorm(i).params);
case 'eval', x = norm_eval_do(x,sNorm(i).params);
otherwise,
error(['Unrecognized method: ' sNorm(i).method]);
end
sNorm(i).status = 'done';
elseif strcmp(operation,'undo'),
if strcmp(sNorm(i).status,'uninit'),
warning('Could not undo: uninitialized normalization struct.')
break;
end
switch(sNorm(i).method),
case 'var', x = norm_scale_undo(x,sNorm(i).params);
case 'range', x = norm_scale_undo(x,sNorm(i).params);
case 'log', x = norm_log_undo(x,sNorm(i).params);
case 'logistic', x = norm_logistic_undo(x,sNorm(i).params);
case 'histD', x = norm_histeqD_undo(x,sNorm(i).params);
case 'histC', x = norm_histeqC_undo(x,sNorm(i).params);
case 'eval', x = norm_eval_undo(x,sNorm(i).params);
otherwise,
error(['Unrecognized method: ' sNorm(i).method]);
end
sNorm(i).status = 'undone';
elseif ~strcmp(operation,'init'),
error(['Unrecognized operation: ' operation])
end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
% linear scaling
function p = norm_variance_init(x)
inds = find(~isnan(x) & isfinite(x));
p = [mean(x(inds)), std(x(inds))];
if p(2) == 0, p(2) = 1; end
%end of norm_variance_init
function p = norm_scale01_init(x)
inds = find(~isnan(x) & isfinite(x));
mi = min(x(inds));
ma = max(x(inds));
if mi == ma, p = [mi, 1]; else p = [mi, ma-mi]; end
%end of norm_scale01_init
function x = norm_scale_do(x,p)
x = (x - p(1)) / p(2);
% end of norm_scale_do
function x = norm_scale_undo(x,p)
x = x * p(2) + p(1);
% end of norm_scale_undo
% logarithm
function p = norm_log_init(x)
inds = find(~isnan(x) & isfinite(x));
p = min(x(inds));
% end of norm_log_init
function x = norm_log_do(x,p)
x = log(x - p +1);
% if any(~isreal(x)), ok = 0; end
% end of norm_log_do
function x = norm_log_undo(x,p)
x = exp(x) -1 + p;
% end of norm_log_undo
% logistic
function p = norm_logistic_init(x)
inds = find(~isnan(x) & isfinite(x));
p = [mean(x(inds)), std(x(inds))];
if p(2)==0, p(2) = 1; end
% end of norm_logistic_init
function x = norm_logistic_do(x,p)
x = (x-p(1))/p(2);
x = 1./(1+exp(-x));
% end of norm_logistic_do
function x = norm_logistic_undo(x,p)
x = log(x./(1-x));
x = x*p(2)+p(1);
% end of norm_logistic_undo
% histogram equalization for discrete values
function p = norm_histeqD_init(x)
inds = find(~isnan(x) & ~isinf(x));
p = unique(x(inds));
% end of norm_histeqD_init
function x = norm_histeqD_do(x,p)
bins = length(p);
inds = find(~isnan(x) & ~isinf(x))';
for i = inds,
[dummy ind] = min(abs(x(i) - p));
% data item closer to the left-hand bin wall is indexed after RH wall
if x(i) > p(ind) & ind < bins,
x(i) = ind + 1;
else
x(i) = ind;
end
end
x = (x-1)/(bins-1); % normalization between [0,1]
% end of norm_histeqD_do
function x = norm_histeqD_undo(x,p)
bins = length(p);
x = round(x*(bins-1)+1);
inds = find(~isnan(x) & ~isinf(x));
x(inds) = p(x(inds));
% end of norm_histeqD_undo
% histogram equalization with partially linear functions
function p = norm_histeqC_init(x)
% investigate x
inds = find(~isnan(x) & ~isinf(x));
samples = length(inds);
xs = unique(x(inds));
mi = xs(1);
ma = xs(end);
% decide number of limits
lims = ceil(sqrt(length(xs))); % 2->2,100->10,1000->32,10000->100
% decide limits
if lims==1,
p = [mi, mi+1];
lims = 2;
elseif lims==2,
p = [mi, ma];
else
p = zeros(lims,1);
p(1) = mi;
p(end) = ma;
binsize = zeros(lims-1,1); b = 1; avebinsize = samples/(lims-1);
for i=1:(length(xs)-1),
binsize(b) = binsize(b) + sum(x==xs(i));
if binsize(b) >= avebinsize,
b = b + 1;
p(b) = (xs(i)+xs(i+1))/2;
end
if b==(lims-1),
binsize(b) = samples-sum(binsize); break;
else
avebinsize = (samples-sum(binsize))/(lims-1-b);
end
end
end
% end of norm_histeqC_init
function x = norm_histeqC_do(x,p)
xnew = x;
lims = length(p);
% handle values below minimum
r = p(2)-p(1);
inds = find(x<=p(1) & isfinite(x));
if any(inds), xnew(inds) = 0-(p(1)-x(inds))/r; end
% handle values above maximum
r = p(end)-p(end-1);
inds = find(x>p(end) & isfinite(x));
if any(inds), xnew(inds) = lims-1+(x(inds)-p(end))/r; end
% handle all other values
for i=1:(lims-1),
r0 = p(i); r1 = p(i+1); r = r1-r0;
inds = find(x>r0 & x<=r1);
if any(inds), xnew(inds) = i-1+(x(inds)-r0)/r; end
end
% scale so that minimum and maximum correspond to 0 and 1
x = xnew/(lims-1);
% end of norm_histeqC_do
function x = norm_histeqC_undo(x,p)
xnew = x;
lims = length(p);
% scale so that 0 and 1 correspond to minimum and maximum
x = x*(lims-1);
% handle values below minimum
r = p(2)-p(1);
inds = find(x<=0 & isfinite(x));
if any(inds), xnew(inds) = x(inds)*r + p(1); end
% handle values above maximum
r = p(end)-p(end-1);
inds = find(x>lims-1 & isfinite(x));
if any(inds), xnew(inds) = (x(inds)-(lims-1))*r+p(end); end
% handle all other values
for i=1:(lims-1),
r0 = p(i); r1 = p(i+1); r = r1-r0;
inds = find(x>i-1 & x<=i);
if any(inds), xnew(inds) = (x(inds)-(i-1))*r + r0; end
end
x = xnew;
% end of norm_histeqC_undo
% eval
function p = norm_eval_init(method)
p = method;
%end of norm_eval_init
function x = norm_eval_do(x,p)
x_tmp = eval(p{1});
if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
size(x_tmp,2)==1 & size(x,2)==1,
x = x_tmp;
end
%end of norm_eval_do
function x = norm_eval_undo(x,p)
x_tmp = eval(p{2});
if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
size(x_tmp,2)==1 & size(x,2)==1,
x = x_tmp;
end
%end of norm_eval_undo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
cca.m
|
.m
|
mirtooloct-master/somtoolbox/cca.m
| 7,987 |
utf_8
|
f9446f8801dde781d7e8f400842fb0c2
|
function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0)
%CCA Projects data vectors using Curvilinear Component Analysis.
%
% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])
%
% P = cca(D,2,10); % projects the given data to a plane
% P = cca(D,pcaproj(D,2),5); % same, but with PCA initialization
% P = cca(D, 2, 10, Dist); % same, but the given distance matrix is used
%
% Input and output arguments ([]'s are optional):
% D (matrix) the data matrix, size dlen x dim
% (struct) data or map struct
% P (scalar) output dimension
% (matrix) size dlen x odim, the initial projection
% epochs (scalar) training length
% [Dist] (matrix) pairwise distance matrix, size dlen x dlen.
% If the distances in the input space should
% be calculated otherwise than as euclidian
% distances, the distance from each vector
% to each other vector can be given here,
% size dlen x dlen. For example PDIST
% function can be used to calculate the
% distances: Dist = squareform(pdist(D,'mahal'));
% [alpha0] (scalar) initial step size, 0.5 by default
% [lambda0] (scalar) initial radius of influence, 3*max(std(D)) by default
%
% P (matrix) size dlen x odim, the projections
%
% Unknown values (NaN's) in the data: projections of vectors with
% unknown components tend to drift towards the center of the
% projection distribution. Projections of totally unknown vectors are
% set to unknown (NaN).
%
% See also SAMMON, PCAPROJ.
% Reference: Demartines, P., Herault, J., "Curvilinear Component
% Analysis: a Self-Organizing Neural Network for Nonlinear
% Mapping of Data Sets", IEEE Transactions on Neural Networks,
% vol 8, no 1, 1997, pp. 148-154.
% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% juuso 171297 040100
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check arguments
error(nargchk(3, 6, nargin)); % check the number of input arguments
% input data
if isstruct(D),
if strcmp(D.type,'som_map'), D = D.codebook; else D = D.data; end
end
[noc dim] = size(D);
noc_x_1 = ones(noc, 1); % used frequently
me = zeros(1,dim); st = zeros(1,dim);
for i=1:dim,
me(i) = mean(D(find(isfinite(D(:,i))),i));
st(i) = std(D(find(isfinite(D(:,i))),i));
end
% initial projection
if prod(size(P))==1,
P = (2*rand(noc,P)-1).*st(noc_x_1,1:P) + me(noc_x_1,1:P);
else
% replace unknown projections with known values
inds = find(isnan(P)); P(inds) = rand(size(inds));
end
[dummy odim] = size(P);
odim_x_1 = ones(odim, 1); % this is used frequently
% training length
train_len = epochs*noc;
% random sample order
rand('state',sum(100*clock));
sample_inds = ceil(noc*rand(train_len,1));
% mutual distances
if nargin<4 | isempty(Mdist) | all(isnan(Mdist(:))),
fprintf(2, 'computing mutual distances\r');
dim_x_1 = ones(dim,1);
for i = 1:noc,
x = D(i,:);
Diff = D - x(noc_x_1,:);
N = isnan(Diff);
Diff(find(N)) = 0;
Mdist(:,i) = sqrt((Diff.^2)*dim_x_1);
N = find(sum(N')==dim); %mutual distance unknown
if ~isempty(N), Mdist(N,i) = NaN; end
end
else
% if the distance matrix is output from PDIST function
if size(Mdist,1)==1, Mdist = squareform(Mdist); end
if size(Mdist,1)~=noc,
error('Mutual distance matrix size and data set size do not match');
end
end
% alpha and lambda
if nargin<5 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end
alpha = potency_curve(alpha0,alpha0/100,train_len);
if nargin<6 | isempty(lambda0) | isnan(lambda0), lambda0 = max(st)*3; end
lambda = potency_curve(lambda0,0.01,train_len);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Action
k=0; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs);
for i=1:train_len,
ind = sample_inds(i); % sample index
dx = Mdist(:,ind); % mutual distances in input space
known = find(~isnan(dx)); % known distances
if ~isempty(known),
% sample vector's projection
y = P(ind,:);
% distances in output space
Dy = P(known,:) - y(noc_x_1(known),:);
dy = sqrt((Dy.^2)*odim_x_1);
% relative effect
dy(find(dy==0)) = 1; % to get rid of div-by-zero's
fy = exp(-dy/lambda(i)) .* (dx(known) ./ dy - 1);
% Note that the function F here is e^(-dy/lambda))
% instead of the bubble function 1(lambda-dy) used in the
% paper.
% Note that here a simplification has been made: the derivatives of the
% F function have been ignored in calculating the gradient of error
% function w.r.t. to changes in dy.
% update
P(known,:) = P(known,:) + alpha(i)*fy(:,odim_x_1).*Dy;
end
% track
if rem(i,noc)==0,
k=k+1; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% clear up
% calculate error
error = cca_error(P,Mdist,lambda(train_len));
fprintf(2,'%d iterations, error %f \n', epochs, error);
% set projections of totally unknown vectors as unknown
unknown = find(sum(isnan(D)')==dim);
P(unknown,:) = NaN;
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% tips
% to plot the results, use the code below
%subplot(2,1,1),
%switch(odim),
% case 1, plot(P(:,1),ones(dlen,1),'x')
% case 2, plot(P(:,1),P(:,2),'x');
% otherwise, plot3(P(:,1),P(:,2),P(:,3),'x'); rotate3d on
%end
%subplot(2,1,2), dydxplot(P,Mdist);
% to a project a new point x in the input space to the output space
% do the following:
% Diff = D - x(noc_x_1,:); Diff(find(isnan(Diff))) = 0;
% dx = sqrt((Diff.^2)*dim_x_1);
% p = project_point(P,x,dx); % this function can be found from below
% tlen = size(p,1);
% plot(P(:,1),P(:,2),'bx',p(tlen,1),p(tlen,2),'ro',p(:,1),p(:,2),'r-')
% similar trick can be made to the other direction
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function vals = potency_curve(v0,vn,l)
% curve that decreases from v0 to vn with a rate that is
% somewhere between linear and 1/t
vals = v0 * (vn/v0).^([0:(l-1)]/(l-1));
function error = cca_error(P,Mdist,lambda)
[noc odim] = size(P);
noc_x_1 = ones(noc,1);
odim_x_1 = ones(odim,1);
error = 0;
for i=1:noc,
known = find(~isnan(Mdist(:,i)));
if ~isempty(known),
y = P(i,:);
Dy = P(known,:) - y(noc_x_1(known),:);
dy = sqrt((Dy.^2)*odim_x_1);
fy = exp(-dy/lambda);
error = error + sum(((Mdist(known,i) - dy).^2).*fy);
end
end
error = error/2;
function [] = dydxplot(P,Mdist)
[noc odim] = size(P);
noc_x_1 = ones(noc,1);
odim_x_1 = ones(odim,1);
Pdist = zeros(noc,noc);
for i=1:noc,
y = P(i,:);
Dy = P - y(noc_x_1,:);
Pdist(:,i) = sqrt((Dy.^2)*odim_x_1);
end
Pdist = tril(Pdist,-1);
inds = find(Pdist > 0);
n = length(inds);
plot(Pdist(inds),Mdist(inds),'.');
xlabel('dy'), ylabel('dx')
function p = project_point(P,x,dx)
[noc odim] = size(P);
noc_x_1 = ones(noc,1);
odim_x_1 = ones(odim,1);
% initial projection
[dummy,i] = min(dx);
y = P(i,:)+rand(1,odim)*norm(P(i,:))/20;
% lambda
lambda = norm(std(P));
% termination
eps = 1e-3; i_max = noc*10;
i=1; p(i,:) = y;
ready = 0;
while ~ready,
% mutual distances
Dy = P - y(noc_x_1,:); % differences in output space
dy = sqrt((Dy.^2)*odim_x_1); % distances in output space
f = exp(-dy/lambda);
fprintf(2,'iteration %d, error %g \r',i,sum(((dx - dy).^2).*f));
% all the other vectors push the projected one
fy = f .* (dx ./ dy - 1) / sum(f);
% update
step = - sum(fy(:,odim_x_1).*Dy);
y = y + step;
i=i+1;
p(i,:) = y;
ready = (norm(step)/norm(y) < eps | i > i_max);
end
fprintf(2,'\n');
|
github
|
martinarielhartmann/mirtooloct-master
|
sompak_sammon.m
|
.m
|
mirtooloct-master/somtoolbox/sompak_sammon.m
| 4,311 |
utf_8
|
e86c45a58b8ef54fa7ae35f5aa42efc7
|
function sMap=sompak_sammon(sMap,ft,cout,ct,rlen)
%SOMPAK_SAMMON Call SOM_PAK Sammon's mapping program from Matlab.
%
% P = sompak_sammon(sMap,ft,cout,ct,rlen)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sMap (struct) map struct
% (string) filename
% [ft] (string) 'pak' or 'box'. Argument must be defined, if
% input file is used.
% [cout] (string) output file name. If argument is not defined
% (i.e argument is '[]') temporary file '__abcdef' is
% used in operations and *it_is_removed* after
% operations!!!
% [ct] (string) 'pak' or 'box'. Argument must be defined, if
% output file is used.
% rlen (scalar) running length
%
% RETURNS:
% P (matrix) the mapping coordinates
%
% Calls SOM_PAK Sammon's mapping program (sammon) from Matlab. Notice
% that to use this function, the SOM_PAK programs must be in your
% search path, or the variable 'SOM_PAKDIR' which is a string
% containing the program path, must be defined in the workspace.
% SOM_PAK programs can be found from:
% http://www.cis.hut.fi/research/som_lvq_pak.shtml
%
% See also SOMPAK_INIT, SOMPAK_SAMMON, SOMPAK_SAMMON_GUI,
% SOMPAK_GUI, SAMMON.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
NO_FILE = 0;
nargchk(5,5,nargin);
if ~(isstruct(sMap) | isstr(sMap))
error('Argument ''sMap'' must be a struct or filename.');
end
if isstr(sMap)
if isempty(ft) | ~isstr(ft) | ~(strcmp(ft,'pak') | strcmp(ft,'box'))
error('Argument ''ft'' must be string ''pak'' or ''box''.');
end
if strcmp(ft,'pak')
sMap=som_read_cod(sMap);
else
new_var=diff_varname;
varnames=evalin('base','who');
loadname=eval(cat(2,'who(''-file'',''',sMap,''')'));
if any(strcmp(loadname{1},evalin('base','who')))
assignin('base',new_var,evalin('base',loadname{1}));
evalin('base',cat(2,'load(''',sMap,''');'));
new_var2=diff_varname;
assignin('base',new_var2,evalin('base',loadname{1}));
assignin('base',loadname{1},evalin('base',new_var));
evalin('base',cat(2,'clear ',new_var));
sMap=evalin('base',new_var2);
evalin('base',cat(2,'clear ',new_var2));
else
evalin('base',cat(2,'load(''',sMap,''');'));
sMap=evalin('base',loadname{1});
evalin('base',cat(2,'clear ',loadname{1}));
end
end
end
if ~isstr(cout) & isempty(cout)
NO_FILE = 1;
cout = '__abcdef';
elseif ~isstr(cout) | isempty(cout)
error('Argument ''cout'' must be a string or ''[]''.');
end
if ~NO_FILE & (isempty(ct) | ~(strcmp(ct,'pak') | strcmp(ct,'box')))
error('Argument ''ct'' must be string ''pak'' or ''box''.');
end
som_write_cod(sMap,cout);
if ~is_positive_integer(rlen)
error('Argument ''rlen'' must be a positive integer.');
end
if any(strcmp('SOM_PAKDIR',evalin('base','who')))
command=cat(2,evalin('base','SOM_PAKDIR'),'sammon ');
else
command='sammon ';
end
str = sprintf('%s -cin %s -cout %s -rlen %d',command,cout,cout,rlen);
if isunix
unix(str);
else
dos(str);
end
sMap=som_read_cod(cout);
if ~NO_FILE
if isunix
unix(cat(2,'/bin/rm ',cout));
else
dos(cat(2,'del ',cout));
end
if strcmp(ct,'box');
sMap=sMap.codebook;
eval(cat(2,'save ',cout,' sMap'));
disp(cat(2,'Output is saved to the file ',sprintf('''%s.mat''.',cout)));
else
som_write_cod(sMap,cout);
sMap=sMap.codebook;
disp(cat(2,'Output is saved to the file ',cout,'.'));
end
else
if isunix
unix('/bin/rm __abcdef');
else
dos('del __abcdef');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool = is_positive_integer(x)
bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0;
if ~isempty(bool)
if bool & x~=round(x)
bool = 0;
end
else
bool = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function str = diff_varname();
array=evalin('base','who');
if isempty(array)
str='a';
return;
end
for i=1:length(array)
lens(i)=length(array{i});
end
ind=max(lens);
str(1:ind+1)='a';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_show_add.m
|
.m
|
mirtooloct-master/somtoolbox/som_show_add.m
| 48,954 |
utf_8
|
2f99faff178e5d12162026e4b926a00c
|
function h=som_show_add(mode,D,varargin)
%SOM_SHOW_ADD Shows hits, labels and trajectories on SOM_SHOW visualization
%
% h = som_show_add(mode, D, ['argID',value,...])
%
% som_show_add('label',sMap)
% som_show_add('hit',som_hits(sMap,sD))
% som_show_add('traj',som_bmus(sMap,sD))
% som_show_add('comet',som_bmus(sMap,sD))
% som_show_add('comet',inds, 'markersize', [1 0.2])
%
% Input and output arguments ([]'s are optional):
% mode (string) operation mode 'label', 'hit', 'traj', 'comet'
% D (varies) depending on operation mode
% In 'label' mode gives the labels
% (struct) map struct, the .labels field of which is used
% (cell array of strings) size munits x number_of_labels
% In 'hit' mode gives the hit histogram(s)
% (matrix) size munits x k, if k>1, D gives hit histograms
% for k different sets of data (e.g. k classes).
% In 'traj' and 'comet' modes gives the trace of the trajectory
% (vector) size N x 1, D(1) is the current and D(end)
% is oldest item of the trajectory
% [argID, (string) Additional arguments are given as argID, value
% value] (varies) pairs. Depend on the operation mode (see below).
%
% h (vector) handles to the created objects
%
% Here are the valid argument IDs and corresponding values. Most of
% them depend on the operation mode:
%
% all modes
% 'SubPlot' (vector) which subplots are affected (default: current)
% (string) 'all': all subplots are affected
% mode = 'label'
% 'TextSize' (scalar) text size in points
% 'TextColor' (string) ColorSpec, 'xor' or 'none': label color
%
% mode = 'hit'
% 'EdgeColor' (string) ColorSpec, 'none'
% 'MarkerSize' (scalar) maximum marker size
% if k == 1,
% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'none'
% 'MarkerColor'(string) Colorspec, 'none': fill color for markers
% 'Text' (string) 'on', 'off': whether to write the number of hits
% 'TextColor' (string) ColorSpec, 'xor': text color if Text is 'on'
% 'TextSize' (scalar) text font size in points if Text is 'on'
% if k > 1,
% 'SizeFactor' (string) 'common', 'separate': size scaling
% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'pie', 'none'
% (cell array) size k x 1, marker style for each histogram
% 'MarkerColor'(string) Colorspec, 'none': fill color for markers
% (matrix) size k x 3, color for each histogram
%
% mode = 'traj'
% 'TrajWidth' (scalar) basic trajectory line width in points
% 'WidthFactor'(string) 'hit' or 'equal': effect of hits on line width
% 'TrajColor' (string) ColorSpec, 'xor': color for trajectory line
% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'none'
% 'MarkerSize' (scalar) basic marker size (in points)
% 'SizeFactor' (string) 'equal', 'hit' (equal size/size depends on freq.)
% 'MarkerColor'(string) Colorspec, 'none': color of markers
% 'EdgeColor' (string) ColorSpec, 'none': edgecolor of markers
%
% mode = 'comet'
% 'Marker' (string) 'lattice', Matlab's built-in markerstyles
% 'MarkerColor'(string) ColorSpec, 'none': color for the markers
% (matrix) size N x 3, RGB color for each step
% 'EdgeColor' (string) ColorSpec, 'none': edgecolor for markers
% 'MarkerSize' (vector) size 1 x 2, size of comet core and tail
%
% For more help, try 'type som_show_add' or check out online documentation.
% See also SOM_SHOW.
%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_show_add
%
% PURPOSE
%
% Shows hits, labels and trajectories on SOM_SHOW visualization
%
% SYNTAX
%
% h = som_show_add(mode, D);
% h = som_show_add(..., 'argID', value);
%
% DESCRIPTION
%
% The SOM_SHOW function makes the basic visualization of the SOM.
% With SOM_SHOW_ADD one can set labels, hit histogarms or different
% trajectories on this visualization.
%
% labels (mode = 'label')
%
% Labels are strings describing the units. They may be, e.g., a result
% of SOM_AUTOLABEL function. Labels are centered on the unit so that
% multiple labels are in a column.
%
% hit histograms (mode = 'hit')
%
% Hit histograms indicate how the best matching units of a data
% set/some data sets are distribited on a SOM. The hit histogram can
% be calculated using function SOM_HITS.
%
% trajectories (mode = 'traj' or mode = 'comet')
%
% Trajectories show the best matching units for a data set that is
% time (or any ordered) series. It may be either a line connecting the
% consecutive best matching units ('traj' mode) or a "comet"
% trajectory where the current (first sample in D) best matching unit
% has biggest marker and the oldest (last sample) has smallest
% marker ('comet' mode).
%
% NOTE: that the SOM_SHOW_ADD function can only be applied to
% figures that have been drawn by SOM_SHOW.
%
% KNOWN BUGS
%
% for 'hit' mode, if the given hit matrix is all zeros, a series of
% error messages is generated
%
% REQUIRED INPUT ARGUMENTS
%
% mode (string) Visuzalization mode
% 'label' map labeling
% 'hit' hit histograms
% 'traj' line style trajectory
% 'comet' comet style trajectory
%
% D (vector, map struct, cell array of strings) Data
%
% The valid value of D depends on the visualization mode:
%
% Mode Valid D
% 'label' map struct or Mxl cell array of strings, where
% M is number of map units and l maximum numer of
% labels in unit.
%
% 'hit' Mx1 vector or MxK matrix, where M is number of map
% units and K is number of hit histograms (of K
% different classes of data) to be shown
%
% 'comet' Lx1 vector of best matchig unit indices that have to
% 'traj' be in range of the map that is in the figure. L is
% the length of trajectory
%
% OPTIONAL INPUT ARGUMENTS
%
% Optional arguments must be given as 'argument identifier', value
% -pairs. This section is divided in four parts because each mode
% functions in a different way, though they may have same identifier
% names.
%
% If user specifies an identifier that is not operational in the
% specified mode, the functions gives a warning message. If the
% identifier does not exist in any mode the execution is terminated
% and an error message is returned.
%
% GENERAL OPTIONAL INPUT ARGUMENTS (in all modes)
%
% 'SubPlot' Target subplots in the figure
% (vector) Subplots' ordinal numbers in a vector. By default
% the target is the current subplot (see GCA).
% (string) String 'all' means all subplots.
%
% 'Marker' Data marker (not in use in 'label' mode)
% (string) 'none': sets the markers off
% 'lattice': sets the marker shape according to the
% lattice of the underlying map, i.e. it gives
% rectangles if underlying map lattice is 'rect' and
% hexagons for 'hexa', respectively
% any of the Matlab's built-in marker styles: 'o', 's',
% 'd', 'v', '^', '<' ,'> ', 'p', 'h', 'x', '.', '*', '+'
%
% NOTE that '.','x','+' or '*' are not recommended since
% they have only edgecolor and many visualizations are
% based on _face_ color.
%
% NOTE there is an important difference between built-in
% markers. If figure size is changed the 'lattice'
% markers are rescaled but the built-in markers stay at
% fixed size, and consequently, the size unit for
% 'lattice' markers is normalized but for built-in
% markers the size is given in points. For 'lattice'
% markers size 1 means the size of the map unit.
%
% NOTE that in 'hit' mode there are some additional features.
%
% 'EdgeColor' Sets edgecolor for the markers (not in use in 'label' mode)
% (string) ColorSpec, e.g. 'r', gives each edge the specified color
% 'none': sets markers edges invisible
% Default is 'none' - except if MarkerColor is set to 'none' the
% defaults is 'black'.
%
% OPTIONAL INPUT ARGUMENTS mode 'label'
%
% Labels are centered on the unit so that multiple labels are in
% a single column.
%
% 'SubPlot' see General Optional Input Arguments
%
% 'TextSize' Text size for labels
% (scalar) Text size in points. Default is 10.
%
% 'TextColor' Text color
% (string) ColorSpec specifies the text color for all labels
% 'xor': gives Matlab's "xor" text color mode where the
% label color depends on background color
% 'none': sets labels invisble (but creates the objects)
%
% OPTIONAL INPUT ARGUMENTS mode 'hit'
%
% The function in mode 'hit' depends on the input argument size. If
% only one hit histogram is drawn (K==1), it is possible to show the
% hits using numbers. This is not possible for multiple hit
% histograms (K>1).
%
% 'SubPlot' see General Optional Input Arguments
%
% 'Marker' Marker style(s)
% (string) As in General Optional Input Arguments. In addition
% 'pie': sets pie charts for markers. The size of the
% pie in each unit describes the number of total hits in the
% unit and the share of each sector is the relative amount of
% hits in each class (requires multiple histograms). Color for
% each class is set by MarkerColor. Default coloring
% is hsv(K), where K is the number of hit histograms (classes).
% (cell array) size K x 1, of built-in marker style characters. K is
% number of histograms (classes), i.e., same as size(D,2)
% where D is the second input argument. Cell value is
% valid only if multiple histograms are specified (K>1).
%
% NOTE if multiple histograms (classes) are specified
% and Marker is one of the built-in marker styles or
% 'lattice', the markers are drawn in size order from
% largest to smallest. This insures that all markers are
% visible (or at least their edges are). But if two
% markers for different classes in the same node were of
% same size, the other would be totally hidden. In order
% to prevent this, the markers for different classes are
% shifted different amounts from the exact centre of the
% unit. (Evidently, if Marker is 'pie' this problem does
% not exist.)
%
% Default marker is 'lattice' for one histogram and
% 'pie' for multiple histograms.
%
% 'MarkerColor' Marker color(s)
% (string) ColorSpec gives all markers the same color
% 'none': leaves the markes transparent (only edges are visible)
% (matrix) size K x 3, RGB triples for each histogram class
% giving each hit histogram an own color
%
% NOTE that markers '*','+','x', or '.' cannot use
% MarkerColor since these objects have no face (fill)
% color. For them only EdgeColor matters.
%
% 'MarkerSize' Maximum size for marker
% (scalar) set the _maximum_ marker size that corresponds to
% maximum hit count. If Marker is 'pie' or 'lattice' the
% MarkerSize is in normalized scale: 1 correspons to unit size.
% If Marker is one of the built-in styles, MarkerSize is given
% in points.
%
% Marker Default MarkerSize
% 'lattice' 1 (normalized units)
% 'pie' 1 (normalized units)
% 'o','s', etc. 6 (points)
%
% 'SizeFactor' Defines the scaling of the marker sizes in multiple
% histogram case (when Marker is one of the built-in marker
% styles or 'lattice').
% (string) 'separate' (the default) means that marker size shows
% the share of the data which hits the unit compared to
% amount of data in that class. That is, the size of
% markers show the relative distribution of data on the map
% in each class separately. The maximum size is SizeFactor.
% 'common' means that marker size shows the distribution of
% the data in the different classes compared to
% _the total amount of data_.
%
% 'EdgeColor' Sets edgecolor for the markers, see General
% Optional Input Arguments. Default is 'none' -
% except if MarkerColor is 'none' or Marker is
% 'x','*,'x', or '.'. In these cases default EdgeColor is 'black'.
%
% 'Text' Write/don't write the number of hits on the
% units. This option is not in use for multiple histograms.
% (string) 'on' or 'off' (the default)
%
% 'TextColor' Text color
% (string) ColorSpec gives each letter the same color
% 'xor' gives a "xor" coloring for the text
%
% 'TextSize' Text size (in points)
% (scalar) text size in points, default is 10
%
% OPTIONAL INPUT ARGUMENTS mode 'traj'
%
% Input D is a Nx1 vector of N BMU indices that describe the trace of the
% comet. First element D(1) is "newest" and D(end) "oldest". Note
% that at least two indeces are expected: size of D must be at
% least 2x1.
%
% 'SubPlot' see General Optional Input Arguments
%
% 'TrajColor' Color for trajectory line
% (string) ColorSpec gives each marker the same color, 'w' by default
% 'none' sets the marker fill invisible: only edges are shown
%
% 'TrajWidth' Maximum width of trajectory line
% (scalar) width in points. Default is 3.
%
% 'WidthFactor' Shows how often edge between two units has been traversed.
% (string) 'hit': the size of the marker shows how frequent the
% trajectory visits the unit (TrajWidth sets the
% maximum size). This is the default.
% 'equal': all lines have the same width (=TrajWidth)
%
% 'Marker' Marker style, see General Optional Input
% Arguments. Default is 'o'.
%
% NOTE Marker style 'lattice' is not valid in mode 'traj'.
% NOTE Markers can be turned off by setting MarkerSize to zero.
%
% 'MarkerSize' Maximum size of markers
% (scalar) Default is 12 (points).
%
% 'SizeFactor' Sets the frequency based marker size or constant marker size.
% (string) 'hit': the size of the marker shows how frequent the
% trajectory visits the unit (MarkerSize sets the
% maximum size). This is the default.
% 'equal': all markers have th esame size (=MarkerSize)
%
% 'MarkerColor' The fill color(s) for hit markers
% (string) ColorSpec gives each marker the same color, default is 'w'
% 'none' sets the marker fill invisible: only edges are shown
%
% NOTE markers '*','+','x', or '.' can't use MarkerColor since
% these objects have no face (fill) color: only EdgeColor
% matters for these markers.
%
% 'EdgeColor' see General Optional Input Arguments. Default is
% 'none' - except if MarkerColor is 'none' or Marker
% is 'x','*','x', or '.'. In these cases default
% EdgeColor is 'white'.
%
% OPTIONAL INPUT ARGUMENTS mode 'comet'
%
% Input D is a Nx1 vector of N BMU indices that describe the trace of
% the comet. First element D(1) is "newest" and D(end) "oldest". Note
% that at least two indeces are expected: size of D must be at least
% 2x1.
%
% 'SubPlot' see General Optional Input Arguments
%
% 'Marker' Marker style, see General Optional Input
% Arguments. Default is 'lattice'.
%
% 'MarkerColor' The fill color(s) for comet markers
% (string) ColorSpec gives each marker the same color, default is 'w'
% 'none' sets the marker fill invisible: only edges are shown
% (matrix) size N x 3, consisting of RGB triples as rows
% sets different color for each marker. This may be
% used to code the time series using color/grayscale.
%
% NOTE Markers '*','+','x', or '.' can't use MarkerColor
% since these objects have no face (fill) color: only
% EdgeColor matters for these markers.
%
% 'EdgeColor' see General Optional Input Arguments. Default is
% 'none' - except if MarkerColor is 'none' or Marker
% is 'x','*,'x', or '.'. In these cases default
% EdgeColor is 'white'.
%
% 'MarkerSize' The size of "comet core" and tail
% (vector) size 1 x 2: first element sets the size for the marker
% representing D(1) and the second set size for D(end)
% the size (area) of the markes between these changes linearly.
% Note that size units for 'lattice' marker style are
% normalized so that 1 means map unit size but for built-in
% marker styles the size is given points.
%
% Marker default value
% 'lattice' [0.8 0.1]
% 'o','v', etc. [20 4]
%
% OUTPUT ARGUMENTS
%
% h (vector) handles to all objects created by the function
%
% OBJECT TAGS
%
% Field Tag in every object is set to
%
% 'Lab' for objects created in mode 'label'
% 'Hit' -"- 'hit'
% 'Traj' -"- 'traj'
% 'Comet' -"- 'comet'
%
% EXAMPLES
%
% Not yet ready
%
% SEE ALSO
%
% som_show Basic map visualization
% som_show_clear Clear hit marks, labels or trajectories from current figure.
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 131199
%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error(nargchk(2,Inf,nargin)) % check no. of input args
% Get data from the SOM_SHOW figure, exit if error
[handles,msg,lattice,msize,dim]=vis_som_show_data('all',gcf);
error(msg);
munits=prod(msize);
% Initialize some variables: these must exist later;
% the default values are set by subfunctions
Property=init_properties;
Property.handles=handles;
%%% Check mode and that D is of right type & size for that mode
% mode has to be string
if ~vis_valuetype(mode,{'string'}),
error('String value expected for first input argument (mode).');
else
mode=lower(mode); % case insensitive
mode_=mode; % 'mode' is internal variable;
% for program constructs 'mode_' is shown to
% user in some error messags
end
switch mode % check mode
case 'hit'
%%% Hit histogram visualization: vector [msize k]
if ~vis_valuetype(D,{'nxm'}),
error('Hit visualization: a matrix expected for data input.');
elseif size(D,1) ~= prod(msize)
error('Hit visualization: data and map size do not match.');
end
% Multiple hit histograms
if size(D,2)>1
mode='mhit';
% Hit count musn't be negative
if any(D(:)<0),
error('Hit visualization: negative hit count in data not allowed!');
end
end
case {'traj','comet'}
%%% Trajectory like visualizations
if ~vis_valuetype(D,{'nx1'}),
error('Trajectory/Comet: a Nx1 vector expected for data input.');
elseif any(D>prod(msize))| any(D<1),
error('Trajectory/Comet: BMU indices out of range in data input.');
elseif any(fix(D)~=D),
warning('Trajectory/Comet: BMU indices not integer. Rounding...');
elseif size(D,1)<2
error('At least two BMU indexes expected.');
end
case 'label'
%%% Label visualizations
if isstruct(D), % check if D is a map
[tmp,ok,tmp]=som_set(D);
if all(ok) & strcmp(D.type,'som_map')
;
else
error('Map struct is invalid!');
end
% Size check
if length(msize) ~= length(D.topol.msize) | ...
munits ~= prod(D.topol.msize),
error(['The size of the input map and the map in the figure' ...
' do not match.']);
end
D=D.labels;
% Cell input
elseif vis_valuetype(D,{'2Dcellarray_of_char'})
;
% Char input
elseif vis_valuetype(D,{'char_array'}),
D=cellstr(D);
else
error(['Labels has to be in a map struct or in a cell array' ...
' of strings']);
end
if size(D,1) ~= munits
error(['The number of labels does not match the size of the map' ...
' in the figure.']);
end
otherwise
error('Invalid visualization mode.');
end
if rem(length(varargin),2)
error('Mismatch in identifier-value pairs or wrong input argument order.');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read in optional arguments
for i=1:2:length(varargin),
%% Check that all argument types are strings
if ~ischar(varargin{i})
error('Invalid identifier name or input argument order.');
end
%% Lower/uppercase in identifier types doesn't matter:
identifier=lower(varargin{i}); % identifier (lowercase)
value=varargin{i+1};
% Check property identifiers and values and store the values.
% Struct used_in is set to initiate warning messages:
% if a don't care propersty is set, the user is warned.
switch identifier
case 'marker'
%%% Marker for hits or trajectories
switch mode
case 'mhit'
if vis_valuetype(value,{'markerstyle'}) | ...
(vis_valuetype(value,{'string'}) & ...
any(strcmp(value,{'lattice','pie'}))),
; % ok
elseif vis_valuetype(value,{'cellcolumn_of_char'}),
if size(value,1) ~= size(D,2)
error([' If a cell of Markers is specified its size must be' ...
' number_of_hit_histograms x 1.']);
else
for i=1:size(D,2),
if ~vis_valuetype(value{i},{'markerstyle'})
error('Cell input for ''Marker'' contains invalid styles.')
end
end
end
else
error([' Invalid ''Marker'' in case of multiple hit histograms.' ...
char(10) ' See detailed documentation.'])
end
case {'comet','hit'}
if vis_valuetype(value,{'markerstyle'}) | isempty(value),
% ok;
elseif ischar(value) & strcmp(value,'lattice'),
% ok;
else
error(['Marker must be Matlab''s marker style, or string' ...
' ''lattice''.']);
end
case 'traj'
if ~vis_valuetype(value,{'markerstyle'}) & ~isempty(value),
error('In mode ''traj'' Marker must be one of Matlab''s built-in marker styles');
end
end
used_in.comet=1; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=1;
used_in.mhit=1;
case 'markersize'
%%% Marker for hits or trajectories
switch mode
case 'comet'
if ~vis_valuetype(value,{'1x2'}) & ~isempty(value),
error('In mode ''comet'' MarkerSize'' must be a 1x2 vector.');
end
case {'hit','traj'}
if ~vis_valuetype(value,{'1x1'}) & ~isempty(value),
error(['In mode ''' mode_ ...
''' ''MarkerSize'' must be a scalar.']);
end
end
used_in.comet=1; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=1;
used_in.mhit=1;
case 'sizefactor'
%%% Hit dependent size factor
switch mode
case 'traj'
if ~vis_valuetype(value,{'string'}) | ...
~any(strcmp(value,{'hit', 'equal'})),
error(['In mode ''traj'' ''SizeFactor'' must be ' ...
'string ''equal'' or ''hit''.']);
end
case 'mhit'
if ~vis_valuetype(value,{'string'}) | ...
~any(strcmp(value,{'common', 'separate'})),
error(['In mode ''hit'' ''SizeFactor'' must be ' ...
'string ''common'' or ''separate''.']);
end
end
used_in.comet=0; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=0;
used_in.mhit=1;
case 'markercolor'
%%% Markercolor
switch mode
case 'comet'
if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ...
~vis_valuetype(value,{'nx3rgb',[size(D,1) 3]},'all') & ...
~isempty(value),
error(['MarkerColor in mode ''comet'' must be a ColorSpec,' ...
' string ''none'' or Mx3 matrix of RGB triples.']);
end
case 'mhit'
if ~vis_valuetype(value,{[size(D,2) 3],'nx3rgb'},'all') & ...
~vis_valuetype(value,{'colorstyle','1x3rgb'}),
error([' If multiple hit histograms in mode ''hit'' are' ...
char(10) ...
' given MarkerColor must be ColorSpec or a Kx3 matrix' ...
char(10)...
' of RGB triples where K is the number of histograms.']);
end
case 'hit'
if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ...
~isempty(value),
error(['MarkerColor in mode ''hit'' ' ...
'must be a ColorSpec or string ''none''.']);
end
case 'traj'
if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ...
~isempty(value),
error(['MarkerColor in mode ''traj'' ' ...
'must be a ColorSpec or string ''none''.']);
end
end
used_in.comet=1; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=1;
used_in.mhit=1;
case 'edgecolor'
%%% Color for marker edges
if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ~isempty(value),
error('''EdgeColor'' must be a ColorSpec or string ''none''.')
end
used_in.comet=1; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=1;
used_in.mhit=1;
case 'text'
%%% Labeling for trajectories/hits
switch mode
case 'hit'
%%% Hit count using numbers?
if isempty(value),
value='off';
elseif vis_valuetype(value,{'string'}) & ...
~any(strcmp(value,{'on','off'})),
error('Value for Text in mode ''hit'' should be ''on'' or ''off''.');
else
; % ok
end
%case 'traj','comet'
% if ~vis_valuetype(value,{'char_array','cellcolumn_of_char'}) & ...
% ~isempty(value)
% error('Value for Text is of wrong type or size.')
% elseif ischar(value)
% value=strcell(value) % ok, convert to cell
% end
% if size(traj_label,1)~=size(D,1)
% error(['The number of labels in Text and the length of the' ...
% ' trajectory do not match.']);
% end
case 'label'
; % not used
end
used_in.comet=0; % Set relevance flags
used_in.traj=0;
used_in.label=0;
used_in.hit=1;
used_in.mhit=0;
case 'textsize'
%%% Text size for labels
if ~vis_valuetype(value,{'1x1'}) & ~isempty(value),
error('TextSize must be scalar.');
end
used_in.comet=0; % Set relevance flags
used_in.traj=0;
used_in.label=1;
used_in.hit=1;
used_in.mhit=0;
case 'textcolor'
%%% Color for labels
if ~vis_valuetype(value,{'colorstyle','1x3rgb','xor'}) & ~isempty(value),
error('''TextColor'' must be ColorSpec, ''xor'' or ''none''.')
end
used_in.comet=0; % Set relevance flags
used_in.traj=0;
used_in.label=1;
used_in.hit=1;
used_in.mhit=0;
case 'trajwidth'
%%% Basic line width for a line trajectory
if ~vis_valuetype(value,{'1x1'}) & ~isempty(value),
error('TrajWidth must be a scalar.');
end
used_in.comet=0; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=0;
used_in.mhit=0;
case 'widthfactor'
%%% Hit factor for a line trajectory
if ~vis_valuetype(value,{'string'}) | ...
~any(strcmp(value,{'hit', 'equal'})),
error(['In mode ''traj'' ''WidthFactor'' must be ' ...
'string ''equal'' or ''hit''.']);
end
used_in.comet=0; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=0;
used_in.mhit=0;
case 'trajcolor'
%%% Color for trajectory line
if ~vis_valuetype(value,{'colorstyle','1x3rgb','xor'}) & ~isempty(value),
error('''TrajColor'' must be a ColorSpec or string ''xor''.')
end
used_in.comet=0; % Set relevance flags
used_in.traj=1;
used_in.label=0;
used_in.hit=0;
used_in.mhit=0;
case 'uselabel'
%%% Which labels to show
error('Not yet implemented.');
case 'shift'
if ~vis_valuetype(value,{'1x1'}) | ((value < 0) | (value > 1)),
error('''Shift'' must be a scalar in range [0,1].')
end
used_in.comet=0; % Set relevance flags
used_in.traj=0;
used_in.label=0;
used_in.hit=0;
used_in.mhit=1;
case 'subplot'
%%% The subplots which are affected
if vis_valuetype(value,{'1xn','nx1','string'}),
if ischar(value),
if ~strcmp(value,'all'),
error('Only valid string value for subplot indices is ''all''.');
else
value=1:length(handles);
end
elseif any(value<1) | any(value>length(handles)),
error('Subplot indices must be in range 1...number_of_subplots!');
end
elseif ~isempty(value)
error('Invalid subplot indices!');
end
used_in.comet=1; % Set relevance flags
used_in.traj=1;
used_in.label=1;
used_in.hit=1;
used_in.mhit=1;
otherwise
error([ 'Unknown identifier ''' identifier '''.']);
end
% Warn user if the property that was set has no effect in the
% selected visuzlization mode
if ~getfield(used_in, mode),
warning(['Property ''' identifier ''' has no effect in mode ''' ...
mode_ '''.']);
else
Property=setfield(Property,identifier,value);
end
end
% set default subplot
if isempty(Property.subplot)
% search the subplot number for current axis
value=find(gca==handles);
if isempty(value) | value>length(handles)
error('SubPlot default value setting: current axis is not in the figure!');
else
Property.subplot=value;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%% Main switch: select the right subfunction %%%%%%%%%%%%%%%%%%%
switch mode
case 'hit'
h_=hit(D, lattice, msize, Property);
case 'mhit'
h_=mhit(D, lattice, msize, Property);
case 'label'
h_=label(D, lattice, msize, Property);
case 'traj'
h_=traj(D, lattice, msize, Property);
case 'comet'
%error('Not yet implemented.');
h_=comet(D, lattice, msize, Property);
otherwise
error('Whoops! Internal error: unknown mode!');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Build output if necessary %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>0
h=h_;
end
%%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h_=hit(Hits, lattice, msize, Property);
% number of map units
munits=prod(msize);
% subplots
p=Property.subplot;
handles=Property.handles;
% Set default marker
if isempty(Property.marker),
if strcmp(Property.text,'on')
Property.marker='none';
else
Property.marker='lattice';
end
end
% Set default markersize
if isempty(Property.markersize)
if strcmp(Property.marker,'none'),
warning('MarkerSize is not meaningful since Marker is set to ''none''.');
elseif strcmp(Property.marker,'lattice'),
Property.markersize=1; % normalized size
else
Property.markersize=12; % points
end
end
% Set default colors
if ~isempty(Property.markercolor),
if strcmp(Property.marker,'none')
warning('MarkerColor is not used since Marker is set to ''none''.');
Property.markercolor=[]; % not used
else
; % ok
end
elseif any(strcmp(Property.marker,{'+','*','.','x'})),
% these don't use fill color: 'none' will cause default
% edgecolor to be 'k'.
Property.markercolor='none';
else
Property.markercolor='k';
end
if ~isempty(Property.edgecolor),
if strcmp(Property.marker,'none')
warning(['EdgeColor is not used since Marker is set to' ...
' ''none''.']);
else
; %ok
end
elseif ~strcmp(Property.markercolor,'none'),
Property.edgecolor='none';
else
Property.edgecolor='k';
end
% Set default text
if isempty(Property.text),
Property.text='off';
end
% Set default textsize
if isempty(Property.textsize)
Property.textsize=10;
elseif strcmp(Property.text,'off')
warning('TextSize not used as hits are not set to be shown as numbers.');
end
% Set default textcolor
if isempty(Property.textcolor)
Property.textcolor='w';
elseif strcmp(Property.text,'off')
warning('TextColor not used as hits are not set to be shown as numbers.');
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
h_=[]; % this variable is for collecting the object handles
% Select the drawing mode
if ~strcmp(Property.marker,'none')
%%%%% Draw spots %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% unit coordinates
coord=som_vis_coords(lattice,msize);
% Calculate the size of the spots
mx=max(Hits);
if mx==0,
% nothing to draw!
h_=[];
return
else
Size=sqrt(Hits./mx);
end
% coordinates for non-zero hits (only those are drawn)
coord=coord(Size~=0,:);
Size=Size(Size~=0);
N=size(Size,1);
% som_cplane can't draw one unit with arbitrary
% coordinates as it its mixed with msize:
if size(coord,1)==1 & strcmp(Property.marker,'lattice'),
Size=[Size;Size];
coord=[coord;coord];
end
for i=1:length(p),
% Set axes
axes(handles(p(i)));
% Get hold state and caxis
memhold=ishold; cax=caxis;
hold on;
switch Property.marker
case 'lattice'
h_(i,1)=som_cplane(lattice, coord, Property.markercolor, ...
Property.markersize*Size);
otherwise
[S,m]=som_grid(lattice, [N 1],...
'Coord',coord, ...
'Line','none',...
'Marker',Property.marker,...
'MarkerColor',Property.markercolor,...
'MarkerSize', Size*Property.markersize);
h_=[h_;m(:)];
end
% Restore hold state
if ~memhold
hold off;
end
end
% Set edgecolor
if strcmp(Property.marker,'lattice')
set(h_,'edgecolor',Property.edgecolor);
else
set(h_,'markeredgecolor',Property.edgecolor);
end
end
if strcmp(Property.text,'on'),
%%%%% Draw numbers %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do numbers
Hits=reshape(Hits,[munits 1]);
labels=cell([munits 1]);
for i=1:length(Hits)
if Hits(i) % zero hit won't be shown
labels(i)={num2str(Hits(i))};
end
end
for i=1:length(p),
axes(handles(p(i))); % Set axes
memhold=ishold; % Get hold state
hold on;
[S,m,l,t]=som_grid(lattice, msize, ...
'Line','none',...
'Marker','none', ...
'Label',labels, ...
'LabelColor', Property.textcolor, ...
'LabelSize', Property.textsize);
% Get handles
h_=[h_;t(:)];
% Restore hold state and caxis
if ~memhold
hold off;
end
caxis(cax);
end
% Remove zero object handles (missing objects)
h_=setdiff(h_,0);
end
%% Set object tags (for som_show_clear) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','Hit')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h_=mhit(Hits, lattice, msize, Property);
% number of map units
munits=prod(msize);
% subplots
p=Property.subplot;
handles=Property.handles;
% Set default marker
if isempty(Property.marker),
Property.marker=lattice;
end
% variable 'mode' indicates which kind of markers are used:
if iscell(Property.marker),
mode='marker';
elseif vis_valuetype(Property.marker,{'markerstyle'}),
mode='marker';
elseif strcmp(Property.marker,'pie'),
mode='pie';
else
mode='lattice';
end
% Set default size scaling
if isempty(Property.sizefactor)
Property.sizefactor='separate';
end
% Set default markersize
if isempty(Property.markersize)
if any(strcmp(mode,{'lattice','pie'})),
Property.markersize=1; % normalized
else
Property.markersize=12; % points
end
end
% Set default colors
if isempty(Property.markercolor),
Property.markercolor=hsv(size(Hits,2));
end
if isempty(Property.edgecolor),
if vis_valuetype(Property.markercolor,{'none'}),
Property.edgecolor='k';
else
Property.edgecolor='none';
end
end
% Set default shift
if isempty(Property.shift)
Property.shift=0;
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
h_=[]; % this variable is for collecting the object handles
switch mode
case {'marker','lattice'}
% Number of hits histograms
n_Hits=size(Hits,2);
% Calculate the size of the spots
if strcmp(Property.sizefactor,'common')
mx=max(max(Hits));
if mx==0 % nothing to draw!
h_=[]; return
end
spotSize=sqrt(Hits./mx);
else
mx=repmat(max(Hits),munits,1);
mx(mx==0)=1; % Prevent division by zero
spotSize=sqrt(Hits./mx);
end
%%% Make spotSize
%reshape Size to a vector [spotSizeforHist(:,1); spotSizeforHist(:,2);...]
spotSize=spotSize(:);
% indices for non-zero hits (only those are drawn)
notZero=find(spotSize ~= 0);
% Drop zeros away from spotSize
spotSize=spotSize(notZero);
% Order spots so that bigger will be drawn first, so that they
% won't hide smaller ones
[dummy, sizeOrder]=sort(spotSize); sizeOrder=sizeOrder(end:-1:1);
spotSize=spotSize(sizeOrder);
%%% Make unit coordinates
coord=som_vis_coords(lattice,msize);
move=repmat(linspace(-.1,.1,n_Hits),size(coord,1),1)*Property.shift;
move=repmat(move(:),1,2);
% do n_Hits copies of unit coordinates so that they match spotSize
coord=repmat(coord,n_Hits,1)+move;
% Drop zeros away from coords and order
coord=coord(notZero,:);
coord=coord(sizeOrder,:);
%%% Make unit colors
if vis_valuetype(Property.markercolor,{'nx3'}),
% If multiple colors Copy unit colors so that they match spotSize
color=Property.markercolor(reshape(repmat([1:n_Hits]',1,munits)',...
munits*n_Hits,1),:);
% drop zeros away & order
color=color(notZero,:);
color=color(sizeOrder,:);
else
% only on color
color=Property.markercolor;
end
%%% Make unit markers
if iscell(Property.marker),
%marker shows class:
marker=char(Property.marker);
marker=marker(reshape(repmat([1:n_Hits]',1,munits)',...
munits*n_Hits,1),:);
% Drop zeros, order & make to cell array (for som_grid)
marker=marker(notZero,:);
marker=cellstr(marker(sizeOrder,:));
else
marker=Property.marker;
end
% som_cplane can't draw one unit with arbitrary
% coordinates as it its mixed with msize:
if size(coord,1)==1 & strcmp(mode,'lattice'),
spotSize = [spotSize; spotSize];
coord = [coord; coord];
end
N=length(notZero); % for som_grid visuzalization routine
case 'pie'
% marker 'pie' requires size parameter totHits
if strcmp(mode,'pie')
coord=som_vis_coords(lattice, msize);
notZero=sum(Hits,2)>0;
Hits=Hits(notZero,:);
coord=coord(notZero,:);
N=size(notZero,1);
totHits=sqrt(sum(Hits,2)./max(sum(Hits,2)));
end
% som_pieplane can't draw one unit with arbitrary
% coordinates as it its mixed with msize:
if size(coord,1)==1,
Hits= [Hits; Hits];
coord = [coord; coord];
end
otherwise
error('Whoops: internal error. Bad mode in subfunction mhit');
end
for i=1:length(p), %%% Main loop begins
% Set axis
axes(handles(p(i)));
% Get hold state and caxis
memhold=ishold; cax=caxis;
hold on;
switch mode
case 'lattice'
h_(i,1)=som_cplane(lattice, coord, color, spotSize*Property.markersize);
case 'marker'
[S,m]=som_grid(lattice, [N 1],...
'Coord',coord, ...
'Line','none',...
'Marker',marker,...
'MarkerColor',color,...
'MarkerSize', spotSize*Property.markersize);
h_=[h_;m(:)];
case 'pie'
h_(i)=som_pieplane(lattice, coord, ...
Hits, Property.markercolor, ...
totHits*Property.markersize);
end
% Restore hold state and caxis
if ~memhold
hold off;
end
caxis(cax);
end
% Set edgecolor
if any(strcmp(mode,{'lattice','pie'})),
set(h_,'edgecolor',Property.edgecolor);
else
set(h_,'markeredgecolor',Property.edgecolor);
end
%% Set object tags (for som_show_clear) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','Hit')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h_=label(Labels, lattice, msize, Property)
% number of map units
munits=prod(msize);
% subplots and handles
p=Property.subplot;
handles= Property.handles;
% Set default text size
if isempty(Property.textsize) % default point size
Property.textsize=10;
end
% Check color/set default
if isempty(Property.textcolor),
Property.textcolor='k';
end
% handles will be collected in h_ for output
h_=[];
%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:length(p);
% set axes
axes(handles(p(i)));
% store hold state and caxis (for some reason matlab may
% change caxis(!?)
memhold=ishold;
hold on;
cax=caxis;
% Write labels
[S,m,l,t]=som_grid(lattice, msize, ...
'Line','none', ...
'Marker', 'none', ...
'Label', Labels, ...
'LabelColor', Property.textcolor, ...
'LabelSize', Property.textsize);
% Get handles
h_=[h_;m(:);l(:);t(:)];
% reset hold state and caxis
if ~memhold
hold off;
end
caxis(cax);
end
%%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','Lab');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h_=traj(bmu, lattice, msize, Property)
% number of map units
munits=prod(msize);
% subplots and handles
p=Property.subplot;
handles=Property.handles;
% Set default text color
%if isempty(Property.textcolor),
% Property.textcolor='k';
%end
% Set default text size
%if isempty(Property.textsize)
% Property.textsize=10;
%end
% Set default marker
if isempty(Property.marker)
Property.marker='o';
end
% Set default markersize
if isempty(Property.markersize)
Property.markersize=10;
end
% Set default markercolor
if isempty(Property.markercolor)
Property.markercolor='w';
end
% Set default sizefactor
if isempty(Property.sizefactor)
%Property.sizefactor=0;
Property.sizefactor='hit';
end
% Set default trajwidth
if isempty(Property.trajwidth)
Property.trajwidth=3;
end
% Set default widthfactor
if isempty(Property.widthfactor)
Property.widthfactor='hit';
end
% Set default trajcolor
if isempty(Property.trajcolor)
Property.trajcolor='w';
end
% if no labels, do a empty cell array for syntax reasons
%if isempty(Property.text),
% Property.text=cell(munits,1);
%end
h_=[]; % handles will be collected in h_ for output
l=length(bmu); % length of trajectory
C=sparse(munits, munits); % init a connection matrix
%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate the connection matrix that describes the trajectory
for i=1:l-1,
% The following if structure removes the possible redundancy due
% to travels in both directions between two nodes of trajectory
% (i.e. reflexivity)
I=bmu(i+1);J=bmu(i);
%if bmu(i)>bmu(i+1)
%else
% I=bmu(i);J=bmu(i+1);
%end
C(I,J)=C(I,J)+1;
end
% transitive connections are equal
C=C+C';
% drop reflexive conncetions away
C=spdiags(zeros(munits,1),0,C);
% Do labels of trajectory nodes
%traj_lab=cell(munits,1);
hits=zeros(munits,1);
for i=1:l,
% traj_lab{bmu(i)}=strvcat(traj_lab{bmu(i)},Property.text{i});
hits(bmu(i))=(hits(bmu(i))+1);
end
% Calculate unit coordinates
unit_coord=som_vis_coords(lattice, msize);
% Calculate line width
if strcmp(Property.widthfactor,'equal')
TrajWidth=(C>0)*Property.trajwidth;
else
TrajWidth=Property.trajwidth.*sqrt(C./max(max(C)));
end
% Calculate marker sizes
if strcmp(Property.sizefactor,'hit')
MarkerSize=Property.markersize*sqrt(hits/max(hits));
else
MarkerSize=Property.markersize*(hits>0);
end
for i=1:length(p),
axes(handles(p(i)));
% Get hold state and caxis
memhold=ishold; cax=caxis;
hold on;
%'Label', traj_lab, ...
%'LabelColor', Property.textcolor, ...
%'LabelSize', Property.textsize, ...
% Draw
[S,m,l,t,s]=som_grid(C,msize,'coord',unit_coord,...
'Line','-', ...
'LineColor', Property.trajcolor, ...
'LineWidth', TrajWidth, ...
'Marker', Property.marker, ...
'MarkerColor', Property.markercolor, ...
'MarkerSize', MarkerSize);
% Restore hold state and caxis
if ~memhold
hold off;
end
caxis(cax);
% Get handles
h_=[h_;m(:);l(:);t(:);s(:)];
end
%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','Traj');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h_=comet(bmu, lattice, msize, Property)
% number of map units
munits=prod(msize);
% subplots and handles
p=Property.subplot;
handles=Property.handles;
% Set default text color
%if isempty(Property.textcolor),
% Property.textcolor='k';
%end
%% Set default text size
%if isempty(Property.textsize)
% Property.textsize=10;
%end
% Set default marker
if isempty(Property.marker)
Property.marker='o';
end
% Set default markersize
if isempty(Property.markersize),
if strcmp(Property.marker,'lattice'),
Property.markersize=linspace(0.8,0.1,length(bmu))';
else
Property.markersize=sqrt(linspace(400,16,length(bmu)))';
end
else
if strcmp(Property.marker,'lattice'),
Property.markersize=linspace(Property.markersize(1),...
Property.markersize(2), ...
length(bmu))';
else
Property.markersize=sqrt(linspace(Property.markersize(1).^2,...
Property.markersize(2).^2, ...
length(bmu)))';
end
end
% Set default markercolor
if isempty(Property.markercolor)
Property.markercolor='w';
end
% Set default edgecolor
if isempty(Property.edgecolor),
if vis_valuetype(Property.markercolor,{'nx3rgb'}),
Property.edgecolor='none';
else
Property.edgecolor=Property.markercolor;
end
end
h_=[];l_=[]; % handles will be collected in h_ for output
N_bmus=length(bmu); % length of trajectory
% if no labels, do a empty cell array for syntax reasons
%if isempty(Property.text),
% Property.text=cell(N_bmus,1);
%end
%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate unit coordinates for trajectory points
unit_coord=som_vis_coords(lattice, msize);
coord=unit_coord(bmu,:);
% Make labels for the _unique_ units that the comet hits
unique_bmu=unique(bmu); % count units
%N_labels=length(unique_bmu);
%traj_lab=cell(N_labels,1); % cell for labels
%label_coord=unit_coord(unique_bmu,:); % label coordinates
% Make labels
%for i=1:N_bmus,
% index=find(unique_bmu==bmu(i));
% traj_lab{index}=strvcat(traj_lab{index},Property.text{i});
%end
%Main loop for drawing comets
for i=1:length(p),
% set axis
axes(handles(p(i)));
% Get hold state and caxis
memhold=ishold; cax=caxis;
hold on;
if strcmp(Property.marker,'lattice'),
% Draw: marker is a patch ('hexa','rect')
l_=som_cplane(lattice, coord, Property.markercolor, ...
Property.markersize);
% Set edgecolor
set(l_,'edgecolor',Property.edgecolor);
else
% Draw: other markers than 'hexa' or 'rect'
[S,m,l,t,s]=som_grid(lattice, [N_bmus 1], 'coord', coord,...
'Line','none', ...
'Marker', Property.marker, ...
'MarkerColor', Property.markercolor, ...
'MarkerSize',Property.markersize);
% Set edgecolor
set(m, 'markeredgecolor', Property.edgecolor);
% Get handles from markers
h_=[h_;l_(:);m(:);l(:);t(:);s(:)];
end
% Set labels
%[S,m,l,t,s]=som_grid(lattice, [N_labels 1], 'coord', label_coord,...
% 'Marker','none','Line','none',...
% 'Label', traj_lab, ...
% 'LabelColor', Property.textcolor, ...
% 'LabelSize', Property.textsize);
% Get handles from labels
%h_=[h_;m(:);l(:);t(:);s(:)];
% Restore hold state and caxis
if ~memhold
hold off;
end
caxis(cax);
end
%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','Comet');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function P=init_properties;
% Initialize an empty property struct
P.marker=[];
P.markersize=[];
P.sizefactor=[];
P.markercolor=[];
P.edgecolor=[];
P.trajwidth=[];
P.widthfactor=[];
P.trajcolor=[];
P.text=[];
P.textsize=[];
P.textcolor=[];
P.subplot=[];
P.shift=[];
|
github
|
martinarielhartmann/mirtooloct-master
|
som_fuzzycolor.m
|
.m
|
mirtooloct-master/somtoolbox/som_fuzzycolor.m
| 6,305 |
utf_8
|
ba6ea6d7d1079610c8988bcb30365eb4
|
function [color,X]=som_fuzzycolor(sM,T,R,mode,initRGB,S)
% SOM_FUZZYCOLOR Heuristic contraction projection/soft cluster color coding for SOM
%
% function [color,X]=som_fuzzycolor(map,[T],[R],[mode],[initRGB],[S])
%
% sM (map struct)
% [T] (scalar) parameter that defines the speed of contraction
% T<1: slow contraction, T>1: fast contraction. Default: 1
% [R] (scalar) number of rounds, default: 30
% [mode] (string) 'lin' or 'exp', default: 'lin'
% [initRGB] (string) Strings accepted by SOM_COLORCODE, default: 'rgb2'
% [S] (matrix) MxM matrix a precalculated similarity matrix
% color (matrix) of size MxRx3 resulting color codes at each step
% X (matrix) of size MxRx2 coordiantes for projected unit weight vectors
% at each step of iteration. (Color code C is calculated using this
% projection.)
%
% The idea of the projection is to use a naive contraction model which
% pulls the units together. Units that are close to each other in the
% output space (clusters) contract faster into the same point in the
% projection. The original position for each unit is its location in
% the topological grid.
%
% This is an explorative tool to color code the map units so that
% similar units (in the sense of euclidean norm) have similar coloring
% (See also SOM_KMEANSCOLOR) The tool gives a series of color codings
% which start from an initial color coding (see SOM_COLORCODE) and
% show the how the fuzzy clustering process evolves.
%
% The speed of contraction is controlled by the input parameter T. If
% it is high the projection contracts more slowly and reveals more
% intermediate stages (hierarchy). A good value for T must be
% searched manually. It is probable that the default values do not
% yield good results.
%
% The conatrction process may be slow. In this case the mode can be
% set to 'exp' instead of 'lin', however, then the computing becomes
% heavier.
%
% EXAMPLE
%
% load iris; % or any other map struct sM
% [color]=som_fuzzycolor(sM,'lin',10);
% som_show(sM,'color',color);
%
% See also SOM_KMEANSCOLOR, SOM_COLORCODE, SOM_CLUSTERCOLOR
%
% REFERENCES
%
% Johan Himberg, "A SOM Based Cluster Visualization and Its
% Application for False Coloring", in Proceedings of International
% Joint Conference on Neural Networks (IJCNN2000)},
% pp. 587--592,Vol. 3, 2000
%
% Esa Alhoniemi, Johan Himberg, and Juha Vesanto, Probabilistic
% Measures for Responses of Self-Organizing Map Units, pp. 286--290,
% in Proceedings of the International ICSC Congress on Computational
% Intelligence Methods and Applications (CIMA '99)}, ICSC Academic
% Press}, 1999
%
% Outline of the heuristic
%
% First a matrix D of squared pairwise euclidean distances
% D(i,j)=d(i,j)^2 between map weight vectors is calculated. This
% matrix is transformed into a similarity matrix S,
% s(i,j)=exp(-(D(i,j)/(T.^2*v)), where T is a free input parameter and
% v the variance of all elements of D v=var(D(:)). The matrix is
% further normalized so that all rows sum to one. The original
% topological coordinates X=som_unit_coords(sM) are successively
% averaged using this matrix. X(:,:,i)=S^i*X(:,:,1); As the process is
% actually a series of successive weighted averagings of the initial
% coordinates, all projected points eventually contract into one
% point. T is a user defined parameter that defines how fast the
% projection contracts into this center point. If T is too small, the
% process will end into the center point at once.
%
% In practise, we don't calculate powers of S, but compute
%
% X(:,:,i)=S.*X(:,:,i-1); % mode: 'lin'
%
% The contraction process may be slow if T is selected to be large,
% then for each step the similarity matrix is squared
%
% X(:,:,i)=S*X(:,:,1); S=S*S % mode: 'exp'
%
% The coloring is done using the function SOM_COLORCODE according to
% the projections in X, The coordinates are rescaled in order to
% achieve maximum color resolution.
% Contributed to SOM Toolbox vs2, 2000 by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
% Previously rownorm function normalized the rows of S erroneously
% into unit length, this major bug was corrected 14042003. Now the
% rownorm normalizes the rows to have unit sum as it should johan 14042003
%% Check input arguments
if isstruct(sM),
if ~isfield(sM,'topol')
error('Topology field missing.');
end
M=size(sM.codebook,1);
else
error('Requires a map struct.');
end
if nargin<2 | isempty(T),
T=1;
end
if ~vis_valuetype(T,{'1x1'})
error('Input for T must be a scalar.');
end
if nargin<3 | isempty(R),
R=30;
end
if ~vis_valuetype(R,{'1x1'})
error('Input for R must be a scalar.');
end
if nargin < 4 | isempty(mode),
mode='lin';
end
if ~ischar(mode),
error('String input expected for mode.');
else
mode=lower(mode);
switch mode
case {'lin','exp'}
;
otherwise
error('Input for mode must be ''lin'' or ''exp''.');
end
end
if nargin < 5 | isempty(initRGB)
initRGB='rgb2';
end
if ischar(initRGB),
try
dummy=som_colorcode(sM,initRGB);
catch
error(['Color code ''' initRGB ''' not known, see SOM_COLORCODE.']);
end
else
error('Invalid color code string');
end
if nargin<6 | isempty(S),
S=fuzzysimilarity(sM,1./T);
end
if ~vis_valuetype(S,{[M M]}),
error('Similarity matrix must be a MunitsxMunits matrix.')
end
x = maxnorm(som_unit_coords(sM.topol.msize,sM.topol.lattice,'sheet'));
x = x-repmat(mean(x),size(x,1),1);
X(:,:,1)=x;
color(:,:,1)=som_colorcode(x,'rgb2',1);
%%% Actions
for i=1:R,
switch mode
case 'exp'
S=rownorm(S*S);
tmpX=S*X(:,:,1);
case 'lin'
tmpX=S*X(:,:,i);
end
X(:,:,i+1)=tmpX;
color(:,:,i+1)=som_colorcode(X(:,:,i+1),initRGB);
end
color(isnan(color))=0;
function r=fuzzysimilarity(sM,p)
% Calculate a "fuzzy response" similarity matrix
% sM: map
% p: sharpness factor
d=som_eucdist2(sM,sM);
v=std(sqrt(d(:))).^2;
r=rownorm(exp(-p^2*(d./v)));
r(~isfinite(r))=0;
return;
function X = rownorm(X)
r = sum(X,2);
X = X ./ r(:,ones(size(X,2),1));
return;
function X = maxnorm(X)
for i=1:size(X,2), r = (max(X(:,i))-min(X(:,i))); if r, X(:,i) = X(:,i) / r; end, end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_stats.m
|
.m
|
mirtooloct-master/somtoolbox/som_stats.m
| 9,256 |
utf_8
|
913c885c15a80104f02cd88b0bcbd0c8
|
function csS = som_stats(D,varargin)
%SOM_STATS Calculate descriptive statistics for the data.
%
% csS = som_stats(D,[sort]);
%
% csS = som_stats(D);
% csS = som_stats(D,'nosort');
% som_table_print(som_stats_table(csS))
%
% Input and output arguments ([]'s are optional):
% D (matrix) a matrix, size dlen x dim
% (struct) data or map struct
% [sort] (string) 'sort' (default) or 'nosort'
% If 'nosort' is specified, the data is not
% sorted, and therefore the values of
% nunique, uvalues, ucount, fvalues, fcount, and tiles fields
% are not calculated. This may be useful if
% there is a very large amount of data, and
% one wants to reduce calculation time.
%
% csS (cell array) size dim x 1, of statistics structs with
% the following fields
% .type (string) 'som_stat'
% .name (string) name of the variable
% .normalization (struct array) variable normalization (see SOM_NORMALIZE)
% .ntotal (scalar) total number of values
% .nvalid (scalar) number of valid values (not Inf or NaN)
% .min (scalar) minimum value
% .max (scalar) maximum value
% .mean (scalar) mean value (not Inf or NaN)
% .std (scalar) standard deviation (not Inf or NaN)
% .nunique (scalar) number of unique values
% .mfvalue (vector) most frequent value
% .mfcount (vector) number of occurances of most frequent value
% .values (vector) at most MAXDISCRETE (see below) sample values
% .counts (vector) number of occurances for each sampled value
% .tiles (vector) NT-tile values, for example
% NT=4 for quartiles: 25%, 50% and 75%
% NT=100 for percentiles: 1%, 2%, ... and 99%
% .hist (struct) histogram struct with the following fields
% .type (string) 'som_hist'
% .bins (vector) histogram bin centers
% .counts (vector) count of values in each bin
% .binlabels (cellstr) labels for the bins (denormalized bin
% center values)
% .binlabels2 (cellstr) labels for the bins (denormalized bin
% edge values, e.g. '[1.4,2.5['
%
% Constants:
% MAXDISCRETE = 10
% NT = 10
%
% See also SOM_STATS_PLOT, SOM_STATS_TABLE, SOM_TABLE_PRINT, SOM_STATS_REPORT.
% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 311201
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% arguments
% default values
nosort = 0;
nbins = 10;
maxdiscrete = 20;
ntiles = 10;
% first argument
if isstruct(D),
switch D.type,
case 'som_map', cn = D.comp_names; sN = D.comp_norm; D = D.codebook;
case 'som_data', cn = D.comp_names; sN = D.comp_norm; D = D.data;
otherwise, error('Invalid first argument')
end
else
cn = cell(size(D,2),1);
cn(:) = {'Variable'};
for i=1:length(cn), cn{i} = sprintf('%s%d',cn{i},i); end
sN = cell(size(D,2),1);
end
[dlen dim] = size(D);
% other arguments
if length(varargin)>0,
if strcmp(varargin{1},'nosort'), nosort = 1; end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% action
sStat = struct('type','som_stat','name','','normalization',[],...
'min',NaN,'max',NaN,'mean',NaN,'std',NaN,...
'nunique',NaN,'values',[],'counts',[],'mfvalue',NaN,'mfcount',NaN,'tiles',[],...
'ntotal',dlen,'nvalid',NaN,'hist',[]);
csS = cell(0);
for i=1:dim,
sS = sStat;
sS.name = cn{i};
sS.normalization = sN{i};
x = D(:,i);
x(find(~isfinite(x))) = [];
% basic descriptive statistics
sS.nvalid = length(x);
if length(x),
sS.min = min(x);
sS.max = max(x);
sS.mean = mean(x);
sS.std = std(x);
bins = [];
if ~nosort,
xsorted = sort(x);
% number of unique values
repeated = (xsorted(1:end-1)==xsorted(2:end));
j = [1; find(~repeated)+1];
xunique = xsorted(j);
sS.nunique = length(xunique);
ucount = diff([j; length(xsorted)+1]);
% most frequent value
[fcount,j] = max(ucount);
sS.mfvalue = xunique(j);
sS.mfcount = fcount;
% -tiles (k*100/ntiles % of values, k=1..)
pickind = round(linspace(1,sS.nvalid,ntiles+1));
pickind = pickind(2:end-1);
sS.tiles = xsorted(pickind);
if sS.nunique <= sS.nvalid/2,
% unique values
sS.values = xunique;
sS.counts = ucount;
bins = sS.values;
else
% just maxdiscrete values, evenly picked
pickind = round(linspace(1,sS.nunique,maxdiscrete));
sS.values = xunique(pickind);
sS.counts = ucount(pickind);
%% OPTION 2: maxdiscrete most frequent values
%[v,j] = sort(ucount);
%pickind = j(1:maxdiscrete);
%sS.values = xunique(pickind);
%sS.counts = ucount(pickind);
% OPTION 3: representative values - calculated using k-means
%[y,bm,qe] = kmeans(x,maxdiscrete);
%sS.values = y;
%sS.counts = full(sum(sparse(bm,1:length(bm),1,maxdiscrete,length(bm)),2));
end
end
if isempty(bins),
bins = linspace(sS.min,sS.max,nbins+1);
bins = (bins(1:end-1)+bins(2:end))/2;
end
sS.hist = som_hist(x,bins,sS.normalization);
else
sS.hist = som_hist(x,0);
end
csS{end+1} = sS;
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% subfunctions
function sH = som_hist(x,bins,sN)
binlabels = [];
binlabels2 = [];
if nargin<2 | isempty(bins) | isnan(bins),
bins = linspace(min(x),max(x),10);
end
if isstruct(bins),
bins = sH.bins;
binlabels = sH.binlabels;
binlabels2 = sH.binlabels2;
end
if nargin<3, sN = []; end
sH = struct('type','som_hist','bins',bins,'counts',[],...
'binlabels',binlabels,'binlabels2',binlabels2);
if length(bins)==1,
sH.counts = [length(x)];
edges = bins;
elseif length(x),
edges = (bins(1:end-1)+bins(2:end))/2;
counts = histc(x,[-Inf; edges(:); Inf]);
sH.counts = counts(1:end-1);
end
if isempty(sH.binlabels),
b = som_denormalize(bins(:),sN);
sH.binlabels = numtostring(b,4);
end
if isempty(sH.binlabels2),
if length(edges)==1,
sH.binlabels2 = numtostring(som_denormalize(edges,sN),2);
if length(bins)>1,
sH.binlabels2 = sH.binlabels2([1 1]);
sH.binlabels2{1} = [']' sH.binlabels2{1} '['];
sH.binlabels2{2} = ['[' sH.binlabels2{2} '['];
end
else
if size(edges,1)==1, edges = edges'; end
bstr = numtostring(som_denormalize(edges,sN),4);
sH.binlabels2 = bstr([1:end end]);
sH.binlabels2{1} = [bstr{1} '['];
for i=2:length(sH.binlabels2)-1,
sH.binlabels2{i} = ['[' bstr{i-1} ',' bstr{i} '['];
end
sH.binlabels2{end} = ['[' bstr{end}];
end
end
if 0,
if length(bins)==1, sH.binlabels2 = {'constant'};
else
ntiles = 10;
plim = [1:ntiles-1] / ntiles;
cp = cumsum(sH.counts)/sum(sH.counts);
[dummy,i] = histc(cp,[-Inf plim Inf]);
l2 = cell(length(bins),1);
for j=1:length(bins), l2{j} = sprintf('Q%d',i(j)); end
if i(1) > 1, l2{1} = ['...' l2{1}]; end
k = 0;
for j=2:length(bins),
if i(j)==i(j-1),
if k==0, l2{j-1} = [l2{j-1} '.1']; k = 1; end
k = k + 1;
l2{j} = [l2{j} '.' num2str(k)];
else k = 0; end
end
if i(end) < ntiles, l2{end} = [l2{end} '...']; end
sH.binlabels2 = l2;
end
end
return;
function vstr = numtostring(v,d)
r = max(v)-min(v);
if r==0, r=1; end
nearzero = (abs(v)/r < 10.^-d);
i1 = find(v > 0 & nearzero);
i2 = find(v < 0 & nearzero);
vstr = strrep(cellstr(num2str(v,d)),' ','');
vstr(i1) = {'0.0'};
vstr(i2) = {'-0.0'};
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
knn_old.m
|
.m
|
mirtooloct-master/somtoolbox/knn_old.m
| 7,196 |
utf_8
|
91ff9ef390bf0c8610ff647a8a3e29bd
|
function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old(sM, sData, [], 3);
% [sD_class,P]=knn_old(sD, sM, class);
% [class,P]=knn_old(data, proto, class);
% [class,P]=knn_old(sData, sM, class,5);
%
% Input and output arguments ([]'s are optional):
% Data (matrix) size Nxd, vectors to be classified (=classifiees)
% (struct) map or data struct: map codebook vectors or
% data vectors are considered as classifiees.
% Proto (matrix) size Mxd, prototype vector matrix (=prototypes)
% (struct) map or data struct: map codebook vectors or
% data vectors are considered as prototypes.
% [proto_class] (vector) size Nx1, integers 1,2,...,k indicating the
% classes of corresponding protoptypes, default: see the
% explanation below.
% [K] (scalar) the K in KNN classifier, default is 1
%
% Class (matrix) size Nx1, vector of 1,2, ..., k indicating the class
% desicion according to the KNN rule
% P (matrix) size Nxk, the relative amount of prototypes of
% each class among the K closest prototypes for
% each classifiee.
%
% If 'proto_class' is _not_ given, 'Proto' _must_ be a labeled SOM
% Toolbox struct. The label of the data vector or the first label of
% the map model vector is considered as class label for th prototype
% vector. In this case the output 'Class' is a copy of 'Data' (map or
% data struct) relabeled according to the classification. If input
% argument 'proto_class' _is_ given, the output argument 'Class' is
% _always_ a vector of integers 1,2,...,k indiacating the class.
%
% If there is a tie between representatives of two or more classes
% among the K closest neighbors to the classifiee, the class is
% selected randomly among these candidates.
%
% IMPORTANT
%
% ** Even if prototype vectors are given in a map struct the mask _is not
% taken into account_ when calculating Euclidean distance
% ** The function calculates the total distance matrix between all
% classifiees and prototype vectors. This results to an MxN matrix;
% if N is high it is recommended to divide the matrix 'Data'
% (the classifiees) into smaller sets in order to avoid memory
% overflow or swapping. Also, if K>1 this function uses 'sort' which is
% considerably slower than 'max' which is used for K==1.
%
% See also KNN, SOM_LABEL, SOM_AUTOLABEL
% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 040200
%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This must exist later
classnames='';
% Check K
if nargin<4 | isempty(K),
K=1;
end
if ~vis_valuetype(K,{'1x1'})
error('Value for K must be a scalar.');
end
% Take data from data or map struct
if isstruct(Data);
if isfield(Data,'type') & ischar(Data.type),
;
else
error('Invalid map/data struct?');
end
switch Data.type
case 'som_map'
data=Data.codebook;
case 'som_data'
data=Data.data;
end
else
% is already a matrix
data=Data;
end
% Take prototype vectors from prototype struct
if isstruct(Proto),
if isfield(Proto,'type') & ischar(Proto.type),
;
else
error('Invalid map/data struct?');
end
switch Proto.type
case 'som_map'
proto=Proto.codebook;
case 'som_data'
proto=Proto.data;
end
else
% is already a matrix
proto=Proto;
end
% Check that inputs are matrices
if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
error('Prototype or data input not valid.')
end
% Record data&proto sizes and check their dims
[N_data dim_data]=size(data);
[N_proto dim_proto]=size(proto);
if dim_proto ~= dim_data,
error('Data and prototype vector dimension does not match.');
end
% Check if the classes are given as labels (no class input arg.)
% if they are take them from prototype struct
if nargin<3 | isempty(proto_class)
if ~isstruct(Proto)
error(['If prototypes are not in labeled map or data struct' ...
'class must be given.']);
% transform to interger (numerical) class labels
else
[proto_class,classnames]=class2num(Proto.labels);
end
end
% Check class label vector: must be numerical and of integers
if ~vis_valuetype(proto_class,{[N_proto 1]});
error(['Class vector is invalid: has to be a N-of-data_rows x 1' ...
' vector of integers']);
elseif sum(fix(proto_class)-proto_class)~=0
error('Class labels in vector ''Class'' must be integers.');
end
% Find all class labels
ClassIndex=unique(proto_class);
N_class=length(ClassIndex); % number of different classes
% Calculate euclidean distances between classifiees and prototypes
d=distance(proto,data);
%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if K==1, % sort distances only if K>1
% 1NN
% Select the closest prototype
[tmp,proto_index]=min(d);
class=proto_class(proto_index);
else
% Sort the prototypes for each classifiee according to distance
[tmp,proto_index]=sort(d);
%% Select K closest prototypes
proto_index=proto_index(1:K,:);
knn_class=proto_class(proto_index);
for i=1:N_class,
classcounter(i,:)=sum(knn_class==ClassIndex(i));
end
%% Vote between classes of K neighbors
[winner,vote_index]=max(classcounter);
%% Handle ties
% set index to clases that got as amuch votes as winner
equal_to_winner=(repmat(winner,N_class,1)==classcounter);
% set index to ties
tie_index=find(sum(equal_to_winner)>1); % drop the winner from counter
% Go through equal classes and reset vote_index randomly to one
% of them
for i=1:length(tie_index),
tie_class_index=find(equal_to_winner(:,tie_index(i)));
fortuna=randperm(length(tie_class_index));
vote_index(tie_index(i))=tie_class_index(fortuna(1));
end
class=ClassIndex(vote_index);
end
%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Relative amount of classes in K neighbors for each classifiee
if K==1,
P=zeros(N_data,N_class);
if nargout>1,
for i=1:N_data,
P(i,ClassIndex==class(i))=1;
end
end
else
P=classcounter'./K;
end
% xMake class names to struct if they exist
if ~isempty(classnames),
Class=Data;
for i=1:N_data,
Class.labels{i,1}=classnames{class(i)};
end
else
Class=class;
end
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [nos,names] = class2num(class)
% Change string labels in map/data struct to integer numbers
names = {};
nos = zeros(length(class),1);
for i=1:length(class)
if ~isempty(class{i}) & ~any(strcmp(class{i},names))
names=cat(1,names,class(i));
end
end
tmp_nos = (1:length(names))';
for i=1:length(class)
if ~isempty(class{i})
nos(i,1) = find(strcmp(class{i},names));
end
end
function d=distance(X,Y);
% Euclidean distance matrix between row vectors in X and Y
U=~isnan(Y); Y(~U)=0;
V=~isnan(X); X(~V)=0;
d=X.^2*U'+V*Y'.^2-2*X*Y';
|
github
|
martinarielhartmann/mirtooloct-master
|
som_trajectory.m
|
.m
|
mirtooloct-master/somtoolbox/som_trajectory.m
| 9,582 |
utf_8
|
943fbebf146286d22761e716b557cf75
|
function som_trajectory(bmus,varargin)
%SOM_TRAJECTORY Launch a "comet" trajectory visualization GUI.
%
% som_show(sM,'umat','all')
% bmus = som_bmus(sM,sD);
% som_trajectory(bmus)
% som_trajectory(bmus, 'data1', sD, 'trajsize', [12 6 3 1]')
% som_trajectory(bmus, 'data1', sD.data(:,[1 2 3]), 'name1', {'fii' 'faa' 'foo'})
%
% Input arguments ([]'s are optional):
% bmus (matrix) size Nx1, vector of BMUS
% ['argID', (string) Other arguments can be given as 'argID', value
% value] (varies) pairs. See list below for valid values.
%
% NOTE: the GUI only works on a figure which has been made with SOM_SHOW.
%
% Here are the valid argument IDs (case insensitive) and associated values:
% 'color' string 'xor' or ColorSpec, default: 'xor'.
% (default: according to lattice as in som_cplane)
% 'TrajSize' vector of size Nx1 to define the length of comet
% (N) and size of the comet dots in points.
% default: [16 12 10 8 6 4]'
% 'Data1' SOM Toolbox data struct or matrix. The size of
% data matrix (in data struct the field .data) is
% Nxd, where N must be the same as the amount of
% BMUS given in the first input argument 'bmus'
% This data is shown in a new window in d subplots.
% Default: BMU indices (first input argument)
% 'Name1' cell array of d strings which contains names
% for the components in 'Data1'. If 'Data1' is a SOM
% Toolbox data struct, the existing component names
% are overdone.
% 'Figure' scalar that must be a handle to an existing figure
% which has been made using SOM_SHOW function.
% Default: current active figure (gcf).
%
% The following tools can be found in the 'Tools' -menu.
%
% Remove Trajectory: removes trajectory from the map.
% Dye Nodes : opens GUI for selecting color for the nodes
% and points selected.
% Clear Markers : removes markers from map and data figure.
% Save : saves the current situation as a struct.
% Load : loads the struct from workspace and draws markers.
%
% Mouse operation
%
% In data window: Left button is used to drag the operation point ruler
% if left button is on blank area, it starts
% In map window : Left button starts a polygon; right button
% finishes; middle button toggles a unit.
%
% SOM_TRAJECTORY is an application for observing trajectory behavior.
%
% Using mouse the line in data figure can be dragged and the
% trajectory moves in the SOM SHOW figure. It is also possible to move
% trajectory by pressing keys '>' and '<' when mouse pointer is above
% data figure.
%
% Regions can be chosen from the data and the points in that region
% are mapped to the component planes. Regions can be chosen also in
% the map. In this situation data points and map nodes are also
% marked (Left mouse button adds point to the polygon indicating the
% region and right button finals the polygon). By clicking a node (the
% middle button) that node is either added or removed from selection.
%
% It should be noticed that choosing intervals from data may cause
% situations that seem to be bugs. If there exisist marks of different
% color, removing them by clicking the map may left some marks in the
% data, because more than one point in the data is mapped to the same
% node in the map and the removing operation depends on the color of
% the marks. However, all the marks can be removed by using the 'Clear
% Markers' -operation.
%
% FEATURES
%
% The first input argument 'bmus' may also be a munits x N matrix
% In this case each column defines a "fuzzy response". That is,
% each column defines a hit histogram function). The element
% bmus(i,t) sets the size of marker on unit i at time t.
% NOTE: - in this case no regions can be selcted on the map!
% - only > and < keys can be used to move the operation point
% line: it can't be dragged
% - "fuzzy response is always black (hope this will be fixed)
%
% It is possible to open a second data window showing different data:
% use indetifiers 'Data2' (and 'Name2'). The argument syntax is
% identical to 'Data1' (and 'Name1').
%
% See also SOM_SHOW, SOM_SHOW_ADD, SOM_BMUS.
% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan
% Himberg and Juha Parhankangas
% Copyright (c) 2000 by the Johan Himberg and Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Check arguments
error(nargchk(1,Inf,nargin)); % Check no. of input arguments
%% Init input argument struct (see subfunction)
Traj=iniTraj(bmus);
% Check tentative BMU input validity
if ~vis_valuetype(bmus,{'nxm'}),
error(['First input should be a vector of BMU indices or' ...
' a "response matrix"']);
end
%% Check optional arguments
for i=1:2:length(varargin)
identifier=lower(varargin{i});
value=varargin{i+1};
% Trajectory color
switch identifier
case 'color'
if isempty(value)
value='xor';
end
if vis_valuetype(value,{'colorstyle','xor'})
Traj.color=value;
else
error('''Color'' has to be ColorSpec or string ''xor''.');
end
% 1st data
case 'data1'
if isempty(value),
value=[];
elseif vis_valuetype(value,{'nxm'})
Traj.primary_data=value;
elseif isstruct(value) & isfield(value,'type') & ...
ischar(value.type) & strcmp(value.type,'som_data'),
Traj.primary_data=value.data;
if isempty(Traj.primary_names),
Traj.primary_names=value.comp_names;
end
end
% 2nd data
case 'data2'
if isempty(value),
value=[];
elseif vis_valuetype(value,{'nxm'})
Traj.secondary_data=value;
elseif isstruct(value) & isfield(value,'type') & ...
ischar(value.type) & strcmp(value.type,'som_data'),
Traj.secondary_data=value.data;
if isempty(Traj.secondary_names),
Traj.secondary_names=value.comp_names;
end
end
% Trajectory length & size
case 'trajsize'
if isempty(value),
Traj.size=[16 12 10 8 6 4]';
end
if vis_valuetype(value,{'nx1'})
Traj.size=value
else
error('''TrajSize'' has to be a nx1 vector.');
end
% Names for first data variables
case 'name1'
if isempty(value),
Traj.primary_names=[];
elseif ~vis_valuetype(value,{'cellcolumn_of_char'}),
error('''Name1'': variable names must be in a cell column array.')
else
Traj.primary_names = value;
end
% Names for 2nd data variables
case 'name2'
if isempty(value),
Traj.secondary_names=[];
elseif ~vis_valuetype(value,{'cellcolumn_of_char'}),
error('''Name2'': variable names must be in a cell column array.')
else
Traj.secondary_names = value;
end
% Figure number
case 'figure'
if isempty(value)
Traj.figure='gcf';
end
if vis_valuetype(value,{'1x1'})
Traj.figure=value;
else
error('''Figure'' should be number of an existing figure.')
end
end
end
%% Get SOM data from figure
[h,msg,lattice,msize,dim]=vis_som_show_data('all',Traj.figure);
%% Not a SOM_SHOW figure?
if ~isempty(msg);
error('Figure is invalid: use SOM_SHOW to draw the figure.');
end
% Get map size from figure data
Traj.lattice=lattice;
Traj.msize=msize;
if length(msize)>2,
error(['This function works only with 2D maps: figure contains' ...
' something else.']);
end
munits=prod(msize);
% Check BMU (or response) and map match
if vis_valuetype(bmus,{'nx1'});
if max(bmus)>prod(msize) | min(bmus) <1
error('BMU indexes out of range.')
elseif any(round(bmus)~=bmus)
error('BMU indexes must be integer.');
elseif isempty(Traj.primary_data),
Traj.primary_data=bmus;
end
elseif size(bmus,1) ~= munits
error(['Response matrix column number must match with the number of' ...
' map units.']);
else
bmus=bmus';
if isempty(Traj.primary_data),
Traj.primary_data=[1:size(bmus,1)]';
Traj.primary_names={'BMU Index'};
end
end
size1=size(Traj.primary_data);
size2=size(Traj.secondary_data);
% Data2 must not be defined alone
if isempty(Traj.primary_data)&~isempty(Traj.secondary_data),
error('If ''Data2'' is specified ''Data1'' must be specified, too.');
elseif ~isempty(Traj.secondary_data) ...
& size1~= size2
% If data1 and data2 exist both, check data1 and data2 match
error('''Data1'' and ''Data2'' have different amount of data vectors.')
end
% Check BMU and data1 match (data2 matches with 1 anyway)
if ~isempty(Traj.primary_data) & size(bmus,1) ~= size1,
error(['The number of data vectors in ''data1'' must match with' ...
' the number of rows in the first input argument (bmus).']);
end
% Check that number of names and data dimension is consistent
if ~isempty(Traj.primary_names) & (size1(2)~=length(Traj.primary_names)),
error('Number of component names and ''Data1'' dimension mismatch.');
end
if ~isempty(Traj.secondary_names) & ...
(size2(2)~=length(Traj.secondary_names)),
error('Number of component names and ''Data2'' dimension mismatch.');
end
%% Call the function that does the job
vis_trajgui(Traj);
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Traj=iniTraj(bmus)
Traj.figure=gcf;
Traj.primary_data=[];
Traj.secondary_data=[];
Traj.primary_names = [];
Traj.secondary_names = [];
Traj.size=[16 12 10 8 6 4]';
Traj.bmus=bmus;
Traj.color='xor';
Traj.msize=[];
Traj.lattice=[];
|
github
|
martinarielhartmann/mirtooloct-master
|
som_vs1to2.m
|
.m
|
mirtooloct-master/somtoolbox/som_vs1to2.m
| 7,005 |
utf_8
|
ff7eede3183ba5dfa54884255dc76c4e
|
function sS = som_vs1to2(sS)
%SOM_VS1TO2 Convert version 1 structure to version 2.
%
% sSnew = som_vs1to2(sSold)
%
% sMnew = som_vs1to2(sMold);
% sDnew = som_vs1to2(sDold);
%
% Input and output arguments:
% sSold (struct) a SOM Toolbox version 1 structure
% sSnew (struct) a SOM Toolbox version 2 structure
%
% For more help, try 'type som_vs1to2' or check out online documentation.
% See also SOM_SET, SOM_VS2TO1.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_vs1to2
%
% PURPOSE
%
% Transforms SOM Toolbox 1 version structs from to 2 version structs.
%
% SYNTAX
%
% sS2 = som_vs1to2(sS1)
%
% DESCRIPTION
%
% This function is offered to allow the change of old map and data structs
% to new ones. There are quite a lot of changes between the versions,
% especially in the map struct, and this function makes it easy to update
% the structs.
%
% WARNING!
%
% 'som_unit_norm' normalization type is not supported by version 2,
% so this type of normalization will be lost.
%
% REQUIRED INPUT ARGUMENTS
%
% sS1 (struct) any SOM Toolbox version 1 struct (map, data,
% training or normalization struct)
%
% OUTPUT ARGUMENTS
%
% sS2 (struct) the corresponding SOM Toolbox 2 version struct
%
% EXAMPLES
%
% sM = som_vs1to2(sMold);
% sD = som_vs1to2(sDold);
% sT = som_vs1to2(sMold.train_sequence{1});
% sN = som_vs1to2(sDold.normalization);
%
% SEE ALSO
%
% som_set Set values and create SOM Toolbox structs.
% som_vs2to1 Transform structs from version 2.0 to 1.0.
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 101199
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
error(nargchk(1, 1, nargin)); % check no. of input arguments is correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set field values
if isfield(sS,'codebook'), type='som_map';
elseif isfield(sS,'data'), type='som_data';
elseif isfield(sS,'algorithm'), type = 'som_train';
elseif isfield(sS,'inv_params'), type = 'som_norm';
else
error('Unrecognized input struct.');
end
switch type,
case 'som_map',
msize = sS.msize; munits = prod(msize); dim = prod(size(sS.codebook))/munits;
M = reshape(sS.codebook,[munits dim]);
% topology
if strcmp(sS.shape,'rect'), shape = 'sheet'; else shape = sS.shape; end
sTopol = struct('type','som_topol','msize',msize,'lattice',sS.lattice,'shape',shape);
% labels
labels = cell(munits,1);
for i=1:munits,
for j=1:length(sS.labels{i}), labels{i,j} = sS.labels{i}{j}; end
end
% trainhist
tl = length(sS.train_sequence);
if strcmp(sS.init_type,'linear'); alg = 'lininit'; else alg = 'randinit'; end
trh = struct('type','som_train');
trh.algorithm = alg;
trh.neigh = sS.neigh;
trh.mask = sS.mask;
trh.data_name = sS.data_name;
trh.radius_ini = NaN;
trh.radius_fin = NaN;
trh.alpha_ini = NaN;
trh.alpha_type = '';
trh.trainlen = NaN;
trh.time = '';
for i=1:tl,
trh(i+1) = som_vs1to2(sS.train_sequence{i});
trh(i+1).mask = sS.mask;
trh(i+1).neigh = sS.neigh;
trh(i+1).data_name = sS.data_name;
end
% component normalizations
cnorm = som_vs1to2(sS.normalization);
if isempty(cnorm),
cnorm = cell(dim,1);
elseif length(cnorm) ~= dim,
warning('Incorrect number of normalizations. Normalizations ignored.\n');
cnorm = cell(dim,1);
else
if strcmp(cnorm{1}.method,'histD'),
M = redo_hist_norm(M,sS.normalization.inv_params,cnorm);
end
end
% map
sSnew = struct('type','som_map');
sSnew.codebook = M;
sSnew.topol = sTopol;
sSnew.labels = labels;
sSnew.neigh = sS.neigh;
sSnew.mask = sS.mask;
sSnew.trainhist = trh;
sSnew.name = sS.name;
sSnew.comp_norm = cnorm;
sSnew.comp_names = sS.comp_names;
case 'som_data',
[dlen dim] = size(sS.data);
% component normalizations
cnorm = som_vs1to2(sS.normalization);
if isempty(cnorm),
cnorm = cell(dim,1);
elseif length(cnorm) ~= dim,
warning('Incorrect number of normalizations. Normalizations ignored.\n');
cnorm = cell(dim,1);
else
if strcmp(cnorm{1}.method,'histD'),
sS.data = redo_hist_norm(sS.data,sS.normalization.inv_params,cnorm);
end
end
% data
sSnew = struct('type','som_data');
sSnew.data = sS.data;
sSnew.name = sS.name;
sSnew.labels = sS.labels;
sSnew.comp_names = sS.comp_names;
sSnew.comp_norm = cnorm;
sSnew.label_names = [];
case 'som_norm',
if isempty(sS.inv_params),
sSnew = [];
else
dim = size(sS.inv_params,2);
sSnew = cell(dim,1);
switch sS.name,
case 'som_var_norm', method = 'var';
case 'som_lin_norm', method = 'range';
case 'som_hist_norm', method = 'histD';
case 'som_unit_norm', method = '';
warning(['Normalization method ''som_unit_norm'' is not available' ...
' in version 2 of SOM Toolbox.\n']);
end
if ~isempty(method),
for i=1:dim,
sSnew{i} = struct('type','som_norm');
sSnew{i}.method = method;
sSnew{i}.params = [];
sSnew{i}.status = 'done';
switch method,
case 'var',
me = sS.inv_params(1,i); st = sS.inv_params(2,i);
sSnew{i}.params = [me, st];
case 'range',
mi = sS.inv_params(1,i); ma = sS.inv_params(2,i);
sSnew{i}.params = [mi, ma-mi];
case 'histD',
vals = sS.inv_params(1:(end-1),i);
bins = sum(isfinite(vals));
vals = vals(1:bins);
sSnew{i}.params = vals;
end
end
end
end
case 'som_train',
sSnew = struct('type','som_train');
sSnew.algorithm = sS.algorithm;
sSnew.neigh = 'gaussian';
sSnew.mask = [];
sSnew.data_name = 'unknown';
sSnew.radius_ini = sS.radius_ini;
sSnew.radius_fin = sS.radius_fin;
sSnew.alpha_ini = sS.alpha_ini;
sSnew.alpha_type = sS.alpha_type;
sSnew.trainlen = sS.trainlen;
sSnew.time = sS.time;
case 'som_topol',
disp('Version 1.0 of SOM Toolbox did not have topology structure.\n');
case {'som_grid','som_vis'}
disp('Version 1.0 of SOM Toolbox did not have visualization structs.\n');
otherwise,
error('Unrecognized struct.');
end
sS = sSnew;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function D = redo_hist_norm(D,inv_params,cnorm)
dim = size(D,2);
% first - undo the old way
n_bins = inv_params(end,:);
D = round(D * sparse(diag(n_bins)));
for i = 1:dim,
if any(isnan(D(:, i))), D(isnan(D(:, i)), i) = n_bins(i); end
D(:, i) = inv_params(D(:, i), i);
end
% then - redo the new way
for i=1:dim,
bins = length(cnorm{i}.params);
x = D(:,i);
inds = find(~isnan(x) & ~isinf(x))';
for j = inds,
[dummy ind] = min(abs(x(j) - cnorm{i}.params));
if x(j) > cnorm{i}.params(ind) & ind < bins, x(j) = ind + 1;
else x(j) = ind;
end
end
D(:,i) = (x-1)/(bins-1);
end
|
github
|
martinarielhartmann/mirtooloct-master
|
rep_utils.m
|
.m
|
mirtooloct-master/somtoolbox/rep_utils.m
| 18,698 |
utf_8
|
729db4f7bcce5f1f91eab7a63c3acdc1
|
function aout = rep_utils(action,fmt,fid)
%REP_UTILS Utilities for print reports and report elements.
%
% aout = rep_utils(action,fmt,[fid])
%
% Input and output arguments ([]'s are optional):
% action (string) action identifier
% (cell array) {action,par1,par2,...}
% the action identifier, followed by action
% parameters
% [fmt] (string) format of output, 'txt' by default
% [fid] (scalar) output file id, by default NaN in which
% case output is not written, only returned
% in aout
%
% aout (varies) output of the action
%
% Here are the actions and their arguments:
% 'printlines' par1 (cellstr) print par1, each cell on a new line
% 'header' par1 (string) print document header using par1 as title
% 'footer' print document footer
% 'compile' par1 (string) compile the named document (only 'ps' and 'pdf')
% 'inserttable' par1 (struct) print given table
% par2 (scalar) print lines between rows if par2=1
% par3 (scalar) use longtable format (only 'ps' and 'pdf')
% 'printfigure' par1 (string) print current figure to file, par1 = filename
% par2 (scalar) used resolution (150 dpi by default)
% par3 (scalar) if par3=1, insert figure in minipage
% 'insertfigure' par1 (string) insert figure to report, par1 = filename of figure
% par2 (vector) size 2 x 1, size of figure relative to page size
% NaN = automatic scaling
% par3 (scalar) if par3=1, insert figure in minipage (only 'ps' and 'pdf')
% 'insertbreak' insert paragraph break into report
%
% See also REP_STATS.
% Contributed to SOM Toolbox 2.0, January 2nd, 2002 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 020102
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
pars = {''};
if iscell(action),
if length(action)>1, pars = action(2:end); end
action = action{1};
end
if nargin<2 | isempty(fmt), fmt = 'txt'; end
global REPORT_OUTPUT_FMT
REPORT_OUTPUT_FMT = fmt;
if nargin<3 | isempty(fid), fid = NaN; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
aout = [];
printable = 0;
switch action,
case 'printlines',
aout = pars{1};
case 'header',
switch fmt,
case {'ps','pdf'}, aout = tex_startdocument(pars{1});
case 'html', aout = html_startpage(pars{1});
case 'txt', aout = cell(0);
end
printable = 1;
case 'footer',
switch fmt,
case {'ps','pdf'}, aout = tex_enddocument;
case 'html', aout = html_endpage;
case 'txt', aout = cell(0);
end
printable = 1;
case 'compile', aout = compiledocument(pars{1});
case 'inserttable', aout = inserttable(pars{:}); printable = 1;
case 'printfigure', printfigure(pars{:});
case 'insertfigure', aout = insertfigure(pars{:}); printable = 1;
case 'insertbreak', aout = insertbreak; printable = 1;
case 'joinstr', aout = joinstr(pars{:}); printable = 1;
case 'rulestr', aout = rulestr(pars{:}); printable = 1;
case 'c_and_p_str', aout = c_and_p_str(pars{:}); printable = 1;
case 'p_str', aout = p_str(pars{:}); printable = 1;
end
% if output file is given, print lines
if ~isnan(fid) & printable,
if ~iscell(aout), aout = {aout}; end
for i = 1:length(aout), fprintf(fid,'%s\n',fmtline(aout{i})); end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
%% simple formatter strings
function s = joinstr(cs, sep1, sep2)
if nargin==1, sep1 = ', '; sep2 = ' and '; end
if nargin<3, sep2 = sep1; end
if isempty(cs),
s = '';
elseif strcmp(sep1,'\n'),
if size(cs,1)==1, cs = cs'; end
s = char(cs);
else
s = cs{1};
for i=2:length(cs)-1, s = [s sep1 cs{i}]; end
if length(cs)>1, s = [s sep2 cs{end}]; end
end
return;
function str = c_and_p_str(n,m)
% return a string of form # (%), e.g. '23 (12%)'
if n==m, p = '100';
elseif n==0, p = '0';
else p = sprintf('%.2g',100*n/m);
end
str = sprintf('%d (%s%%)',round(n),p);
return;
function str = p_str(p)
% return a string of form %, e.g. '12%'
if round(p*100)>=100, p = sprintf('%3g',100*p);
elseif abs(p)<eps, p = '0';
else p = sprintf('%.2g',100*p);
end
str = sprintf('%s%%',p);
return;
function cs = rulestr(sR,cnames)
global REPORT_OUTPUT_FMT
switch REPORT_OUTPUT_FMT
case {'ps','pdf'}, [leq,geq,infi,m,less,in] = deal('\leq','\geq','\inf','$','<','\in');
case 'html', [leq,geq,infi,m,less,in] = deal('<=','>=','Inf',' ','<',' ');
case 'txt', [leq,geq,infi,m,less,in] = deal('<=','>=','inf',' ','<','');
end
nr = length(sR);
cs = cell(nr,1);
fmt = '%.2g';
if nargin<2, cnames = {sR.name}; end
if isempty(cnames), cnames = cell(nr,1); cnames(:) = {''}; end
for i=1:nr,
low = sR(i).low;
high = sR(i).high;
switch isfinite(low) + 2*isfinite(high),
case 0, cs{i} = [cnames{i} ' ' 'any'];
case 1, cs{i} = [cnames{i} ' ' m geq sprintf(fmt,low) m];
case 2, cs{i} = [cnames{i} ' ' m less sprintf(fmt,high) m];
case 3, cs{i} = [cnames{i} ' ' m in '[' sprintf(fmt,low) ',' sprintf(fmt,high) ']' m];
end
end
return;
%% print figure
function imgfmt = fmt2imgfmt
global REPORT_OUTPUT_FMT
switch REPORT_OUTPUT_FMT,
case 'ps', imgfmt = 'ps';
case 'pdf', imgfmt = 'pdf';
case 'html', imgfmt = 'png';
case 'txt', imgfmt = '';
end
return;
function printfigure(fname,resolution)
if nargin<2, resolution = 150; end
fnameps = [fname '.ps'];
switch fmt2imgfmt,
case 'ps',
print('-dpsc2',fnameps);
case 'pdf',
print('-dpsc2',fnameps);
eval(sprintf('!ps2pdf %s',fnameps));
case 'gif',
print('-dpsc2',fnameps);
cmd = 'pstogif';
opt = sprintf('-depth 1 -density %d',resolution);
unix(sprintf('%s %s -out %s %s',cmd,opt,[fname '.gif'],fnameps));
case 'png',
opt = sprintf('-r%d',resolution);
print('-dpng',opt,[fname '.png']);
end
return;
%% headers and footers, and compilation
function cs = tex_startdocument(title)
% tex document headers
global REPORT_OUTPUT_FMT
cs = cell(0);
cs{end+1} = '\documentclass[10pt,a4paper]{article}';
cs{end+1} = '\usepackage[dvips]{epsfig,graphicx,color}';
cs{end+1} = '\usepackage{float,graphics,subfigure}';
cs{end+1} = '\usepackage{multirow,rotating,portland,lscape,longtable,pifont}';
cs{end+1} = '\usepackage[T1]{fontenc}';
if strcmp(REPORT_OUTPUT_FMT,'pdf'), cs{end+1} = '\usepackage{pslatex}'; end
cs{end+1} = '\usepackage[english]{babel}';
cs{end+1} = '\oddsidemargin 0 mm';
cs{end+1} = '\evensidemargin 0 mm';
cs{end+1} = '\textwidth 17 cm';
cs{end+1} = '\topmargin 0 mm';
cs{end+1} = '\textheight 21 cm';
cs{end+1} = '\voffset 0 mm';
cs{end+1} = '\begin{document}';
cs{end+1} = ['\title{' title '}'];
cs{end+1} = '\maketitle';
%cs{end+1} = '\tableofcontents';
%cs{end+1} = '\clearpage';
return;
function cs = tex_enddocument
cs = cell(0);
cs{end+1} = '\end{document}';
return;
function cs = html_startpage(title)
% print HTML document headers
cs = cell(0);
cs{end+1} = '<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">';
cs{end+1} = '<HTML>';
cs{end+1} = '<HEAD>';
cs{end+1} = sprintf(' <TITLE>%s</TITLE>',title);
cs{end+1} = '</HEAD>';
cs{end+1} = '<BODY bgcolor=white vlink="#000033" link="#0000ff" text="#000000">';
if ~isempty(title), cs{end+1} = sprintf('<H1>%s</H1>',title); end
return;
function cs = html_endpage
% print HTML document footers
cs = cell(0);
cs{end+1} = '<P><HR>';
cs{end+1} = '</BODY>';
cs{end+1} = '</HTML>';
return;
function files = compiledocument(filename)
global REPORT_OUTPUT_FMT
switch REPORT_OUTPUT_FMT,
case 'pdf',
eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename));
eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename));
%eval(sprintf('!acroread %s.pdf &',filename));
files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.pdf']};
case 'ps',
eval(sprintf('!latex --interaction batchmode %s.tex',filename));
eval(sprintf('!latex --interaction batchmode %s.tex',filename));
eval(sprintf('!dvips %s.dvi',filename));
eval(sprintf('!ps2pdf %s.ps',filename));
%eval(sprintf('!ghostview %s.ps &',filename));
files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.dvi'],[filename '.pdf']};
case 'html',
case 'txt',
end
return;
function vstr = defaultformat(val)
global REPORT_OUTPUT_FMT
if ischar(val), vstr = val;
elseif iscellstr(val), vstr = char(val);
elseif isempty(val), vstr = '';
elseif isnumeric(val),
if val==round(val), fmt = '%d'; else fmt = '%.3g'; end
if abs(val)<=eps, vstr = '0'; else vstr = sprintf(fmt,val); end
elseif isstruct(val) & isfield(val,'values') & isfield(val,'headers'),
% a table
vstr = joinstr(inserttable(val,0),'\n');
if any(strcmp(REPORT_OUTPUT_FMT,{'ps','pdf'})),
vstr= inserttominipage(vstr);
end
else
vstr = ''; fprintf(1,'defaultformat unable to handle input\n');
whos val
end
return;
%% report elements (list, table, image, link)
function str = fmtline(str)
% replace some formatting elements depeding on output format
global REPORT_OUTPUT_FMT
if isempty(str), str = ''; return; end
switch REPORT_OUTPUT_FMT,
case {'ps','pdf'},
str = strrep(str,'<B>', '{\bf ');
str = strrep(str,'<I>', '{\em ');
str = strrep(str,'<TT>', '{\tt ');
str = strrep(str,'</B>', '}');
str = strrep(str,'</I>', '}');
str = strrep(str,'</TT>','}');
str = strrep(str,'#','\#');
str = strrep(str,'%','\%');
case 'html', % nil
case 'txt',
str = strrep(str,'<B>', '*');
str = strrep(str,'<I>', '*');
str = strrep(str,'<TT>', '');
str = strrep(str,'</B>', '*');
str = strrep(str,'</I>', '*');
str = strrep(str,'</TT>','');
end
return;
function cs = insertbreak
global REPORT_OUTPUT_FMT
cs = cell(0);
switch REPORT_OUTPUT_FMT
case {'ps','pdf'}, cs{end+1} = '';
case 'html', cs{end+1} = '<P>';
case 'txt', cs{end+1} = '';
end
return;
function insertlist(list,enum)
% make list
global REPORT_OUTPUT_FMT
if nargin<2, enum = 0; end
cs = cell(0);
switch REPORT_OUTPUT_FMT
case {'ps','pdf'},
if enum, tag = 'enumerate'; else tag = 'itemize'; end
starttag = ['\begin{' tag '}'];
listtag = '\item ';
endtag = ['\end{' tag '}'];
case 'html',
if enum, tag = 'OL'; else tag = 'UL'; end
starttag = ['<' tag '>'];
listtag = '<LI>';
endtag = ['</' tag '>'];
case 'txt',
starttag = '';
listtag = '- ';
endtag = '';
end
cs{end+1} = starttag;
for i=1:length(list), cs{end+1} = sprintf('%s %s',listtag,list{i}); end
cs{end+1} = endtag;
return;
function csout = tablerow(cs,emp,span)
% construct one table row
global REPORT_OUTPUT_FMT
if nargin<2 | isempty(emp), emp = 'none'; end
if nargin<3 | isempty(span), span = ones(length(cs),2); end
rowspan = span(:,1); colspan = span(:,2);
switch emp,
case 'bold', emp1 = '<B>'; emp2 = '</B>';
case 'italic', emp1 = '<I>'; emp2 = '</I>';
case 'fixed', emp1 = '<TT>'; emp2 = '</TT>';
case 'none', emp1 = ''; emp2 = '';
case 'header', emp1 = ''; emp2 = ''; tag = 'TH';
end
csout = cell(0);
switch REPORT_OUTPUT_FMT,
case {'pdf','ps'},
%switch emp,
% case 'bold', emp1 = '{\bf '; emp2 = '}';
% case 'italic', emp1 = '{\em '; emp2 = '}';
% case 'fixed', emp1 = '{\tt '; emp2 = '}';
% case 'none', emp1 = ''; emp2 = '';
%end
s0 = '';
for i=1:length(cs),
if rowspan(i) & colspan(i),
sp1 = ''; sp2 = '';
if colspan(i)>1, sp1 = [sp1 ' \multicolumn{' num2str(colspan(i)) '}{|c|}{']; sp2 = [sp2 '}']; end
if rowspan(i)>1, sp1 = [sp1 ' \multirow{' num2str(rowspan(i)) '}{2cm}{']; sp2 = [sp2 '}']; end
s = s0;
content = cellstr(defaultformat(cs{i}));
csout{end+1} = [s sp1 emp1 content{1}];
for j=2:length(content), csout{end+1} = content{j}; end
csout{end} = [csout{end} emp2 sp2];
s0 = ' & ';
end
end
csout{end} = [csout{end} ' \\'];
case 'html',
tag = 'TD';
csout{end+1} = '<TR>';
for i=1:length(cs),
if rowspan(i) & colspan(i),
sp = '';
if rowspan(i)>1, sp = [sp ' ROWSPAN=' num2str(rowspan(i))]; end
if colspan(i)>1, sp = [sp ' COLSPAN=' num2str(colspan(i))]; end
s = sprintf('<%s%s>%s',tag,sp,emp1);
content = cellstr(defaultformat(cs{i}));
csout{end+1} = [s content{1}];
for j=2:length(content), csout{end+1} = content{j}; end
csout{end} = [csout{end} emp2 '</' tag '>'];
end
end
csout{end+1} = '</TR>';
case 'txt',
for i=1:length(cs), csout{end+1} = defaultformat(cs{i}); end
end
return;
function cs = inserttable(sTable,rowlines,long)
% put table contents to cellstr
global REPORT_OUTPUT_FMT
if nargin<2, rowlines = 1; end
if nargin<3, long = 0; end
[rows cols] = size(sTable.values);
cs = cell(0);
if isempty(sTable.colfmt), cf = 'c'; sTable.colfmt = cf(ones(1,cols)); end
if isempty(sTable.span), sTable.span = ones([rows cols 2]); end
switch REPORT_OUTPUT_FMT
case {'ps','pdf','tex','latex'}
li1 = ' \hline';
if rowlines>0, li2 = li1; li3 = li1;
elseif rowlines==0, li2 = ''; li3 = li1;
else li1 = ''; li2 = ''; li3 = '';
end
if long, tbl = 'longtable'; else tbl = 'tabular'; end
cs{end+1} = ['\begin{' tbl '}{' sTable.colfmt '}' li1];
if ~isempty(sTable.headers),
row = tablerow(sTable.headers,'bold');
for i=1:length(row), cs{end+1} = row{i}; end
cs{end} = [cs{end} li1 li2];
end
for i=1:rows,
row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:)));
for i=1:length(row), cs{end+1} = row{i}; end
cs{end} = [cs{end} li2];
end
if ~rowlines, cs{end} = [cs{end} li3]; end
cs{end+1} = ['\end{' tbl '}'];
case 'html'
cs{end+1} = ['<TABLE BORDER=' num2str(rowlines>0) '>'];
if ~isempty(sTable.headers),
row = tablerow(sTable.headers,'header');
for i=1:length(row), cs{end+1} = row{i}; end
end
for i=1:rows,
row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:)));
for i=1:length(row), cs{end+1} = row{i}; end
end
cs{end+1} = '</TABLE>';
case 'txt'
cT = [sTable.headers(:)'; sTable.values];
A = cell2char(cT);
for i=1:size(A,1), cs{end+1} = A(i,:); end
end
return;
function A = cell2char(T)
[nrow,ncol] = size(T);
rowsep = 0;
colsep = 1;
% change to strings
for i=1:nrow,
for j=1:ncol,
t = T{i,j};
if ischar(t), % ok
elseif isempty(t), T{i,j} = '';
elseif isstruct(t), % ??
elseif iscell(t), T{i,j} = cell2char(t);
elseif isnumeric(t), T{i,j} = num2str(t,3);
end
end
end
% widths of columns and heights of rows
HW = ones(nrow,ncol,2);
for i=1:nrow, for j=1:ncol, HW(i,j,:) = size(T{i,j}); end, end
colw = max(HW(:,:,2),[],1);
rowh = max(HW(:,:,1),[],2);
% the table itself
A = char(32*ones(sum(rowh)+rowsep*(nrow-1),sum(colw)+colsep*(ncol-1)));
for i=1:nrow,
for j=1:ncol,
i0 = (i-1)*rowsep+sum(rowh(1:i-1));
j0 = (j-1)*colsep+sum(colw(1:j-1));
S = char(32*ones(rowh(i),colw(j)));
si = size(T{i,j}); S(1:si(1),1:si(2)) = T{i,j};
A(i0+[1:rowh(i)],j0+[1:colw(j)]) = S;
end
end
return;
function s = inserttominipage(s,width)
if nargin<2 | isempty(width) | isnan(width), width = 1; end
width = ['{' num2str(width) '\columnwidth}'];
mp1 = '\begin{minipage}[t]'; mp2 = '\end{minipage}';
if size(s,1)==1, s = [mp1 width s mp2];
else s = char({[mp1 width]; s; mp2});
end
return;
function cs = insertfigure(fname,boxsize,inminipage)
global REPORT_OUTPUT_FMT
if nargin<2, boxsize = [NaN 1]; end
if nargin<3, inminipage = 0; end
htmlpagewidth = 800;
si = cell(0);
switch REPORT_OUTPUT_FMT,
case {'ps','pdf'},
if ~isnan(boxsize(1)), si{end+1} = ['height=' num2str(boxsize(1)) '\textheight']; end
if ~isnan(boxsize(2)), si{end+1} = ['width=' num2str(boxsize(2)) '\columnwidth']; end
if length(si), si = [', ' joinstr(si, ', ', ', ')]; end
case 'html',
if ~isnan(boxsize(1)), si{end+1} = ['HEIGHT=' num2str(htmlpagewidth*boxsize(1))]; end
if ~isnan(boxsize(2)), si{end+1} = ['WIDTH=' num2str(htmlpagewidth*boxsize(2))]; end
if length(si), si = [' ' joinstr(si, ' ', ' ')]; end
case 'txt',
% nil
end
switch REPORT_OUTPUT_FMT,
case 'ps', s = ['\epsfig{file=./' fname '.ps ' si '}'];
case 'pdf', s = ['\includegraphics[' si ']{./' fname '.pdf}'];
case 'html',
fn = [fname '.' fmt2imgfmt];
s = ['<IMG SRC="' fn '" ALIGN="center" ALT="' fname '"' si '>'];
s = makelinkfrom(fn,s);
case 'txt',
s = ['[image:' fname ']'];
end
switch REPORT_OUTPUT_FMT,
case {'ps','pdf'},
if inminipage, s = inserttominipage(s,boxsize(2)); end
case 'html',
s = ['<CENTER>' s '</CENTER>'];
case 'txt',
% nil
end
cs = {s};
return;
function str = makelinkfrom(linkto,anchor)
global REPORT_OUTPUT_FMT
if iscell(linkto),
if strcmp(REPORT_OUTPUT_FMT,'html'), linkto = joinstr(linkto,'','#');
else linkto = joinstr(linkto,'','');
end
end
switch REPORT_OUTPUT_FMT,
case 'pdf', str = ['\hyperlink{' linkto '}{' anchor '}'];
case 'ps', str = [anchor ' (p.\pageref{' linkto '})'];
case 'html', str = ['<a href="' linkto '">' anchor '</a>'];
case 'txt', str = '';
end
return;
function str = makelinkto(linkname)
global REPORT_OUTPUT_FMT
switch REPORT_OUTPUT_FMT,
case 'pdf',
fmt = '\pdfdest name {%s} fit \pdfoutline goto name {%s} {%s}';
str = sprintf(fmt,linkname,linkname,linkname);
case 'ps', str = ['\label{' linkname '}'];
case 'html', str = ['<a name="' linkname '"> </a>'];
case 'txt', str = '';
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_vs2to1.m
|
.m
|
mirtooloct-master/somtoolbox/som_vs2to1.m
| 8,359 |
utf_8
|
7ae2b5258b2e375dd754f63d956a5dcd
|
function sS = som_vs2to1(sS)
%SOM_VS2TO1 Convert version 2 struct to version 1.
%
% sSold = som_vs2to1(sSnew)
%
% sMold = som_vs2to1(sMnew);
% sDold = som_vs2to1(sDnew);
%
% Input and output arguments:
% sSnew (struct) a SOM Toolbox version 2 struct
% sSold (struct) a SOM Toolbox version 1 struct
%
% For more help, try 'type som_vs2to1' or check out online documentation.
% See also SOM_SET, SOM_VS1TO2.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_vs2to1
%
% PURPOSE
%
% Converts SOM Toolbox version 2 structs to version 1 structs.
%
% SYNTAX
%
% sS1 = som_vs2to1(sS2)
%
% DESCRIPTION
%
% This function is offered to allow the change of new map and data structs
% to old ones. There are quite a lot of changes between the versions,
% especially in the map struct, and this function makes it possible to
% use the old functions with new structs.
%
% Note that part of the information is lost in the conversion. Especially,
% training history is lost, and the normalization is, except in the simplest
% cases (like all have 'range' or 'var' normalization) screwed up.
%
% REQUIRED INPUT ARGUMENTS
%
% sS2 (struct) som SOM Toolbox version 2.0 struct (map, data,
% training or normalization struct)
%
% OUTPUT ARGUMENTS
%
% sS1 (struct) the corresponding SOM Toolbox version 2.0 struct
%
% EXAMPLES
%
% sM = som_vs2to1(sMnew);
% sD = som_vs2to1(sDnew);
% sT = som_vs2to1(sMnew.trainhist(1));
%
% SEE ALSO
%
% som_set Set values and create SOM Toolbox structs.
% som_vs1to2 Transform structs from 1.0 version to 2.0.
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 101199
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
error(nargchk(1, 1, nargin)); % check no. of input arguments is correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set field values
switch sS.type,
case 'som_map',
msize = sS.topol.msize;
[munits dim] = size(sS.codebook);
% topology
if strcmp(sS.topol.shape,'sheet'), shape = 'rect';
else shape = sS.shape;
end
% labels
labels = cell(munits,1);
nl = size(sS.labels,2);
for i=1:munits,
labels{i} = cell(nl,1);
for j=1:nl, labels{i}{j} = sS.labels{i,j}; end
end
% trainhist
tl = length(sS.trainhist);
if tl==0 | strcmp(sS.trainhist(1).algorithm,'lininit'),
init_type = 'linear';
else
init_type = 'random';
end
if tl>1,
for i=2:tl,
train_seq{i-1} = som_vs2to1(sS.trainhist(i));
end
train_type = sS.trainhist(tl).algorithm;
else
train_seq = [];
train_type = 'batch';
end
if tl>0, data_name = sS.trainhist(tl).data_name; else data_name = ''; end
% component normalizations
sN = convert_normalizations(sS.comp_norm);
if strcmp(sN.name,'som_hist_norm'),
sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
end
% map
sSnew = struct('init_type', 'linear', 'train_type', 'batch', 'lattice' ,...
'hexa', 'shape', 'rect', 'neigh', 'gaussian', 'msize', msize, ...
'train_sequence', [], 'codebook', [], 'labels', [], ...
'mask', [], 'data_name', 'unnamed', 'normalization', [], ...
'comp_names', [], 'name', 'unnamed');
sSnew.init_type = init_type;
sSnew.train_type = train_type;
sSnew.lattice = sS.topol.lattice;
sSnew.shape = shape;
sSnew.neigh = sS.neigh;
sSnew.msize = sS.topol.msize;
sSnew.train_sequence = train_seq;
sSnew.codebook = reshape(sS.codebook,[sS.topol.msize dim]);
sSnew.labels = labels;
sSnew.mask = sS.mask;
sSnew.data_name = data_name;
sSnew.normalization = sN;
sSnew.comp_names = sS.comp_names;
sSnew.name = sS.name;
case 'som_data',
[dlen dim] = size(sS.data);
% component normalizations
sN = convert_normalizations(sS.comp_norm);
if strcmp(sN.name,'som_hist_norm'),
sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
end
% data
sSnew = struct('data', [], 'name', '', 'labels' , [], 'comp_names', ...
[], 'normalization', []);
sSnew.data = sS.data;
sSnew.name = sS.name;
sSnew.labels = sS.labels;
sSnew.comp_names = sS.comp_names;
sSnew.normalization = sN;
case 'som_norm',
sSnew = struct('name','som_var_norm','inv_params',[]);
switch sS.method,
case 'var', sSnew.name = 'som_var_norm';
case 'range', sSnew.name = 'som_lin_norm';
case 'histD', sSnew.name = 'som_hist_norm';
otherwise,
warning(['Method ' method ' does not exist in version 1.'])
end
if strcmp(sS.status,'done'),
switch sS.method,
case 'var',
sSnew.inv_params = zeros(2,1);
sSnew.inv_params(1) = sS.params(1);
sSnew.inv_params(2) = sS.params(2);
case 'range',
sSnew.inv_params = zeros(2,1);
sSnew.inv_params(1) = sS.params(1);
sSnew.inv_params(2) = sS.params(2) + sS.params(1);;
case 'histD',
bins = length(sS.params);
sSnew.inv_params = zeros(bins+1,1) + Inf;
sSnew.inv_params(1:bins,i) = sS.params;
sSnew.inv_params(end,i) = bins;
end
end
case 'som_train',
sSnew = struct('algorithm', sS.algorithm, 'radius_ini', ...
sS.radius_ini, 'radius_fin', sS.radius_fin, 'alpha_ini', ...
sS.alpha_ini, 'alpha_type', sS.alpha_type, 'trainlen', sS.trainlen, ...
'qerror', NaN, 'time', sS.time);
case 'som_topol',
disp('Version 1 of SOM Toolbox did not have topology structure.\n');
otherwise,
error('Unrecognized struct.');
end
sS = sSnew;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function sN = convert_normalizations(cnorm)
dim = length(cnorm);
sN = struct('name','som_var_norm','inv_params',[]);
% check that there is exactly one normalization per component
% and that their status and method is the same
ok = 1;
nof = zeros(dim,1);
for i=1:dim, nof(i) = length(cnorm{i}); end
if any(nof>1), ok=0;
elseif any(nof==1) & any(nof==0), ok=0;
elseif any(nof>0),
status = cnorm{1}.status;
method = cnorm{1}.method;
for i=2:dim,
if ~strcmp(cnorm{i}.status,status) | ~strcmp(cnorm{i}.method,method),
ok = 0;
end
end
elseif all(nof==0),
return;
end
if ~ok,
warning(['Normalization could not be converted. All variables can' ...
' only be normalized with a single, and same, method.']);
return;
end
% method name
switch method,
case 'var', sN.name = 'som_var_norm';
case 'range', sN.name = 'som_lin_norm';
case 'histD', sN.name = 'som_hist_norm';
otherwise,
warning(['Normalization could not be converted. Method ' method ...
'does not exist in version 1.']);
return;
end
% if not done, inv_params is empty
if ~strcmp(status,'done'), return; end
% ok, make the conversion
switch method,
case 'var',
sN.inv_params = zeros(2,dim);
for i=1:dim,
sN.inv_params(1,i) = cnorm{i}.params(1);
sN.inv_params(2,i) = cnorm{i}.params(2);
end
case 'range',
sN.inv_params = zeros(2,dim);
for i=1:dim,
sN.inv_params(1,i) = cnorm{i}.params(1);
sN.inv_params(2,i) = cnorm{i}.params(2) + cnorm{i}.params(1);
end
case 'histD',
bins = zeros(dim,1);
for i=1:dim, bins(i) = length(cnorm{i}.params); end
m = max(bins);
sN.inv_params = zeros(m+1,dim) + Inf;
for i=1:dim,
sN.inv_params(1:bins(i),i) = cnorm{i}.params;
if bins(i)<m, sN.inv_params(bins(i)+1,i) = NaN; end
sN.inv_params(end,i) = bins(i);
end
end
function D = redo_hist_norm(D,cnorm,sN)
dim = size(D,2);
% first - undo the new way
for i=1:dim,
bins = length(cnorm{i}.params);
D(:,i) = round(D(:,i)*(bins-1)+1);
inds = find(~isnan(D(:,i)) & ~isinf(D(:,i)));
D(inds,i) = cnorm{i}.params(D(inds,i));
end
% then - redo the old way
n_bins = sN.inv_params(size(sN.inv_params,1),:);
for j = 1:dim,
for i = 1:size(D, 1)
if ~isnan(D(i, j)),
[d ind] = min(abs(D(i, j) - sN.inv_params(1:n_bins(j), j)));
if (D(i, j) - sN.inv_params(ind, j)) > 0 & ind < n_bins(j),
D(i, j) = ind + 1;
else
D(i, j) = ind;
end
end
end
end
D = D * sparse(diag(1 ./ n_bins));
|
github
|
martinarielhartmann/mirtooloct-master
|
som_dendrogram.m
|
.m
|
mirtooloct-master/somtoolbox/som_dendrogram.m
| 9,039 |
utf_8
|
43f32770e95d19d4a12855bd8bfb91f3
|
function [h,Coord,Color,height] = som_dendrogram(Z,varargin)
%SOM_DENDROGRAM Visualize a dendrogram.
%
% [h,Coord,Color,height] = som_dendrogram(Z, [[argID,] value, ...])
%
% Z = som_linkage(sM);
% som_dendrogram(Z);
% som_dendrogram(Z,sM);
% som_dendrogram(Z,'coord',co);
%
% Input and output arguments ([]'s are optional):
% h (vector) handle to the arc lines
% Z (matrix) size n-1 x 1, the hierarchical cluster matrix
% returned by functions like LINKAGE and SOM_LINKAGE
% n is the number of original data samples.
% [argID, (string) See below. The values which are unambiguous can
% value] (varies) be given without the preceeding argID.
% Coord (matrix) size 2*n-1 x {1,2}, the coordinates of the
% original data samples and cluster nodes used
% in the visualization
% Color (matrix) size 2*n-1 x 3, the colors of ...
% height (vector) size 2*n-1 x 1, the heights of ...
%
% Here are the valid argument IDs and corresponding values. The values
% which are unambiguous (marked with '*') can be given without the
% preceeding argID.
% 'data' *(struct) map or data struct: many other optional
% arguments require this
% (matrix) data matrix
% 'coord' (matrix) size n x 1 or n x 2, the coordinates of
% the original data samples either in 1D or 2D
% (matrix) size 2*n-1 x {1,2}, the coordinates of both
% original data samples and each cluster
% *(string) 'SOM', 'pca#', 'sammon#', or 'cca#': the coordinates
% are calculated using the given data and the
% required projection algorithm. The '#' at the
% end of projection algorithms refers to the
% desired output dimension and can be either 1 or 2
% (2 by default). In case of 'SOM', the unit
% coordinates (given by SOM_VIS_COORDS) are used.
% 'color' (matrix) size n x 3, the color of the original data samples
% (matrix) size 2*n-1 x 3, the colors of both original
% data samples and each cluster
% (string) color specification, e.g. 'r.', used for each node
% 'height' (vector) size n-1 x 1, the heights used for each cluster
% (vector) size 2*n-1 x 1, the heights used for both original
% data samples and each cluster
% *(string) 'order', the order of combination determines height
% 'depth', the depth at which the combination
% happens determines height
% 'linecolor' (string) color specification for the arc color, 'k' by default
% (vector) size 1 x 3
%
% See also SOM_LINKAGE, DENDROGRAM.
% Copyright (c) 2000 by Juha Vesanto
% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 160600
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% read the arguments
% Z
nd = size(Z,1)+1;
nc = size(Z,1);
% varargin
Coordtype = 'natural'; Coord = []; codim = 1;
Colortype = 'none'; Color = [];
height = [zeros(nd,1); Z(:,3)];
M = [];
linecol = 'k';
i=1;
while i<=length(varargin),
argok = 1;
if ischar(varargin{i}),
switch varargin{i},
case 'data', i = i + 1; M = varargin{i};
case 'coord',
i=i+1;
if isnumeric(varargin{i}), Coord = varargin{i}; Coordtype = 'given';
else
if strcmp(varargin{i},'SOM'), Coordtype = 'SOM';
else Coordtype = 'projection'; Coord = varargin{i};
end
end
case 'color',
i=i+1;
if isempty(varargin{i}), Colortype = 'none';
elseif ischar(varargin{i}), Colortype = 'colorspec'; Color = varargin{i};
else Colortype = 'given'; Color = varargin{i};
end
case 'height', i=i+1; height = varargin{i};
case 'linecolor', i=i+1; linecol = varargin{i};
case 'SOM',
Coordtype = 'SOM';
case {'pca','pca1','pca2','sammon','sammon1','sammon2','cca','cca1','cca2'},
Coordtype = 'projection'; Coord = varargin{i};
case {'order','depth'}, height = varargin{i};
end
elseif isstruct(varargin{i}), M = varargin{i};
else
argok = 0;
end
if ~argok,
disp(['(som_dendrogram) Ignoring invalid argument #' num2str(i+1)]);
end
i = i+1;
end
switch Coordtype,
case 'SOM',
if isempty(M) | ~any(strcmp(M.type,{'som_map','som_topol'})) ,
error('Cannot determine SOM coordinates without a SOM.');
end
if strcmp(M.type,'som_map'), M = M.topol; end
case 'projection',
if isempty(M), error('Cannot do projection without the data.'); end
if isstruct(M),
if strcmp(M.type,'som_data'), M = M.data;
elseif strcmp(M.type,'som_map'), M = M.codebook;
end
end
if size(M,1) ~= nd,
error('Given data must be equal in length to the number of original data samples.')
end
case 'given',
if size(Coord,1) ~= nd & size(Coord,1) ~= nd+nc,
error('Size of given coordinate matrix does not match the cluster hierarchy.');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% initialization
% Coordinates
switch Coordtype,
case 'natural', o = leavesorder(Z)'; [dummy,Coord] = sort(o); codim = 1;
case 'SOM', Coord = som_vis_coords(M.lattice,M.msize); codim = 2;
case 'projection',
switch Coord,
case {'pca','pca2'}, Coord = pcaproj(M,2); codim = 2;
case 'pca1', Coord = pcaproj(M,1); codim = 1;
case {'cca','cca2'}, Coord = cca(M,2,20); codim = 2;
case 'cca1', Coord = cca(M,1,20); codim = 1;
case {'sammon','sammon2'}, Coord = sammon(M,2,50); codim = 2;
case 'sammon1', Coord = sammon(M,1,50); codim = 1;
end
case 'given', codim = min(size(Coord,2),2); % nill
end
if size(Coord,1) == nd,
Coord = [Coord; zeros(nc,size(Coord,2))];
for i=(nd+1):(nd+nc),
leaves = leafnodes(Z,i,nd);
if any(leaves), Coord(i,:) = mean(Coord(leaves,:),1); else Coord(i,:) = Inf; end
end
end
% Colors
switch Colortype,
case 'colorspec', % nill
case 'none', Color = '';
case 'given',
if size(Color,1) == nd,
Color = [Color; zeros(nc,3)];
for i=(nd+1):(nd+nc),
leaves = leafnodes(Z,i,nd);
if any(leaves), Color(i,:) = mean(Color(leaves,:),1);
else Color(i,:) = 0.8;
end
end
end
end
% height
if ischar(height),
switch height,
case 'order', height = [zeros(nd,1); [1:nc]'];
case 'depth', height = nodedepth(Z); height = max(height) - height;
end
else
if length(height)==nc, height = [zeros(nd,1); height]; end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% draw
% the arcs
lfrom = []; lto = [];
for i=1:nd+nc,
if i<=nd, ch = [];
elseif ~isfinite(Z(i-nd,3)), ch = [];
else ch = Z(i-nd,1:2)';
end
if any(ch),
lfrom = [lfrom; i*ones(length(ch),1)];
lto = [lto; ch];
end
end
% the coordinates of the arcs
if codim == 1,
Lx = [Coord(lfrom), Coord(lto), Coord(lto)];
Ly = [height(lfrom), height(lfrom), height(lto)];
Lz = [];
else
Lx = [Coord(lfrom,1), Coord(lto,1), Coord(lto,1)];
Ly = [Coord(lfrom,2), Coord(lto,2), Coord(lto,2)];
Lz = [height(lfrom), height(lfrom), height(lto)];
end
washold = ishold;
if ~washold, cla; end
% plot the lines
if isempty(Lz),
h = line(Lx',Ly','color',linecol);
else
h = line(Lx',Ly',Lz','color',linecol);
if ~washold, view(3); end
rotate3d on
end
% plot the nodes
hold on
switch Colortype,
case 'none', % nill
case 'colorspec',
if codim == 1, plot(Coord,height,Color);
else plot3(Coord(:,1), Coord(:,2), height, Color);
end
case 'given',
som_grid('rect',[nd+nc 1],'line','none','Coord',[Coord, height],...
'Markersize',10,'Markercolor',Color);
end
if ~washold, hold off, end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function depth = nodedepth(Z)
nd = size(Z,1)+1;
nc = size(Z,1);
depth = zeros(nd+nc,1);
ch = nc+nd-1;
while any(ch),
c = ch(1); ch = ch(2:end);
if c>nd & isfinite(Z(c-nd,3)),
chc = Z(c-nd,1:2);
depth(chc) = depth(c) + 1;
ch = [ch, chc];
end
end
return;
function inds = leafnodes(Z,i,nd)
inds = [];
ch = i;
while any(ch),
c = ch(1); ch = ch(2:end);
if c>nd & isfinite(Z(c-nd,3)), ch = [ch, Z(c-nd,1:2)]; end
if c<=nd, inds(end+1) = c; end
end
return;
function order = leavesorder(Z)
nd = size(Z,1)+1;
order = 2*nd-1;
nonleaves = 1;
while any(nonleaves),
j = nonleaves(1);
ch = Z(order(j)-nd,1:2);
if j==1, oleft = []; else oleft = order(1:(j-1)); end
if j==length(order), oright = []; else oright = order((j+1):length(order)); end
order = [oleft, ch, oright];
nonleaves = find(order>nd);
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_plotplane.m
|
.m
|
mirtooloct-master/somtoolbox/som_plotplane.m
| 8,877 |
utf_8
|
afada5a6c93a0270c32acbd532af0679
|
function h=som_plotplane(varargin)
%SOM_PLOTPLANE Visualize the map prototype vectors as line graphs
%
% h=som_plotplane(lattice, msize, data, [color], [scaling], [pos])
% h=som_plotplane(topol, data, [color], [scaling], [pos])
%
% som_plotplane('hexa',[5 5], rand(25,4), jet(25))
% som_plotplane(sM, sM.codebook)
%
% Input and output arguments ([]'s are optional)
% lattice (string) grid 'hexa' or 'rect'
% msize (vector) size 1x2, defines the grid size
% (matrix) size Mx2, defines explicit coordinates: in
% this case the first argument does not matter
% topol (struct) map or topology struct
% data (matrix) Mxd matrix, M=prod(msize)
% [color] (matrix) size Mx3, gives an individual color for each graph
% (string) ColorSpec gives the same color for each
% graph, default is 'k' (black)
% [scaling] (string) 'on' or 'off', default is 'on'
% [pos] (vector) 1x2 vector that determines translation.
% Default is no translation.
%
% h (vector) the object handles for the LINE objects
%
% If scaling is set on, the data will be linearly scaled in each
% unit so that min and max values span from lower to upper edge
% in each unit. If scaling is 'off', the proper scaling is left to
% the user: values in range [-.5,.5] will be plotted within the limits of the
% unit while values exceeding this range will be out of the unit.
% Axis are set as in SOM_CPLANE.
%
% For more help, try 'type som_plotplane' or check out online documentation.
% See also SOM_PLANE, SOM_PIEPLANE, SOM_BARPLANE
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_plotplane
%
% PURPOSE
%
% Visualizes the map prototype vectors as line graph
%
% SYNTAX
%
% h = som_plotplane(topol, data)
% h = som_plotplane(lattice, msize, data)
% h = som_plotplane(..., color)
% h = som_plotplane(..., color, scaling)
% h = som_plotplane(..., color, scaling, pos)
%
% DESCRIPTION
%
% Visualizes the map prototype vectors as line graph
%
% KNOWN BUGS
%
% It is not possible to specify explicit coordinates for map
% consistig of just one unit as then the msize is interpreted as
% map size.
%
% FEATURES
%
% - the colors are fixed: changing colormap in the figure (see
% COLORMAP) will not affect the coloring of the plots
%
% REQUIRED INPUT ARGUMENTS
%
% lattice The basic topology
%
% (string) 'hexa' or 'rect' positions the plots according to hexagonal or
% rectangular map lattice.
%
% msize The size of the map grid
%
% (vector) [n1 n2] vector defines the map size (height n1 units, width n2
% units, total M=n1 x n2 units). The units will be placed to their
% topological locations in order to form a uniform hexagonal or
% rectangular grid.
% (matrix) Mx2 matrix defines arbitary coordinates for the M units.
% In this case the argument 'lattice' has no effect.
%
% topol Topology of the map grid
%
% (struct) map or topology struct from which the topology is taken
%
% data The data to be visualized
%
% (matrix) Mxd matrix of data vectors.
%
% OPTIONAL INPUT ARGUMENTS
%
% If unspecified or given empty values ('' or []), default values
% will be used for optional input arguments.
%
% color The color of the plots
%
% (string) Matlab's ColorSpec (see help plot) string gives the same color
% for each line.
%
% (matrix) Mx3 matrix assigns an RGB color determined by the Nth row of
% the matrix to the Nth plot.
%
% (vector) 1x3 RGB vector gives the same color for each line.
%
% scaling The data scaling mode
%
% (string) 'on or 'off': if scaling is set on, the data will be
% linearly scaled in each unit so that min and max values span from
% lower to upper edge in each unit. If scaling is 'off', the proper
% scaling is left to the user: values in range [-.5,.5] will be plotted
% within the limits of the unit while values exceeding this
% range will be out of the unit.
%
% pos Position of the origin
%
% (vector) This is meant for drawing the plane in arbitary location in a
% figure. Note the operation: if this argument is given, the
% axis limits setting part in the routine is skipped and the limits
% setting will be left to be done by MATLAB's
% defaults. By default no translation is done.
%
% OUTPUT ARGUMENTS
%
% h (scalar) Handle to the created patch object
%
% OBJECT TAG
%
% Object property 'Tag' is set to 'planePlot'.
%
% EXAMPLES
%
% %%% Create the data and make a map
%
% data=rand(1000,20); map=som_make(data);
%
% %%% Create a 'gray' colormap that has 64 levels
%
% color_map=gray(64);
%
% %%% Draw plots using red color
%
% som_plotplane(map, map.codebook,'r');
%
% %%% Calculate hits on the map and calculate colors so that
% black = min. number of hits and white = max. number of hits
%
% hit=som_hits(map,data); color=som_normcolor(hit(:),color_map);
%
% %%% Draw plots again. Now the gray level indicates the number of hits to
% each node
%
% som_plotplane(map, map.codebook, color);
%
% SEE ALSO
%
% som_cplane Visualize a 2D component plane, u-matrix or color plane
% som_barplane Visualize the map prototype vectors as bar diagrams.
% som_pieplane Visualize the map prototype vectors as pie charts
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 160799 juuso 151199 070600
%%% Init & Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[nargin, lattice, msize, data, color, scaling, pos] = vis_planeGetArgs(varargin{:});
error(nargchk(3, 5, nargin)); % check no. of input args is correct
s=0.8; % size of plot
if nargin < 6 | isempty(pos)
pos=NaN;
end
if nargin < 5 | isempty(scaling)
scaling='on';
elseif ~vis_valuetype(scaling,{'string'}) | ...
~any(strcmp(scaling,{'on','off'})),
error('Scaling should be string ''on'' or ''off''.');
end
l=size(data,2);
if ~isnumeric(msize) | ndims(msize) ~= 2 | size(msize,2)~=2,
error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
elseif size(msize,1) == 1,
xdim=msize(2);
ydim=msize(1);
N=xdim*ydim;
y=repmat(repmat([1:ydim]',xdim,1),1,l);
x=reshape(repmat([1:xdim],ydim*l,1),l,N)';
else
x=repmat(msize(:,1),1,l);y=repmat(msize(:,2),1,l);
N=size(msize,1);
lattice='rect';
if isnan(pos),
pos=[0 0];
end
end
switch lattice
case {'hexa', 'rect'}
;
otherwise
error(['Lattice' lattice ' not implemented!']);
end
if ~isnumeric(data) | size(data,1) ~= N
error('Data matrix is invalid or has wrong size!');
end
if nargin < 4 | isempty(color),
color='k';
elseif vis_valuetype(color, {'colorstyle',[N 3]}),
if ischar(color) & strcmp(color,'none'),
error('Colorstyle ''none'' not allowed in som_plotplane.');
end
elseif vis_valuetype(color,{'1x3rgb'})
;
elseif ~vis_valuetype(color,{'nx3rgb',[N 3]},'all'),
error('The color matrix has wrong size or contains invalid RGB values or colorstyle.');
end
[linesx, linesy]=vis_line(data,scaling);
%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Making the lattice.
% Command view([0 90]) shows the map in 2D properly oriented
switch lattice
case 'hexa'
t=find(rem(y(:,1),2)); % move even rows by .5
x(t,:)=x(t,:)-.5;
x=(x./s+linesx).*s+.5; y=(y./s+linesy).*s; % scale with s
case 'rect'
x=(x./s+linesx).*s; y=(y./s+linesy).*s; % scale with s
end
%% Draw the map! ...
h_=plot(x',y');
if size(color,1) == 1
set(h_,'Color',color);
else
for i=1:N,
set(h_(i,:),'Color',color(i,:));
end
end
if ~isnan(pos)
x=x+pos(1);y=y+pos(2); % move upper left corner
end % to pos(1),pos(2)
%% Set axes properties
ax=gca;
vis_PlaneAxisProperties(ax, lattice, msize, pos);
%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(h_,'Tag','planePlot'); % tag the lineobject
if nargout>0, h=h_; end % Set h only,
% if there really is output
%% Subfuntion %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [x,y]=vis_line(data, scaling)
s=size(data);
% normalization between [0,1] if scaling is on
if strcmp(scaling,'on')
mn=repmat(min(data,[],2),1,s(2));
mx=repmat(max(data,[],2),1,s(2));
y=-((data-mn)./(mx-mn))+.5;
else % -sign is beacuse we do axis ij
y=-data;
end
x=repmat(linspace(-.5, .5, size(data,2)), size(data,1),1);
|
github
|
martinarielhartmann/mirtooloct-master
|
som_seqtrain.m
|
.m
|
mirtooloct-master/somtoolbox/som_seqtrain.m
| 20,925 |
utf_8
|
10804404d112d52c0bf9538861655c63
|
function [sMap, sTrain] = som_seqtrain(sMap, D, varargin)
%SOM_SEQTRAIN Use sequential algorithm to train the Self-Organizing Map.
%
% [sM,sT] = som_seqtrain(sM, D, [[argID,] value, ...])
%
% sM = som_seqtrain(sM,D);
% sM = som_seqtrain(sM,sD,'alpha_type','power','tracking',3);
% [M,sT] = som_seqtrain(M,D,'ep','trainlen',10,'inv','hexa');
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct, the trained and updated map is returned
% (matrix) codebook matrix of a self-organizing map
% size munits x dim or msize(1) x ... x msize(k) x dim
% The trained map codebook is returned.
% D (struct) training data; data struct
% (matrix) training data, size dlen x dim
% [argID, (string) See below. The values which are unambiguous can
% value] (varies) be given without the preceeding argID.
%
% sT (struct) learning parameters used during the training
%
% Here are the valid argument IDs and corresponding values. The values which
% are unambiguous (marked with '*') can be given without the preceeding argID.
% 'mask' (vector) BMU search mask, size dim x 1
% 'msize' (vector) map size
% 'radius' (vector) neighborhood radiuses, length 1, 2 or trainlen
% 'radius_ini' (scalar) initial training radius
% 'radius_fin' (scalar) final training radius
% 'alpha' (vector) learning rates, length trainlen
% 'alpha_ini' (scalar) initial learning rate
% 'tracking' (scalar) tracking level, 0-3
% 'trainlen' (scalar) training length
% 'trainlen_type' *(string) is the given trainlen 'samples' or 'epochs'
% 'train' *(struct) train struct, parameters for training
% 'sTrain','som_train ' = 'train'
% 'alpha_type' *(string) learning rate function, 'inv', 'linear' or 'power'
% 'sample_order'*(string) order of samples: 'random' or 'ordered'
% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss',
% 'ep' or 'bubble'
% 'topol' *(struct) topology struct
% 'som_topol','sTopo l' = 'topol'
% 'lattice' *(string) map lattice, 'hexa' or 'rect'
% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid'
%
% For more help, try 'type som_seqtrain' or check out online documentation.
% See also SOM_MAKE, SOM_BATCHTRAIN, SOM_TRAIN_STRUCT.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_seqtrain
%
% PURPOSE
%
% Trains a Self-Organizing Map using the sequential algorithm.
%
% SYNTAX
%
% sM = som_seqtrain(sM,D);
% sM = som_seqtrain(sM,sD);
% sM = som_seqtrain(...,'argID',value,...);
% sM = som_seqtrain(...,value,...);
% [sM,sT] = som_seqtrain(M,D,...);
%
% DESCRIPTION
%
% Trains the given SOM (sM or M above) with the given training data
% (sD or D) using sequential SOM training algorithm. If no optional
% arguments (argID, value) are given, a default training is done, the
% parameters are obtained from SOM_TRAIN_STRUCT function. Using
% optional arguments the training parameters can be specified. Returns
% the trained and updated SOM and a train struct which contains
% information on the training.
%
% REFERENCES
%
% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
% Berlin, 1995, pp. 78-82.
% Kohonen, T., "Clustering, Taxonomy, and Topological Maps of
% Patterns", International Conference on Pattern Recognition
% (ICPR), 1982, pp. 114-128.
% Kohonen, T., "Self-Organized formation of topologically correct
% feature maps", Biological Cybernetics 43, 1982, pp. 59-69.
%
% REQUIRED INPUT ARGUMENTS
%
% sM The map to be trained.
% (struct) map struct
% (matrix) codebook matrix (field .data of map struct)
% Size is either [munits dim], in which case the map grid
% dimensions (msize) should be specified with optional arguments,
% or [msize(1) ... msize(k) dim] in which case the map
% grid dimensions are taken from the size of the matrix.
% Lattice, by default, is 'rect' and shape 'sheet'.
% D Training data.
% (struct) data struct
% (matrix) data matrix, size [dlen dim]
%
% OPTIONAL INPUT ARGUMENTS
%
% argID (string) Argument identifier string (see below).
% value (varies) Value for the argument (see below).
%
% The optional arguments can be given as 'argID',value -pairs. If an
% argument is given value multiple times, the last one is
% used. The valid IDs and corresponding values are listed below. The values
% which are unambiguous (marked with '*') can be given without the
% preceeding argID.
%
% 'mask' (vector) BMU search mask, size dim x 1. Default is
% the one in sM (field '.mask') or a vector of
% ones if only a codebook matrix was given.
% 'msize' (vector) map grid dimensions. Default is the one
% in sM (field sM.topol.msize) or
% 'si = size(sM); msize = si(1:end-1);'
% if only a codebook matrix was given.
% 'radius' (vector) neighborhood radius
% length = 1: radius_ini = radius
% length = 2: [radius_ini radius_fin] = radius
% length > 2: the vector given neighborhood
% radius for each step separately
% trainlen = length(radius)
% 'radius_ini' (scalar) initial training radius
% 'radius_fin' (scalar) final training radius
% 'alpha' (vector) learning rate
% length = 1: alpha_ini = alpha
% length > 1: the vector gives learning rate
% for each step separately
% trainlen is set to length(alpha)
% alpha_type is set to 'user defined'
% 'alpha_ini' (scalar) initial learning rate
% 'tracking' (scalar) tracking level: 0, 1 (default), 2 or 3
% 0 - estimate time
% 1 - track time and quantization error
% 2 - plot quantization error
% 3 - plot quantization error and two first
% components
% 'trainlen' (scalar) training length (see also 'tlen_type')
% 'trainlen_type' *(string) is the trainlen argument given in 'epochs'
% or in 'samples'. Default is 'epochs'.
% 'sample_order'*(string) is the sample order 'random' (which is the
% the default) or 'ordered' in which case
% samples are taken in the order in which they
% appear in the data set
% 'train' *(struct) train struct, parameters for training.
% Default parameters, unless specified,
% are acquired using SOM_TRAIN_STRUCT (this
% also applies for 'trainlen', 'alpha_type',
% 'alpha_ini', 'radius_ini' and 'radius_fin').
% 'sTrain', 'som_train' (struct) = 'train'
% 'neigh' *(string) The used neighborhood function. Default is
% the one in sM (field '.neigh') or 'gaussian'
% if only a codebook matrix was given. Other
% possible values is 'cutgauss', 'ep' and 'bubble'.
% 'topol' *(struct) topology of the map. Default is the one
% in sM (field '.topol').
% 'sTopol', 'som_topol' (struct) = 'topol'
% 'alpha_type'*(string) learning rate function, 'inv', 'linear' or 'power'
% 'lattice' *(string) map lattice. Default is the one in sM
% (field sM.topol.lattice) or 'rect'
% if only a codebook matrix was given.
% 'shape' *(string) map shape. Default is the one in sM
% (field sM.topol.shape) or 'sheet'
% if only a codebook matrix was given.
%
% OUTPUT ARGUMENTS
%
% sM the trained map
% (struct) if a map struct was given as input argument, a
% map struct is also returned. The current training
% is added to the training history (sM.trainhist).
% The 'neigh' and 'mask' fields of the map struct
% are updated to match those of the training.
% (matrix) if a matrix was given as input argument, a matrix
% is also returned with the same size as the input
% argument.
% sT (struct) train struct; information of the accomplished training
%
% EXAMPLES
%
% Simplest case:
% sM = som_seqtrain(sM,D);
% sM = som_seqtrain(sM,sD);
%
% To change the tracking level, 'tracking' argument is specified:
% sM = som_seqtrain(sM,D,'tracking',3);
%
% The change training parameters, the optional arguments 'train',
% 'neigh','mask','trainlen','radius','radius_ini', 'radius_fin',
% 'alpha', 'alpha_type' and 'alpha_ini' are used.
% sM = som_seqtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0);
%
% Another way to specify training parameters is to create a train struct:
% sTrain = som_train_struct(sM,'dlen',size(D,1),'algorithm','seq');
% sTrain = som_set(sTrain,'neigh','cutgauss');
% sM = som_seqtrain(sM,D,sTrain);
%
% By default the neighborhood radius goes linearly from radius_ini to
% radius_fin. If you want to change this, you can use the 'radius' argument
% to specify the neighborhood radius for each step separately:
% sM = som_seqtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]);
%
% By default the learning rate (alpha) goes from the alpha_ini to 0
% along the function defined by alpha_type. If you want to change this,
% you can use the 'alpha' argument to specify the learning rate
% for each step separately:
% alpha = 0.2*(1 - log([1:100]));
% sM = som_seqtrain(sM,D,'alpha',alpha);
%
% You don't necessarily have to use the map struct, but you can operate
% directly with codebook matrices. However, in this case you have to
% specify the topology of the map in the optional arguments. The
% following commads are identical (M is originally a 200 x dim sized matrix):
% M = som_seqtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl');
%
% M = som_seqtrain(M,D,'msize',[20 10],'hexa','cyl');
%
% sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl');
% M = som_seqtrain(M,D,sT);
%
% M = reshape(M,[20 10 dim]);
% M = som_seqtrain(M,D,'hexa','cyl');
%
% The som_seqtrain also returns a train struct with information on the
% accomplished training. This is the same one as is added to the end of the
% trainhist field of map struct, in case a map struct is given.
% [M,sTrain] = som_seqtrain(M,D,'msize',[20 10]);
%
% [sM,sTrain] = som_seqtrain(sM,D); % sM.trainhist{end}==sTrain
%
% SEE ALSO
%
% som_make Initialize and train a SOM using default parameters.
% som_batchtrain Train SOM with batch algorithm.
% som_train_struct Determine default training parameters.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 1.0beta juuso 220997
% Version 2.0beta juuso 101199
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check arguments
error(nargchk(2, Inf, nargin)); % check the number of input arguments
% map
struct_mode = isstruct(sMap);
if struct_mode,
sTopol = sMap.topol;
else
orig_size = size(sMap);
if ndims(sMap) > 2,
si = size(sMap); dim = si(end); msize = si(1:end-1);
M = reshape(sMap,[prod(msize) dim]);
else
msize = [orig_size(1) 1];
dim = orig_size(2);
end
sMap = som_map_struct(dim,'msize',msize);
sTopol = sMap.topol;
end
[munits dim] = size(sMap.codebook);
% data
if isstruct(D),
data_name = D.name;
D = D.data;
else
data_name = inputname(2);
end
D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data
[dlen ddim] = size(D); % check input dimension
if dim ~= ddim, error('Map and data input space dimensions disagree.'); end
% varargin
sTrain = som_set('som_train','algorithm','seq','neigh', ...
sMap.neigh,'mask',sMap.mask,'data_name',data_name);
radius = [];
alpha = [];
tracking = 1;
sample_order_type = 'random';
tlen_type = 'epochs';
i=1;
while i<=length(varargin),
argok = 1;
if ischar(varargin{i}),
switch varargin{i},
% argument IDs
case 'msize', i=i+1; sTopol.msize = varargin{i};
case 'lattice', i=i+1; sTopol.lattice = varargin{i};
case 'shape', i=i+1; sTopol.shape = varargin{i};
case 'mask', i=i+1; sTrain.mask = varargin{i};
case 'neigh', i=i+1; sTrain.neigh = varargin{i};
case 'trainlen', i=i+1; sTrain.trainlen = varargin{i};
case 'trainlen_type', i=i+1; tlen_type = varargin{i};
case 'tracking', i=i+1; tracking = varargin{i};
case 'sample_order', i=i+1; sample_order_type = varargin{i};
case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i};
case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i};
case 'radius',
i=i+1;
l = length(varargin{i});
if l==1,
sTrain.radius_ini = varargin{i};
else
sTrain.radius_ini = varargin{i}(1);
sTrain.radius_fin = varargin{i}(end);
if l>2, radius = varargin{i}; tlen_type = 'samples'; end
end
case 'alpha_type', i=i+1; sTrain.alpha_type = varargin{i};
case 'alpha_ini', i=i+1; sTrain.alpha_ini = varargin{i};
case 'alpha',
i=i+1;
sTrain.alpha_ini = varargin{i}(1);
if length(varargin{i})>1,
alpha = varargin{i}; tlen_type = 'samples';
sTrain.alpha_type = 'user defined';
end
case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i};
case {'topol','sTopol','som_topol'},
i=i+1;
sTopol = varargin{i};
if prod(sTopol.msize) ~= munits,
error('Given map grid size does not match the codebook size.');
end
% unambiguous values
case {'inv','linear','power'}, sTrain.alpha_type = varargin{i};
case {'hexa','rect'}, sTopol.lattice = varargin{i};
case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i};
case {'epochs','samples'}, tlen_type = varargin{i};
case {'random', 'ordered'}, sample_order_type = varargin{i};
otherwise argok=0;
end
elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
switch varargin{i}(1).type,
case 'som_topol',
sTopol = varargin{i};
if prod(sTopol.msize) ~= munits,
error('Given map grid size does not match the codebook size.');
end
case 'som_train', sTrain = varargin{i};
otherwise argok=0;
end
else
argok = 0;
end
if ~argok,
disp(['(som_seqtrain) Ignoring invalid argument #' num2str(i+2)]);
end
i = i+1;
end
% training length
if ~isempty(radius) | ~isempty(alpha),
lr = length(radius);
la = length(alpha);
if lr>2 | la>1,
tlen_type = 'samples';
if lr> 2 & la<=1, sTrain.trainlen = lr;
elseif lr<=2 & la> 1, sTrain.trainlen = la;
elseif lr==la, sTrain.trainlen = la;
else
error('Mismatch between radius and learning rate vector lengths.')
end
end
end
if strcmp(tlen_type,'samples'), sTrain.trainlen = sTrain.trainlen/dlen; end
% check topology
if struct_mode,
if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ...
~strcmp(sTopol.shape,sMap.topol.shape) | ...
any(sTopol.msize ~= sMap.topol.msize),
warning('Changing the original map topology.');
end
end
sMap.topol = sTopol;
% complement the training struct
sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% initialize
M = sMap.codebook;
mask = sTrain.mask;
trainlen = sTrain.trainlen*dlen;
% neighborhood radius
if length(radius)>2,
radius_type = 'user defined';
else
radius = [sTrain.radius_ini sTrain.radius_fin];
rini = radius(1);
rstep = (radius(end)-radius(1))/(trainlen-1);
radius_type = 'linear';
end
% learning rate
if length(alpha)>1,
sTrain.alpha_type ='user defined';
if length(alpha) ~= trainlen,
error('Trainlen and length of neighborhood radius vector do not match.')
end
if any(isnan(alpha)),
error('NaN is an illegal learning rate.')
end
else
if isempty(alpha), alpha = sTrain.alpha_ini; end
if strcmp(sTrain.alpha_type,'inv'),
% alpha(t) = a / (t+b), where a and b are chosen suitably
% below, they are chosen so that alpha_fin = alpha_ini/100
b = (trainlen - 1) / (100 - 1);
a = b * alpha;
end
end
% initialize random number generator
rand('state',sum(100*clock));
% distance between map units in the output space
% Since in the case of gaussian and ep neighborhood functions, the
% equations utilize squares of the unit distances and in bubble case
% it doesn't matter which is used, the unitdistances and neighborhood
% radiuses are squared.
Ud = som_unit_dists(sTopol).^2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Action
update_step = 100;
mu_x_1 = ones(munits,1);
samples = ones(update_step,1);
r = samples;
alfa = samples;
qe = 0;
start = clock;
if tracking > 0, % initialize tracking
track_table = zeros(update_step,1);
qe = zeros(floor(trainlen/update_step),1);
end
for t = 1:trainlen,
% Every update_step, new values for sample indeces, neighborhood
% radius and learning rate are calculated. This could be done
% every step, but this way it is more efficient. Or this could
% be done all at once outside the loop, but it would require much
% more memory.
ind = rem(t,update_step); if ind==0, ind = update_step; end
if ind==1,
steps = [t:min(trainlen,t+update_step-1)];
% sample order
switch sample_order_type,
case 'ordered', samples = rem(steps,dlen)+1;
case 'random', samples = ceil(dlen*rand(update_step,1)+eps);
end
% neighborhood radius
switch radius_type,
case 'linear', r = rini+(steps-1)*rstep;
case 'user defined', r = radius(steps);
end
r=r.^2; % squared radius (see notes about Ud above)
r(r==0) = eps; % zero radius might cause div-by-zero error
% learning rate
switch sTrain.alpha_type,
case 'linear', alfa = (1-steps/trainlen)*alpha;
case 'inv', alfa = a ./ (b + steps-1);
case 'power', alfa = alpha * (0.005/alpha).^((steps-1)/trainlen);
case 'user defined', alfa = alpha(steps);
end
end
% find BMU
x = D(samples(ind),:); % pick one sample vector
known = ~isnan(x); % its known components
Dx = M(:,known) - x(mu_x_1,known); % each map unit minus the vector
[qerr bmu] = min((Dx.^2)*mask(known)); % minimum distance(^2) and the BMU
% tracking
if tracking>0,
track_table(ind) = sqrt(qerr);
if ind==update_step,
n = ceil(t/update_step);
qe(n) = mean(track_table);
trackplot(M,D,tracking,start,n,qe);
end
end
% neighborhood & learning rate
% notice that the elements Ud and radius have been squared!
% (see notes about Ud above)
switch sTrain.neigh,
case 'bubble', h = (Ud(:,bmu)<=r(ind));
case 'gaussian', h = exp(-Ud(:,bmu)/(2*r(ind)));
case 'cutgauss', h = exp(-Ud(:,bmu)/(2*r(ind))) .* (Ud(:,bmu)<=r(ind));
case 'ep', h = (1-Ud(:,bmu)/r(ind)) .* (Ud(:,bmu)<=r(ind));
end
h = h*alfa(ind);
% update M
M(:,known) = M(:,known) - h(:,ones(sum(known),1)).*Dx;
end; % for t = 1:trainlen
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Build / clean up the return arguments
if tracking, fprintf(1,'\n'); end
% update structures
sTrain = som_set(sTrain,'time',datestr(now,0));
if struct_mode,
sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh);
tl = length(sMap.trainhist);
sMap.trainhist(tl+1) = sTrain;
else
sMap = reshape(M,orig_size);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
%%%%%%%%
function [] = trackplot(M,D,tracking,start,n,qe)
l = length(qe);
elap_t = etime(clock,start);
tot_t = elap_t*l/n;
fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
switch tracking
case 1,
case 2,
plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
title('Quantization errors for latest samples')
drawnow
otherwise,
subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
title('Quantization error for latest samples');
subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b.');
title('First two components of map units (o) and data vectors (+)');
drawnow
end
% end of trackplot
|
github
|
martinarielhartmann/mirtooloct-master
|
som_kmeanscolor2.m
|
.m
|
mirtooloct-master/somtoolbox/som_kmeanscolor2.m
| 5,856 |
utf_8
|
b167d8ae2e8d9e2099002b0b6c376772
|
function [color,centroids]=som_kmeanscolor2(mode,sM,C,initRGB,contrast,R)
% SOM_KMEANSCOLOR2 Color codes a SOM according to averaged or best K-means clustering
%
% color = som_kmeanscolor2('average',sM, C, [initRGB], [contrast],[R])
%
% color=som_kmeanscolor2('average',sM,[2 4 8 16],som_colorcode(sM,'rgb1'),'enhanced');
% [color,centroid]=som_kmeanscolor2('best',sM,15,[],'flat',R);
%
% Input and output arguments ([]'s are optional):
%
% mode (string) 'average' or 'best', defalut: 'average'
% sM (struct) a map struct
% C (vector) number of clusters
% [initRGB] (string, matrix) a color code string accepted by SOM_COLORCODE
% or an Mx3 matrix of RGB triples, where M is the number
% of map units. Default: SOM_COLORCODEs default
% [contrast] (string) 'flat', 'enhanced' color contrast mode, default:
% 'enhanced'.
% [R] (scalar) number of K-means trials, default: 30.
% color (matrix) Mx3xC of RGB triples
% centroid (array of matrices) centroid{i} includes codebook for the best
% k-means for C(i) clusters, i.e. the cluster centroids corresponding to
% the color code color(:,:,i).
%
% The function gives a set of color codes for the SOM according to K-means
% clustering. It has two operation modes:
%
% 'average': The idea of coloring is that the color of the units belonging to the same
% cluster is the mean of the original RGB values (see SOM_COLORCODE) of the map units
% belonging to the cluster (see SOM_CLUSTERCOLOR). The K-means clustering is made,
% by default, 30 times and the resulting color codes are averaged for
% each specified number of clusters C(i), i=1,...,k. In a way, the resulting averaged color
% codes reflect the stability of the K-means clustering made on the map units.
%
% 'best': runs the k-means R times for C(i), i=1,...,n clusters as in previous mode,
% but instead of averaging all the R color codes, it picks the one that corresponds to the
% best k-means clustering for each C(i). The 'best' is the one with the lowest
% quantization error. The result may differ from run to run.
%
% EXAMPLE
%
% load iris; % or any other map struct sM
% color=som_kmeanscolor2('average',sM,[2:6]);
% som_show(sM,'umat','all','color',color);
%
% See also SOM_KMEANS, SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR
% Contributed to SOM Toolbox 2.0, 2001 February by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
%%% Check number of inputs
error(nargchk(3, 6, nargin)); % check no. of input args
%%% Check input args & set defaults
if ~vis_valuetype(mode,{'string'}),
error('Mode must be a string.');
end
switch lower(mode),
case{'average','best'}
;
otherwise
error('Mode must be string ''average'' or ''best''.');
end
if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
[tmp,lattice,msize]=vis_planeGetArgs(sM);
munits=prod(msize);
if length(msize)>2
error('Does not work with 3D maps.')
end
else
error('Map struct required for the second input argument!');
end
if ~vis_valuetype(C,{'1xn','nx1'}),
error('Vector value expected for cluster number.');
end
% Round C and check
C=round(C(:)');
if any(C<2),
error('Cluster number must be 2 or more.');
end
% check initial color coding
if nargin<4 | isempty(initRGB)
initRGB=som_colorcode(sM);
end
% check contrast checking
if nargin<5 | isempty(contrast),
contrast='enhanced';
end
if ~ischar(contrast),
error('String input expected for input arg. ''contrast''.');
else
switch lower(contrast)
case {'flat','enhanced'}
;
otherwise
error(['''flat'' or ''enhanced'' expected for '...
'input argument ''contrast''.']);
end
end
if ischar(initRGB),
try
initRGB=som_colorcode(sM,initRGB);
catch
error(['Color code ' initRGB ...
'was not recognized by SOM_COLORCODE.']);
end
elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
;
else
error(['The initial color code must be a string '...
'or an Mx3 matrix of RGB triples.']);
end
if nargin<6|isempty(R),
R=30;
end
if ~vis_valuetype(R,{'1x1'}),
error('''R'' must be scalar.');
end
%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Wait...');
index=0; hit_=zeros(munits,munits);
switch mode,
%% Averaged k-means coloring
case 'average'
for k=C,
disp(['Running K-means for ' num2str(k) ' clusters...']);
color_=zeros(munits,3);
colord_=color_;
% Average R k-means colorings for C clusters
for j=1:R,
[dummy,c]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off
color_=color_+som_clustercolor(sM,c,initRGB);
end
index=index+1;
color(:,:,index)=color_./R;
end
%% coloring for 'best' k-means coloring
case 'best'
for k=C,
disp(['Running K-means for ' num2str(k) ' clusters...']);
c=[];err=Inf; div=[];
%% look for the best k-means among R trials
for i=1:R,
[c_,div_,err_(i)]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off
if err_(i)<err,
err=err_(i); c=c_; div=div_;
end
end
% record the 'best' k-means for C clusters
index=index+1;
color(:,:,index)=som_clustercolor(sM,div,initRGB);
centroid{index}=c;
end
end
%%% Build output
switch contrast
case 'flat'
;
case 'enhanced'
warning off;
ncolor=maxnorm(color);
ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
color=ncolor;
warning on;
end
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function X=maxnorm(x)
% normalize columns of x between [0,1]
x=x-repmat(min(x),[size(x,1) 1 1]);
X=x./repmat(max(x),[size(x,1) 1 1]);
|
github
|
martinarielhartmann/mirtooloct-master
|
som_stats_plot.m
|
.m
|
mirtooloct-master/somtoolbox/som_stats_plot.m
| 4,911 |
utf_8
|
04eb5e46e18587318f497985e2ef3672
|
function som_stats_plot(csS,plottype,varargin)
%SOM_STATS_PLOT Plots of data set statistics.
%
% som_stats_plot(csS, plottype, [argID, value, ...])
%
% som_stats_plot(csS,'stats')
% som_stats_plot(csS,'stats','p','vert','color','r')
%
% Input and output arguments ([]'s are optional):
% csS (cell array) of statistics structs
% (struct) a statistics struct
% plottype (string) some of the following
% 'hist' histogram
% 'box' min, max, mean, and std shown as a boxplot
% 'stats' both histogram (with black) and the boxplot
% [argID, (string) See below. The values which are unambiguous can
% value] (varies) be given without the preceeding argID.
%
% Here are the valid argument IDs and corresponding values. The values which
% are unambiguous (marked with '*') can be given without the preceeding argID.
% 'counts' *(string) 'c' (for counts, the default) or 'p' (for percentages)
% 'color' (vector) size 1 x 3, color to be used
% (string) a color string
% 'title' (string) 'on' (default) or 'off'
% 'orientation' *(string) 'horiz' or 'vert' (default): orientation for the
% bin values (horizontally or vertically)
%
% See also SOM_STATS, SOM_STATS_TABLE, SOM_TABLE_PRINT, SOM_STATS_REPORT.
% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 311201
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% arguments
% statistics
if isstruct(csS), csS = {csS}; end
% default values
useprob = 0;
color = [0 0 1];
showtitle = 1;
horiz = 0;
% varargin
i=1;
while i<=length(varargin),
argok = 1;
if ischar(varargin{i}),
switch varargin{i},
% argument IDs
case 'counts', i=i+1; useprob = strcmp(varargin{i}(1),'p');
case 'color', i=i+1; color = varargin{i};
case 'title', i=i+1; showtitle = strcmp(varargin{i},'on');
case 'orientation', i=i+1; horiz = strcmp(varargin{i},'horiz');
% unambiguous values
case {'horiz','vert'}, horiz = strcmp(varargin{i},'horiz');
case {'c','p'}, useprob = strcmp(varargin{i}(1),'p');
otherwise argok=0;
end
elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
argok = 0;
else
argok = 0;
end
if ~argok,
disp(['(som_stats_plot) Ignoring invalid argument #' num2str(i+2)]);
end
i = i+1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% action
ss = ceil(sqrt(length(csS))); ss = [ss, ceil(length(csS)/ss)];
for j = 1:length(csS),
sS = csS{j};
subplot(ss(1),ss(2),j);
switch plottype,
case 'stats',
cla, hold on
Counts = sS.hist.counts;
if useprob, for i=1:size(Counts,2), Counts(:,i) = Counts(:,i)/sum(Counts(:,i)); end, end
hist_plot(sS.hist.bins,sS.hist.binlabels,Counts,color);
box_plot(sS.min,sS.max,sS.mean,sS.std,[0 0 0]);
case 'hist',
cla, hold on
Counts = sS.hist.counts;
if useprob, for i=1:size(Counts,2), Counts(:,i) = Counts(:,i)/sum(Counts(:,i)); end, end
hist_plot(sS.hist.bins,sS.hist.binlabels,Counts,color);
case 'box',
cla
box_plot(sS.min,sS.max,sS.mean,sS.std,color);
end
if showtitle, title(sprintf('%s (valid: %d/%d)',sS.name,sS.nvalid,sS.ntotal)); end
if ~horiz, view(90,-90); end
a = axis; a(1) = sS.min; a(2) = sS.max; axis(a);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% subfunctions
function hist_plot(bins,binlabels,Counts,color)
if nargin<4, color = jet(size(Counts,2)); end
h = bar(bins,Counts);
for j=1:length(h), set(h(j),'facecolor',color(j,:),'edgecolor','none'); end
a = axis; a(3:4) = [0 max(Counts(:))]; axis(a);
set(gca,'XTick',bins,'XTickLabel',binlabels);
return;
function vstr = numtostring(v,d)
nearzero = (abs(v)/(max(v)-min(v)) < 10.^-d);
i1 = find(v > 0 & nearzero);
i2 = find(v < 0 & nearzero);
vstr = strrep(cellstr(num2str(v,d)),' ','');
vstr(i1) = {'0.0'};
vstr(i2) = {'-0.0'};
return;
function box_plot(mi,ma,me,st,Color)
if nargin < 5, Color = jet(length(mi)); end
a = axis;
y = linspace(a(3),a(4),length(mi)+2); y = y(2:end);
d = (y(2)-y(1))/20;
for i=1:length(mi),
h1 = line([mi(i) ma(i)],[y(i) y(i)]);
h2 = line([mi(i) mi(i) NaN ma(i) ma(i)],[y(i)-d y(i)+d NaN y(i)-d y(i)+d]);
h3 = line([me(i)-st(i) me(i)+st(i)],[y(i) y(i)]);
h4 = line([me(i) me(i)],[y(i)-2*d y(i)+2*d]);
set([h1 h2 h3 h4],'color',Color(i,:));
set([h1 h2],'linewidth',1);
set([h3 h4],'linewidth',3);
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
sompak_train.m
|
.m
|
mirtooloct-master/somtoolbox/sompak_train.m
| 6,477 |
utf_8
|
c1f8430b537283c08dd052265ce60c99
|
function sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius)
%SOMPAK_TRAIN Call SOM_PAK training program from Matlab.
%
% sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sMap (struct) map struct
% (string) filename
% [ft] (string) 'pak' or 'box'. Argument must be defined, if input file
% is used.
% [cout] (string) filename for output SOM, if argument is not defined
% (i.e. argument is '[]') temporary file '__abcdef' is
% used in operations and *it_is_removed* after
% operations!!!
% [ct] (string) 'pak' or 'box'. Argument must be defined, if output
% file is used.
% din (struct) data struct to be used in teaching
% (matrix) data matrix
% (string) filename
% If argument is not a filename or file is .mat -file,
% temporary file '__din' is used in operations
% and *it_is_removed* after operations!!!
% [dt] (string) 'pak' or 'box'. Argument must be defined, if input file
% is used.
% rlen (scalar) running length of teaching
% alpha (float) initial alpha value
% radius (float) initial radius of neighborhood
%
% RETURNS
% sMap (struct) map struct
%
% Calls SOM_PAK training program (vsom) from Matlab. Notice that to
% use this function, the SOM_PAK programs must be in your search path,
% or the variable 'SOM_PAKDIR' which is a string containing the
% program path, must be defined in the workspace. SOM_PAK programs can
% be found from: http://www.cis.hut.fi/research/som_lvq_pak.shtml
%
% See also SOMPAK_TRAIN, SOMPAK_SAMMON, SOMPAK_TRAIN_GUI,
% SOMPAK_GUI, SOM_SEQTRAIN.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
nargchk(9,9,nargin);
NO_FILE=0;
DIN_FILE = 0;
if ~isstruct(sMap) & ~isstr(sMap)
error('Argument ''sMap'' must be a struct or string.');
end
if isstr(sMap)
if isempty(ft)
error('Argument ''ft'' must be defined.');
end
if strcmp(ft,'pak')
sMap=som_read_cod(sMap);
elseif strcmp(ft,'box')
new_var=diff_varname;
varnames=evalin('base','who');
loadname=eval(cat(2,'who(''-file'',''',sMap,''')'));
if any(strcmp(loadname{1},evalin('base','who')))
assignin('base',new_var,evalin('base',loadname{1}));
evalin('base',cat(2,'load(''',sMap,''');'));
new_var2=diff_varname;
assignin('base',new_var2,evalin('base',loadname{1}));
assignin('base',loadname{1},evalin('base',new_var));
evalin('base',cat(2,'clear ',new_var));
sMap=evalin('base',new_var2);
evalin('base',cat(2,'clear ',new_var2));
else
evalin('base',cat(2,'load(''',sMap,''');'));
sMap=evalin('base',loadname{1});
evalin('base',cat(2,'clear ',loadname{1}));
end
end
end
if ~isstr(cout) & isempty(cout)
cout = '__abcdef';
NO_FILE = 1;
elseif ~isstr(cout) | (isstr(cout) & isempty(cout))
error('Argument ''cout'' must be a string or ''[]''.');
end
if ~NO_FILE & (isempty(ct) | ~(~isempty(ct) & ...
(strcmp(ct,'pak') | strcmp(ct,'box'))))
error('Argument ''ct'' must be string ''pak'' or ''box''.');
end
map_name=sMap.name;
som_write_cod(sMap,cout);
if ~isempty(din)
som_write_data(din, '__din');
DIN_FILE = 1;
din = '__din';
else
DIN_FILE=0;
end
if ~DIN_FILE
if isempty(dt) | ~isstr(dt) | ~(strcmp(dt,'box') | strcmp(dt,'pak'))
error('Argument ''dt'' must be string ''pak'' or ''box''.');
end
if strcmp(dt,'box');
DIN_FILE = 1;
din_var=diff_varname;
varnames=evalin('base','who');
loadname=eval(cat(2,'who(''-file'',''',din,''')'));
if any(strcmp(loadname{1},evalin('base','who')))
assignin('base',din_var,evalin('base',loadname{1}));
evalin('base',cat(2,'load(''',din,''');'));
din_var2=diff_varname;
assignin('base',new_var2,evalin('base',loadname{1}));
assignin('base',loadname{1},evalin('base',din_var));
evalin('base',cat(2,'clear ',din_var));
din=evalin('base',din_var2);
else
evalin('base',cat(2,'load(''',din,''')'));
din=evalin('base',loadname{1});
evalin('base',cat(2,'clear ',loadname{1}));
end
som_write_data(din,'__din');
din = '__din';
end
end
if ~is_positive_integer(rlen)
error('Argument ''rlen'' must be positive integer.');
end
if ~(isreal(alpha) & all(size(alpha)==1))
error('Argument ''alpha'' must be a floating point number.');
end
if ~(isreal(radius) & all(size(radius)==1) & radius > 0)
error('Argument ''radius'' must be a positive floating point number.');
end
if any(strcmp('SOM_PAKDIR',evalin('base','who')))
traincommand=cat(2,evalin('base','SOM_PAKDIR'),'vsom ');
else
traincommand='vsom ';
end
str=cat(2,traincommand,sprintf('-cin %s -din %s -cout %s ',cout,din,cout),...
sprintf(' -rlen %d -alpha %f -radius %f',rlen,alpha,radius));
if isunix
unix(str);
else
dos(str);
end
sMap=som_read_cod(cout);
sMap.name=map_name;
if ~NO_FILE
if isunix
unix(cat(2,'/bin/rm ',cout));
else
dos(cat(2,'del ',cout));
end
if isempty(ct) | ~isstr(ct) | ~(strcmp(ct,'pak') | strcmp(ct,'box'))
error('Argument ''ct'' must be string ''pak'' or ''box''.');
elseif strcmp(ct,'box');
eval(cat(2,'save ',cout,' sMap'));
disp(cat(2,'Output written to the file ',sprintf('''%s.mat''.',cout)));
else
som_write_cod(sMap,cout);
end
else
if isunix
unix('/bin/rm __abcdef');
else
dos('del __abcdef');
end
end
if DIN_FILE
if isunix
unix('/bin/rm __din');
else
dos('del __abcdef');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool = is_positive_integer(x)
bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0;
if ~isempty(bool)
if bool & x~=round(x)
bool = 0;
end
else
bool = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function str = diff_varname();
array=evalin('base','who');
if isempty(array)
str='a';
return;
end
for i=1:length(array)
lens(i)=length(array{i});
end
ind=max(lens);
str(1:ind+1)='a';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_kmeanscolor.m
|
.m
|
mirtooloct-master/somtoolbox/som_kmeanscolor.m
| 4,375 |
utf_8
|
b90f1bc73191e2f5c68e2e8afe11269a
|
function [color,best,kmeans]=som_kmeanscolor(sM,C,initRGB,contrast)
% SOM_KMEANSCOLOR Map unit color code according to K-means clustering
%
% [color, best, kmeans] = som_kmeanscolor(sM, C, [initRGB],[contrast])
%
% color = som_kmeanscolor(sM,15,som_colorcode(sM,'rgb1'),'enhance');
% [color,best] = som_kmeanscolor(sM,15,[],'normal');
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct
% C (scalar) maximum number of clusters
% initRGB (string, matrix) color code string accepted by SOM_COLORCODE
% or an Mx3 matrix of RGB triples, where M is the number
% of map units. Default: SOM_COLORCODEs default
% contrast (string) 'flat', 'enhanced' color contrast mode, default:
% 'enhanced'
%
% color (matrix) MxCx3 of RGB triples
% best (scalar) index for "best" clustering according to
% Davies-Boulding index; color(:,:,best) includes the
% corresponding color code.
% kmeans (cell) output of KMEANS_CLUSTERS in a cell array.
%
% The function gives a set of color codings according to K-means
% clustering. For clustering, it uses function KMEANS_CLUSTERS for map units,
% and it calculates color codings for 1,2,...,C clusters.
% The idea of coloring is that the color of a cluster is the mean of the
% original colors (RGB values) of the map units belonging to that cluster,
% see SOM_CLUSTERCOLOR. The original colors are defined by SOM_COLORCODE
% by default. Input 'contrast' simply specifies whether or not
% to linearly redistribute R,G, and B values so that minimum is 0 and
% maximum 1 ('enahanced') or to use directly the output of
% SOM_CLUSTERCOLOR ('flat'). KMEANS_CLUSTERS uses certain heuristics to
% select the best of 5 trials for each number of clusters. Evaluating the
% clustering multiple times may take some time.
%
% EXAMPLE
%
% load iris; % or any other map struct sM
% [color,b]=som_kmeanscolor(sM,10);
% som_show(sM,'color',color,'color',{color(:,:,b),'"Best clustering"');
%
% See also SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, KMEANS_CLUSTERS
% Contributed to SOM Toolbox 2.0, April 1st, 2000 by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
% corrected help text 11032005 johan
%%% Check number of inputs
error(nargchk(2, 4, nargin)); % check no. of input args
%%% Check input args & set defaults
if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
[tmp,lattice,msize]=vis_planeGetArgs(sM);
munits=prod(msize);
if length(msize)>2
error('Does not work with 3D maps.')
end
else
error('Map struct requires for first input argument!');
end
if ~vis_valuetype(C,{'1x1'}),
error('Scalar value expect for maximum number of clusters.');
end
% check initial color coding
if nargin<3 | isempty(initRGB)
initRGB=som_colorcode(sM);
end
% check contrast checking
if nargin<4 | isempty(contrast),
contrast='enhanced';
end
if ~ischar(contrast),
error('String input expected for input arg. ''contrast''.');
else
switch lower(contrast)
case {'flat','enhanced'}
;
otherwise
error(['''flat'' or ''enhanced'' expected for '...
'input argument ''contrast''.']);
end
end
if ischar(initRGB),
try
initRGB=som_colorcode(sM,initRGB);
catch
error(['Color code ' initRGB ...
'was not recognized by SOM_COLORCODE.']);
end
elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
;
else
error(['The initial color code must be a string '...
'or an Mx3 matrix of RGB triples.']);
end
%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Wait...');
[c,p,err,ind]=kmeans_clusters(sM,C,5,0); % use 5 trials, verbose off
% Store outputs to kmeans
kmeans{1}=c;
kmeans{2}=p;
kmeans{3}=err;
kmeans{4}=ind;
%%% Build output
color=som_clustercolor(sM,cat(2,p{:}),initRGB);
[tmp,best]=min(ind);
switch contrast
case 'flat'
;
case 'enhanced'
warning off;
ncolor=maxnorm(color);
ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
color=ncolor;
warning on;
end
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function X=maxnorm(x)
% normalize columns of x between [0,1]
x=x-repmat(min(x),[size(x,1) 1 1]);
X=x./repmat(max(x),[size(x,1) 1 1]);
|
github
|
martinarielhartmann/mirtooloct-master
|
vis_valuetype.m
|
.m
|
mirtooloct-master/somtoolbox/vis_valuetype.m
| 7,502 |
utf_8
|
cb7a373fcda9120d69231b2748371740
|
function flag=vis_valuetype(value, valid, str);
% VIS_VALUETYPE Used for type checks in SOM Toolbox visualization routines
%
% flag = vis_valuetype(value, valid, str)
%
% Input and output arguments:
% value (varies) variable to be checked
% valid (cell array) size 1xN, cells are strings or vectors (see below)
% str (string) 'all' or 'any' (default), determines whether
% all or just any of the types listed in argument 'valid'
% should be true for 'value'
%
% flag (scalar) 1 or 0 (true or false)
%
% This is an internal function of SOM Toolbox visualization. It makes
% various type checks. For example:
%
% % Return 1 if X is a numeric scalar otherwise 0:
% f=vis_valuetype(X,{'1x1'});
%
% % Return 1 if X is a ColorSpec, that is, a 1x3 vector presenting an RGB
% % value or any of strings 'red','blue','green','yellow','magenta','cyan',
% % 'white' or 'black' or their shortenings 'r','g','b','y','m','c','w','k':
% f=vis_valueype(X,{'1x3rgb','colorstyle'})
%
% % Return 1 if X is _both_ 10x3 size numeric matrix and has RGB values as rows
% f=vis_valuetype(X,{'nx3rgb',[10 3]},'all')
%
% Strings that may be used in argument valid:
% id is true if value is
%
% [n1 n2 ... nn] any n1 x n2 x ... x nn sized numeric matrix
% '1x1' scalar (numeric)
% '1x2' 1x2 vector (numeric)
% 'nx1' any nx1 numeric vector
% 'nx2' nx2
% 'nx3' nx3
% 'nxn' any numeric square matrix
% 'nxn[0,1]' numeric square matrix with values in interval [0,1]
% 'nxm' any numeric matrix
% '1xn' any 1xn numeric vector
% '1x3rgb' 1x3 vector v for which all(v>=0 & v<=1), e.g., a RGB code
% 'nx3rgb' nx3 numeric matrix that contains n RGB values as rows
% 'nx3dimrgb' nx3xdim numeric matrix that contains RGB values
% 'nxnx3rgb' nxnx3 numeric matrix of nxn RGB triples
% 'none' string 'none'
% 'xor' string 'xor'
% 'indexed' string 'indexed'
% 'colorstyle' strings 'red','blue','green','yellow','magenta','cyan','white'
% or 'black', or 'r','g','b','y','m','c','w','k'
% 'markerstyle' any of Matlab's marker chars '.','o','x','+','*','s','d','v',
% '^','<','>','p'or 'h'
% 'linestyle' any or Matlab's line style strings '-',':','--', or '-.'
% 'cellcolumn' a nx1 cell array
% 'topol_cell' {lattice, msize, shape}
% 'topol_cell_no_shape' {lattice, msize}
% 'string' any string (1xn array of char)
% 'chararray' any MxN char array
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 201099 juuso 280800
if nargin == 2
str='any';
end
flag=0;
sz=size(value);
dims=ndims(value);
% isnumeric
numeric=isnumeric(value);
character=ischar(value);
% main loop: go through all types in arg. 'valid'
for i=1:length(valid),
if isnumeric(valid{i}), % numeric size for double matrix
if numeric & length(valid{i}) == dims,
flag(i)=all(sz == valid{i});
else
flag(i)=0; % not numeric or wrong dimension
end
else
msg=''; % for a error message inside try
try
switch valid{i}
% scalar
case '1x1'
flag(i)=numeric & dims == 2 & sz(1)==1 & sz(2) ==1;
% 1x2 numeric vector
case '1x2'
flag(i)=numeric & dims == 2 & sz(1)==1 & sz(2) == 2;
% 1xn numeric vector
case '1xn'
flag(i)=numeric & dims == 2 & sz(1) == 1;
% any numeric matrix
case 'nxm'
flag(i)=numeric & dims == 2;
% nx3 numeric matrix
case 'nx3'
flag(i)=numeric & dims == 2 & sz(2) == 3;
% nx2 numeric matrix
case 'nx2'
flag(i)=numeric & dims == 2 & sz(2) == 2;
% nx1 numeric vector
case 'nx1'
flag(i)=numeric & dims == 2 & sz(2) == 1;
% nx1xm numric matrix
case 'nx1xm'
flag(i)=numeric & dims == 3 & sz(2) == 1;
% nx3 matrix of RGB triples
case 'nx3rgb'
flag(i)=numeric & dims == 2 & sz(2) == 3 & in0_1(value);
% RGB triple (ColorSpec vector)
case '1x3rgb'
flag(i) = numeric & dims == 2 & sz(1)==1 & sz(2) == 3 & in0_1(value);
% any square matrix
case 'nxn'
flag(i)=numeric & dims == 2 & sz(1) == sz(2);
% nx3xdim array of nxdim RGB triples
case 'nx3xdimrgb'
flag(i)=numeric & dims == 3 & sz(2) == 3 & in0_1(value);
% nxnx3 array of nxn RGB triples
case 'nxnx3rgb'
flag(i)= numeric & dims == 3 & sz(1) == sz(2) & sz(3) == 3 ...
& in0_1(value);
% nxn matrix of values between [0,1]
case 'nxn[0,1]'
flag(i)=numeric & dims == 2 & sz(1) == sz(2) & in0_1(value);
% string 'indexed'
case 'indexed'
flag(i) = ischar(value) & strcmp(value,'indexed');
% string 'none'
case 'none'
flag(i) = character & strcmp(value,'none');
% string 'xor'
case 'xor'
flag(i) = character & strcmp(value,'xor');
% any string (1xn char array)
case 'string'
flag(i) = character & dims == 2 & sz(1)<=1;
% any char array
case 'chararray'
flag(i) = character & dims == 2 & sz(1)>0;
% ColorSpec string
case 'colorstyle'
flag(i)=(character & sz(1) == 1 & sz(2) == 1 & ...
any(ismember('ymcrgbwk',value))) | ...
(ischar(value) & any(strcmp(value,{'none','yellow','magenta',...
'cyan','red','green','blue','white','black'})));
% any valid Matlab's Marker
case 'markerstyle'
flag(i)=character & sz(1) == 1 & sz(2) == 1 & ...
any(ismember('.ox+*sdv^<>ph',value));
% any valid Matlab's LineStyle
case 'linestyle'
str=strrep(strrep(strrep(value,'z','1'),'--','z'),'-.','z');
flag(i)=character & any(ismember(str,'z-:')) & sz(1)==1 & (sz(2)==1 | sz(2)==2);
% any struct
case 'struct'
flag(i)=isstruct(value);
% nx1 cell array of strings
case 'cellcolumn_of_char'
flag(i)=iscell(value) & dims == 2 & sz(2)==1;
try, char(value); catch, flag(i)=0; end
% mxn cell array of strings
case '2Dcellarray_of_char'
flag(i)=iscell(value) & dims == 2;
try, char(cat(2,value{:})); catch, flag(i)=0; end
% valid {lattice, msize}
case 'topol_cell_no_shape'
flag(i)=1;
if ~iscell(value) | length(size(value)) ~= 2 | size(value,2)~=2
flag(i)=0;
else
if vis_valuetype(value{1},{'string'}),
switch value{1}
case { 'hexa','rect'}
;
otherwise
flag(i)=0;
end
end
if ~vis_valuetype(value{2},{'1xn'}),
flag(i)=0;
end
end
% valid {lattice, msize, shape}
case 'topol_cell'
flag(i)=1;
if ~iscell(value) | length(size(value)) ~= 2 | size(value,2) ~= 3,
flag(i)=0;
else
if vis_valuetype(value{1},{'string'}),
switch value{1}
case { 'hexa','rect'}
;
otherwise
flag(i)=0;
end
end
if ~vis_valuetype(value{2},{'1xn'})
flag(i)=0;
end
if ~vis_valuetype(value{3},{'string'})
flag(i)=0;
else
switch value{3}
case { 'sheet','cyl', 'toroid'}
;
otherwise
flag(i)=0;
end
end
end
otherwise
msg='Unknown valuetype!';
end
catch
% error during type check is due to wrong type of value:
% lets set flag(i) to 0
flag(i)=0;
end
% Unknown indetifier?
error(msg);
end
% set flag according to 3rd parameter (all ~ AND, any ~ OR)
if strcmp(str,'all');
flag=all(flag);
else
flag=any(flag);
end
end
function f=in0_1(value)
f=all(value(:) >= 0 & value(:)<=1);
|
github
|
martinarielhartmann/mirtooloct-master
|
som_neighf.m
|
.m
|
mirtooloct-master/somtoolbox/som_neighf.m
| 3,518 |
utf_8
|
d37974390d75ef52b98cff7dc7fa6a53
|
function H = som_neighf(sMap,radius,neigh,ntype)
%SOM_NEIGHF Return neighborhood function values.
%
% H = som_neighf(sMap,[radius],[neigh],[ntype]);
%
% Input and output arguments ([]'s are optional):
% sMap (struct) map or topology struct
% [radius] (scalar) neighborhood radius (by default, the last used value
% in sMap.trainhist is used, or 1 if that is unavailable)
% [neigh] (string) neighborhood function type (by default, ..., or
% 'gaussian' if that is unavailable)
% [ntype] (string) 'normal' (default), 'probability' or 'mirror'
%
% H (matrix) [munits x munits] neighborhood function values from
% each map unit to each other map unit
%
% For more help, try 'type som_batchtrain' or check out online documentation.
% See also SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check arguments
% defaults
rdefault = 1;
ndefault = 'gaussian';
tdefault = 'normal';
% map
switch sMap.type,
case 'som_map',
sTopol = sMap.topol;
sTrain = sMap.trainhist(end);
if isempty(sTrain.radius_fin) | isnan(sTrain.radius_fin),
rdefault = 1;
else
rdefault = sTrain.radius_fin;
end
if ~isempty(sTrain.neigh) & ~isnan(sTrain.neigh),
ndefault = sTrain.neigh;
end
case 'som_topol', sTopol = sMap;
end
munits = prod(sTopol.msize);
% other parameters
if nargin<2 | isempty(radius), radius = rdefault; end
if nargin<3 | isempty(neigh), neigh = ndefault; end
if nargin<4 | isempty(ntype), ntype = tdefault; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% initialize
% basic neighborhood
Ud = som_unit_dists(sTopol);
Ud = Ud.^2;
radius = radius.^2;
if radius==0, radius = eps; end % zero neighborhood radius may cause div-by-zero error
switch ntype,
case 'normal',
H = neighf(neigh,Ud,radius);
case 'probability',
H = neighf(neigh,Ud,radius);
for i=1:munits, H(i,:) = H(i,:)/sum(H(i,:)); end
case 'mirror', % only works for 2-dim grid!!!
H = zeros(munits,munits);
Co = som_unit_coords(sTopol);
for i=-1:1,
for j=-1:1,
Ud = gridmirrordist(Co,i,j);
H = H + neighf(neigh,Ud,radius);
end
end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function H = neighf(neigh,Ud,radius)
switch neigh,
case 'bubble', H = (Ud<=radius);
case 'gaussian', H = exp(-Ud/(2*radius));
case 'cutgauss', H = exp(-Ud/(2*radius)) .* (Ud<=radius);
case 'ep', H = (1-Ud/radius) .* (Ud<=radius);
end
return;
function Ud = gridmirrordist(Co,mirrorx,mirrory)
[munits,mdim] = size(Co);
if mdim>2, error('Mirrored neighborhood only works for 2-dim map grids.'); end
% width and height of the grid
dx = max(Co(:,1))-min(Co(:,1));
dy = max(Co(:,2))-min(Co(:,2));
% calculate distance from each location to each other location
Ud = zeros(munits,munits);
for i=1:munits,
inds = [i:munits];
coi = Co(i,:); % take hexagonal shift into account
coi(1) = coi(1)*(1-2*(mirrorx~=0)) + 2*dx*(mirrorx==1); % +mirrorx * step
coi(2) = coi(2)*(1-2*(mirrory~=0)) + 2*dy*(mirrory==1); % +mirrory * step
Dco = (Co(inds,:) - coi(ones(munits-i+1,1),:))';
Ud(i,inds) = sqrt(sum(Dco.^2));
Ud(inds,i) = Ud(i,inds)';
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_gui.m
|
.m
|
mirtooloct-master/somtoolbox/som_gui.m
| 99,745 |
utf_8
|
46047f777569e35ebc2596223c5cc512
|
function som_gui(varargin)
%SOM_GUI A GUI for initialization and training of SOM.
%
% som_gui([sD])
%
% som_gui
% som_gui(sD)
%
% Input and output arguments ([]'s are optional)
% [sD] (struct) SOM data struct
% (matrix) a data matrix, size dlen x dim
%
% Actually, there are more arguments the function takes, but
% they are for internal action of the function only. DO NOT use
% them.
%
% For a more throughout description, see the online documentation.
% See also PREPROCESS.
%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IN FILES: som_gui.html,browsewin.jpg,wspace.jpg,loadgui.jpg,initgui.jpg,questdlg.jpg,paragui.jpg,mwindow.jpg,visgui.gif,reload.gif,savemap.gif,browse.gif
%
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Mika Pollari
% Copyright (c) by Mika Pollari and SOM Toolbox Team
% http://www.cis.hut.fi/projects/somtoolbox/
% Mika Pollari 31.1.2000 vs 1.1
global NEWMAP NEWST MAPSAVED MAP DATA LOAD_NAME LOAD_DATA;
global SAVEMAP ALGORITHM HANDLE2 STOPOLINIT INIT_TYPE;
global STRAIN1 STRAIN2 SOTHERS;
if nargin == 0
main_gui;
action = 'dummy';
elseif nargin == 1
temp = varargin{1};
if isstruct(temp),
DATA = temp;
main_gui;
action = 'input_data';
elseif isnumeric(temp),
DATA = som_data_struct(temp);
main_gui;
action = 'input_data';
else
action = temp;
end
end
switch(action)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LOAD %%%%%%%%%%%%%%%%%%%%%%%%%%
case 'load_data'
loadgui3; %%% Activates load GUI
case 'workspace'
workspace; %%% Workspace selected
case 'file'
file; %%% File Selected
case 'file_select'
file_select;
case 'missing'
Handle = findobj(gcf,'Tag','Checkbox1');
set(Handle,'Value',1);
case 'load_ok' %%% <Load OK> pushed
load_ok;
case 'input_data' %%% GUI activated with data as arg1
input_data; %%% eg. som_gui(data)
case 'browse' %%% Activates Browse GUI
browse; %%% Browse files or workspace variables
case 'works_ok' %%% <OK> pushed in (workspace) browse GUI
works_ok;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%% Initialization %%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 'def_initialization' %%% Finds default initialization ...
def_initialization; %%% parameters
case 'change_initialization' %%% Activates change (init) parameters GUI
change_initialization;
case 'change_initialization_ok'%%% Set new init. parameters
change_initialization_ok;
case 'change_initialization_cancel'
close(gcf);
return;
case 'map_size' %%% Checks that 'map_size' is given in correct form
map_size;
case 'munits' %%% Checks that 'munits' is given in correct form
munits;
case 'init' %%% Initialize Map
init;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%% Train %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 'def_values_others'
def_values_others;
case 'def_values_train'
STRAIN1 = som_train_struct('algorithm',ALGORITHM,'phase','rough','data',DATA);
STRAIN2 = som_train_struct('previous',STRAIN1);
case 'fill_fields' %%% Fill text fields in GUI
fill_fields;
case 'def_train' %%% Train Map
def_train;
case 'change_def' %%% Change default training parameters
change_def; %%% Activate GUI
case 'fill_new_defaults'
fill_new_defaults;
case 'set_batch_mask'
set_batch_mask;
case 'set_new_parameters'
set_new_parameters;
case 'only_finetune' %%% Train only once with finetune parameters
only_finetune;
%%%%%%% Next function check correctnes of new training parameters.
case 'check_rough_radini'
check_rough_radini;
case 'check_fine_radini'
check_fine_radini;
case 'check_rough_radfin'
check_rough_radfin;
case 'check_fine_radfin'
check_fine_radfin;
case 'check_rough_alphaini'
check_rough_alphaini;
case 'check_fine_alphaini'
check_fine_alphaini;
case 'check_rough_trainlen'
check_rough_trainlen;
case 'check_fine_trainlen'
check_fine_trainlen;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%% Save Map %%%%%%%%%%%%%%%%%%%%%%
case 'savemap' %%% Save as <.cod> file
savemap;
case 'save_workspace' %%% Save in workspace
save_workspace;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%% Help & Info %%%%%%%%%%%%%%%%%%%%%%%
case 'help'
web file:///share/somtoolbox/vs2/html/som_GUI.html;
case 'helpwin'
helpwin1;
case 'helpwin2'
helpwin som_gui;
case 'data_info'
data_info; %%% Info about data
case 'map_info' %%% Info about map
map_info;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%% Other Functions %%%%%%%%%%%%%%%%%%%%%%%
case 'preprocess'
preprocess_gui; %%%%% Call preprocess GUI
case 'visualize'
visualize; %%%%% Call visualization GUI
case 'clear_all' %%%%% Clear all filds
clear_all;
case 'close'
close_fig; %%%%% Close active GUI
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% END OF SWITCH-STATEMENT %%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%% (SUB) FUNCTIONS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% LOAD SECTION STARTS %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = workspace()
Handle = findobj(gcbf,'Tag','Radiobutton2');
Value = get(Handle,'Value');
HandleTemp = findobj(gcbf,'Tag','Radiobutton1');
if Value == 1
set(HandleTemp,'Value',0);
HandleBar = findobj(gcbf,'Tag','PopupMenu1');
set(HandleBar,'Enable','off');
set(HandleBar,'Visible','off');
Handle3 = findobj(gcbf,'Tag','StaticText3');
set(Handle3,'Visible','off');
Handle3 = findobj(gcbf,'Tag','Checkbox1');
set(Handle3,'Visible','off');
Handle3 = findobj(gcbf,'Tag','EditText3');
set(Handle3,'Visible','off');
Handle = findobj(gcbf,'Tag','EditText2');
set(Handle,'String','');
end
function [] = file()
Handle = findobj(gcbf,'Tag','Radiobutton1');
Value = get(Handle,'Value');
HandleTemp = findobj(gcbf,'Tag','Radiobutton2');
if Value == 1
set(HandleTemp,'Value',0);
HandleBar = findobj(gcbf,'Tag','PopupMenu1');
set(HandleBar,'Enable','on');
set(HandleBar,'Visible','on');
Handle3 = findobj(gcbf,'Tag','StaticText3');
set(Handle3,'Visible','on');
Handle3 = findobj(gcbf,'Tag','Checkbox1');
set(Handle3,'Visible','on');
Handle3 = findobj(gcbf,'Tag','EditText3');
set(Handle3,'Visible','on');
Handle = findobj(gcbf,'Tag','EditText1');
set(Handle,'String','');
end
function [] = file_select()
Handle = findobj(gcbf,'Tag','PopupMenu1');
temp = get(Handle,'String');
val = get(Handle,'Value');
Handle1 = findobj(gcbf,'Tag','Checkbox1');
Handle2 = findobj(gcbf,'Tag','EditText3');
if strcmp(temp{val},'dat file')
set(Handle2,'String','x');
set(Handle1,'Enable','on');
set(Handle2,'Enable','on');
set(Handle1,'Visible','on');
set(Handle2,'Visible','on');
else
set(Handle1,'Value',0);
set(Handle1,'Enable','off');
set(Handle2,'Enable','off');
set(Handle1,'Visible','off');
set(Handle2,'Visible','off');
end
function [] = load_ok()
global MAP DATA LOAD_DATA LOAD_NAME;
Handle1 = findobj(gcbf,'Tag','EditText1');
Handle2 = findobj(gcbf,'Tag','EditText2');
Name1 = get(Handle1,'String');
Name2 = get(Handle2,'String');
if isempty(Name1) & not(isempty(Name2))
Handle = findobj(gcbf,'Tag','PopupMenu1')
type = get(Handle,'String');
val = get(Handle,'Value');
type = type{val};
if strcmp(type,'mat file')
ltemp = 'load:::';
ltemp = strcat(ltemp,Name2);
ltemp = strrep(ltemp,':::',' ');
evalin('base',ltemp);
DATA = evalin('base','sD');
LOAD_DATA = evalin('base','sD.data');
LOAD_NAME = evalin('base','sD.name');
LOAD_NAME = strrep(LOAD_NAME,'.','_');
load_labels = evalin('base','sD.labels');
load_comp_names = evalin('base','sD.comp_names');
DATA = som_data_struct(LOAD_DATA);
DATA.name = LOAD_NAME;
DATA.comp_names = load_comp_names;
DATA.labels = load_labels;
else
Handle = findobj(gcbf,'Tag','Checkbox1');
value = get(Handle,'Value');
if value == 0
temp = 'som_read_data(''';
temp = strcat(temp,Name2,''');');
else
Handle = findobj(gcbf,'Tag','EditText3');
missing = get(Handle,'String');
if not(isempty(missing))
temp = 'som_read_data(''';
temp = strcat(temp,Name2,'''',',','''',missing,''');');
else
temp = 'som_read_data(''';
temp = strcat(temp,Name2,''');');
end
end
evalin('base',temp);
DATA = evalin('base','ans');
name = DATA.name;
temp = findstr('/',name);
if not(isempty(temp))
name = name(temp(end)+1:end);
end
name = strrep(name,'.','_');
LOAD_NAME = name;
DATA.name = name;
end
elseif isempty(Name2) & not(isempty(Name1))
LOAD_DATA = evalin('base',Name1);
if not(isstruct(LOAD_DATA))
DATA = som_data_struct(LOAD_DATA);
LOAD_NAME = Name1;
DATA.name = Name1;
else
DATA = LOAD_DATA;
name = DATA.name;
temp = findstr('/',name);
if not(isempty(temp))
name = name(temp(end)+1:end);
end
name = strrep(name,'.','_');
LOAD_NAME = name;
DATA.name = name;
end
else
errmsg = {'Give name of data before loading'};
errordlg(errmsg,'Empty data name!');
return;
end
close(gcbf);
if not(isempty(MAP))
clear MAP;
global MAP;
str1 = 'Map: <empty>';
str2 = 'Train';
Handle = findobj(gcf,'Tag','StaticText3');
set(Handle,'String',str1);
Handle = findobj(gcf,'Tag','StaticText8');
set(Handle,'String',str2);
end
temp = 'Data:';
temp = strcat(temp,' <',LOAD_NAME,'>');
Handle = findobj(gcf,'Tag','StaticText4');
set(Handle,'String',temp);
som_gui('def_initialization');
Handle = findobj(gcf,'Tag','Pushbutton2');
set(Handle,'Enable','off');
Handle = findobj(gcf,'Tag','Pushbutton4');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Pushbutton9');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Subuimenu2');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Init&Trainuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Init&TrainInitialize1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Subuimenu1');
set(Handle,'Enable','off'); %%%%%%????????
Handle = findobj(gcf,'Tag','StaticText10');
set(Handle,'String','Status <data loaded>');
function [] = input_data()
global DATA;
name = DATA.name;
newname = strrep(name,'.','_');
DATA.name = newname;
temp = strcat('Data: <',newname,'>');
Handle = findobj(gcf,'Tag','StaticText4');
set(Handle,'String',temp);
som_gui('def_initialization');
Handle = findobj(gcf,'Tag','Pushbutton2');
set(Handle,'Enable','off');
Handle = findobj(gcf,'Tag','Pushbutton4');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Pushbutton9');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Subuimenu2');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Init&Trainuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','&Init&TrainInitialize1');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Tag','Subuimenu1');
set(Handle,'Enable','off'); %%%%%%????????
Handle = findobj(gcf,'Tag','StaticText10');
set(Handle,'String','Status <data loaded>');
function [] = browse()
global HANDLE2;
HandleWorkspace = findobj(gcbf,'Tag','Radiobutton2');
HandleFile = findobj(gcbf,'Tag','Radiobutton1');
WorkspaceVal = get(HandleWorkspace,'Value');
FileVal = get(HandleFile,'Value');
if FileVal == 1
Handle = findobj(gcbf,'Tag','PopupMenu1');
str = get(Handle,'String');
value = get(Handle,'Value');
str = str{value};
if strcmp(str,'mat file')
filtter = '*.mat';
else
filtter = '*.dat*';
end
[filename pathname] = uigetfile(filtter,'Load file.');
temp = strcat(pathname,filename);
Handle = findobj(gcbf,'Tag','EditText2');
set(Handle,'String',temp);
elseif WorkspaceVal == 1
HANDLE2 = gcf;
works;
temp = evalin('base','who');
index2 = 1;
names = '';
for index = 1:length(temp)
if isnumeric(evalin('base',temp{index}))
test = size(evalin('base',temp{index}));
if test(1) ~= 1 & test(2) ~= 1
names{index2} = temp{index};
index2 = index2 + 1;
end
end
end
for index = 1:length(temp)
variable = evalin('base',temp{index});
if isstruct(variable)
fnames = fieldnames(variable);
if size(fnames,1) == 6 & strcmp(fnames(1),'type') & strcmp(variable.type,'som_data')
names{index2} = temp{index};
index2 = index2 + 1;
end
end
end
Handle = findobj(gcf,'Tag','Listbox1');
%%%%%% if is empty string#%%%
set(Handle,'String',names);
else
errmsg = 'Select browse type: Workspace or file.';
errordlg(errmsg,'Browse error!');
return;
end
function [] = works_ok()
global HANDLE2;
Handle = findobj(gcbf,'Tag','Listbox1');
temp = get(Handle,'String');
val = get(Handle,'Value');
data = temp{val};
Handle = findobj(HANDLE2,'Tag','EditText1');
set(Handle,'String',data);
close;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF LOAD SECTION %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% START OF INITIALIZATION %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = def_initialization()
global DATA STOPOLINIT INIT_TYPE;
sTopol = som_topol_struct('data',DATA);
Handle = findobj(gcf,'Tag','StaticText5');
temp = num2str(sTopol.msize);
temp = strcat('map size:',' [',temp,']');
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText6');
set(Handle,'String','type: linear');
Handle = findobj(gcf,'Tag','StaticText20');
temp = strcat('lattice:',sTopol.lattice);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText21');
temp = strcat('shape:',sTopol.shape);
set(Handle,'String',temp);
STOPOLINIT = sTopol;
INIT_TYPE = 'linear';
function [] = change_initialization()
global INIT_TYPE STOPOLINIT;
initialization2;
Handle = findobj(gcf,'Tag','PopupMenu1');
temp = get(Handle,'String');
val = loop(temp,INIT_TYPE);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','PopupMenu2');
temp = get(Handle,'String');
val = loop(temp,STOPOLINIT.lattice);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','PopupMenu3');
temp = get(Handle,'String');
val = loop(temp,STOPOLINIT.shape);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','EditText1');
temp = num2str(STOPOLINIT.msize);
msize = strcat('[',temp,']');
set(Handle,'String',msize);
function [] = change_initialization_ok()
Handle = findobj(gcbf,'Tag','PopupMenu1');
temp = get(Handle,'String');
val = get(Handle,'Value');
INIT_TYPE = temp{val};
Handle = findobj(gcbf,'Tag','PopupMenu2');
temp = get(Handle,'String');
val = get(Handle,'Value');
lattice = temp{val};
Handle = findobj(gcbf,'Tag','PopupMenu3');
temp = get(Handle,'String');
val = get(Handle,'Value');
shape = temp{val};
Handle = findobj(gcbf,'Tag','EditText1');
temp = get(Handle,'String');
msize = str2num(temp);
STOPOLINIT = som_set('som_topol','msize',msize,'lattice',lattice,'shape',shape);
close(gcf);
Handle = findobj(gcf,'Tag','StaticText5');
temp = num2str(STOPOLINIT.msize);
temp = strcat('map size:',' [',temp,']');
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText6');
temp = strcat('type:',INIT_TYPE);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText20');
temp = strcat('lattice:',STOPOLINIT.lattice);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText21');
temp = strcat('shape:',STOPOLINIT.shape);
set(Handle,'String',temp);
function [] = def_values_others()
global SOTHERS;
Handle = findobj(gcf,'Tag','StaticText19');
temp = strcat('tracking:',SOTHERS.tracking);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText12');
temp = strcat('order:',SOTHERS.oder);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText14');
temp = strcat('length_type:',SOTHERS.length_type);
set(Handle,'String',temp);
function [] = fill_fields()
global STRAIN1 STRAIN2 ALGORITHM
neigh = STRAIN1.neigh;
mask = STRAIN1.mask;
rad_ini1 = STRAIN1.radius_ini;
rad_ini2 = STRAIN2.radius_ini;
rad_fin1 = STRAIN1.radius_fin;
rad_fin2 = STRAIN2.radius_fin;
trainlen1 = num2str(STRAIN1.trainlen);
trainlen2 = num2str(STRAIN2.trainlen);
alpha_ini1 = num2str(STRAIN1.alpha_ini);
alpha_ini2 = num2str(STRAIN2.alpha_ini);
if strcmp(ALGORITHM,'seq')
alpha_type = STRAIN1.alpha_type; %%% only in sequential
Handle = findobj(gcf,'Tag','StaticText28');
temp = strcat('alpha type:',alpha_type);
set(Handle,'String',temp);
end
Handle = findobj(gcf,'Tag','StaticText11');
temp = strcat('neigh: ',neigh);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText22');
temp = num2str(rad_fin1);
temp = strcat('radius final:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText25');
temp = num2str(rad_fin2);
temp = strcat('radius final:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText11');
temp = strcat('neigh: ',neigh);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText17');
temp = num2str(rad_ini1);
temp = strcat('radius initial:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText24');
temp = num2str(rad_ini2);
temp = strcat('radius initial:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText16');
temp = num2str(trainlen1);
temp = strcat('training length:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText23');
temp = num2str(trainlen2);
temp = strcat('training length:',temp);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText26');
temp = strcat('alpha initial:',alpha_ini1);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText27');
temp = strcat('alpha initial:',alpha_ini2);
set(Handle,'String',temp);
function [] = init();
global INIT_TYPE MAP NEWMAP ALGORITHM SOTHERS DATA STOPOLINIT;
if strcmp(INIT_TYPE,'random')
MAP = som_randinit(DATA,STOPOLINIT);
else
MAP = som_lininit(DATA,STOPOLINIT);
end
NEWMAP = MAP;
temp = 'Map:';
temp = strcat(temp,' <',MAP.name,'>');
Handle = findobj(gcbf,'Tag','StaticText3');
set(Handle,'String',temp);
Handle = findobj(gcbf,'Tag','StaticText10');
set(Handle,'String','Status <map initialized>');
ALGORITHM = 'batch';
Handle = findobj(gcbf,'Tag','Pushbutton4');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton6');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','Pushbutton5');
set(Handle,'Enable','on');
SOTHERS.tracking = '1';
SOTHERS.length_type = 'epochs';
SOTHERS.oder = 'random';
som_gui('def_values_topol');
som_gui('def_values_train');
som_gui('def_values_others');
som_gui('fill_fields');
Handle = findobj(gcbf,'Tag','Pushbutton4');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton9');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Radiobutton1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','Subuimenu2');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1');
set(Handle,'Enable','off'); %%%%%%%%%%%??????????
Handle = findobj(gcbf,'Tag','StaticText9');
set(Handle,'String','training type: batch');
function [] = set_batch_mask()
Handle = findobj(gcbf,'Tag','Listbox2');
temp = get(Handle,'String');
mask = str2num(temp);
Handle = findobj(gcbf,'Tag','Listbox1');
replace = get(Handle,'Value');
Handle = findobj(gcbf,'Tag','EditText2');
temp = get(Handle,'String');
value = str2num(temp);
if not(isempty(value))
mask(replace) = value;
Handle = findobj(gcbf,'Tag','Listbox2');
temp = num2str(mask);
set(Handle,'String',temp);
end
function [] = munits()
global DATA STOPOLINIT;
msgs = {'Correct map units is number';'Correct map units is number'};
[msgs_nro, value] = check_ok('EditText2');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect map units!')
return;
end
STOPOLINIT = som_topol_struct('munits',value,'data',DATA);
Handle = findobj(gcbf,'Tag','EditText1');
temp = num2str(STOPOLINIT.msize);
msize = strcat('[',temp,']');
set(Handle,'String',msize);
function [] = map_size()
global STOPOLINIT;
msgs = {'Map size must be in form [x y]';...
'Map size must be in form [x y]'};
[msgs_nro, value, Handle] = msize_ok('EditText1');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect map size!');
temp = num2str(STOPOLINIT.msize);
temp = strcat('[',temp,']');
set(Handle,'String',temp);
return;
end
STOPOLINIT.msize = value;
Handle = findobj(gcbf,'Tag','EditText2');
set(Handle,'String','');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF INITIALIZATION %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% START OF TRAINING %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = def_train()
global SOTHERS ALGORITHM MAP NEWST DATA STRAIN1 STRAIN2 MAPSAVED;
tlen_type = SOTHERS.length_type;
sample_order = SOTHERS.oder;
tracking = SOTHERS.tracking;
test = str2num(tracking);
Handle = findobj(gcbf,'Tag','Radiobutton1');
tempval = get(Handle,'Value');
if strcmp(ALGORITHM,'seq')
if tempval ~= 1
[MAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN1,tlen_type,sample_order);
end
if test > 1
figure;
set(gcf,'Name',MAP.name);
set(gcf,'NumberTitle','off');
end
[NEWMAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN2,'tracking',test,tlen_type,sample_order);
else
if tempval ~= 1
[MAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN1);
end
if test > 1
figure;
set(gcf,'Name',MAP.name);
set(gcf,'NumberTitle','off');
end
[NEWMAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN2,'tracking',test);
end
MAP = NEWMAP;
clear MAPSAVED;
Handle = findobj(gcbf,'Tag','StaticText10');
set(Handle,'String','Status <map trained>');
Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
set(Handle,'Enable','off');
function [] = change_def()
global ALGORITHM STRAIN1 DATA;
ButtonName = questdlg('Select training type!',...
'Change values.',...
'Batch','Sequential','Cancel',...
'Batch');
if strcmp(ButtonName,'Sequential')
Handle = findobj(gcbf,'Visible','off');
set(Handle,'Visible','on');
ALGORITHM = 'seq';
Handle = findobj(gcf,'Tag','StaticText9');
set(Handle,'String','training type: sequential');
new_para2_2;
Handle = findobj(gcf,'Tag','StaticText1');
set(Handle,'String','Change parameters for sequential training');
Handle = findobj(gcf,'Enable','off');
set(Handle,'Enable','on');
Handle = findobj(gcf,'Visible','off');
set(Handle,'Visible','on');
elseif strcmp(ButtonName,'Batch')
ALGORITHM = 'batch';
Handle = findobj(gcbf,'Tag','StaticText26');
set(Handle,'Visible','off');
Handle = findobj(gcbf,'Tag','StaticText27');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText9');
set(Handle,'String','training type: batch');
Handle = findobj(gcf,'Tag','StaticText12');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText28');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText14');
set(Handle,'Visible','off');
new_para2_2;
Handle = findobj(gcf,'Tag','StaticText1');
set(Handle,'String','Change parameters for batch training');
Handle = findobj(gcf,'Tag','PopupMenu3');
set(Handle,'Enable','off');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','PopupMenu4');
set(Handle,'Enable','off');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','PopupMenu5');
set(Handle,'Enable','off');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText17');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText18');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText19');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText13');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','StaticText14');
set(Handle,'Visible','off');
Handle = findobj(gcf,'Tag','EditText6');
set(Handle,'Visible','off');
set(Handle,'Enable','off');
Handle = findobj(gcf,'Tag','EditText10');
set(Handle,'Visible','off');
set(Handle,'Enable','off');
else
return;
end
som_gui('def_values_train');
mask = STRAIN1.mask;
Handle = findobj(gcf,'Tag','Listbox1');
set(Handle,'String',DATA.comp_names);
som_gui('fill_new_defaults');
function [] = fill_new_defaults()
global STRAIN1 STRAIN2 SOTHERS ALGORITHM;
Handle = findobj(gcf,'Tag','EditText4');
temp = num2str(STRAIN1.radius_ini);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText8');
temp = num2str(STRAIN2.radius_ini);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText5');
temp = num2str(STRAIN1.radius_fin);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText9');
temp = num2str(STRAIN2.radius_fin);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText6');
temp = num2str(STRAIN1.alpha_ini);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText10');
temp = num2str(STRAIN2.alpha_ini);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText7');
temp = num2str(STRAIN1.trainlen);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','EditText11');
temp = num2str(STRAIN2.trainlen);
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','Listbox2');
temp = num2str(STRAIN1.mask');
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','PopupMenu2');
string = get(Handle,'String');
val = loop(string,SOTHERS.tracking);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','PopupMenu1');
string = get(Handle,'String');
val = loop(string,STRAIN1.neigh);
set(Handle,'Value',val);
if strcmp(ALGORITHM,'seq')
Handle = findobj(gcf,'Tag','PopupMenu3');
string = get(Handle,'String');
val = loop(string,SOTHERS.length_type);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','PopupMenu4');
string = get(Handle,'String');
val = loop(string,SOTHERS.oder);
set(Handle,'Value',val);
Handle = findobj(gcf,'Tag','PopupMenu5');
string = get(Handle,'String');
val = loop(string,STRAIN1.alpha_type);
set(Handle,'Value',val);
end
function [] = set_new_parameters()
global STRAIN1 STRAIN2 ALGORITHM SOTHERS;
Handle = findobj(gcbf,'Tag','Listbox2');
temp = get(Handle,'String');
mask = str2num(temp); %%%%%%%%%%%%% Do somthing
mask = mask';
Handle = findobj(gcbf,'Tag','PopupMenu1');
temp = get(Handle,'String');
val = get(Handle,'Value');
neigh = temp{val};
Handle = findobj(gcbf,'Tag','PopupMenu2');
temp = get(Handle,'String');
val = get(Handle,'Value');
SOTHERS.tracking = temp{val}; %%%%% finetune phase!
Handle = findobj(gcbf,'Tag','EditText4');
temp = get(Handle,'String');
rad_ini1 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText8');
temp = get(Handle,'String');
rad_ini2 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText5');
temp = get(Handle,'String');
rad_fin1 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText9');
temp = get(Handle,'String');
rad_fin2 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText6');
temp = get(Handle,'String');
alpha_ini1 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText10');
temp = get(Handle,'String');
alpha_ini2 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText7');
temp = get(Handle,'String');
train_length1 = str2num(temp);
Handle = findobj(gcbf,'Tag','EditText11');
temp = get(Handle,'String');
train_length2 = str2num(temp);
if strcmp(ALGORITHM,'seq')
Handle = findobj(gcbf,'Tag','PopupMenu3');
temp = get(Handle,'String');
val = get(Handle,'Value');
SOTHERS.length_type = temp{val};
Handle = findobj(gcbf,'Tag','PopupMenu4');
temp = get(Handle,'String');
val = get(Handle,'Value');
SOTHERS.oder= temp{val};
Handle = findobj(gcbf,'Tag','PopupMenu5');
temp = get(Handle,'String');
val = get(Handle,'Value');
alpha_type = temp{val};
else
alpha_type = 'inv';
end
STRAIN1.neigh = neigh;
STRAIN2.neigh = neigh;
STRAIN1.mask = mask;
STRAIN2.mask = mask;
STRAIN1.radius_ini = rad_ini1;
STRAIN2.radius_ini = rad_ini2;
STRAIN1.radius_fin = rad_fin1;
STRAIN2.radius_fin = rad_fin2;
STRAIN1.alpha_ini = alpha_ini1;
STRAIN2.alpha_ini = alpha_ini2;
STRAIN1.alpha_type = alpha_type;
STRAIN2.alpha_type = alpha_type;
STRAIN1.trainlen = train_length1;
STRAIN2.trainlen = train_length2;
close(gcbf);
som_gui('fill_fields');
som_gui('def_values_others');
function [] = only_finetune()
Handle = findobj(gcbf,'Tag','Radiobutton1');
test = get(Handle,'Value');
if test == 1
Handle = findobj(gcbf,'Tag','StaticText16');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','StaticText17');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','StaticText22');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','StaticText26');
set(Handle,'Enable','off');
else
Handle = findobj(gcbf,'Tag','StaticText16');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','StaticText17');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','StaticText22');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','StaticText26');
set(Handle,'Enable','on');
end
function [] = check_rough_radini()
global STRAIN1;
msgs = {'Initial radius must be number!';...
'Initial radius must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText4');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect initial radius!')
temp = num2str(STRAIN1.radius_ini);
set(Handle,'String',temp);
return;
end
function [] = check_fine_radini()
global STRAIN2;
msgs = {'Initial radius must be number!';...
'Initial radius must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText8');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect initial radius!')
temp = num2str(STRAIN2.radius_ini);
set(Handle,'String',temp);
return;
end
function [] = check_rough_radfin()
global STRAIN1;
msgs = {'Final radius must be number!';...
'Final radius must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText5');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect final radius!')
temp = num2str(STRAIN1.radius_fin);
set(Handle,'String',temp);
return;
end
function [] = check_fine_radfin()
global STRAIN2;
msgs = {'Final radius must be number!';...
'Final radius must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText9');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect final radius!')
temp = num2str(STRAIN2.radius_fin);
set(Handle,'String',temp);
return;
end
function [] = check_rough_alphaini()
global STRAIN1;
msgs = {'Alpha initial must be number!';...
'Alpha initial must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText6');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect initial alpha!')
temp = num2str(STRAIN1.alpha_ini);
set(Handle,'String',temp);
return;
end
function [] = check_fine_alphaini()
global STRAIN2;
msgs = {'Alpha initial must be number!';...
'Alpha initial must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText10');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect initial alpha!')
temp = num2str(STRAIN2.alpha_ini);
set(Handle,'String',temp);
return;
end
function [] = check_rough_trainlen()
global STRAIN1;
msgs = {'Training length must be number!';...
'Training length must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText7');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect training length!')
temp = num2str(STRAIN1.trainlen);
set(Handle,'String',temp);
return;
end
function [] = check_fine_trainlen()
global STRAIN2;
msgs = {'Training length must be number!';...
'Training length must be single valued number!'};
[msgs_nro, value, Handle] = check_ok('EditText11');
if msgs_nro > 0
errordlg({msgs{msgs_nro}},'Incorrect training length!')
temp = num2str(STRAIN2.trainlen);
set(Handle,'String',temp);
return;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF TRAINING %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% START OF SAVING %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = savemap()
global MAP MAPSAVED;
if isempty(MAP)
str = {'There is no map to be saved! Train map before saving.'};
helpdlg(str,'Empty map!');
return;
end
[FileName Path] = uiputfile('*.cod','Save file!');
if FileName ~= 0
temp = strcat(Path,FileName);
som_write_cod(MAP,temp);
MAPSAVED = 'SAVED';
end
Handle = findobj(gcf,'Tag','StaticText10');
set(Handle,'String','Status <map saved>');
function [] = save_workspace()
global MAP MAPSAVED;
if isempty(MAP)
str = {'There is no map to be saved! Train map before saving.'};
helpdlg(str,'Empty map!');
return;
else
prompt = {'Save map as?'};
title = 'Save map!';
lineNo = 1;
answer = inputdlg(prompt,title,lineNo);
if isempty(answer)
return;
end
if not(isempty(answer{1}))
ws_variable = evalin('base','who');
max_length = 0;
for index = 1:size(ws_variable,1)
if max_length < size(ws_variable{index},2)
max_length = size(ws_variable{index},2);
end
end
length = max_length + 1;
tempfoo(1:1:length) = 'A';
assignin('base',tempfoo,answer{1});
str = ['exist(' tempfoo ')'];
temp = evalin('base',str); %%%%%%%%%%@@@@@@@@@
evalin('base',['clear ' tempfoo ])
if temp == 0
assignin('base',answer{1},MAP);
MAPSAVED = 'SAVED';
elseif temp ~= 0
Questmsg = strcat('Variable',' ''',answer{1},'''',...
' exist. Overwrite?');
ButtonName = questdlg(Questmsg);
switch(ButtonName)
case 'Yes'
assignin('base',answer{1},MAP);
MAPSAVED = 'SAVED';
case 'No'
som_gui('save_workspace');
end
end
else
helpmsg = {'There cannot be any empty field in ''save'''};
helpdlg(helpmsg,'Help Save!');
som_gui('save');
end
end
Handle = findobj(gcf,'Tag','StaticText10');
set(Handle,'String','Status <map saved>');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF SAVING %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% START OF HELP & INFO %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%HEREXX
function [] = data_info()
global DATA;
if isempty(DATA)
helpmsg = 'Load data first!';
helpdlg(helpmsg,'Empty data!');
return;
end
file_name = tempname;
file_name = strcat(file_name,'.m');
fid = fopen(file_name,'w');
fprintf(fid,'%% %+35s\n','DATA INFO');
fprintf(fid,'%%\n');
print_info(DATA,2,fid);
directory = tempdir;
addpath (directory);
helpwin (file_name);
fclose(fid);
delete(file_name);
rmpath (directory);
function [] = map_info()
global MAP;
if isempty(MAP)
helpmsg = 'There is no map!';
helpdlg(helpmsg,'Empty map!');
return;
end
file_name = tempname;
file_name = strcat(file_name,'.m');
fid = fopen(file_name,'w');
fprintf(fid,'%% %+35s\n','MAP INFO');
fprintf(fid,'%%\n');
print_info(MAP,2,fid);
directory = tempdir;
addpath (directory);
helpwin (file_name);
fclose(fid);
delete(file_name);
rmpath (directory);
function [] = helpwin1()
file1 = tempname;
file1 = strcat(file1,'.m');
directory = tempdir;
html2tex('file:///share/somtoolbox/vs2/html/som_GUI.html',file1);
addpath (directory);
helpwin (file1);
rmpath (directory);
delete (file1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF HELP & INFO %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% START OF OTHER FUNC %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [msgs_nro, value, Handle] = check_ok(Tag)
Handle = findobj(gcbf,'Tag',Tag);
temp = get(Handle,'String');
value = str2num(temp);
if isempty(value)
msgs_nro = 1;
return;
end
[test1 test2] = size(value);
if test1 ~= 1 | test2 ~= 1
msgs_nro = 2;
return;
end
msgs_nro = 0;
function [msgs_nro, value, Handle] = msize_ok(Tag)
Handle = findobj(gcbf,'Tag',Tag);
temp = get(Handle,'String');
value = str2num(temp);
if isempty(value)
msgs_nro = 1;
return;
end
[test1 test2] = size(value);
if test1 ~= 1 | test2 ~= 2
msgs_nro = 2;
return;
end
msgs_nro = 0;
%%% Changed 1.2.2000
function [] = visualize()
global MAP;
if isempty(MAP)
helpmsg = {'Train map before tryinig to visualize it!'};
helpdlg(helpmsg,'Empty Map!');
return;
end
dim = size(MAP.codebook,2);
odim = 2;
[P,V] = pcaproj(MAP.codebook,odim);
ccode = som_colorcode(MAP, 'rgb1');
figure;
som_show(MAP,'umat','all','comp',1:dim,'norm','d');
figure;
subplot(1,2,1)
som_grid(MAP,'Coord',P,'MarkerColor',ccode,'Markersize',5, ...
'Linewidth',1,'Linecolor','k');
xlabel('PC1'), ylabel('PC2')
title('PCA-projection (on the left), color coding (on the right)')
axis tight, axis equal
subplot(1,2,2)
som_cplane(MAP.topol.lattice,MAP.topol.msize,ccode);
%msgbox('Save map in workspace. Load it from there.');
%som_gui('save_workspace');
%som_comp_vis;
%%%%%%%%%%%%%%%%
function [] = clear_all()
Handle = findobj(gcbf,'Enable','off');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','Radiobutton1');
set(Handle,'Value',0);
Handle = findobj(gcbf,'Tag','StaticText10');
set(Handle,'String','Status <no action>');
Handle = findobj(gcbf,'Tag','StaticText3');
set(Handle,'String','Map: <empty>');
Handle = findobj(gcbf,'Tag','StaticText4');
set(Handle,'String','Data: <empty>');
Handle = findobj(gcbf,'Tag','StaticText20');
set(Handle,'String','lattice:');
Handle = findobj(gcbf,'Tag','StaticText11');
set(Handle,'String','neigh:');
Handle = findobj(gcbf,'Tag','StaticText16');
set(Handle,'String','training length:');
Handle = findobj(gcbf,'Tag','StaticText23');
set(Handle,'String','training length:');
Handle = findobj(gcbf,'Tag','StaticText17');
set(Handle,'String','radius initial:');
Handle = findobj(gcbf,'Tag','StaticText24');
set(Handle,'String','radius initial:');
Handle = findobj(gcbf,'Tag','StaticText5');
set(Handle,'String','map size:');
Handle = findobj(gcbf,'Tag','StaticText21');
set(Handle,'String','shape:');
Handle = findobj(gcbf,'Tag','StaticText12');
set(Handle,'String','order:');
set(Handle,'Visible','off');
Handle = findobj(gcbf,'Tag','StaticText14');
set(Handle,'String','length type:');
set(Handle,'Visible','off');
Handle = findobj(gcbf,'Tag','StaticText22');
set(Handle,'String','radius final:');
Handle = findobj(gcbf,'Tag','StaticText25');
set(Handle,'String','radius final:');
Handle = findobj(gcbf,'Tag','StaticText19');
set(Handle,'String','tracking:');
Handle = findobj(gcbf,'Tag','StaticText7');
set(Handle,'String','Initialization');
Handle = findobj(gcbf,'Tag','StaticText28');
set(Handle,'String','alpha type:');
set(Handle,'Visible','off');
Handle = findobj(gcbf,'Tag','StaticText26');
set(Handle,'String','alpha initial:');
Handle = findobj(gcbf,'Tag','StaticText27');
set(Handle,'String','alpha initial:');
Handle = findobj(gcbf,'Tag','StaticText6');
set(Handle,'String','type:');
Handle = findobj(gcbf,'Tag','StaticText9');
set(Handle,'String','training type:');
Handle = findobj(gcbf,'Tag','Pushbutton9');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton6');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton4');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton5');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Pushbutton2');
set(Handle,'Enable','on');
Handle = findobj(gcbf,'Tag','Radiobutton1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Subuimenu2');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Help/InfoHelp windowuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1');
set(Handle,'Enable','off');
Handle = findobj(gcbf,'String','alpha initial:');
set(Handle,'Visible','off');
clear;
clear global;
function [] = close_fig()
global MAPSAVED NEWMAP;
if isempty(MAPSAVED)
if not(isempty(NEWMAP))
quest = 'Save map before closing?';
ButtonName = questdlg(quest);
switch ButtonName
case 'Yes'
som_gui('savemap');
som_gui('clear');
clear global;
close(gcbf);
case 'No'
som_gui('clear');
clear global;
close(gcbf);
case 'Cancel'
end
else
som_gui('clear');
clear global;
close(gcbf);
end
else
som_gui('clear');
clear global;
close(gcbf);
end
function [] = preprocess_gui()
global DATA;
if isempty(DATA)
helpmsg = {'Load data before tryinig to preprocess!'};
helpdlg(helpmsg,'Empty Data!');
return;
end
preprocess(DATA);
waitfor(gcf);
prompt = {'Name of preprocessed data in workspace?'};
tittle = 'Reload preprocessed data!';
lineNo = 1;
def = {DATA.name};
answer = inputdlg(prompt,tittle,lineNo,def);
if isempty(answer)
return;
end
data = answer{1};
new_name = retname;
assignin('base',new_name,data);
str = ['exist(' new_name ')'];
temp = evalin('base',str);
if temp ~= 1
temp = strcat('Variable ''',data,''' doesn''t exist in workspace.',...
'Old Data which is not preprocessed will be used.');
errordlg(temp,'Unknown variable!');
return;
end
evalin('base',['clear ' new_name ])
Handle = findobj(gcf,'Tag','StaticText4');
temp = strcat('Data: <',data,'>');
set(Handle,'String',temp);
Handle = findobj(gcf,'Tag','StaticText10');
set(Handle,'String','Status <data preprocessed>');
temp = evalin('base',data);
DATA.data = temp;
som_gui('def_initialization');
function [val] = loop(cell_data, search_data)
for val = 1: length(cell_data)
if strcmp(cell_data{val},search_data)
break;
end
end
if not(strcmp(cell_data{val},search_data))
val = -1;
end
function [] = comp_names(names,fid)
last = size(names);
for index=1:last
fprintf(fid,'%% %s\n',names{index})
end
function [] = fill_field(names,mask,fid)
last = size(mask);
for index=1:last
num = num2str(mask(index))
fprintf(fid,'%% %-15s %-2s\n',names{index},num)
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% END OF OTHER FUNC %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function fig = main_gui()
v = version;
ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
h0 = figure('Units','normalized', ...
'Color',[0.85 0.85 0.85], ...
'Name','SOM Toolbox -- Initialization & Training', ...
'NumberTitle','off', ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[0.3296875 0.28125 0.3828125 0.576171875], ...
'Tag','Fig1');
if ver_53_or_newer, set(h0,'ToolBar','none'); end
h1 = uimenu('Parent',h0, ...
'Label','&Load/Save', ...
'Tag','uimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''load_data'');',...
'Label','Load Data', ...
'Tag','Subuimenu1');
h2 = uimenu('Parent',h1, ...
'Label','Save map', ...
'Enable','off',...
'Tag','Load/SaveSubuimenu1');
h3 = uimenu('Parent',h2, ...
'Callback','som_gui(''save_workspace'');', ...
'Enable','off', ...
'Label','Save in workspace', ...
'Tag','Load/SaveSave mapuimenu1');
h3 = uimenu('Parent',h2, ...
'Callback','som_gui(''savemap'');', ...
'Enable','off', ...
'Label','Write cod-file', ...
'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
h1 = uimenu('Parent',h0, ...
'Label','&Utilities', ...
'Tag','uimenu2');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''preprocess'');', ...
'Enable','off', ...
'Label','Preprocess Data', ...
'Tag','Subuimenu2');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''visualize'');', ...
'Enable','off', ...
'Label','Visualize Map', ...
'Tag','&ToolsSubuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''clear_all'');', ...
'Label','Clear all', ...
'Tag','&ToolsSubuimenu2');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''close'');', ...
'Label','Close Figure', ...
'Tag','&ToolsClear alluimenu1');
h1 = uimenu('Parent',h0, ...
'Label','&Info', ...
'Tag','&ToolsClose Figureuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''help'');', ...
'Label','WWW Help', ...
'Tag','Helpuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''helpwin'');', ...
'Label','Help window', ...
'Tag','Helpuimenu2');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''helpwin2'');', ...
'Label','About GUI', ...
'Tag','&Help/InfoHelp windowuimenu2');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''data_info'');', ...
'Enable','off', ...
'Label','Data info', ...
'Tag','&Help/InfoHelp windowuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''map_info'');', ...
'Enable','off', ...
'Label','Map info', ...
'Tag','&Help/InfoData infouimenu1');
h1 = uimenu('Parent',h0, ...
'Label','&Init/Train', ...
'Tag','&Init/Train1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''change_initialization'');', ...
'Enable','off', ...
'Label','Change initialization values', ...
'Tag','&Init&Trainuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''init'');', ...
'Enable','off', ...
'Label','Initialize', ...
'Tag','&Init&TrainInitialize1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''change_def'');', ...
'Enable','off', ...
'Label','Change training values', ...
'Tag','&Init&TrainChange initialization valuesuimenu1');
h2 = uimenu('Parent',h1, ...
'Callback','som_gui(''def_train'');', ...
'Enable','off', ...
'Label','Train', ...
'Tag','&Init&TrainTrain1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.04081632653061224 0.01129943502824859 0.7619047619047619 0.9717514124293786], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.06802721088435373 0.7909604519774012 0.7074829931972788 0.1807909604519774], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.8527570621468927 0.6530612244897959 0.03389830508474576], ...
'FontUnits','normalized',...
'String','Map <empty>', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.8075593220338984 0.6530612244897959 0.03389830508474576], ...
'String','Data <empty>', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.06802721088435373 0.5988700564971752 0.7074829931972788 0.1694915254237288], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.1041 0.7356 0.6286 0.0271], ...
'String','Initialization', ...
'FontUnits','normalized',...
'Style','text', ...
'FontWeight','bold', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.7005649717514124 0.2993197278911565 0.03389830508474576], ...
'String','map size:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.6553672316384182 0.2993197278911565 0.03389830508474576], ...
'String','lattice:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText20');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.7000000000000001 0.2993197278911565 0.03389830508474576], ...
'String','type:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.6553672316384182 0.2993197278911565 0.03389830508474576], ...
'String','shape:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText21');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.3129251700680272 0.6101694915254238 0.217687074829932 0.03389830508474576], ...
'String','Change values', ...
'FontUnits','normalized',...
'Callback','som_gui(''change_initialization'');', ...
'Enable','off', ...
'Tag','Pushbutton9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.06802721088435373 0.02259887005649718 0.7074829931972788 0.5536723163841808], ...
'Style','frame', ...
'Tag','Frame4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.1041 0.5316 0.6429 0.0339], ...
'String','Training', ...
'FontUnits','normalized',...
'Style','text', ...
'FontWeight','bold', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.4971751412429379 0.6530612244897959 0.03389830508474576], ...
'String','training type', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.4519774011299435 0.2993197278911565 0.03389830508474576], ...
'String','tracking:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText19');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.4519774011299435 0.2993197278911565 0.03389830508474576], ...
'String','neigh:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText11');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.36519774011299435 0.2993197278911565 0.03389830508474576], ...
'String','alpha type:', ...
'FontUnits','normalized',...
'Style','text', ...
'Visible','off',...
'Tag','StaticText28');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'ListboxTop',0, ...
'HorizontalAlignment','left', ...
'Position',[0.09523809523809523 0.4067796610169492 0.2993197278911565 0.03389830508474576], ...
'String','length type:', ...
'FontUnits','normalized',...
'Style','text', ...
'Visible','off',...
'Tag','StaticText14');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.4067796610169492 0.2993197278911565 0.03389830508474576], ...
'String','order:', ...
'FontUnits','normalized',...
'Style','text', ...
'Visible','off',...
'Tag','StaticText12');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.07909604519774012 0.2993197278911565 0.2711864406779661], ...
'Style','frame', ...
'Tag','Frame5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.4353741496598639 0.07909604519774012 0.2993197278911565 0.2711864406779661], ...
'Style','frame', ...
'Tag','Frame6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.108843537414966 0.3050847457627119 0.2721088435374149 0.03389830508474576], ...
'String','Rough', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText13');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.3050847457627119 0.2721088435374149 0.03389830508474576], ...
'String','Finetune', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText15');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.108843537414966 0.1807909604519774 0.2721088435374149 0.03389830508474576], ...
'String','training length:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText16');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.108843537414966 0.2694915254237288 0.2714285714285714 0.03389830508474576], ...
'String','radius initial:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText17');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1088 0.2260 0.2721 0.0339], ...
'String','radius final:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText22');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'ListboxTop',0, ...
'Position',[0.108843537414966 0.13694915254237288 0.2714285714285714 0.03389830508474576], ...
'String','alpha initial:', ...
'FontUnits','normalized',...
'HorizontalAlignment','left', ...
'Style','text', ...
'Visible','off',...
'Tag','StaticText26');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.1807909604519774 0.2721088435374149 0.03389830508474576], ...
'String','training length:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText23');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.2711864406779661 0.2721088435374149 0.03389830508474576], ...
'String','radius initial:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText24');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4490 0.2260 0.2721 0.0339], ...
'String','radius final:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText25');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'ListboxTop',0, ...
'Position',[0.4489795918367346 0.13694915254237288 0.2721088435374149 0.03389830508474576], ...
'String','alpha initial:', ...
'FontUnits','normalized',...
'HorizontalAlignment','left', ...
'Style','text', ...
'Visible','off',...
'Tag','StaticText27');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.3129251700680272 0.03389830508474576 0.217687074829932 0.03389830508474576], ...
'String','Change values', ...
'FontUnits','normalized',...
'Callback','som_gui(''change_def'');', ...
'Enable','off', ...
'Tag','Pushbutton6');
if ver_53_or_newer, set(h1,'TooltipString','Change default values in training.'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.8163265306122448 0.8152542372881356 0.163265306122449 0.05593220338983051], ...
'String','LOAD', ...
'FontUnits','normalized',...
'Callback','som_gui(''load_data'');', ...
'Tag','Pushbutton2');
if ver_53_or_newer, set(h1,'TooltipString','Load data file.'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.8163265306122448 0.6457627118644068 0.163265306122449 0.05593220338983051], ...
'String','INITIALIZE', ...
'FontUnits','normalized',...
'Callback','som_gui(''init'');', ...
'Enable','off', ...
'Tag','Pushbutton4');
if ver_53_or_newer, set(h1,'TooltipString','Initialize map.'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.8163265306122448 0.384180790960452 0.163265306122449 0.05649717514124294], ...
'String','TRAIN', ...
'FontUnits','normalized',...
'Callback','som_gui(''def_train'');', ...
'Enable','off', ...
'Tag','Pushbutton5');
if ver_53_or_newer, set(h1,'TooltipString','Train map whit default values.'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.8163265306122448 0.06779661016949153 0.163265306122449 0.05649717514124294], ...
'Callback','som_gui(''close'');', ...
'String','CLOSE', ...
'FontUnits','normalized',...
'Tag','Pushbutton8');
if ver_53_or_newer, set(h1,'TooltipString','Close figure.'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.9 0.9 0.9], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09387755102040815 0.897954802259887 0.6530612244897959 0.03389830508474576], ...
'String','Status <no action>', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText10');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.108843537414966 0.0903954802259887 0.2721088435374149 0.03389830508474576], ...
'String','Only finetune', ...
'FontUnits','normalized',...
'Callback','som_gui(''only_finetune'');', ...
'Enable','off', ...
'Style','radiobutton', ...
'Tag','Radiobutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.9418531073446328 0.6530612244897959 0.0259887005649718], ...
'String','Information', ...
'FontUnits','normalized',...
'FontWeight','bold', ...
'Style','text', ...
'Tag','StaticText18');
if nargout > 0, fig = h0; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function fig = loadgui3()
temp = {'dat file';'mat file'};
h0 = figure('Units','normalized', ...
'Color',[0.8 0.8 0.8], ...
'Name','Load data!', ...
'NumberTitle','off', ...
'PaperType','a4letter', ...
'Position',[0.3828125 0.5 0.3421875 0.189453125], ...
'Tag','Fig1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.02853881278538813 0.06443298969072164 0.7705479452054794 0.8698453608247422], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.04337899543378995 0.547680412371134 0.7420091324200913 0.354381443298969], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.04280821917808219 0.09664948453608246 0.7420091324200913 0.4188144329896907], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'FontWeight','bold', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.05717762557077625 0.7881958762886597 0.2853881278538812 0.09664948453608246], ...
'String','From', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','som_gui(''workspace'');', ...
'ListboxTop',0, ...
'Position',[0.05107762557077625 0.7087628865979381 0.1997716894977169 0.09664948453608246], ...
'String','Ws', ...
'FontUnits','normalized',...
'Style','radiobutton', ...
'Tag','Radiobutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','som_gui(''file'');', ...
'ListboxTop',0, ...
'Position',[0.05107762557077625 0.5773195876288659 0.2009132420091324 0.09793814432989689], ...
'String','File', ...
'FontUnits','normalized',...
'Style','radiobutton', ...
'Tag','Radiobutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','Handle = findobj(gcbf,''Tag'',''EditText2'');set(Handle,''String'','''');',...
'FontUnits','normalized',...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2893881278538812 0.7087628865979381 0.3139269406392694 0.09664948453608246], ...
'Style','edit', ...
'Tag','EditText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','Handle = findobj(gcbf,''Tag'',''EditText1'');set(Handle,''String'','''');',...
'FontUnits','normalized',...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2893881278538812 0.5798969072164948 0.3139269406392694 0.09664948453608246], ...
'Style','edit', ...
'Tag','EditText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''browse'');', ...
'ListboxTop',0, ...
'Position',[0.6279 0.5799 0.1427 0.2255], ...
'String','Browse', ...
'FontUnits','normalized',...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''load_ok'');', ...
'ListboxTop',0, ...
'Position',[0.8276 0.5577 0.1427 0.2255], ...
'String','Load', ...
'FontUnits','normalized',...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','close;',...
'ListboxTop',0, ...
'Position',[0.8276 0.2577 0.1427 0.2255], ...
'String','Cancel', ...
'FontUnits','normalized',...
'Tag','Pushbutton3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''file_select'');', ...
'ListboxTop',0, ...
'Max',2, ...
'Min',1, ...
'String',temp,...
'FontUnits','normalized',...
'Position',[0.3995433789954338 0.2977319587628866 0.1997716894977169 0.08664948453608246], ...
'Style','popupmenu', ...
'Tag','PopupMenu1', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'FontWeight','bold', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.05707762557077625 0.3865979381443299 0.7134703196347032 0.09664948453608246], ...
'String','Parameters for file', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.05707762557077625 0.2777319587628866 0.2568493150684931 0.09664948453608246], ...
'String','File type ', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.05707762557077625 0.1288659793814433 0.2996575342465753 0.09664948453608246], ...
'String','Missing value', ...
'Style','checkbox', ...
'FontUnits','normalized',...
'Tag','Checkbox1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''missing'');',...
'ListboxTop',0, ...
'Position',[0.5136986301369862 0.1258659793814433 0.08561643835616438 0.10664948453608246], ...
'String','x', ...
'FontUnits','normalized',...
'Style','edit', ...
'Tag','EditText3');
if nargout > 0, fig = h0; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function fig = works()
v = version;
ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
h0 = figure('Units','normalized', ...
'Color',[0.8 0.8 0.8], ...
'Name','Load from workspace!', ...
'NumberTitle','off', ...
'PaperPosition',[18 180 576 432], ...
'PaperType','a4letter', ...
'PaperUnits','points', ...
'Position',[0.5390625 0.2490234375 0.203125 0.251953125], ...
'Tag','Fig1');
if ver_53_or_newer, set(h0,'ToolBar','none'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.05384615384615385 0.1472868217054263 0.9076923076923078 0.8255813953488372], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''works_ok'');', ...
'ListboxTop',0, ...
'Position',[0.1077 0.0194 0.2885 0.1202], ...
'String','OK', ...
'FontUnits','normalized',...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','close;', ...
'ListboxTop',0, ...
'Position',[0.6115 0.0155 0.2885 0.1202], ...
'String','Cancel', ...
'FontUnits','normalized',...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Position',[0.1192 0.1977 0.7692 0.6395], ...
'String',' ', ...
'FontUnits','normalized',...
'Style','listbox', ...
'Tag','Listbox1', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'FontWeight','bold', ...
'ListboxTop',0, ...
'Position',[0.2115384615384616 0.8720930232558139 0.576923076923077 0.06976744186046512], ...
'String','Your options', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText1');
if nargout > 0, fig = h0; end
function fig = initialization2()
temp1 = {'random';'linear'};
temp2 = {'hexa';'rect'};
temp3 = {'sheet';'cyl';'toroid'};
% position bug in following corrected 1.12.04 KimmoR
h0 = figure('Units','normalized', ...
'Color',[0.8 0.8 0.8], ...
'Name','Change initialization parameters!', ...
'NumberTitle','off', ...
'PaperType','a4letter', ...
'Position',[0.48828125 0.4267578125 0.3515625 0.146484375], ...
'Tag','Fig1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.02777777777777778 0.08333333333333333 0.8055555555555556 0.8333333333333334], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''change_initialization_ok'');', ...
'ListboxTop',0, ...
'Position',[0.8472222222222222 0.55 0.125 0.25], ...
'FontUnits','normalized',...
'String','OK', ...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''change_initialization_cancel'');', ...
'ListboxTop',0, ...
'Position',[0.8472222222222222 0.25 0.125 0.25], ...
'FontUnits','normalized',...
'String','Cancel', ...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'FontWeight','bold', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.08333333333333334 0.6666666666666666 0.7066666666666667 0.1933333333333333], ...
'String','Initialization parameters:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0556 0.200 0.1667 0.1250],...
'String','type:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Max',2, ...
'Min',1, ...
'Position',[0.2500 0.200 0.1667 0.1250], ...
'String',temp1, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu1', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.05555555555555556 0.6 0.1666666666666667 0.125], ...
'String','map size:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''map_size'');', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.25 0.6 0.1666666666666667 0.125], ...
'FontUnits','normalized',...
'Style','edit', ...
'Tag','EditText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.05555555555555556 0.4033333333333333 0.1666666666666667 0.125], ...
'String','lattice:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Max',2, ...
'Min',1, ...
'Position',[0.25 0.4333333333333333 0.1666666666666667 0.125], ...
'String',temp2, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu2', ...
'Value',2);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4444444444444445 0.4033333333333333 0.1666666666666667 0.125], ...
'String','shape:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Max',3, ...
'Min',1, ...
'Position',[0.638888888888889 0.4333333333333333 0.1666666666666667 0.125], ...
'String',temp3, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu3', ...
'Value',2);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4444444444444445 0.6 0.1666666666666667 0.125], ...
'FontUnits','normalized',...
'String','munits:', ...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''munits'');', ...
'ListboxTop',0, ...
'Position',[0.638888888888889 0.6 0.1666666666666667 0.125], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText2');
if nargout > 0, fig = h0; end
function fig = new_para2_2()
temp1 = {'0';'1';'2';'3'};
temp2 = {'gaussian';'cutgauss';'ep';'bubble'};
temp3 = {'epochs';'samples'};
temp4 = {'random';'ordered'};
temp5 = {'inv';'linear';'power'};
v = version;
ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
h0 = figure('Units','normalized', ...
'Color',[0.8 0.8 0.8], ...
'Name','Change training parameters!', ...
'NumberTitle','off', ...
'PaperPosition',[18 180 576 432], ...
'PaperType','a4letter', ...
'PaperUnits','points', ...
'Position',[0.59140625 0.4560546875 0.3046875 0.4619140625], ...
'Tag','Fig3');
if ver_53_or_newer, set(h0,'ToolBar','none'); end
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.02051282051282051 0.08456659619450317 0.9641025641025641 0.8921775898520086], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.5308 0.1374 0.4000 0.3742], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.08012820512820512 0.1416490486257928 0.4102564102564102 0.3699788583509514], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','close(gcbf);', ...
'ListboxTop',0, ...
'Position',[0.6410 0.0036 0.2897 0.0740], ...
'FontUnits','normalized',...
'String','Cancel', ...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''set_new_parameters'');', ...
'ListboxTop',0, ...
'Position',[0.1026 0.0036 0.2897 0.0740], ...
'String','Set parameters', ...
'FontUnits','normalized',...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Max',4, ...
'Min',1, ...
'Position',[0.7051282051282051 0.6723044397463003 0.1923076923076923 0.040169133192389], ...
'String',temp1, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu2', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Max',4, ...
'Min',1, ...
'Position',[0.2948717948717949 0.6670190274841438 0.1923076923076923 0.03964059196617336], ...
'String',temp2, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu1', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','som_gui(''batch_cancel'');', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5076923076923077 0.6575052854122622 0.1923076923076923 0.05285412262156448], ...
'String','tracking', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'Callback','som_gui(''batch_cancel'');', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09615384615384615 0.6553911205073996 0.1923076923076923 0.05285412262156448], ...
'String','neigh.', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09615384615384615 0.7526427061310783 0.09487179487179487 0.04228329809725159], ...
'String','mask:', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Position',[0.2948717948717949 0.7399577167019028 0.6025641025641025 0.07399577167019028], ...
'String',' ', ...
'FontUnits','normalized',...
'Style','listbox', ...
'Tag','Listbox2', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0962 0.8060 0.1154 0.0529], ...
'FontUnits','normalized',...
'String','Set', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''set_batch_mask'');', ...
'Position',[0.2948717948717949 0.8165961945031712 0.3205128205128205 0.05285412262156448], ...
'String',' ', ...
'FontUnits','normalized',...
'Style','listbox', ...
'Tag','Listbox1', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.6250 0.8060 0.1603 0.0529], ...
'String','to value', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''set_batch_mask'');', ...
'ListboxTop',0, ...
'Position',[0.7923076923076923 0.8181818181818182 0.09487179487179487 0.05285412262156448], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_fine_trainlen'');', ...
'ListboxTop',0, ...
'Position',[0.7923 0.2352 0.0974 0.0402], ...
'FontUnits','normalized',...
'Style','edit', ...
'Tag','EditText11');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_fine_alphaini'');', ...
'Enable','off', ...
'ListboxTop',0, ...
'Position',[0.7923076923076923 0.1664904862579281 0.09743589743589742 0.03805496828752643], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText10', ...
'Visible','off');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_fine_radfin'');', ...
'ListboxTop',0, ...
'Position',[0.7923076923076923 0.3002114164904862 0.09743589743589742 0.040169133192389], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_fine_radini'');', ...
'ListboxTop',0, ...
'Position',[0.7923076923076923 0.3657505285412262 0.09743589743589742 0.040169133192389], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5590 0.2326 0.2179 0.0402], ...
'String','training length', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText16');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5590 0.1665 0.2179 0.0381], ...
'String','alpha initial', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText14', ...
'Visible','off');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5590 0.2981 0.2179 0.0402], ...
'String','radius final', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText12');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5590 0.3636 0.2179 0.0402], ...
'String','radius initial', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText10');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_rough_trainlen'');', ...
'ListboxTop',0, ...
'Position',[0.3590 0.2352 0.0949 0.0402], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_rough_alphaini'');', ...
'Enable','off', ...
'ListboxTop',0, ...
'Position',[0.3590 0.1691 0.0949 0.0381], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText6', ...
'Visible','off');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_rough_radfin'');', ...
'ListboxTop',0, ...
'Position',[0.358974358974359 0.3044397463002114 0.09487179487179487 0.040169133192389], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Callback','som_gui(''check_rough_radini'');', ...
'ListboxTop',0, ...
'Position',[0.358974358974359 0.3699788583509514 0.09487179487179487 0.040169133192389], ...
'Style','edit', ...
'FontUnits','normalized',...
'Tag','EditText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0962 0.2326 0.2179 0.0402], ...
'String','training length', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText15');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0962 0.1691 0.2179 0.0381], ...
'String','alpha initial', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText13', ...
'Visible','off');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0962 0.3023 0.2179 0.0402], ...
'String','radius final', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText11');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.0962 0.3679 0.2179 0.0402], ...
'FontUnits','normalized',...
'String','radius initial', ...
'Style','text', ...
'Tag','StaticText9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Position',[0.5948717948717949 0.4291754756871036 0.2871794871794872 0.05285412262156448], ...
'String','Finetune', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Position',[0.1205128205128205 0.4355179704016914 0.3153846153846154 0.04862579281183932], ...
'String','Rough', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'FontWeight','bold', ...
'ListboxTop',0, ...
'Position',[0.1641025641025641 0.8900634249471459 0.7025641025641025 0.05285412262156448], ...
'String','Change parameters for batch training', ...
'Style','text', ...
'FontUnits','normalized',...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09615384615384615 0.6025369978858351 0.1743589743589744 0.040169133192389], ...
'String','length type:', ...
'Style','text', ...
'FontUnits','normalized',...
'Tag','StaticText17');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Max',2, ...
'Min',1, ...
'Position',[0.2948717948717949 0.6062367864693446 0.1923076923076923 0.03964059196617336], ...
'String',temp3, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu3', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5102564102564102 0.6004228329809724 0.1641025641025641 0.040169133192389], ...
'String','order', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText18');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Max',2, ...
'Min',1, ...
'Position',[0.7051282051282051 0.6109936575052853 0.1923076923076923 0.040169133192389], ...
'String',temp4, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu4', ...
'Value',1);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.09615384615384615 0.5369978858350951 0.2051282051282051 0.040169133192389], ...
'String','learning func', ...
'FontUnits','normalized',...
'Style','text', ...
'Tag','StaticText19');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
'ListboxTop',0, ...
'Max',3, ...
'Min',1, ...
'Position',[0.2948717948717949 0.5454545454545455 0.1923076923076923 0.03964059196617336], ...
'String',temp5, ...
'FontUnits','normalized',...
'Style','popupmenu', ...
'Tag','PopupMenu5', ...
'Value',1);
if nargout > 0, fig = h0; end
function print_info(sS,level,fid)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
%error(nargchk(1, 2, nargin)) % check no. of input args is correct
if ~isstruct(sS),
if ~iscell(sS) | ~isstruct(sS{1}),
error('Input argument is not a struct or a cell array of structs.')
end
csS = sS;
else
csS = {sS};
end
if nargin<2 | isempty(level) | isnan(level), level = 1; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% print struct information
for c=1:length(csS),
sS = csS{c};
switch sS.type,
case 'som_map',
mdim = length(sS.topol.msize);
[munits dim] = size(sS.codebook);
t = length(sS.trainhist);
if t==0, st='uninitialized';
elseif t==1, st = 'initialized';
else st = sprintf('initialized, trained %d times',t-1);
end
% level 1
fprintf(fid,'%% Struct type : %s\n', sS.type);
fprintf(fid,'%% Map name : %s\n', sS.name);
fprintf(fid,'%% Input dimension : %d\n', dim);
fprintf(fid,'%% Map grid size : ');
for i = 1:mdim - 1, fprintf(fid,'%d x ',sS.topol.msize(i)); end
fprintf(fid,'%d\n', sS.topol.msize(mdim));
fprintf(fid,'%% Lattice type (rect/hexa) : %s\n', sS.topol.lattice);
fprintf(fid,'%% Shape (sheet/cyl/toroid) : %s\n', sS.topol.shape);
fprintf(fid,'%% Neighborhood type : %s\n', sS.neigh);
fprintf(fid,'%% Mask : ');
if dim,
for i = 1:dim-1, fprintf(fid,'%d ',sS.mask(i)); end;
fprintf(fid,'%d\n',sS.mask(dim));
else fprintf(fid,'%% \n');
end
fprintf(fid,'%% Training status : %s\n', st);
% level 1,
status = cell(dim,1);
for i=1:dim,
n = length(sS.comp_norm{i});
if n,
uninit = strcmp('uninit',{sS.comp_norm{i}.status});
done = strcmp('done',{sS.comp_norm{i}.status});
undone = strcmp('undone',{sS.comp_norm{i}.status});
if sum(uninit)==n, status{i} = 'no normalization';
elseif sum(done)==n, status{i} = 'normalized';
elseif sum(undone)==n, status{i} = 'denormalized';
else status{i} = 'partial';
end
else status{i} = 'no normalization'; end
end
if level>1,
fprintf(fid,'%% Vector components\n');
M = sS.codebook;
fprintf(fid,'%% # name mask min mean max std status\n');
fprintf(fid,'%% --- ------------ ---- ------ ------ ------ ------ ------\n');
for i = 1:dim,
fprintf(fid,'%% %-3d %-12s %-4.2f %6.1g %6.1g %6.1g %6.1g %s\n', ...
i,sS.comp_names{i}, sS.mask(i), ...
min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i});
end
end
% level 3
if level>2,
fprintf(fid,'%% Vector component normalizations\n');
fprintf(fid,'%% # name method (i=uninit,u=undone,d=done)\n');
fprintf(fid,'%% --- ------------ ---------------------------------------\n');
for i=1:dim,
fprintf(fid,'%% %-3d %-12s ',i,sS.comp_names{i});
n = length(sS.comp_norm{i});
for j=1:n,
m = sS.comp_norm{i}(j).method;
s = sS.comp_norm{i}(j).status;
if strcmp(s,'uninit'), c='i';
elseif strcmp(s,'undone'), c='u';
else c='d';
end
fprintf(fid,'%% %s[%s] ',m,c);
end
fprintf(fid,'%% \n');
end
end
% level 4
if level>3,
fprintf(fid,'%% Training history\n');
for i=1:t,
sT = sS.trainhist(i);
fprintf(fid,'%% * Algorithm: %8s Data: %13s Trainlen: %8d\n',...
sT.algorithm,sT.data_name,sT.trainlen);
%if i>1,
fprintf(fid,'%% Neighborh: %8s Mask: ',sT.neigh);
for i = 1:dim-1, fprintf(fid,'%% %d ',sT.mask(i)); end;
fprintf(fid,'%% %d\n',sT.mask(mdim));
fprintf(fid,'%% Radius: %4.2f->%4.2f Alpha: %5.3f (%s)\n', ...
sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type);
%end
fprintf(fid,'%% Time: %s\n',sT.time);
end
end
case 'som_data',
[dlen dim] = size(sS.data);
if dlen*dim
ind = find(~isnan(sum(sS.data),2));
else ind = []; end
complete = size(sS.data(ind,:),1);
partial = dlen - complete;
values = prod(size(sS.data));
missing = sum(sum(isnan(sS.data)));
% level 1
fprintf(fid,'%% Struct type : %s\n', sS.type);
fprintf(fid,'%% Data name : %s\n', sS.name);
fprintf(fid,'%% Vector dimension : %d\n', dim);
fprintf(fid,'%% Number of data vectors : %d\n', dlen);
fprintf(fid,'%% Complete data vectors : %d\n', complete);
fprintf(fid,'%% Partial data vectors : %d\n', partial);
if values, r = floor(100 * (values - missing) / values); else r = 0; end
fprintf(fid,'%% Complete values : %d of %d (%d%%)\n', ...
values-missing, values, r);
% level 2,
status = cell(dim,1);
for i=1:dim,
n = length(sS.comp_norm{i});
if n,
uninit = strcmp('uninit',{sS.comp_norm{i}.status});
done = strcmp('done',{sS.comp_norm{i}.status});
undone = strcmp('undone',{sS.comp_norm{i}.status});
if sum(uninit)==n, status{i} = 'no normalization';
elseif sum(done)==n, status{i} = 'normalized';
elseif sum(undone)==n, status{i} = 'denormalized';
else status{i} = 'partial';
end
else status{i} = 'no normalization'; end
end
if level>1,
fprintf(fid,'%% Vector components\n');
D = sS.data;
fprintf(fid,'%% # name min mean max std missing status\n');
fprintf(fid,'%% --- ------------ ------ ------ ------ ------ ----------- ------\n');
for i = 1:dim,
known = find(~isnan(D(:,i)));
miss = dlen-length(known);
fprintf(fid,'%% %-3d %-12s %6.1g %6.1g %6.1g %6.1g %5d (%2d%%) %s\n', ...
i,sS.comp_names{i}, ...
min(D(known,i)),mean(D(known,i)),max(D(known,i)),std(D(known,i)), ...
miss,floor(100*miss/dlen),status{i});
end
end
% level 3
if level>2,
fprintf(fid,'%% Vector component normalizations\n');
fprintf(fid,'%% # name method (i=uninit,u=undone,d=done)\n');
fprintf(fid,'%% --- ------------ ---------------------------------------\n');
for i=1:dim,
fprintf(fid,'%% %-3d %-12s ',i,sS.comp_names{i});
n = length(sS.comp_norm{i});
for j=1:n,
m = sS.comp_norm{i}(j).method;
s = sS.comp_norm{i}(j).status;
if strcmp(s,'uninit'), c='i';
elseif strcmp(s,'undone'), c='u';
else c='d';
end
fprintf(fid,'%% %s[%s] ',m,c);
end
fprintf(fid,'%% \n');
end
end
case 'som_topol',
mdim = length(sS.msize);
% level 1
fprintf(fid,'%% Struct type : %s\n',sS.type);
fprintf(fid,'%% Map grid size : ');
for i = 1:mdim - 1, fprintf(fid,'%% %d x ',sS.msize(i)); end
fprintf(fid,'%% %d\n', sS.msize(mdim));
fprintf(fid,'%% Lattice type (rect/hexa) : %s\n', sS.lattice);
fprintf(fid,'%% Shape (sheet/cyl/toroid) : %s\n', sS.shape);
case 'som_train',
% level 1
fprintf(fid,'%% Struct type : %s\n',sS.type);
fprintf(fid,'%% Training algorithm : %s\n',sS.algorithm);
fprintf(fid,'%% Training data : %s\n',sS.data_name);
fprintf(fid,'%% Neighborhood function : %s\n',sS.neigh);
fprintf(fid,'%% Mask : ');
dim = length(sS.mask);
if dim,
for i = 1:dim-1, fprintf(fid,'%% %d ',sS.mask(i)); end;
fprintf(fid,'%% %d\n',sS.mask(end));
else fprintf(fid,'%% \n'); end
fprintf(fid,'%% Initial radius : %-6.1f\n',sS.radius_ini);
fprintf(fid,'%% Final radius : %-6.1f\n',sS.radius_fin);
fprintf(fid,'%% Initial learning rate (alpha) : %-6.1f\n',sS.alpha_ini);
fprintf(fid,'%% Alpha function type (linear/inv) : %s\n',sS.alpha_type);
fprintf(fid,'%% Training length : %d\n',sS.trainlen);
fprintf(fid,'%% Average quantization error : %-6.1f\n',sS.qerror);
fprintf(fid,'%% When training was done : %s\n',sS.time);
case 'som_norm',
% level 1
fprintf(fid,'%% Struct type : %s\n',sS.type);
fprintf(fid,'%% Normalization method : %s\n',sS.method);
fprintf(fid,'%% Status : %s\n',sS.status);
% level 2
if level>1,
fprintf(fid,'%% Parameters:\n');
sS.params
end
end
end
function [] = html2tex(html_addres,texfile)
tempfile = tempname;
fid = fopen(texfile,'w');
eval(['!lynx -dump ' html_addres ' > ' tempfile]);
fid2 = fopen(tempfile,'r');
while not(feof(fid2))
line = fgets(fid2);
line = strcat('%',line);
fprintf(fid,'%s',line);
end
fclose(fid);
fclose(fid2);
delete (tempfile);
function [name] = retname
resnames = who;
if size(resnames,1) > 0
max_length = size(resnames{1},2);
for index = 1:size(resnames,1)
if size(resnames{index},2) > max_length
max_length = size(resnames{index},2);
end
end
length = max_length + 1;
name(:,1:1:length) = 'A'
else
name = 'A';
end
%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_dmatminima.m
|
.m
|
mirtooloct-master/somtoolbox/som_dmatminima.m
| 2,009 |
utf_8
|
9e535d4906164073484193ea7ef10560
|
function minima = som_dmatminima(sM,U,Ne)
%SOM_DMATMINIMA Find clusters based on local minima of U-matrix.
%
% minima = som_dmatminima(sM,[U],[Ne])
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct
% U (matrix) the distance matrix from which minima is
% searched from
% size msize(1) x ... x msize(end) or
% 2*msize(1)-1 x 2*msize(2)-1 or
% munits x 1
% Ne (matrix) neighborhood connections matrix
%
% minima (vector) indeces of the map units where locla minima of
% of U-matrix (or other distance matrix occured)
%
% See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT.
% Copyright (c) 2000 by Juha Vesanto
% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 220800
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% map
if isstruct(sM),
switch sM.type,
case 'som_map', M = sM.codebook; mask = sM.mask;
case 'som_data', M = sM.data; mask = ones(size(M,2),1);
end
else
M = sM; mask = ones(size(M,2),1);
end
[munits dim] = size(M);
% distances between map units
if nargin<2, U = []; end
% neighborhoods
if nargin<3, Ne = som_neighbors(sM); end
% distance matrix
if nargin<2 | isempty(U), U = som_dmat(sM,Ne,'median'); end
if prod(size(U))>munits, U = U(1:2:size(U,1),1:2:size(U,2)); end
U = U(:);
if length(U) ~= munits, error('Distance matrix has incorrect size.'); end
% find local minima
minima = [];
for i=1:munits,
ne = find(Ne(i,:));
if all(U(i)<=U(ne)) & ~anycommon(ne,minima), minima(end+1)=i; end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function t = anycommon(i1,i2)
if isempty(i1) | isempty(i2), t = 0;
else
m = max(max(i1),max(i2));
t = any(sparse(i1,1,1,m,1) & sparse(i2,1,1,m,1));
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_stats_table.m
|
.m
|
mirtooloct-master/somtoolbox/som_stats_table.m
| 3,684 |
utf_8
|
0dec0a499ac0af8b81c9d1eae7c1e819
|
function [sTstats,csThist] = som_stats_table(csS,histlabel)
%SOM_STATS_TABLE Statistics table.
%
% [sTstats,csThist] = som_stats_table(csS)
%
% sTstats = som_stats_table(csS);
% som_table_print(sTstats);
%
% Input and output arguments ([]'s are optional):
% csS (cell array) of statistics structs
% (struct) a statistics struct
%
% sTstats (struct) a table struct with basic descriptive
% statistics for each variable
% csThist (cell array) of table structs, with histograms for
% each variable
%
% See also SOM_STATS, SOM_STATS_PLOT, SOM_TABLE_PRINT, SOM_STATS_REPORT.
% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 311201
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% arguments
if isstruct(csS), csS = {csS}; end
dim = length(csS);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% action
sTable = struct('colfmt','','headers',[],'values',[],'span',[]);
% summary table of all variables
sT = sTable;
sT.headers = {'name','min','mean','max','std','missing'};
if ~isnan(csS{1}.nunique), sT.headers{end+1} = 'unique'; end
%if length(col_values), sT.headers = [sT.headers, col_headers]; end
sT.values = cell(dim,length(sT.headers));
sT.span = ones([size(sT.values) 2]);
%if length(col_values), sT.values(:,end-size(col_values,2)+1:end) = col_values; end
%if length(col_spans), sT.span(:,end-size(col_spans,2)+1:end,:) = col_spans; end
for i=1:dim,
sT.values{i,1} = csS{i}.name;
v = [csS{i}.min,csS{i}.mean,csS{i}.max,csS{i}.std];
v = som_denormalize(v,csS{i}.normalization);
vstr = numtostring(v,6);
sT.values(i,2:5) = vstr';
sT.values{i,6} = c_and_p_str(csS{i}.ntotal-csS{i}.nvalid,csS{i}.ntotal);
if ~isnan(csS{1}.nunique),
sT.values{i,7} = c_and_p_str(csS{i}.nunique,csS{i}.nvalid);
end
end
sTstats = sT;
% histograms
csThist = cell(dim,1);
for i=1:dim,
sH = csS{i}.hist;
nvalid = csS{i}.nvalid;
nbins = length(sH.bins);
sT = sTable;
sT.headers = {[csS{i}.name ' values'],'frequency #','frequency %'};
sT.values = cell(nbins,length(sT.headers));
sT.span = ones(nbins,length(sT.headers),2);
for j=1:nbins,
if length(sH.bins) < csS{i}.nunique, sT.values{j,1} = sH.binlabels2{j};
else sT.values{j,1} = sH.binlabels{j}; end
sT.values{j,2} = sprintf('%d',round(sH.counts(j)));
sT.values{j,3} = p_str(sH.counts(j)/nvalid);
end
csThist{i} = sT;
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
%% subfunctions
function vstr = numtostring(v,d)
tp = (size(v,2)>1);
if tp, v = v'; end
nearzero = (abs(v)/(max(v)-min(v)) < 10.^-d);
i1 = find(v > 0 & nearzero);
i2 = find(v < 0 & nearzero);
vstr = strrep(cellstr(num2str(v,d)),' ','');
vstr(i1) = {'0.0'};
vstr(i2) = {'-0.0'};
if tp, vstr = vstr'; end
return;
function str = c_and_p_str(n,m)
% return a string of form # (%), e.g. '23 (12%)'
if n==m, p = '100';
elseif n==0, p = '0';
else p = sprintf('%.2g',100*n/m);
end
str = sprintf('%d (%s%%)',round(n),p);
return;
function str = p_str(p)
% return a string of form %, e.g. '12%'
if round(p*100)>100, p = sprintf('%3g',100*p);
elseif p==1, p = '100';
elseif abs(p)<eps, p = '0';
else p = sprintf('%.2g',100*p);
end
str = sprintf('%s%%',p);
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
som_barplane.m
|
.m
|
mirtooloct-master/somtoolbox/som_barplane.m
| 13,935 |
utf_8
|
75467b21870c4a890f9d50bd7db8c647
|
function h = som_barplane(varargin)
%SOM_BARPLANE Visualize the map prototype vectors as bar charts
%
% h = som_barplane(lattice, msize, data, [color], [scaling], [gap], [pos])
% h = som_barplane(topol, data, [color], [scaling], [gap], [pos])
%
% som_barplane('hexa',[5 5], rand(25,4), jet(4))
% som_barplane(sM, sM.codebook,'none')
%
% Input and output argumetns ([]'s are optional):
% lattice (string) grid 'hexa' or 'rect'
% msize (vector) size 1x2, defines the map grid size msize, M=prod(msize)
% (matrix) size Mx2, gives explicit coordinates for each node:
% in this case the first argument does not matter.
% topol (struct) map or topology struct
% data (matrix) size Mxd, each row defines heights of the bars
% [color] (matrix) size dx3, of RGB triples. The rows define colors
% for each bar in a node. Default is hsv(d). A ColorSpec or
% (string) A ColorSpec or 'none' gives each bar the same color.
% [scaling] (string) 'none', 'unitwise' or 'varwise'. The scaling
% mode for the values. Default is 'varwise'.
% [gap] (scalar) Defines the gap between bars, limits: 0 <= gap <= 1
% where 0=no gap, 1=bars are thin lines. Default is 0.25.
% [pos] (vector) 1x2 vector defines the position of origin.
% Default is [1 1].
%
% h (scalar) the object handle to the PATCH object
%
% Axis are set as in SOM_CPLANE.
%
% For more help, try 'type som_barplane' or check out online documentation.
% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_PIEPLANE.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_barplane
%
% PURPOSE
%
% Visualizes the map prototype vectors as bar charts.
%
% SYNTAX
%
% h = som_barplane(topol, data)
% h = som_barplane(lattice, msize, data)
% h = som_barplane(..., color)
% h = som_barplane(..., color, scaling)
% h = som_barplane(..., color, scaling, gap)
% h = som_barplane(..., color, scaling, gap, pos)
%
% DESCRIPTION
%
% Visualizes the map prototype vectors as bar charts.
%
% REQUIRED INPUT ARGUMENTS
%
% lattice The basic shape of the map units
% (string) 'hexa' or 'rect' positions the bar charts according to
% hexagonal or rectangular map lattice
%
% msize The size of the map grid
% (vector) [n1 n2] vector defines the map size (height: n1 units widht: n2
% units, total: M=n1xn2 units). The units will be placed to their
% topological locations in order to form a uniform hexagonal or
% rectangular grid.
% (matrix) Mx2 matrix defines arbitary coordinates for the N units. In
% this case the argument 'lattice' has no effect
%
% topol Topology of the map grid
%
% (struct) map or topology struct from which the topology is taken
%
% data The data to use when constructing the bar charts.
% Typically, the map codebook or some of its components.
% (matrix) Mxd matrix. A row defines heights of the bars.
%
% OPTIONAL INPUT ARGUMENTS
%
% Note: if unspecified or given an empty value ('' or []), default
% values are used for optional input arguments.
%
% color The color of the bars in each pie
% (ColorSpec) or (string) 'none' gives the same color for each slice.
% (matrix) dx3 matrix assigns an RGB color determined by the dth row of
% the matrix to the dth bar (variable) in each bar plot.
% Default value is hsv(d).
%
% scaling How to scale the values
% (string) 'none', 'unitwise' or 'varwise'. This determines the
% scaling of codebook values when drawing the bars.
%
% 'none' don't scale at all. The bars are not limited
% to remain inside he units' area: That is, if value of
% some variable exceeds [-.625,.625] for 'rect' (and
% in "worst case" [-.5,-.5] for 'hexa') the bars may
% overlap other units.
%
% Base line (zero value line)
% - is in the middle of the unit if data (codebook) contains both
% negative and positive values (or is completely zero).
% - is in the top the unit if data (codebook) contains only
% non-positive values (everything <=0).
% - is in the bottom the unit if data (codebook) contains only
% non-negative values (everything >=0).
%
% 'varwise' scales values so that each variable is scaled separately
% so that when it gets its overall maximum value, the
% corresponding bar gets maximum range and for minimum value
% it gets the minimum range. Baseline: see scaling 'none'
% This is the default.
%
% 'unitwise' scales values in each unit individually so that the
% bars for variables having minimum and maximum values have minimum
% and maximum range inside each unit, respectively.
% In this case the zero value line may move depending on the values.
%
% gap The gap between bars
% (scalar) 0: no gap: bars are glued together
% ... default value is 0.25
% 1: maximum gap: bars are thin lines
%
% pos Position of origin
% (vector) size 1x2. This is meant for drawing the plane in arbitrary
% location in a figure. Note the operation: if this argument is
% given, the axis limits setting part in the routine is skipped and
% the limits setting will be left to be done by MATLAB's defaults.
% Default is [1 1].
%
% OUTPUT ARGUMENTS
%
% h (scalar) handle to the created patch object
%
% OBJECT TAGS
%
% One object handle is returned: field Tag is set to 'planeBar'
%
% FEATURES
%
% - The colors are fixed: changing colormap in the figure (see help
% colormap) will not change the coloring of the bars.
%
% EXAMPLES
%
% %%% Create the data and make a map
%
% data=rand(100,5); map=som_make(data);
%
% %%% Create a 'jet' colormap that has as many rows as the data has variables
%
% colors=jet(5);
%
% %%% Draw bars
%
% som_barplane(map.topol.lattice, map.topol.msize, map.codebook, colors);
% or som_barplane(map.topol, map.codebook, colors);
% or som_barplane(map, map.codebook, colors);
%
% %%% Draw the bars so that the gap between the bars is bigger and all
% bars are black
%
% som_barplane(map, map.codebook, 'k', '', 0.6);
%
% SEE ALSO
%
% som_cplane Visualize a 2D component plane, u-matrix or color plane
% som_plotplane Visualize the map prototype vectors as line graphs
% som_pieplane Visualize the map prototype vectors as pie charts
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Juha P 110599, Johan 140799, juuso 151199 140300 070600
%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[nargin, lattice, msize, data, color, scaling, gap, pos] = vis_planeGetArgs(varargin{:});
error(nargchk(3, 7, nargin)) % check that no. of input args is correct
% Check pos
if nargin < 7 | isempty(pos)
pos=NaN; % default value for pos (no translation)
elseif ~vis_valuetype(pos,{'1x2'})
error('Position of origin has to be given as an 1x2 vector');
end
% Check gap
if nargin < 6 | isempty(gap),
gap=0.25; % default value for gap
elseif ~vis_valuetype(gap, {'1x1'}),
error('Gap value must be scalar.');
elseif ~(gap >= 0 & gap<=1)
error('Gap value must be in interval [0,1].')
end
% Check scaling
if nargin < 5 | isempty(scaling),
scaling='varwise';
elseif ~vis_valuetype(scaling,{'string'}) | ...
~any(strcmp(scaling,{'none','unitwise','varwise'})),
error('scaling sholud be ''none'', ''unitwise'' or ''varwise''.');
end
% Check msize
if ~vis_valuetype(msize,{'1x2','nx2'}),
error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
end
% Check data
if ~isnumeric(data),
error('Data matrix has to be numeric.');
elseif length(size((data)))>2
error('Data matrix has too many dimensions!');
else
d=size(data,2);
N=size(data,1);
end
s=.8; % patch size scaling factor
switch scaling,
case 'none'
% no scaling: don't scale
% Check data max and min values
positive=any(data(:)>0); negative=any(data(:)<0);
if (positive & negative) | (~positive & ~negative),
% Data contains both negative and positive values (or is
% completely zero) baseline to centre
zeroline='zero';
elseif positive & ~negative
% Data contains only positive values: baseline to bottom
zeroline='bottom';
elseif ~positive & negative
% Data contains only negative values: baseline to top
zeroline='top';
end
case 'unitwise'
% scale the variables so that the bar for variable with the maximum
% value in the unit spans to the upper edge of the unit
% and the bar for the variable with minimum value spans to the lower edge,
% respectively.
zeroline='moving';
case 'varwise'
% Check data max and min values
positive=any(data(:)>0); negative=any(data(:)<0);
if (positive & negative) | (~positive & ~negative),
% Data contains both negative and positive values (or is
% completely zero) baseline to
% centre, scale data so that it doesn't overflow
data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
zeroline='zero';
elseif positive & ~negative
% Data contains only positive values: baseline to
% bottom, scale data so that it doesn't overflow
data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
zeroline='bottom';
elseif ~positive & negative
% Data contains only negative values: baseline to
% top, scale data so that it doesn't overflow
zeroline='top';
data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
end
otherwise
error('Unknown scaling mode?');
end
for i=1:N, % calculate patch coordinates for
v=data(i,:);
[nx,ny]=vis_barpatch(v,gap,zeroline); % bars
barx(:,(1+(i-1)*d):(i*d))=s*nx;
bary(:,(1+(i-1)*d):(i*d))=s*ny;
end
l=size(barx,1);
if size(msize,1) == 1,
xdim=msize(2);
ydim=msize(1);
if xdim*ydim~=N
error('Data matrix has wrong size.');
else
y=reshape(repmat(1:ydim,d,1),1,d*ydim); y=repmat(repmat(y,l,1),1,xdim);
x=reshape(repmat(1:xdim,l*ydim*d,1),l,N*d);
end
else
x=reshape(repmat(msize(:,1),1,l*d)',l,d*N);
y=reshape(repmat(msize(:,2),1,l*d)',l,d*N);
if N ~= size(msize,1),
error('Data matrix has wrong size.');
else
lattice='rect';
if isnan(pos),
pos=[0 0];
end
end
end
% Check lattice
if ~ischar(lattice)
error('Invalid lattice.');
end
switch lattice
case {'hexa','rect'}
pos=pos-1;
otherwise
error([ 'Lattice' lattice ' not implemented!']);
end
% Check color
% C_FLAG is for color 'none'
if nargin < 4 | isempty(color)
color=hsv(d); % default n hsv colors
end
if ~vis_valuetype(color, {[d 3],'nx3rgb'},'all') & ...
~vis_valuetype(color,{'colorstyle','1x3rgb'})
error('The color matrix has wrong size or has invalid values.');
elseif ischar(color) & strcmp(color,'none')
C_FLAG=1;
color='w';
else
C_FLAG=0;
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Making lattice.
% Command view([0 90]) shows the map in 2D properly oriented
switch lattice
case 'hexa'
t=find(rem(y(1,:),2)); % move even rows by .5
x(:,t)=x(:,t)-.5;
x=x+barx+.5;
y=y+bary;
case 'rect'
x=x+barx;
y=y+bary;
end
% NB: The coordinates in hexa are not uniform in order to get even
% y-coordinates for the nodes. This is handled by setting _axis scaling_
% so that the hexa-nodes look like uniform hexagonals. See
% vis_PlaneAxisProperties
if ~isnan(pos)
x=x+pos(1);y=y+pos(2); % move upper left corner
end % to pos
%% Set axes properties
ax=newplot; % get current axis
vis_PlaneAxisProperties(ax,lattice, msize, pos);
%% Rearrange dx3 color matrix
if ~isstr(color) & size(color,1)~=1,
color=reshape(repmat(color,N,1),[1 N*d 3]);
end
%% Draw the plane!
if isnumeric(color),
% explicit color settings by RGB-triplets won't work with
% patch in 'painters' mode, unless there only a single triplet
si = size(color);
if length(si)~=2 | any(si==[1 3]), set(gcf,'renderer','zbuffer'); end
end
h_=patch(x,y,color);
if C_FLAG
set(h_,'FaceColor','none');
end
set(h_,'Tag','planeBar'); % tag the object
%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>0, h=h_; end % Set h only if
% there really is output
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [xcoord,ycoord]=vis_barpatch(y,gap,zeroline)
x = length(y);
d = gap/(2*(x-1)+2);
step= -.5:1/x:.5;
miny=min(y);
maxy=max(y);
switch(zeroline)
case 'moving'
if miny < 0
if maxy > 0
zl = .5 - (abs(miny)/(maxy-miny)); %reverse mode
y= .5 - ((y-miny*ones(1,x))./(maxy-miny));
else
zl = -.5;
y=-.5+abs(y./miny);
end
else
zl = .5; %reverse mode
y=.5-y./maxy;
end
case 'moveNotScale'
if miny < 0
if maxy > 0
zl = 0.5+miny;
y = zl - y;
else
zl=-.5;
y=-.5+abs(y);
end
else
zl=.5;
y =.5-y;
end
case 'zero'
zl=0; y=zl-y;
case 'top'
zl=-.5; y=zl-2*y;
case 'bottom'
zl=.5; y=zl-2*y;
end
for i=1:x
xcoord(:,i) = [d+step(i);d+step(i);step(i+1)-d;step(i+1)-d;d+step(i)];
ycoord(:,i) = [zl;y(i);y(i);zl;zl];
end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_recolorbar.m
|
.m
|
mirtooloct-master/somtoolbox/som_recolorbar.m
| 12,433 |
utf_8
|
f6539ba0228a1c13d3b9e317d75ddabf
|
function h=som_recolorbar(p, ticks, scale, labels)
%SOM_RECOLORBAR Refresh and rescale colorbars in the current SOM_SHOW fig.
%
% h = som_recolorbar([p], [ticks], [scaling], [labels])
%
% colormap(jet); som_recolorbar
%
% Input and output arguments ([]'s are optional)
% [p] (vector) subplot number vector
% (string) 'all' (the default), 'comp' to process only
% component planes
% [ticks] (string) 'auto' or 'border', default: 'auto'
% (cell array) p x 1 cell array of p row vectors
% (vector) the same ticks are applied to all given subplots
% (scalar) value is at least 2: the number of ticks to show,
% evenly spaced between and including minimum and maximum
% [scale] (string) 'denormalized' or 'normalized' (the default)
% [labels] (cell array) p x 1 cell array of cells containing strings
%
% h (vector) handles to the colorbar objects.
%
% This function refreshes the colorbars in the figure created by SOM_SHOW.
% Refreshing is necessary if you have changed the colormap.
% Each colorbar has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means
% that the scale is denormalized, letter 'n' that the scale is
% normalized, and 'u' is for user specified labels.
%
% For more help, try 'type som_recolorbar' or check out online documentation.
% See also SOM_SHOW
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_recolorbar
%
% PURPOSE
%
% Refreshes the the colorbars in the figure.
%
% SYNTAX
%
% h = som_recolorbar
% h = som_recolorbar(p)
% h = som_recolorbar(p, ticks)
% h = som_recolorbar(p, ticks, scaling)
% h = som_recolorbar(p, ticks, scaling, labels)
%
% DESCRIPTION
%
% This function refreshes the colorbars in the figure created by SOM_SHOW.
% Refreshing is necessary if you have changed the colormap. Each colorbar
% has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means that the
% scale is denormalized, letter 'n' that the scale is normalized, and 'u' is
% for user specified labels.
%
% Different argument combinations:
%
% 1. Argument 'ticks' has string values:
% - 'auto' for input argument ticks sets the automatic tick
% marking on (factory default).
% - 'border' sets the tick marks to the color borders. This is
% convenient if there are only few colors in use.
%
% Argument scale controls the scaling of the tick mark label values.
% 'normalized' means that the tick mark labels are directly the values
% of the ticks, that is, they refer to the map codebook values.
% Value 'denormalized' scales the tick mark label values back to the original
% data scaling. This is made using som_denormalize_data.
%
% 2. Argument 'ticks' is a cell array of vectors:
% The values are set to be the tick marks to the colorbar specified by p.
% - if arg. scale is 'normalized' the ticks are set directly to the colorbar.
% - if arg. scale is 'denormalized' the tick values are first normalized
% in the same way as the data.
%
% 3. Argument 'ticks' is a vector
% As above, but the same values are used for all (given) subplots.
%
% 4. Argument 'ticks' is a scalar
% The ticks are set to equally spaced values between (and including)
% minimum and maximum.
%
% Argument 'labels' specify user defined labels to the tick marks
%
% NOTE: ticks are rounded to contain three significant digits.
%
% OPTIONAL INPUT ARGUMENTS
%
% p (vector) subplot number vector
% (string) 'all' (the default), 'comp' to effect only
% component planes
%
% ticks (string) 'auto' or 'border', default: 'auto'
% (cell array) p x 1 cell array of p row vectors
% (vector) as the cell array, but the same vector is
% applied to all given subplots
% (scalar) the number of ticks to show: these are
% evenly space between minimum and maximum
%
% scale (string) 'denormalized' or 'normalized' (the default)
%
% labels (cell array) p x 1 cell array of cells containing strings
%
% OUTPUT ARGUMENTS
%
% h (vector) handles to the colorbar objects.
%
% EXAMPLE
%
% colormap(jet(5)); som_recolorbar('all','border','denormalized')
% % Uses five colors and sets the ticks on the color borders.
% % Tick label values are denormalized back to the original data scaling
%
% colormap(copper(64));som_recolorbar
% % changes to colormap copper and resets default ticking and labeling
%
% som_recolorbar('all',3)
% % To put 3 ticks to each colorbar so that minimum, mean and
% % maximum values on the colorbar are shown.
%
% som_recolorbar([1 3],{[0.1 0.2 0.3];[0.2 0.4]},'denormalized')
% % Ticks colorbar 1 by first normalizing values 0.1, 0.2, 0.3 and
% % then setting the ticks to the colorbar. Labels are of course
% % 0.1, 0.2 and 0.3. Ticks colorbar 3 in the same way using values
% % 0.2 and 0.4.
%
% som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}})
% % Ticks colorbar 2 and 4 directly to the specified values. Sets labels
% % '1' '2' and 'a' 'b' to the ticks.
%
% som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}})
% % as previous one, but normalizes tick values first
%
% SEE ALSO
%
% som_show Basic SOM visualization.
% som_normalize Normalization operations.
% som_denormalize Denormalization operations.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 1.0beta Johan 061197
% Version 2.0beta juuso 151199 130300 160600 181101
%% Init & check %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error(nargchk(0, 4, nargin)) % check no. of input args
% Check the subplot vector p and get the handles, exit if error
% Default subplot vector is 'all'
if nargin < 1 | isempty(p) % default p
p= 'all';
end
% check SOM_SHOW and get the figure data. Exit, if error
[handles, msg, lattice, msize, dim, normalization, comps]= ...
vis_som_show_data(p, gcf);
error(msg);
if nargin < 2 | isempty(ticks) % default tick mode is 'auto'
ticks = 'auto';
elseif isa(ticks,'cell') % check for cell
tickValues = ticks;
ticks= 'explicit';
elseif isa(ticks,'double') & length(ticks)>1,
tickValues = {ticks};
ticks = 'explicit';
elseif isa(ticks,'double') & length(ticks)==1,
tickValues = max(2,round(ticks));
ticks = 'evenspace';
end
if ~ischar(ticks) % invalid argument
error('The second argument should be a string or a cell array of vectors.');
end
switch ticks % check ticks
case {'auto','border'}, % nill
case 'evenspace',
tickValues_tmp = cell(length(handles),1);
for i=1:length(handles), tickValues_tmp{i} = tickValues; end
tickValues = tickValues_tmp;
case 'explicit',
if length(tickValues)==1 & length(handles)>1,
tickValues_tmp = cell(length(handles),1);
for i=1:length(handles), tickValues_tmp{i} = tickValues{1}; end
tickValues = tickValues_tmp;
end
if length(tickValues) ~= length(handles),
error('Cell containing the ticks has wrong size.')
end
otherwise
error('''auto'' or ''border'' expected for the second argument.');
end
if nargin < 3 | isempty(scale) % default mode is normalized
scale= 'normalized';
end
if ~ischar(scale) % check scale type
error('The third argument should be a string.');
end
switch scale % check the string
case { 'normalized', 'denormalized'} % ok
case 'n', scale = 'normalized';
case 'd', scale = 'denormalized';
otherwise
error('''normalized'' or ''denormalized'' expected for the third argument.')
end
if nargin < 4 | isempty(labels) % default is autolabeling
labels = 'auto';
elseif ~isa(labels,'cell') % check type
error('The fourth argument should be a cell array of cells containing strings.')
else
labelValues=labels; % set labels
labels = 'explicit';
if length(labelValues) == length(handles) % check size
;
else
error('Cell containing the labels has wrong size')
end
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
n = size(colormap,1)+1; % number of colors+1
h_ = zeros(length(handles),1);
for i=1:length(handles), % MAIN LOOP BEGINS
axes(handles(i)); % set axes, refres colorbar and
if comps(i)>=0,
h_(i)=colorbar; % get colorbar handles
colorbardir=get(h_(i),'YaxisLocation');
switch colorbardir % get colorbar direction &
case 'left' % set some strings
Tick='Xtick'; Lim='Xlim'; LabelMode='XTickLabelMode'; Label='XtickLabel';
case 'right'
Tick='Ytick'; Lim='Ylim'; LabelMode='YTickLabelMode'; Label='YtickLabel';
otherwise
error('Internal error: unknown value for YaxisLocation'); % fatal
end
switch ticks
case 'auto'
set(h_(i),LabelMode,'auto'); % factory default ticking
tickValues{i}=get(h_(i),Tick); % get tick values
case 'border'
limit=caxis;
t=linspace(limit(1),limit(2),n); % set n ticks between min and max
t([1 length(t)])=get(h_(i),Lim); % <- caxis is not necerraily the same
tickValues{i}=t; % as the colorbar min & max values
case 'evenspace'
limit = caxis;
t = linspace(limit(1),limit(2),tickValues{i});
t([1 length(t)])=get(h_(i),Lim);
tickValues{i}=t;
case 'explicit'
if comps(i)>0,
if strcmp(scale,'normalized') % normalize tick values
tickValues{i} = som_normalize(tickValues{i},normalization{comps(i)});
end
end
otherwise
error('Internal error: unknown tick type') % this shouldn't happen
end
%tickValues{i} = epsto0(tickValues{i});
switch labels
case 'auto'
switch scale
case 'normalized'
labelValues{i} = round2(tickValues{i}); % use the raw ones
case 'denormalized' % denormalize tick values
if comps(i)>0,
labelValues{i} = som_denormalize(tickValues{i},normalization{comps(i)});
labelValues{i} = round2(labelValues{i}); % round the scale
else
labelValues{i} = round2(tickValues{i});
end
otherwise
error('Internal error: unknown scale type'); % this shouldn't happen
end
case 'explicit'
; % they are there already
otherwise
error('Internal error: unknown label type'); % this shouldn't happen
end
set(h_(i),Tick,tickValues{i}); % set ticks and labels
set(h_(i),Label,labelValues{i});
if comps(i)>0,
% Label the colorbar with letter 'n' if normalized, with letter 'd'
% if denormalized and 'u' if the labels are user specified
mem_axes=gca; axes(h_(i));
ch=' ';
if strcmp(scale,'normalized'), ch(1)='n'; end
if strcmp(scale,'denormalized'), ch(1)='d'; end
if strcmp(labels,'explicit'), ch(2)='u'; end
xlabel(ch);
axes(mem_axes);
end
end
end % MAIN LOOP ENDS
%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>0
h=h_;
end
return;
%% Subfunction: ROUND2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ROUND2 rounds the labels to tol significant digits
function r=round2(d)
tol=3;
zero=(d==0);
d(zero)=1;
k=floor(log10(abs(d)))-(tol-1);
r=round(d./10.^k).*10.^k;
r(zero)=0;
%r=epsto0(r);
%% Subfunction: ISVECTOR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function t=isvector(v)
% ISVECTOR checks if a matrix is a vector or not
t=(ndims(v) == 2 & min(size(v)) == 1) & isnumeric(v);
%% Subfunction: EPSTO0
function t=epsto0(t)
% EPSTO0 checks whether first tick value is *very* close to zero,
% if so sets it to zero.
if (t(end)-t(1))/t(end) > 1-0.005 & abs(t(1))<1, t(1) = 0; end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_show.m
|
.m
|
mirtooloct-master/somtoolbox/som_show.m
| 28,122 |
utf_8
|
d4dcabfc93b9206fb6cb14eeffb497f1
|
function h=som_show(sMap, varargin)
% SOM_SHOW Basic SOM visualizations: component planes, u-matrix etc.
%
% h = som_show(sMap, ['argID', value, ...])
%
% som_show(sMap);
% som_show(sMap,'bar','none');
% som_show(sMap,'comp',[1:3],'umat','all');
% som_show(sMap,'comp',[1 2],'umat',{[1 2],'1,2 only'},'comp',[3:6]);
% som_show(sMap,'size',m,'bar','vert','edge','off');
%
% Input and output arguments ([]'s are optional):
% sMap (struct) map struct
% [argID, (string) Additional parameters are given as argID, value
% value] (varies) pairs. See below for list of valid IDs and values.
%
% h (struct) struct with the following fields:
% .plane (vector) handles to the axes objecets (subplots)
% .colorbar (vector) handles to the colorbars. Colorbars for empty
% grids & RGB color planes do not exist: the
% value for them in the vector is -1.
% .label (vector) handles to the axis labels
%
% Here are the valid argument IDs and corresponding values. M is
% the number of map units
% 'comp' Which component planes to draw, title is
% the name of the component (from sMap.comp_names)
% (vector) a vector of component indices
% (string) 'all' (or '' or []) for all components
% 'compi' as 'comp' but uses interpolated shading
% 'umat' Show u-matrix calculated using specified
% components
% (vector) a vector of component indeces
% (string) 'all' (or '' or []) to use all components
% (cell) of form {v, str} uses v as the vector, and put
% str as title instead of the default 'U-matrix'
% 'umati' as 'umat' but uses interpolated shading of colors
% 'empty' (string) Make an empty plane using given string as title
% 'color' Set arbitrary unit colors explicitly
% (matrix) size Mx1 or Mx3, Mx1 matrix uses indexed
% coloring; Mx3 matrix (RGB triples as rows)
% defines fixed unit colors
% (cell) of from {color, str}. 'color' is the Mx1
% or Mx3 RGB triple matrix and 'str' is title
% string
% 'colori' as 'color' but uses interpolated shading of colors
% 'norm' (string) 'n' or 'd': Whether to show normalized 'n' or
% denormalized 'd' data values on the
% colorbar. By default denormalized values are used.
% 'bar' (string) Colorbar direction: 'horiz', 'vert' (default)
% or 'none'
% 'size' size of the units
% (scalar) same size for each unit, default is 1
% (vector) size Mx1, individual size for each unit
% 'edge' (string) Unit edges on component planes 'on'
% (default) or 'off'
% 'footnote' (string) Footnote string, sMap.name by default
% 'colormap' (matrix) user defined colormap
% 'subplots' (vector) size 1 x 2, the number of subplots in y- and
% and x-directions (as in SUBPLOT command)
%
% If identifiers 'comp', 'compi', 'umat', 'umati', 'color', 'colori'
% or 'empty' are not specified at all, e.g. som_show(sMap) or
% som_show(sMap,'bar','none'), the U-matrix and all component planes
% are shown.
%
% For more help, try 'type som_show' or check out online documentation.
% See also SOM_SHOW_ADD, SOM_SHOW_CLEAR, SOM_UMAT, SOM_CPLANE, SOM_GRID.
%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_show
%
% PURPOSE
%
% Shows basic visualizations of SOM: component planes, unified distance
% matrices as well as empty planes and fixed color planes.
%
% SYNTAX
%
% h = som_show(sMap)
% h = som_show(sMap, 'argID', value, ...)
%
% DESCRIPTION
%
% This function is used for basic visualization of the SOM. Four
% kinds of SOM planes can be shown:
%
% 1. U-matrix (see SOM_UMAT) which shows clustering structure of
% the SOM. Either all or just part of the components can
% be used in calculating the U-matrix.
% 2. component planes: each component plane shows the values of
% one variable in each map unit
% 3. an empty plane which may be used as a base for, e.g., hit
% histogram visualization or labeling (see SOM_SHOW_ADD)
% 4. a fixed or indexed color representation for showing color coding or
% clustering
%
% The component planes and u-matrices may have colorbars showing the
% scale for the variable. The scale shows by default the values that
% variables have in the map struct. It may be changed to show the
% original data values (estimated by SOM_DENORMALIZE). In this case a
% small 'd' appears below the colorbar. The orientation of these
% colorbars may be changed, or they can be removed.
%
% By default the u-matrix - calculated using all variables - and all
% component planes are shown. This is achieved by giving command
% som_show(sMap) without any further arguments
%
% REQUIRED INPUT ARGUMENTS
%
% sMap (struct) Map to be shown. If only this argument is
% specified, the function draws first the u-matrix
% calculated using all the variables followed by all
% the component planes.
%
% OPTIONAL INPUT ARGUMENTS
%
% (M is the number of map units)
%
% Optional arguments must be given as 'argID',value -pairs
%
% 'comp' Defines the variabels to be shown as component planes.
% (vector) 1xN or Nx1 vector with integer positive numbers ranging
% from 1 to the number of variables in the map codebook
% (dim). This vector determines the variables to be show
% as component planes and their order. Note that the same
% component plane (the same variable index) is allowed to
% occur several times.
% (string) 'all' or '' or []. This uses all variables, that is, it's
% the same that using value [1:dim] where dim is the
% number of variables in the codebook.
%
% 'compi' Same as 'comp' but uses a Gouraud shaded color plane
% (made using SOM_GRID function) instead of the cell-like
% visualization of 'comp' (made using SOM_CPLANE). Note
% that the color interpolation doesn't work strictly
% correctly on 'hexa' grid, as it uses rectangular grid
% (see SURF).
%
% 'umat' Show U-matrix: value defines the variables to be used
% for calculating a u-matrix.
% (vector) as in 'comps'. However, multiple occurences of the
% same variable (same variable index) are ignored.
% (string) 'all' or '' or []. This uses all variables, that is,
% is the same as using value [1:dim] where dim is the
% number of variables in the codebook.
% (cell) of form {v, str} where v is a valid index vector for 'umat'
% (see above) and str is a string that is used as a title
% for the u-matrix instead of the default title
% 'U-matrix'. This may be useful if several u-matrices
% are shown in the same figure.
%
% 'umati' Same as 'umat' but uses shaded color plane (see 'compi').
%
% 'empty' Show an empty plane (patch edges only)
% (string) value is used as title
%
% 'color' Define fixed RGB colors for the map units
% (matrix) a Mx3 matrix of RGB triples as rows
% (vector) a Mx1 vector of any values: sets indexed coloring using
% the current colormap (as SURF does)
% (matrix) a Mx3xN matrix of RGB triples as rows. This gives N
% color planes.
% (matrix) a Mx1xN matrix of any values: sets indexed coloring using
% the current colormap (as SURF does). This gives N
% color planes.
% (cell) of form {rgb, str} where rgb is a Mx3 (xN) matrix of RGB
% triples as rows and str is a string that is used as
% title(s).
% (cell) of form {v, str} where v is a Mx1(xN) matrix of values
% and str is a string that is used as title(s).
%
% 'colori' Same as 'color' but uses shaded color plane (see 'compi').
%
% 'norm' Defines whether to use normalized or denormalized
% values in the colorbar. If denormalized values are
% used, they are acquired from SOM_DENORMALIZE function
% using sMap.comp_norm field.
% (string) 'd' (default) for denormalized values and 'n' for
% normalized values. The corresponding letter appears
% below the colorbar.
%
% 'bar' Define the direction of the colorbars for component planes
% and U-matrices or turn them completely off.
% (string) 'vert' (default), 'horiz' or 'none'. 'vert' gives
% vertical and 'horiz' horizontal colorbars. 'none'
% shows no colorbars at all.
%
% 'size' Define sizes of the units.
% (scalar) all units have the same size (1 by default)
% (vector) size Mx1, each unit gets individual size scaling
% (as in SOM_CPLANE)
%
% 'edge' Unit edges on component plane visualizations.
% (string) 'on' or 'off' determines whether the unit edges on component
% planes ('comp') are shown or not. Default is 'off'. Note that
% U-matrix and color planes are _always_ drawn without edges.
%
% 'footnote' Text on the figure
% (string) is printed as a movable text object on the figure
% where it may be moved using mouse. Default value is the
% string in the sMap.name field. Note: value [] gives the
% string, but input value '' gives no footnote a all.
% See VIS_FOOTNOTE for more information on the text object
% and ways to change its font size.
%
% 'colormap' som_show ghages the colormap by default to a gray-level map
% (matrix) This argument is used to set some other colormap.
%
% 'subplots' the number of subplots in y- and x-directions, as in
% (vector) command SUBPLOT
%
% OUTPUT ARGUMENTS
%
% h (struct)
% .plane (vector) handles to the axes objects (subplots)
% .colorbar (vector) handles to the colorbars. Colorbars of empty
% & color planes do not exist: the corresponding
% value in the vector is -1
% .label (vector) handles to the axis labels
%
% OBJECT TAGS
%
% The property field 'Tag' of the axis objects created by this function
% are set to contain string 'Cplane' if the axis contains component plane
% ('comp'), color plane ('color') or empty plane ('empty') and string
% 'Uplane' if it contains a u-matrix ('umat'). The tag is set to
% 'CplaneI' for planes created using 'compi' and 'colori', and
% 'UplaneI' for 'umati'.
%
% FEATURES
%
% Note that when interpolated shading is used in coloring ('compi' and
% 'colori') the standard built-in bilinear Gouraud interpolation for a
% SURF object is used. If the lattice is hexagonal - or anything else than
% rectangular in general - the result is not strictly what is looked
% for, especially if the map is small.
%
% EXAMPLES
%
%% Make random data, normalize it, and give component names
%% Make a map
%
% data=som_data_struct(rand(1000,3),'comp_names',{'One','Two','Three'});
% data=som_normalize(data,'var');
% map=som_make(data);
%
%% Do the basic visualization with som_show: u-matrix and all
%% component planes
%
% som_show(map);
%
%% The values shown in the colorbar are denormalized codebook values
%% (if denormalization is possible). To view the actual values, use
%% the ..., 'norm', 'n' argument pair.
%
% som_show(map,'norm','n')
%
%% Something more complex:
%% Show 1-2. Component planes 1 and 2 (variables 'One' and 'Two')
%% 3. U-matrix that is calculated only using variables
%% 'One' and 'Two'
%% with title '1,2 only'
%% 4. U-matrix that is calculated using all variables with the
%% deafult title 'U-matrix'
%% 5. The color code (in c) with title 'Color code'
%% 6. Component plane 3 (variable 'Three')
%% and use vertical colorbars and and the values
%% But first: make a continuous color code (see som_colorcode)
%
% c=som_colorcode(map,'rgb1');
%
% som_show(map,'comp',[1 2],'umat',{1:2,'1,2 only'},'umat','all', ...
% 'color',{c,'Color code'},'bar','vert','norm','n','comp',3)
%
% SEE ALSO
%
% som_show_add Show hits, labels and trajectories on SOM_SHOW visualization.
% som_show_clear Clear hit marks, labels or trajectories from current figure.
% som_umat Compute unified distance matrix of self-organizing map.
% som_grid Visualization of a SOM grid.
% som_cplane Visualization of component, u-matrix and color planes.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 1.0beta johan 100298
% Version 2.0beta johan 201099 juuso 181199 johan 011299-100200
% juuso 130300 190600
%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error(nargchk(1,Inf,nargin)) % check no. of input args
if isstruct(sMap), % check map
[tmp,ok,tmp]=som_set(sMap);
if all(ok) & strcmp(sMap.type,'som_map')
;
else
error('Map struct is invalid!');
end
else
error('Requires a map struct!')
end
munits=size(sMap.codebook,1); % numb. of map units
d=size(sMap.codebook,2); % numb. of components
msize=sMap.topol.msize; % size of the map
lattice=sMap.topol.lattice; % lattice
if length(msize)>2
error('This visualizes only 2D maps!')
end
if rem(length(varargin),2)
error('Mismatch in identifier-value pairs.');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read in optional arguments
if isempty(varargin),
varargin = { 'umat','all','comp','all'};
end
%% check the varargin and build visualization infostrcuts
% Vis: what kind of planes, in which order, what are the values in
% the units
% Vis_param: general properties
% see subfunction
% The try-catch construction is here just for avoiding the
% possible termination to happen in subfunction because an error
% message containing subfunction line numbers etc. might be confusing, as
% there probably is nothing wrong with the subfunction but with the
% input. Ok, this isn't proper programming sytle...
try
[Plane, General]= check_varargin(varargin, munits, d, sMap.name);
catch
error(lasterr);
end
% Set default values for missing ones
% No planes at all (only general properties given in varargin):
% set default visualization
if isempty(Plane)
varargin = [varargin, { 'umat','all','comp','all'}];
% and again we go...
try
[Plane, General]= check_varargin(varargin, munits, d, sMap.name);
catch
error(lasterr);
end
end
% set defaults for general properties
if isempty(General.colorbardir)
General.colorbardir='vert';
end
if isempty(General.scale)
General.scale='denormalized';
end
if isempty(General.size)
General.size=1;
end
if isempty(General.edgecolor)
General.edgecolor='none';
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% get rid of an annoying warning: "RGB color data not yet supported in
% Painter's mode."
%set(gcf, 'renderer','zbuffer');
%% -> a much more annoying thing results: the output to PostScript is
%% as bitmap, making the files over 6MB in size...
n=length(Plane); % the number of subfigures
% get the unique component indices
c=General.comp(General.comp>0);
c=setdiff(unique(c),[0 -1]);
c=c(~isnan(c));
% estimate the suitable dimension for
if isempty(General.subplots),
y=ceil(sqrt(n)); % subplots
x=ceil(n/y);
else
y = General.subplots(2);
x = General.subplots(1);
if y*x<n,
error(['Given subplots grid size is too small: should be >=' num2str(n)]);
end
end
clf; % clear figure
for i=1:n, % main loop
h_axes(i,1)=subplot(x,y,i); % open a new subplot
% Main switch: select function according to the flags set in comps
switch Plane{i}.mode
case 'comp'
%%% Component plane
tmp_h=som_cplane(lattice,msize, sMap.codebook(:,General.comp(i)), ...
General.size);
set(tmp_h,'EdgeColor', General.edgecolor);
set(h_axes(i),'Tag','Cplane');
h_label(i,1)=xlabel(sMap.comp_names{General.comp(i)});
case 'compi'
%%% Component plane (interpolated shading)
tmp_h=som_grid(lattice, msize, 'surf', sMap.codebook(:,Plane{i}.value), ...
'Marker', 'none', 'Line', 'none');
set(h_axes(i),'Tag','CplaneI');
h_label(i,1)=xlabel(sMap.comp_names(Plane{i}.value));
vis_PlaneAxisProperties(gca,lattice,msize,NaN);
case 'color'
%%% Color plane
tmp_h=som_cplane(lattice,msize,Plane{i}.value,General.size);
set(tmp_h,'EdgeColor','none');
set(h_axes(i),'Tag','Cplane');
h_label(i,1)=xlabel(Plane{i}.name);
case 'colori'
%%% Color plane (interpolated shading)
tmp_h=som_grid(lattice, msize, 'surf', Plane{i}.value, 'Marker', 'none', ...
'Line', 'none');
set(h_axes(i),'Tag','CplaneI');
h_label(i,1)=xlabel(Plane{i}.name);
vis_PlaneAxisProperties(gca,lattice,msize,NaN);
case 'empty'
%%% Empty plane
tmp_h=som_cplane(lattice,msize,'none');
h_label(i,1)=xlabel(Plane{i}.name);
set(h_axes(i),'Tag','Cplane');
case 'umat'
%%% Umatrix
u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'median',...
'mask',sMap.mask(Plane{i}.value)); u=u(:);
tmp_h=som_cplane([lattice 'U'],msize,u);
set(tmp_h,'EdgeColor','none');
set(h_axes(i),'Tag','Uplane');
h_label(i,1)=xlabel(Plane{i}.name);
case 'umati'
%%% Umatrix (interpolated shading)
u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'mean',...
'mask',sMap.mask(Plane{i}.value)); u=u(1:2:end,1:2:end);
u=u(:);
tmp_h=som_grid('rect', msize, 'surf', u, ...
'Marker', 'none', 'Line', 'none', ...
'coord', som_vis_coords(lattice,msize));
set(h_axes(i),'Tag','UplaneI');
h_label(i,1)=xlabel(Plane{i}.name);
vis_PlaneAxisProperties(gca,lattice,msize,NaN);
otherwise
error('INTERNAL ERROR: unknown visualization mode.');
end
%%% Adjust axis ratios to optimal (only 2D!) and put the
%%% title as close to axis as possible
set(h_label,'Visible','on','verticalalignment','top');
set(gca,'plotboxaspectratio',[msize(2) msize(1) msize(1)]);
%%% Draw colorbars if they are turned on and the plane is umat or c-plane
if General.comp(i)> -1 & ~strcmp(General.colorbardir,'none'),
h_colorbar(i,1)=colorbar(General.colorbardir); % colorbars
else
h_colorbar(i,1)=-1;
General.comp(i)=-1;
end
end %% main loop ends
% Set window name
set(gcf,'Name',[ 'Map name: ' sMap.name]);
%% Set axes handles to the UserData field (for som_addxxx functions
%% and som_recolorbar)
%% set component indexes and normalization struct for som_recolorbar
SOM_SHOW.subplotorder=h_axes;
SOM_SHOW.msize=msize;
SOM_SHOW.lattice=lattice;
SOM_SHOW.dim=d;
SOM_SHOW.comps=General.comp;
SOM_SHOW.comp_norm=sMap.comp_norm; %(General.comp(find(General.comp>0)));
set(gcf,'UserData', SOM_SHOW);
% Set text property 'interp' to 'none' in title texts
set(h_label,'interpreter','none');
h_colorbar=som_recolorbar('all', 3, General.scale); %refresh colorbars
% Set a movable text to lower corner pointsize 12.
vis_footnote(General.footnote); vis_footnote(12);
% set colormap
colormap(General.colormap);
%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout > 0
h.plane=h_axes; h.colorbar=h_colorbar; h.label=h_label;
end
%%%%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Plane, General]=check_varargin(args, munits, dim, name)
% args: varargin of the main function
% munits: number of map units
% dim: map codebook dimension
% name: map name
% Define some variables (they must exist later)
Plane={}; % stores the visualization data for each subplot
General.comp=[]; % information stored on SOM_SHOW figure (which component)
General.size=[]; % unit size
General.scale=[]; % normalization
General.colorbardir=[]; % colorbar direction
General.edgecolor=[]; % edge colors
General.footnote=name; % footnote text
General.colormap=colormap; % default colormap (used to be gray(64).^.5;)
General.subplots=[]; % number of subplots in y- and x-directions
for i=1:2:length(args),
%% Check that all argument types are strings
if ~ischar(args{i}),
error('Invalid input identifier names or input argument order.');
end
%% Lower/uppercase in identifier types doesn't matter:
identifier=lower(args{i}); % identifier (lowercase)
value=args{i+1};
%%% Check first the identifiers that define planes and get values
%%% to the visualization data struct array Plane.
%%% (comps,compi,umat,color,empty) Note that name, value and comp_
%%% must be specified in these cases
%%% comp_ are collected to comp in order. This is stored to the
%%% SOM_SHOW user property field to give information for SOM_RECOLROBAR
%%% how to operate, i.e., which component is in which subplot:
%%% comp(i)=0: draw colorbar, but no normalization (umat)
%%% comp(i)=1...N: a component plane of variable comp(i)
%%% comp(i)=-1: no colorbar (color or empty plane)
switch identifier
case {'comp','compi'}
%%% Component planes: check values & set defaults
if ~vis_valuetype(value,{'nx1','1xn','string'}) & ~isempty(value),
error([ 'A vector argument or string ''all'' expected for ''' ...
identifier '''.'])
end
if isempty(value)
value=1:dim;
elseif ischar(value),
if ~strcmp(value,'all')
error([ 'Only string value ''all'' is valid for ''' ...
identifier '''.']);
else
value=1:dim;
end
else
value=round(value);
if min(value)<1 | max(value)>dim,
error([ 'Component indices out of range in ''' identifier '''.'])
end
end
if size(value,1)==1, value=value';end
comp_=value;
name=[]; % name is taken form sMap by index in main loop
case {'umat','umati'}
%%% Check first the possible cell input
if iscell(value),
if ndims(value) ~= 2 | any(size(value) ~= [1 2]) | ...
~vis_valuetype(value{2},{'string'}),
error('Cell input for ''umat'' has to be of form {vector, string}.');
else
name=value{2}; value=value{1};
end
else
name='U-matrix'; % no cell: default title is set
end
if ~vis_valuetype(value,{'nx1','1xn','string'}) & ~isempty(value),
error('Vector, string ''all'', or cell {vector, string} expected for ''umat''.')
end
if isempty(value)
value=1:dim;
elseif ischar(value),
if ~strcmp(value,'all')
error('Only string value ''all'' is valid for ''umat''.')
else
value=1:dim;
end
else
value=unique(round(value));
end
if min(value)<1 | max(value)>dim,
error('Component indices out of range in ''umat''.')
end
if size(value,1)==1, value=value';end
comp_=0;
case 'empty'
%%% Empty plane: check values & set defaults
if ~vis_valuetype(value,{'string'}),
error('A string value for title name expected for ''empty''.');
end
name=value;
comp_=-1;
case { 'color','colori'}
%%% Color plane: check values & set defaults
% Check first the possible cell input
if iscell(value),
if ndims(value)~=2 | any(size(value) ~= [1 2]) | ...
~vis_valuetype(value{2},{'string'}),
error([ 'Cell input for ''' identifier ...
''' has to be of form {M, string}.']);
else
name=value{2}; value=value{1};
end
else
name='Color code'; % no cell: default title is set
end
if size(value,1)~=munits | ...
(~vis_valuetype(value,{'nx3rgb'}) & ...
~vis_valuetype(value,{'nx1'}) & ...
~vis_valuetype(value,{'nx1xm'}) & ...
~vis_valuetype(value,{'nx3xdimrgb'})),
error(['Mx3 or Mx3xN RGBmatrix, Mx1 or Mx1xN matrix, cell '...
'{RGBmatrix, string},' ...
' or {matrix, string} expected for ''' identifier '''.']);
end
% if colormap is fixed, we don't draw colorbar (comp_ flag is -1)
% if colormap is indexed, we draw colorbar as in umat (comp_=0)
if size(value,2)==3
comp_=-1;
else
comp_=0;
end
%%%% The next things are general properties of the visualization---
%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 'size'
%%% Unit size: check & set
if ~vis_valuetype(value,{'1x1',[munits 1]})
error('A munits x 1 vector or a scalar expected for ''size''.')
end
if isempty(value),
General.size=1;
else
General.size=value;
end
case 'bar'
%%% Colorbar existence & direction: check & set
if ~vis_valuetype(value,{'string'})
error('String value expected for ''bar''.')
elseif isempty(value)
value='vert';
end
if any(strcmp(value,{'vert','horiz','none'})),
General.colorbardir=value;
else
error('String ''vert'', ''horiz'' or ''none'' expected for ''bar''.');
end
case 'norm'
%%% Value normalization: check & set
if ~vis_valuetype(value,{'string'})
error('String ''n'' or ''d'' expected for ''norm''.');
elseif isempty(value)
value='n';
end
if strcmp(value(1),'n'),
General.scale='normalized';
elseif strcmp(value(1),'d'),
General.scale='denormalized';
else
error('String ''n(ormalized)'' or ''d(enormalized)'' expected for ''norm''.');
end
case 'edge'
%%% Edge on or off : check % set
if ~vis_valuetype(value,{'string'}) & ~isempty(value),
error('String value expected for ''edge''.')
elseif ~isempty(value),
switch value
case 'on'
General.edgecolor='k';
case 'off'
General.edgecolor='none';
otherwise
error('String value ''on'' or ''off'' expected for ''edge''.')
end
end
case 'footnote'
%%% Set the movable footnote text
if ~vis_valuetype(value,{'string'})
if ~isempty(value),
error('String value expected for ''footnote''.');
else
General.footnote=sMap.name;
end
else
General.footnote=value;
end
case 'colormap'
%%% Set the colormap
if isempty(value)
General.colormap=gray(64).^2;
elseif ~vis_valuetype(value,{'nx3rgb'})
error('Colormap is invalid!');
else
General.colormap=value;
end
case 'subplots'
%%% set the number of subplots
if ~vis_valuetype(value,{'1x2'}) & ~vis_valuetype(value,{'2x1'})
error('Subplots grid size is invalid!');
else
General.subplots=value;
end
otherwise
%%% Unknown identifier
error(['Invalid argument identifier ''' identifier '''!']);
end
%%% Set new entry to the Plane array if the indentifier means
%%% making a new plane/planes
tail=length(Plane);
switch identifier
case {'comp','compi'}
for i=1:length(value)
Plane{tail+i}.mode=identifier;
Plane{tail+i}.value=value(i);
Plane{tail+i}.name=name; % not used actually
end
General.comp = [General.comp; comp_];
case {'umat','umati','empty'}
Plane{tail+1}.mode=identifier;
Plane{tail+1}.value=value;
Plane{tail+1}.name=name;
General.comp = [General.comp; comp_];
case {'color','colori'},
for i=1:size(value,3),
Plane{tail+i}.mode=identifier;
Plane{tail+i}.name=[name '_' num2str(i)];
Plane{tail+i}.value=value(:,:,i);
General.comp = [General.comp; comp_];
end
if size(value,3)==1,
Plane{tail+1}.name=name;
end
otherwise
; % do nothing
end
end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_show_gui.m
|
.m
|
mirtooloct-master/somtoolbox/som_show_gui.m
| 21,167 |
utf_8
|
04711e9a1ccd09acfe6f6d238cf212f5
|
function fig = som_show_gui(input,varargin)
%SOM_SHOW_GUI A GUI for using SOM_SHOW and associated functions.
%
% h = som_show_gui(sM);
%
% Input and output arguments:
% sM (struct) a map struct: the SOM to visualize
% h (scalar) a handle to the GUI figure
%
% This is a graphical user interface to make the usage of SOM_SHOW and
% associated functions somewhat easier for beginning users of the SOM
% Toolbox.
%
% How to use the GUI:
% 1. Start the GUI by giving command som_show_gui(sM);
% 2. Build a list of visualization planes using the buttons
% ('Add components', etc.) on the right
% - the options associated with each of the planes can be
% modified by selecting a plane from the list, and pressing
% the 'Plane options' button
% - the controls below the list apply to all planes
% - the subplot grid size can be controlled using the 'subplots'
% field on top right corner, e.g. '4 3' to get 4 times 3 grid
% 3. To visualize the planes, press the 'Visualize' button on the bottom.
% 4. To add hits, labels, trajectories (or comets) to the
% visualization, or clear them, or reset the colorbars,
% see the tools available from the 'Tools' menu.
% - the arguments to those tools are either given in the tool,
% or read from the workspace ('Select variable' buttons)
% - the tools always apply to the latest figure created
% by the GUI
% 5. To quit, press the 'Close' button on the bottom.
%
% Known bugs:
% - Especially when using the adding tools, you can easily
% give arguments which do not fit each other, and this
% results in a lengthy (and often cryptic) error message.
% In such a case, check the arguments you are giving to see
% if there's something wrong with them. See function
% SOM_SHOW_ADD for more information on how the options
% can be set.
% - The default values in the adding tools may not be
% very reasonable: you may have to try out different
% values for e.g. markersize before getting the kind of
% result you want.
%
% SOM_SHOW_GUI has two subfunctions: VIS_SHOW_GUI_COMP and
% VIS_SHOW_GUI_TOOL. These are for internal use of SOM_SHOW_GUI.
%
% See also SOM_SHOW, SOM_SHOW_ADD, SOM_SHOW_CLEAR, SOM_RECOLORBAR.
% Copyright (c) 2000 by Roman Feldman and Juha Vesanto
% Contributed to SOM Toolbox on August 22nd, 2000
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta roman 160800 juuso 220800
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MAIN %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
warning off;
if (nargin < 1)
errordlg({'Make sure you have SOM as input argument'; ''; ...
'example: som_show_gui(sMap)'},'Error in SOM_VIS: input arguments');
return
end
if isstruct(input)
fig_h = create_main_gui(input);
if (nargout > 0) fig = fig_h; end
return;
elseif ischar(input)
action = lower(input);
%
udata = get(varargin{1},'UserData');
plot_array = udata.plot_array;
l = length(plot_array);
list1_h = udata.h(1);
if (strcmp(action,''))
errordlg('','Error in SOM_VIS: input arguments');
return;
%%%%%%%%%%%%%%%%%%%%
% add_selected_comp
%
elseif (strcmp(action,'add_selected_comp'))
if isempty(plot_array(1).string), tmp = 1; else tmp = l+1; end
[sel,ok] = listdlg('ListString',udata.sM.comp_names,...
'Name','Component selection',...
'PromptString','Select components to add');
if ok & ~isempty(sel),
for i=1:length(sel),
plot_array(tmp+i-1).string = udata.sM.comp_names{sel(i)};
plot_array(tmp+i-1).args = {'comp' sel(i)};
udata.property{tmp+i-1} = {0};
end
set(list1_h,'Value',tmp+i-1, ...
'String',{plot_array(:).string});
end
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% add_all_comps
%
elseif (strcmp(action,'add_all_comps'))
if (strcmp(plot_array(1).string,''))
tmp = 1;
else
tmp = l+1;
end
indx = length(udata.sM.comp_names);
for (i=1:indx)
plot_array(tmp+i-1).string = udata.sM.comp_names{i};
plot_array(tmp+i-1).args = {'comp' i};
udata.property{tmp+i-1} = {0};
end
% update list
set(list1_h,'Value',tmp+indx-1, ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% add_u_matrix
%
elseif (strcmp(action,'add_u_matrix'))
if (strcmp(plot_array(1).string,''))
tmp = 1;
else
tmp = l+1;
end
plot_array(tmp).string = 'U-matrix';
plot_array(tmp).args = {'umat' 'all'};
udata.property{tmp} = {0 'U-matrix' 1:length(udata.sM.comp_names)};
% update list
set(list1_h,'Value',tmp, ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% add_colorplane
%
elseif (strcmp(action,'add_colorplane'))
if (strcmp(plot_array(1).string,''))
tmp = 1;
else
tmp = l+1;
end
plot_array(tmp).string = 'color plane';
c = som_colorcode(udata.sM);
plot_array(tmp).args = {'color' c};
udata.property{tmp} = {0 'Color code' {'rgb1' 'rgb2' 'rgb3' 'rgb4' 'hsv' '-variable-'} 1};
% update list
set(list1_h,'Value',tmp, ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% add_empty
%
elseif (strcmp(action,'add_empty'))
if (strcmp(plot_array(1).string,''))
tmp = 1;
else
tmp = l+1;
end
plot_array(tmp).string = 'empty plane';
plot_array(tmp).args = {'empty' ''};
udata.property{tmp} = {''};
% update list
set(list1_h,'Value',tmp, ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% remove
%
elseif (strcmp(action,'remove'))
rm_indx = get(list1_h,'Value');
rm_l = length(rm_indx);
% rebuild array
incl_inds = setdiff(1:length(plot_array),rm_indx);
if isempty(incl_inds),
clear plot_array;
plot_array(1).args = {};
plot_array(1).string = '';
udata.property = {};
udata.property{1} = {};
else
plot_array = plot_array(incl_inds);
udata.property = udata.property(incl_inds);
end
set(list1_h,'Value',length(plot_array), ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% remove_all
%
elseif (strcmp(action,'remove_all'))
plot_array = [];
plot_array(1).args = {};
plot_array(1).string = '';
udata.property = {};
set(list1_h,'Value',1, ...
'String',{plot_array(:).string});
udata.plot_array = plot_array;
set(varargin{1},'UserData',udata);
%%%%%%%%%%%%%%%%%%%%
% more_options
%
elseif (strcmp(action,'more_options'))
vis_show_gui_comp(varargin{1},get(list1_h,'Value'),'init');
%%%%%%%%%%%%%%%%%%%%
% close
%
elseif (strcmp(action,'close'))
close(varargin{1});
%%%%%%%%%%%%%%%%%%%%
% visualize
%
elseif (strcmp(action,'visualize')) %% s = {k k.^2}; plot(s{:});
current_fig = varargin{1};
figure;
args = [{udata.sM} plot_array(:).args];
% edge
tmp = get(udata.h(2),'UserData');
i = get(udata.h(2),'Value');
args = [args {'edge' tmp{i}}];
% bar
tmp = get(udata.h(3),'UserData');
i = get(udata.h(3),'Value');
args = [args {'bar' tmp{i}}];
% norm
tmp = get(udata.h(4),'UserData');
i = get(udata.h(4),'Value');
args = [args {'norm' tmp{i}}];
% size
tmp = get(udata.h(5),'String');
args = [args {'size' eval(tmp)}];
% colormap
tmp = get(udata.h(6),'String');
if ~isempty(tmp)
args = [args {'colormap' eval(tmp)}];
end
% footnote
tmp = get(udata.h(7),'String');
args = [args {'footnote' tmp}];
% subplots
tmp = get(udata.h(8),'String');
if ~(strcmp(tmp,'default') | isempty(tmp))
tmp2 = sscanf(tmp,'%i %i');
if length(tmp2)<2, tmp2 = sscanf(tmp,'%ix%i'); end
if length(tmp2)<2, tmp = eval(tmp);
else tmp = tmp2';
end
if length(tmp)<2, tmp(2) = 1; end
if tmp(1)*tmp(2)<length(get(udata.h(1),'string')),
close(varargin{1});
errordlg('Too small subplot size', ...
'Error in SOM_VIS: subplot size');
return;
end
args = [args {'subplots' tmp}];
end
som_show(args{:});
% udata.vis_h = varargin{1};
% first refresh plot info
udata.vis_h = setdiff( ...
udata.vis_h, ...
setdiff(udata.vis_h,get(0,'children')));
udata.vis_h = [udata.vis_h gcf];
set(current_fig,'UserData',udata);
else
;
end
else
errordlg({'Make sure you have SOM as input argument'; ''; ...
'example: som_show_gui(sMap)'},'Error in SOM_VIS: input arguments');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ---------------------- SUBFUNCTIONS ----------------------- %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CREATE_MAIN_GUI %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function fig_h = create_main_gui(sM)
oldFigNumber=watchon;
% init variables
FIGURENAME = 'SOM_SHOW_GUI';
plot_array = [];
plot_array(1).args = {};
plot_array(1).string = '';
% colors
fig_color = [0.8 0.8 0.8];
bg_color1 = [0.701960784313725 0.701960784313725 0.701960784313725];
bg_color2 = [0.9 0.9 0.9];
%%%% positions %%%%%
%--------- figure
fig_i = [0.02 0.25];
fig_s = [0.24 0.55];
%---------
ue = 0.02;
th = 0.03;
hint_text_pos = [0.05 0.94 0.8 th];
big_frame_pos = [ue 0.38 (1-2*ue) 0.56];
planes_listbox_text_pos = [0.07 0.87 0.3 th];
planes_listbox_pos = [(ue+0.03) 0.395 0.46 0.47];
subplots_text_pos = [0.53 0.885 0.2 th];
subplots_pos = [0.73 0.88 0.22 0.05]; ah = 0.045; d = (planes_listbox_pos(4) - 10*ah)/7;
add_components_pos = [0.53 (sum(planes_listbox_pos([2 4]))-ah) 0.42 ah]; tmp = add_components_pos(2)-(d+ah);
add_all_components_pos = [0.53 tmp 0.42 ah]; tmp = add_all_components_pos(2)-(d+ah);
add_u_matrix_pos = [0.53 tmp 0.42 ah]; tmp = add_u_matrix_pos(2)-(d+ah);
add_colorplane_pos = [0.53 tmp 0.42 ah]; tmp = add_colorplane_pos(2)-(d+ah);
add_empty_pos = [0.53 tmp 0.42 ah]; tmp = add_empty_pos(2)-2*(d+ah)+d;
remove_pos = [0.53 tmp 0.42 ah]; tmp = remove_pos(2)-(d+ah);
remove_all_pos = [0.53 tmp 0.42 ah]; tmp = remove_all_pos(2)-2*(d+ah)+d;
plane_options_pos = [0.53 tmp 0.42 ah];
ph = 0.041;
dd = (ph-th)/2;
tmp = big_frame_pos(2) - (planes_listbox_pos(2)-big_frame_pos(2)) - ph;
ie1 = 0.25;
tw = 0.21;
iw = 0.28;
unit_edges_text_pos = [ue (tmp+dd) tw th];
unit_edges_pos = [ie1 tmp iw ph]; tmp = unit_edges_pos(2)-(d+ph) - d;
unit_sizes_text_pos = [ue (tmp+dd) tw th];
unit_sizes_pos = [ie1 tmp iw ph]; tmp = unit_sizes_pos(2)-(d+ph) - d;
colorbar_dir_text_pos = [ue (tmp+dd) tw th];
colorbar_dir_pos = [ie1 tmp iw ph]; tmp2 = sum(colorbar_dir_pos([1 3]));
colorbar_norm_text_pos = [(tmp2) (tmp+dd) 0.11 th];
colorbar_norm_pos = [(1-ue-(iw+0.06)) tmp (iw+0.06) ph]; tmp = colorbar_norm_pos(2)-(d+ph) - d;
colormap_text_pos = [ue (tmp+dd) tw th];
colormap_pos = [ie1 tmp iw ph]; tmp = colormap_pos(2)-(d+ph) - d;
footnote_text_pos = [ue (tmp+dd) tw th];
footnote_pos = [ie1 tmp (1-ue-ie1) ph];
tmp = planes_listbox_pos(2)-big_frame_pos(2);
tmp2 = ah+2*tmp;
little_frame_pos = [ue tmp (1-2*ue) tmp2]; tmp2 = little_frame_pos(2)+tmp;
ddd = 0.1;
bw = (little_frame_pos(3)-2*0.03-ddd)/2;
visualize_pos = [(ue+0.03) tmp2 bw ah];
close_pos = [(sum(visualize_pos([1 3]))+ddd) tmp2 bw ah];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% main figure
%
fig_h = figure( ...
'Units','normalized', ...
'Color',fig_color, ...
'PaperPosition',[18 180 576 432], ...
'PaperType','A4', ...
'PaperUnits','normalized', ...
'Position',[fig_i fig_s], ...
'ToolBar','none', ...
'NumberTitle','off', ...
'Name',FIGURENAME, ...
'Visible','off');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% hint text
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',hint_text_pos, ...
'String','Add planes and then visualize', ...
'Style','text');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% planes listbox
%
uicontrol( ...
'Units','normalized', ...
'Position',big_frame_pos, ...
'Style','frame');
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color1, ...
'HorizontalAlignment','left', ...
'Position',planes_listbox_text_pos, ...
'String','Planes', ...
'Style','text');
list1_h = uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color2, ...
'Position',planes_listbox_pos, ...
'String',{plot_array(:).string}, ...
'Style','listbox', ...
'Max',2, ...
'Value',1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% edit subplots
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color1, ...
'HorizontalAlignment','center', ...
'Position',subplots_text_pos, ...
'String','subplots', ...
'Style','text');
edit4_h = uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color2, ...
'Position',subplots_pos, ...
'FontSize',14, ...
'String','', ...
'Style','edit');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Add components'
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color1, ...
'HorizontalAlignment','left', ...
'Position',add_components_pos, ...
'String',' Add components', ...
'Callback',['som_show_gui(''add_selected_comp'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Add all components'
%
uicontrol( ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'Position',add_all_components_pos, ...
'String',' Add all components', ...
'Callback',['som_show_gui(''add_all_comps'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Add U-matrix'
%
uicontrol( ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'Position',add_u_matrix_pos, ...
'String',' Add U-matrix', ...
'Callback',['som_show_gui(''add_u_matrix'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Add colorplane'
%
uicontrol( ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'Position',add_colorplane_pos, ...
'String',' Add colorplane', ...
'Callback',['som_show_gui(''add_colorplane'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Add empty'
%
uicontrol( ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'Position',add_empty_pos, ...
'String',' Add empty', ...
'Callback',['som_show_gui(''add_empty'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Remove'
%
uicontrol( ...
'Units','normalized', ...
'HorizontalAlignment','left', ...
'Position',remove_pos, ...
'String',' Remove', ...
'Callback',['som_show_gui(''remove'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% creat pushbutton 'Remove all'
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color1, ...
'HorizontalAlignment','left', ...
'Position',remove_all_pos, ...
'String',' Remove all', ...
'Callback',['som_show_gui(''remove_all'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Plane options'
%
uicontrol( ...
'Units','normalized', ...
'Position',plane_options_pos, ...
'String',' Plane options', ...
'Callback',['som_show_gui(''more_options'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% popupmenu unitedges
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',unit_edges_text_pos, ...
'String','unit edges are', ...
'Style','text');
popup1_h = uicontrol( ...
'Units','normalized', ...
'Max',2, ...
'Min',1, ...
'Position',unit_edges_pos, ...
'UserData',{'off' 'on'}, ...
'String',{'off' 'on'}, ...
'Style','popupmenu', ...
'Value',1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% unit sizes edit
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',unit_sizes_text_pos, ...
'String','unit sizes', ...
'Style','text');
edit1_h = uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color2, ...
'Position',unit_sizes_pos, ...
'FontSize',12, ...
'String','1', ...
'Style','edit');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% popupmenu colorbardir
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',colorbar_dir_text_pos, ...
'String','colorbar is', ...
'Style','text');
popup2_h = uicontrol( ...
'Units','normalized', ...
'Max',3, ...
'Min',1, ...
'Position',colorbar_dir_pos, ...
'UserData', {'vert' 'horiz' 'none'}, ...
'String','vert| horiz| none', ...
'Style','popupmenu', ...
'Value',1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% popupmenu colorbarnorm
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',colorbar_norm_text_pos, ...
'String',' and ', ...
'Style','text');
popup3_h = uicontrol( ...
'Units','normalized', ...
'Max',2, ...
'Min',1, ...
'Position',colorbar_norm_pos, ...
'UserData', {'d' 'n'}, ...
'String',{'denormalized','normalized'}, ...
'Style','popupmenu', ...
'Value',1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% colormap edittext
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',colormap_text_pos, ...
'String','colormap', ...
'Style','text');
edit2_h = uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color2, ...
'Position',colormap_pos, ...
'FontSize',12, ...
'String','', ...
'Style','edit');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% footnote edittext
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',fig_color, ...
'HorizontalAlignment','left', ...
'Position',footnote_text_pos, ...
'String','footnote', ...
'Style','text');
edit3_h = uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color2, ...
'Position',footnote_pos, ...
'FontSize',12, ...
'String',sM.name, ...
'Style','edit');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Visualize'
%
uicontrol( ...
'Units','normalized', ...
'Position',little_frame_pos, ...
'Style','frame');
uicontrol( ...
'Units','normalized', ...
'Position',visualize_pos, ...
'String','Visualize', ...
'Callback',['som_show_gui(''visualize'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pushbutton 'Close'
%
uicontrol( ...
'Units','normalized', ...
'BackgroundColor',bg_color1, ...
'Position',close_pos, ...
'String','Close', ...
'Callback',['som_show_gui(''close'',' mat2str(gcf) ')']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% menus
%
uimenu('Parent',fig_h','Label',' ','Enable','off');
m = uimenu('Parent',fig_h,'Label','Tools');
a = uimenu('Parent',m,'Label','Add');
s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_label'')');
uimenu('Parent',a,'Label','label','Callback',s);
s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_hit'')');
uimenu('Parent',a,'Label','hit','Callback',s);
s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_traj'')');
uimenu('Parent',a,'Label','traj','Callback',s);
s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_comet'')');
uimenu('Parent',a,'Label','comet','Callback',s);
s = ['vis_show_gui_tool(',mat2str(gcf),',''clear'')'];
c = uimenu('Parent',m,'Label','Clear','Separator','on','callback',s);
s = strcat('vis_show_gui_tool(',mat2str(gcf),',''recolorbar'')');
r = uimenu('Parent',m,'Label','Recolorbar','Separator','on', ...
'Callback',s);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% end
%
ud.sM = sM;
ud.plot_array = plot_array;
ud.property = {};
ud.vis_h = [];
ud.h = [list1_h popup1_h popup2_h popup3_h ...
edit1_h edit2_h edit3_h edit4_h];
watchoff(oldFigNumber);
set(fig_h,'Visible','on', ...
'UserData', ud, ...
'handlevisibility','off');
|
github
|
martinarielhartmann/mirtooloct-master
|
som_label.m
|
.m
|
mirtooloct-master/somtoolbox/som_label.m
| 9,503 |
utf_8
|
8288c9b7322af1c25b20c13d01f82aef
|
function [sTo] = som_label(sTo, mode, inds, labels)
%SOM_LABEL Give/clear labels to/from map or data struct.
%
% sTo = som_label(sTo, mode, inds [, labels])
%
% sD = som_label(sD,'add',20,'a_label');
% sM = som_label(sM,'replace',[2 4],'a_label');
% sM = som_label(sM,'add',som_bmus(sM,x),'BMU');
% sD = som_label(sD,'prune',[1:10]');
% sM = som_label(sM,'clear','all');
%
% Input and output arguments ([]'s are optional):
% sTo (struct) data or map struct to which the labels are put
% mode (string) 'add' or 'replace' or 'prune' or 'clear'
% inds (vector) indeces of the vectors to which the labels
% are put. Note: this must be a column vector!
% (matrix) subscript indeces to the '.labels' field. The vector
% is given by the first index (e.g. inds(i,1)).
% (string) for 'prune' and 'clear' modes, the string 'all'
% means that all vectors should be pruned/cleared
% [labels] The labels themselves. The number of rows much match
% the number of given indeces, except if there is either
% only one index or only one label. If mode is
% 'prune' or 'clear', labels argument is ignored.
% (string) Label.
% (string array) Each row is a label.
% (cell array of strings) All labels in a cell are handled
% as a group and are applied to the same vector given
% on the corresponding row of inds.
%
% Note: If there is only one label/index, it is used for each specified
% index/label.
%
% For more help, try 'type som_label' or check out online documentation.
% See also SOM_AUTOLABEL, SOM_SHOW_ADD, SOM_SHOW.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_label
%
% PURPOSE
%
% Add (or remove) labels to (from) map and data structs.
%
% SYNTAX
%
% sTo = som_label(sTo, 'clear', inds)
% sTo = som_label(sTo, 'prune', inds)
% sTo = som_label(sTo, 'add', inds, labels)
% sTo = som_label(sTo, 'replace', inds, labels)
%
% DESCRIPTION
%
% This function can be used to give and remove labels in map and data
% structs. Of course the same operation could be done by hand, but this
% function offers an alternative and hopefully slightly user-friendlier
% way to do it.
%
% REQUIRED INPUT ARGUMENTS
%
% sTo (struct) data or map struct to which the labels are put
% mode (string) The mode of operation.
% 'add' : adds the given labels
% 'clear' : removes labels
% 'replace' : replaces current labels with given
% labels; basically same as 'clear'
% followed by 'add'
% 'prune' : removes empty labels ('') from between
% non-empty labels, e.g. if the labels of
% a vector were {'A','','','B','','C'}
% they'd become {'A','B','C'}. Some empty
% labels may be left at the end of the list.
%
% inds Identifies the vectors to which the operation
% (given by mode) is applied to.
% (vector) Linear indexes of the vectors, size n x 1.
% Notice! This should be a column vector!
% (matrix) The labels are in a cell matrix. By giving matrix
% argument for inds, you can address this matrix
% directly. The first index gives the vector and the
% second index the vertical position of the label in
% the labels array. Size n x 2, where n is the
% number of labels.
% (string) for 'prune' and 'clear' modes, the string 'all'
% means that all vectors should be pruned/cleared
%
% OPTIONAL INPUT ARGUMENTS
%
% [labels] The labels themselves. The number of rows much match
% the number of given indeces, except if there is either
% only one index or only one label.
% (string) Label, e.g. 'label'
% (string array) Each row is a label,
% e.g. ['label1'; 'label2'; 'label3']
% (cell array of strings) All labels in a cell are handled
% as a group and are applied to the same vector given
% on the corresponding row of inds.
% e.g. three labels: {'label1'; 'label2'; 'label3'}
% e.g. a group of labels: {'label1', 'label2', 'label3'}
% e.g. three groups: {{'la1'},{'la21','la22'},{'la3'}
%
% OUTPUT ARGUMENTS
%
% sTo (struct) the given data/map struct with modified labels
%
% EXAMPLES
%
% This is the basic way to add a label to map structure:
% sMap = som_label(sMap,'add',3,'label');
%
% The following examples have identical results:
% sMap = som_label(sMap,'add',[4; 13], ['label1'; 'label2']);
% sMap = som_label(sMap,'add',[4; 13], {{'label1'};{'label2'}});
%
% Labeling the BMU of a vector x (and removing any old labels)
% sMap = som_label(sMap,'replace',som_bmus(sMap,x),'BMU');
%
% Pruning labels
% sMap = som_label(sMap,'prune','all');
%
% Clearing labels from a struct
% sMap = som_label(sMap,'clear','all');
% sMap = som_label(sMap,'clear',[1:4, 9:30]');
%
% SEE ALSO
%
% som_autolabel Automatically label a map/data set.
% som_show Show map planes.
% som_show_add Add for example labels to the SOM_SHOW visualization.
% Copyright (c) 1997-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 101199
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments
error(nargchk(3, 4, nargin)); % check no. of input args is correct
% sTo
switch sTo.type,
case 'som_map', [dlen dim] = size(sTo.codebook);
case 'som_data', [dlen dim] = size(sTo.data);
end
maxl = size(sTo.labels,2); % maximum number of labels for a single vector
% inds
if ischar(inds) & strcmp(inds,'all'),
inds = [1:dlen]';
end
if length(inds)>2 & size(inds,2)>2, inds = inds'; end
ni = size(inds,1);
n = ni;
% labels
if nargin==4,
% convert labels to a cell array of cells
if ischar(labels), labels = cellstr(labels); end
if iscellstr(labels),
tmplab = labels;
nl = size(labels,1);
labels = cell(nl,1);
for i=1:nl,
if ~iscell(tmplab{i})
if ~isempty(tmplab{i}), labels{i} = tmplab(i,:);
else labels{i} = {}; end
else
labels(i) = tmplab(i);
end
end
clear tmplab;
end
nl = size(labels,1);
end
% the case of a single label/index
if any(strcmp(mode,{'add','replace'})),
n = max(nl,ni);
if n>1,
if ni==1,
inds = zeros(n,1)+inds(1);
elseif nl==1,
label = labels{1};
labels = cell(n,1);
for i=1:n, labels{i} = label; end
elseif ni ~= nl,
error('The number of labels and indexes does not match.');
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
switch mode,
case 'clear',
if size(inds,2)>2,
inds = inds(find(inds(:,2)<=maxl),:); % ignore if subindex is out-of-range
inds = sub2ind([dlen maxl],inds(:,1),inds(:,2));
sTo.labels{inds} = [];
else
sTo.labels(inds,:) = cell(n,maxl);
end
case 'prune',
if size(inds,2)==1,
% subindex gives the index from which the pruning is started
inds = [inds, ones(n,1)]; % from 1 by default
end
select = ones(1,maxl);
for i=1:n,
v = inds(i,1); s = inds(i,2); select(:) = 1;
for j=s:maxl, select(j) = ~isempty(sTo.labels{v,j}); end
if ~all(select),
labs = cell(1,maxl);
labs(1:sum(select)) = sTo.labels(v,find(select));
sTo.labels(v,:) = labs;
end
end
case 'add',
if size(inds,2)==1,
% subindex gives the index from which the adding is started
inds = [inds, ones(n,1)]; % from 1 by default
end
for i=1:n,
v = inds(i,1); s = inds(i,2); l = length(labels{i});
for j=1:l,
while s<=size(sTo.labels,2) & ~isempty(sTo.labels{v,s}), s=s+1; end
sTo.labels{v,s} = labels{i}{j};
s=s+1;
end
end
case 'replace',
if size(inds,2)==1,
% subindex gives the index from which the replacing is started
inds = [inds, ones(n,1)]; % from 1 by default
end
for i=1:n,
v = inds(i,1); s = inds(i,2); l = length(labels(i));
for j=1:l, sTo.labels{v,s-1+j} = labels{i}{j}; end
end
otherwise
error(['Unrecognized mode: ' mode]);
end
sTo.labels = remove_empty_columns(sTo.labels);
[dlen maxl] = size(sTo.labels);
for i=1:dlen,
for j=1:maxl,
if isempty(sTo.labels{i,j}) & ~ischar(sTo.labels{i,j}),
sTo.labels{i,j} = '';
end
end
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function labels = remove_empty_columns(labels)
[dlen maxl] = size(labels);
% find which columns are empty
cols = zeros(1,maxl);
for i=1:dlen,
for j=1:maxl,
cols(j) = cols(j) + ~isempty(labels{i,j});
end
end
while maxl>0 & cols(maxl)==0, maxl = maxl-1; end % check starting from end
if maxl==0, labels = cell(dlen,1);
elseif maxl<size(labels,2), labels = labels(:,1:maxl);
else % ok
end
% end of remove_empty_columns
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_clset.m
|
.m
|
mirtooloct-master/somtoolbox/som_clset.m
| 10,182 |
utf_8
|
38150d23d264f8096f36eb455bd10bdb
|
function [sC,old2new,newi] = som_clset(sC,action,par1,par2)
% SOM_CLSET Create and/or set values in the som_clustering struct.
%
% first argument
% sC (struct) a som_clustering struct
% Z (matrix) size nb-1 x 3, as given by LINKAGE function
% base (vector) size dlen x 1, a partitioning of the data
%
% actions
% 'remove' removes the indicated clusters (par1: vector)
% 'add' add a cluster by making a combination of the indicated
% clusters (par1: vector)
% %'move' moves a child cluster (par1: scalar) from a parent to another
% % (par2: vector 1 x 2)
% 'merge' like 'add', followed by removing the indicated clusters (par1: vector)
% %'split' the indicated cluster (par1: scalar) is partitioned into indicated
% % parts (par2: vector), which are then added, while the indicated cluster
% % (par1) is removed
% 'coord' sets the coordinates of base clusters (par1: matrix nb x *), and
% recalculates coordinates of the derived clusters (by averaging base cluster
% coordinates)
% 'color' sets the colors of base clusters (par1: matrix nb x 3), and recalculates
% colors of the derived clusters (as averages of base cluster colors)
%
% sC
% .type (string) 'som_clustering'
% .name (string) Identifier for the clustering.
% .nb (scalar) Number of base clusters in the clustering.
% .base (vector) Size dlen x 1, the basic groups of data
% forming the base clusters, e.g. as a result
% of partitive clustering. Allowed values are
% 1:nb indicating the base cluster
% to which the data belongs to.
% NaN indicating that the data has
% been ignored in the clustering
% .nc (scalar) Number of clusters in the clustering (nb + derived clusters).
% .children (cellarray) size nc x 1, each cell gives the list of indeces
% of child clusters for the cluster
% .parent (vector) size nc x 1, the index of parent of each cluster
% (or zero if the cluster does not have a parent)
% .coord (matrix) size nc x *, visualization coordinates for each cluster
% By default the coordinates are set so that
% the base clusters are ordered on a line, and the
% position of each combined cluster is average of
% the base clusters that constitute it.
% .color (matrix) size nc x 3, color for each cluster.
% By default the colors are set so that the
% base clusters are ordered on a line,
% and then colors are assigned from the 'hsv'
% colormap to the base clusters. The color
% of each combined cluster is average as above.
% .cldist (string) Default cluster distance function.
inew = [];
if isstruct(sC),
% it should be a som_clustering struct
old2new = [1:sC.nc];
elseif size(sC,2)==3,
% assume it is a cluster hierarchy matrix Z
sC = Z2sC(sC);
old2new = [1:sC.nc];
else
% assume it is a partitioning vector
base = sC;
u = unique(base(isfinite(base)));
old2new = sparse(u,1,1:length(u));
base = old2new(base);
sC = part2sC(base);
end
switch action,
case 'remove',
for i=1:length(par1),
[sC,o2n] = removecluster(sC,old2new(par1(i)));
old2new = o2n(old2new);
end
case 'add',
[sC,old2new,inew] = addmergedcluster(sC,par1);
case 'move',
% not implemented yet
case 'split',
% not implemented yet
case 'merge',
[sC,old2new,inew] = addmergedcluster(sC,par1);
for i=1:length(par1),
[sC,o2n] = removecluster(sC,old2new(par1(i)));
old2new = o2n(old2new);
end
case 'color',
sC.color = derivative_average(sC,par1);
case 'coord',
sC.coord = derivative_average(sC,par1);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function sC = clstruct(nb,nc)
sC = struct('type','som_clustering',...
'name','','base',[],'nb',nb,'nc',nc,...
'parent',[],'children',[],'coord',[],'color',[],'cldist','centroid');
sC.base = [1:nb];
sC.parent = zeros(nc,1);
sC.children = cell(nc,1); sC.children(:) = {[]};
sC.coord = zeros(nc,2);
sC.color = zeros(nc,3);
return;
function Z = sC2Z(sC,height)
if nargin<2, height = 'level'; end
root = find(sC.parent==0);
order = [root];
ch = sC.children(root);
while any(ch), i = ch(1); order = [ch(1), order]; ch = [ch(2:end), sC.children{i}]; end
he = zeros(sC.nc,1);
if strcmp(height,'level'),
ch = sC.children{root};
while any(ch),
i = ch(1); he(i) = he(sC.parent(i))+1;
ch = [ch(2:end), sC.children{i}];
end
he = max(he)-he;
elseif strcmp(height,'level2'),
for i=order, if any(sC.children{i}), he(i) = max(he(sC.children{i}))+1; end, end
else
%he = som_cldist ( between children )
end
Z = zeros(sC.nb-1,3);
i = sC.nb-1;
inds = root;
while i>0,
ch = sC.children{inds(1)}; h = he(inds(1)); inds = [inds(2:end), ch];
if length(ch)>=2,
for k=1:length(ch)-2, Z(i,:) = [i-1, ch(k), h]; i = i - 1; end
Z(i,:) = [ch(end-1) ch(end) h]; i = i - 1;
end
end
return;
function sC = Z2sC(Z)
nb = size(Z,1)+1;
nc = 2*nb-1;
sC = clstruct(nb,nc);
sC.base = [1:nb];
for i=1:nc,
j = find(Z(:,1)==i | Z(:,2)==i);
sC.parent(i) = nb+j;
sC.children{sC.parent(i)}(end+1) = i;
end
% coords and color
order = nc;
nonleaves = 1;
while any(nonleaves),
j = nonleaves(1);
ch = sC.children{order(j)};
if j==1, oleft = []; else oleft = order(1:(j-1)); end
if j==length(order), oright = []; else oright = order((j+1):length(order)); end
order = [oleft, ch, oright];
nonleaves = find(order>nb);
end
[dummy,co] = sort(order);
sC.coord = derivative_average(sC,co');
H = hsv(nb+1);
sC.color = derivative_average(sC,H(co,:));
return;
function sC = part2sC(part)
nb = max(part);
nc = nb+1;
sC = clstruct(nb,nc);
sC.base = part;
sC.parent(1:nb) = nc;
sC.children{nc} = [1:nb];
co = [1:nb]';
sC.coord = derivative_average(sC,co);
H = hsv(nb+1);
sC.color = derivative_average(sC,H(1:nb,:));
return;
function [sC,old2new] = removecluster(sC,ind)
old2new = [1:sC.nc];
parent_ind = sC.parent(ind);
ch = sC.children{ind};
if ~parent_ind,
% trying to remove root cluster - no go
return;
elseif ~any(ch),
% trying to remove a base cluster - no go
return;
else
% ok, proceed
old2new = [1:ind-1 0 ind:sC.nc-1];
% update parent and child fields
sC.parent(ch) = parent_ind;
sC.children{parent_ind} = setdiff([sC.children{parent_ind}, ch],ind);
% remove old cluster
j = [1:ind-1, ind+1:sC.nc];
sC.parent = sC.parent(j);
sC.children = sC.children(j);
sC.color = sC.color(j,:);
sC.coord = sC.coord(j,:);
sC.nc = sC.nc-1;
% update old indeces to new indices
sC.parent = old2new(sC.parent);
for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
end
return;
function [sC,old2new,inew] = addmergedcluster(sC,inds)
old2new = [1:sC.nc];
inew = 0;
p_inds = sC.parent(inds);
if ~all(p_inds(1)==p_inds),
% clusters are not siblings - no go
return;
end
parent_ind = p_inds(1);
if isempty(setdiff(sC.children{parent_ind},inds)),
% such a merged cluster exists already
return;
else
% ok, proceed
inew = parent_ind;
old2new = [1:inew-1,inew+1:sC.nc+1];
% add the new cluster (=copy of parent_ind)
j = [1:inew,inew:sC.nc];
sC.parent = sC.parent(j);
sC.children = sC.children(j);
sC.color = sC.color(j,:);
sC.coord = sC.coord(j,:);
sC.nc = sC.nc+1;
% update old indeces to new indices
sC.parent = old2new(sC.parent);
for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
inds = old2new(inds);
parent_ind = old2new(parent_ind);
% update parent, child, color and coord fields
sC.parent(inds) = inew;
sC.parent(inew) = parent_ind;
sC.children{inew} = inds;
sC.children{parent_ind} = [setdiff(sC.children{parent_ind}, inds), inew];
b = baseind(sC,inew);
sC.color(inew,:) = mean(sC.color(b,:));
sC.coord(inew,:) = mean(sC.coord(b,:));
end
return;
function C = derivative_average(sC,Cbase)
[n dim] = size(Cbase);
if n ~= sC.nb, error('Color / Coord matrix should have nb rows'); end
C = zeros(sC.nc,dim);
for i=1:sC.nc, C(i,:) = mean(Cbase(baseind(sC,i),:)); end
return;
function bi = baseind(sC,ind)
bi = [ind];
i = 1;
while i<=length(bi), bi = [bi, sC.children{bi(i)}]; end
bi = bi(bi<=sC.nb);
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
sompak_init.m
|
.m
|
mirtooloct-master/somtoolbox/sompak_init.m
| 6,118 |
utf_8
|
326ecf24211ec0f7576c9caa06b50998
|
function sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh)
%SOMPAK_INIT Call SOM_PAK initialization programs from Matlab.
%
% sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sData (struct) data struct
% (matrix) data matrix
% (string) filename
% [ft] (string) 'pak' or 'box'. Argument must be defined, if input
% file is used.
% init_type (string) string 'rand' or 'linear'
% [cout] (string) filename for output SOM, if argument is not defined
% (i.e. argument is '[]') temporary file '__abcdef' is
% used in operations and *it_is_removed* after
% operations!!!
% [ct] (string) 'pak' or 'box'. Argument must be defined, if output
% file is used.
% xdim (scalar) Number of units of the map in x-direction.
% ydim (scalar) Number of units of the map in y-direction.
% topol (string) string 'hexa' or 'rect'
% neigh (string) string 'bubble' or 'gaussian'.
%
% RETURNS
% sMap (struct) map struct
%
% Calls SOM_PAK initialization programs (randinit and lininit) from
% Matlab. Notice that to use this function, the SOM_PAK programs must
% be in your search path, or the variable 'SOM_PAKDIR' which is a
% string containing the program path, must be defined in the
% workspace. SOM_PAK programs can be found from:
% http://www.cis.hut.fi/research/som_lvq_pak.shtml
%
% See also SOMPAK_TRAIN, SOMPAK_SAMMON, SOMPAK_INIT_GUI,
% SOMPAK_GUI, SOM_LININIT, SOM_RANDINIT.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
nargchk(9,9,nargin);
NO_FILE = 0;
if isstruct(sData);
sData=sData.data;
elseif ~(isreal(sData) | isstr(sData))
error('Argument ''sData'' must be a struct or a real matrix.');
else
if isempty(ft)
if isstr(sData)
error('Argument ''file_type'' must be defined when input file is used.');
end
elseif strcmp(ft,'pak');
sData=som_read_data(sData);
elseif strcmp(ft,'box')
new_var=diff_varname;
varnames=evalin('base','who');
loadname=eval(cat(2,'who(''-file'',''',sData,''')'));
if any(strcmp(loadname{1},evalin('base','who')))
assignin('base',new_var,evalin('base',loadname{1}));
evalin('base',cat(2,'load(''',sData,''');'));
new_var2=diff_varname;
assignin('base',new_var2,evalin('base',loadname{1}));
assignin('base',loadname{1},evalin('base',new_var));
evalin('base',cat(2,'clear ',new_var));
sData=evalin('base',new_var2);
evalin('base',cat(2,'clear ',new_var2));
else
evalin('base',cat(2,'load(''',sData,''');'));
sData=evalin('base',loadname{1});
evalin('base',cat(2,'clear ',loadname{1}));
end
else
error('Argument ''ft'' must be a string ''pak'' or ''box''.');
end
end
if isstr(init_type)
if strcmp(init_type,'rand')
if any(strcmp('SOM_PAKDIR',evalin('base','who')))
init_command=cat(2,evalin('base','SOM_PAKDIR'),'randinit');
else
init_command='randinit';
end
elseif strcmp(init_type,'linear')
if any(strcmp('SOM_PAKDIR',evalin('base','who')))
init_command=cat(2,evalin('base','SOM_PAKDIR'),'lininit');
else
init_command='lininit';
end
else
error('Argument ''init_type'' must be string ''rand'' or ''linear''.');
end
else
error('Argument ''init_type'' must be string ''rand'' or ''linear''.');
end
if (isstr(cout) & isempty(cout)) | (~isstr(cout) & isempty(cout))
NO_FILE = 1;
cout = '__abcdef';
elseif ~isstr(cout) & ~isempty(cout)
error('Argument ''cout'' must be a string or ''[]''.');
end
if ~is_positive_integer(xdim)
error('Argument ''xdim'' must be a positive integer.');
end
if ~is_positive_integer(ydim)
error('Argument ''ydim'' must be a positive integer.');
end
if isstr(topol)
if isempty(topol) | (~strcmp(topol,'hexa') & ~strcmp(topol,'rect'))
error ('Argument ''topol'' must be either a string ''hexa'' or ''rect''.');
end
else
error ('Argument ''topol'' must be either a string ''hexa'' or ''rect''.');
end
if isstr(neigh)
if isempty(neigh) | (~strcmp(neigh,'bubble') & ~strcmp(neigh,'gaussian'))
error(sprintf(cat(2,'Argument ''neigh'' must be either a string ',...
'''bubble'' or ''gaussian''.')));
end
else
error(sprintf(cat(2,'Argument ''neigh'' must be either a string ',...
'''bubble'' or ''gaussian''.')));
end
som_write_data(sData, cout);
str=cat(2,init_command,sprintf(' -din %s -cout %s ', cout ,cout),...
sprintf('-topol %s ',topol),...
sprintf('-neigh %s ',neigh),...
sprintf('-xdim %d -ydim %d',xdim,ydim));
if isunix
unix(str);
else
dos(str);
end
sMap=som_read_cod(cout);
if ~NO_FILE
if isunix
unix(cat(2,'/bin/rm ',cout));
else
dos(cat(2,'del ',cout));
end
if strcmp(ct,'pak')
som_write_cod(sMap,cout);
disp(cat(2,'Output written to the file ',cout,'.'));
elseif strcmp(ct,'box')
eval(cat(2,'save ',cout,' sMap'));
disp(cat(2,'Output written to the file ',sprintf('''%s.mat''.',cout)));
end
else
sMap.name=cat(2,'SOM ',date);
if isunix
unix('/bin/rm __abcdef');
else
dos('del __abcdef');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool = is_positive_integer(x)
bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0;
if ~isempty(bool)
if bool & x~=round(x)
bool = 0;
end
else
bool = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function str = diff_varname();
array=evalin('base','who');
if isempty(array)
str='a';
return;
end
for i=1:length(array)
lens(i)=length(array{i});
end
ind=max(lens);
str(1:ind+1)='a';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_distortion3.m
|
.m
|
mirtooloct-master/somtoolbox/som_distortion3.m
| 5,404 |
utf_8
|
13e98906922a0ded1181732268ac671c
|
function [Err,sPropTotal,sPropMunits,sPropComps] = som_distortion3(sM,D,rad)
%SOM_DISTORTION3 Map distortion measures.
%
% [sE,Err] = som_distortion3(sM,[D],[rad]);
%
% sE = som_distortion3(sM);
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct
% [D] (matrix) a matrix, size dlen x dim
% (struct) data or map struct
% by default the map struct is used
% [rad] (scalar) neighborhood radius, looked from sM.trainhist
% by default, or = 1 if that has no valid values
%
% Err (matrix) size munits x dim x 3
% distortion error elements (quantization error,
% neighborhood bias, and neighborhood variance)
% for each map unit and component
% sPropTotal (struct) .n = length of data
% .h = mean neighborhood function value
% .err = errors
% sPropMunits (struct) .Ni = hits per map unit
% .Hi = sum of neighborhood values for each map unit
% .Err = errors per map unit
% sPropComps (struct) .e1 = total squared distance to centroid
% .eq = total squared distance to BMU
% .Err = errors per component
%
% See also SOM_QUALITY.
% Contributed to SOM Toolbox 2.0, January 3rd, 2002 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 030102
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% arguments
% map
[munits dim] = size(sM.codebook);
% neighborhood radius
if nargin<3,
if ~isempty(sM.trainhist),
rad = sM.trainhist(end).radius_fin;
else
rad = 1;
end
end
if rad<eps, rad = eps; end
if isempty(rad) | isnan(rad), rad = 1; end
% neighborhood function
Ud = som_unit_dists(sM.topol);
switch sM.neigh,
case 'bubble', H = (Ud <= rad);
case 'gaussian', H = exp(-(Ud.^2)/(2*rad*rad));
case 'cutgauss', H = exp(-(Ud.^2)/(2*rad*rad)) .* (Ud <= rad);
case 'ep', H = (1 - (Ud.^2)/rad) .* (Ud <= rad);
end
Hi = sum(H,2);
% data
if nargin<2, D = sM.codebook; end
if isstruct(D), D = D.data; end
[dlen dim] = size(D);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% quality measures
% find Voronoi sets, and calculate their properties
[bmus,qerr] = som_bmus(sM,D);
M = sM.codebook;
Vn = M;
Vm = M;
Ni = zeros(munits,dim);
for i=1:munits,
inds = find(bmus==i);
Ni(i,:) = sum(isfinite(D(inds,:)),1); % size of Voronoi set
if any(Ni(i,:)), Vn(i,:) = centroid(D(inds,:),M(i,:)); end % centroid of Voronoi set
Vm(i,:) = centroid(M,M(i,:),H(i,:)'); % centroid of neighborhood
end
HN = repmat(Hi,1,dim).*Ni;
%% distortion
% quantization error (in each Voronoi set and for each component)
Eqx = zeros(munits,dim);
Dx = (Vn(bmus,:) - D).^2;
Dx(isnan(Dx)) = 0;
for i = 1:dim,
Eqx(:,i) = full(sum(sparse(bmus,1:dlen,Dx(:,i),munits,dlen),2));
end
Eqx = repmat(Hi,1,dim).*Eqx;
% bias in neighborhood (in each Voronoi set / component)
Enb = (Vn-Vm).^2;
Enb = HN.*Enb;
% variance in neighborhood (in each Voronoi set / component)
Env = zeros(munits,dim);
for i=1:munits, Env(i,:) = H(i,:)*(M-Vm(i*ones(munits,1),:)).^2; end
Env = Ni.*Env;
% total distortion (in each Voronoi set / component)
Ed = Eqx + Enb + Env;
%% other error measures
% squared quantization error (to data centroid)
me = centroid(D,mean(M));
Dx = D - me(ones(dlen,1),:);
Dx(isnan(Dx)) = 0;
e1 = sum(Dx.^2,1);
% squared quantization error (to map units)
Dx = D - M(bmus,:);
Dx(isnan(Dx)) = 0;
eq = sum(Dx.^2,1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% output
% distortion error matrix
Err = zeros(munits,dim,5);
Err(:,:,1) = Eqx;
Err(:,:,2) = Enb;
Err(:,:,3) = Env;
% total errors
sPropTotal = struct('n',sum(Ni),'h',mean(Hi),'e1',sum(e1),'err',sum(sum(Err,2),1));
% properties of map units
sPropMunits = struct('Ni',[],'Hi',[],'Err',[]);
sPropMunits.Ni = Ni;
sPropMunits.Hi = Hi;
sPropMunits.Err = squeeze(sum(Err,2));
% properties of components
sPropComps = struct('Err',[],'e1',[],'eq',[]);
sPropComps.Err = squeeze(sum(Err,1));
sPropComps.e1 = e1;
sPropComps.eq = eq;
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
%% subfunctions
function v = centroid(D,default,weights)
[n dim] = size(D);
I = sparse(isnan(D));
D(I) = 0;
if nargin==3,
W = weights(:,ones(1,dim));
W(I) = 0;
D = D.*W;
nn = sum(W,1);
else
nn = n-sum(I,1);
end
c = sum(D,1);
v = default;
i = find(nn>0);
v(i) = c(i)./nn(i);
return;
function vis
figure
som_show(sM,'color',{Hi,'Hi'},'color',{Ni,'hits'},...
'color',{Ed,'distortion'},'color',{Eqx,'qxerror'},...
'color',{Enb,'N-bias'},'color',{Env,'N-Var'});
ed = Eqx + Enb + Env;
i = find(ed>0);
eqx = 0*ed; eqx(i) = Eqx(i)./ed(i);
enb = 0*ed; enb(i) = Enb(i)./ed(i);
env = 0*ed; env(i) = Env(i)./ed(i);
figure
som_show(sM,'color',Hi,'color',Ni,'color',Ed,...
'color',eqx,'color',enb,'color',env);
|
github
|
martinarielhartmann/mirtooloct-master
|
som_drmake.m
|
.m
|
mirtooloct-master/somtoolbox/som_drmake.m
| 5,514 |
utf_8
|
89709796bebe24488d9640dffa2a6465
|
function [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)
% SOM_DRMAKE Make descriptive rules for given group within the given data.
%
% sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis])
%
% D (struct) map or data struct
% (matrix) the data, of size [dlen x dim]
% [inds1] (vector) indeces belonging to the group
% (the whole data set by default)
% [inds2] (vector) indeces belonging to the contrast group
% (the rest of the data set by default)
% [sigmea] (string) significance measure: 'accuracy',
% 'mutuconf' (default), or 'accuracyI'.
% (See definitions below).
% [nanis] (scalar) value given for NaNs: 0 (=FALSE, default),
% 1 (=TRUE) or NaN (=ignored)
%
% sR (struct array) best rule for each component. Each
% struct has the following fields:
% .type (string) 'som_rule'
% .name (string) name of the component
% .low (scalar) the low end of the rule range
% .high (scalar) the high end of the rule range
% .nanis (scalar) how NaNs are handled: NaN, 0 or 1
%
% best (vector) indeces of rules which make the best combined rule
% sig (vector) significance measure values for each rule, and for the combined rule
% Cm (matrix) A matrix of vectorized confusion matrices for each rule,
% and for the combined rule: [a, c, b, d] (see below).
%
% For each rule, such rules sR.low <= x < sR.high are found
% which optimize the given significance measure. The confusion
% matrix below between the given grouping (G: group - not G: contrast group)
% and rule (R: true or false) is used to determine the significance values:
%
% G not G
% --------------- accuracy = (a+d) / (a+b+c+d)
% true | a | b |
% |-------------- mutuconf = a*a / ((a+b)(a+c))
% false | c | d |
% --------------- accuracyI = a / (a+b+c)
%
% See also SOM_DREVAL, SOM_DRTABLE.
% Contributed to SOM Toolbox 2.0, January 7th, 2002 by Juha Vesanto
% Copyright (c) by Juha Vesanto
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta juuso 070102
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
if isstruct(D),
switch D.type,
case 'som_data', cn = D.comp_names; D = D.data;
case 'som_map', cn = D.comp_names; D = D.codebook;
end
else
cn = cell(size(D,2),1);
for i=1:size(D,2), cn{i} = sprintf('Variable%d',i); end
end
[dlen,dim] = size(D);
if nargin<2 | isempty(inds1), inds1 = 1:dlen; end
if nargin<3 | isempty(inds2), i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end
if nargin<4, sigmea = 'mutuconf'; end
if nargin<5, nanis = 0; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
sig = zeros(dim+1,1);
Cm = zeros(dim+1,4);
sR1tmp = struct('type','som_rule','name','','low',-Inf,'high',Inf,'nanis',nanis,'lowstr','','highstr','');
sR = sR1tmp;
% single variable rules
for i=1:dim,
% bin edges
mi = min(D(:,i));
ma = max(D(:,i));
[histcount,bins] = hist([mi,ma],10);
if size(bins,1)>1, bins = bins'; end
edges = [-Inf, (bins(1:end-1)+bins(2:end))/2, Inf];
% find the rule for this variable
[low,high,s,cm] = onevar_descrule(D(inds1,i),D(inds2,i),sigmea,nanis,edges);
sR1 = sR1tmp;
sR1.name = cn{i};
sR1.low = low;
sR1.high = high;
sR(i) = sR1;
sig(i) = s;
Cm(i,:) = cm;
end
% find combined rule
[dummy,order] = sort(-sig);
maxsig = sig(order(1)); bestcm = Cm(order(1),:);
best = order(1);
for i=2:dim,
com = [best, order(i)];
[s,cm,truex,truey] = som_dreval(sR(com),D(:,com),sigmea,inds1,inds2,'and');
if s>maxsig, best = com; maxsig = s; bestcm = cm; end
end
sig(end) = maxsig;
Cm(end,:) = cm;
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
%% descriptive rules
function [low,high,sig,cm] = onevar_descrule(x,y,sigmea,nanis,edges)
% Given a set of bin edges, find the range of bins with best significance.
%
% x data values in cluster
% y data values not in cluster
% sigmea significance measure
% bins bin centers
% nanis how to handle NaNs
% histogram counts
if isnan(nanis), x = x(~isnan(x)); y = y(~isnan(y)); end
[xcount,xbin] = histc(x,edges);
[ycount,ybin] = histc(y,edges);
xcount = xcount(1:end-1);
ycount = ycount(1:end-1);
xnan=sum(isnan(x));
ynan=sum(isnan(y));
% find number of true items in both groups in all possible ranges
n = length(xcount);
V = zeros(n*(n+1)/2,4);
s1 = cumsum(xcount);
s2 = cumsum(xcount(end:-1:1)); s2 = s2(end:-1:1);
m = s1(end);
Tx = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(xcount',[n 1])+repmat(xcount,[1 n]),0);
s1 = cumsum(ycount);
s2 = cumsum(ycount(end:-1:1)); s2 = s2(end:-1:1);
Ty = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(ycount',[n 1])+repmat(ycount,[1 n]),0);
[i,j] = find(Tx+Ty);
k = sub2ind(size(Tx),i,j);
V = [i, j, Tx(k), Ty(k)];
tix = V(:,3) + nanis*xnan;
tiy = V(:,4) + nanis*ynan;
% select the best range
nix = length(x);
niy = length(y);
Cm = [tix,nix-tix,tiy,niy-tiy];
[s,k] = max(som_drsignif(sigmea,Cm));
% output
low = edges(V(k,1));
high = edges(V(k,2)+1);
sig = s;
cm = Cm(k,:);
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
preprocess.m
|
.m
|
mirtooloct-master/somtoolbox/preprocess.m
| 176,966 |
utf_8
|
a8170717a6766685b74076da97fb351e
|
function preprocess(sData,arg2)
%PREPROCESS A GUI for data preprocessing.
%
% preprocess(sData)
%
% preprocess(sData)
%
% Launches a preprocessing GUI. The optional input argument can be
% either a data struct or a struct array of such. However, primarily
% the processed data sets are loaded to the application using the
% tools in the GUI. Also, the only way to get the preprocessed data
% sets back into the workspace is to use the tools in the GUI (press
% the button DATA SET MANAGEMENT).
%
% For a more throughout description, see online documentation.
% See also SOM_GUI.
%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IN FILES: preprocess.html,preproc.jpg,sDman.jpg,clip.jpg,delay.jpg,window.jpg,selVect.jpg
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas and the SOM Toolbox team
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
global no_of_sc % every Nth component in 'relative values' is drawn stronger.
no_of_sc=5;
if nargin < 1 | nargin > 2
error('Invalid number of input arguments');
return;
end
if nargin == 1, arg2=[]; end
if ~isstr(sData) %%% Preprocess is started...
data.LOG{1}='% Starting the ''Preprocess'' -window...';
data.LOG{2}=cat(2,'preprocess(',...
sprintf('%s);',inputname(1)));
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if ~isempty(pre_h)
figure(pre_h);
msgbox('''Preprocess''-figure already exists.');
return;
end
h0 = figure('Color',[0.8 0.8 0.8], ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[595 216 600 775], ...
'Tag','Preprocess');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.015 0.06064516129032258 0.9550000000000001 0.1458064516129032], ...
'Style','text', ...
'Tag','StaticText1');
data.results_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess close', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.8067 0.0142 0.1667 0.0348],...
'String','CLOSE', ...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.2141935483870968 0.07000000000000001 0.01806451612903226], ...
'String','LOG', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess sel_comp',...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.7983333333333333 0.2090322580645161 0.1666666666666667 0.03483870967741935], ...
'String',' ', ...
'Style','popupmenu', ...
'Tag','sel_comp_h', ...
'Value',1);
data.sel_comp_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.0183 0.2568 0.2133 0.1290], ...
'Style','text', ...
'Tag','StaticText3');
data.sel_cdata_h=h1;
h1 = axes('Parent',h0, ...
'CameraUpVector',[0 1 0], ...
'CameraUpVectorMode','manual', ...
'Color',[1 1 1], ...
'Position',[0.2583 0.2568 0.2133 0.1290], ...
'Tag','Axes1', ...
'XColor',[0 0 0], ...
'XTickLabel',['0 ';'0.5';'1 '], ...
'XTickLabelMode','manual', ...
'XTickMode','manual', ...
'YColor',[0 0 0], ...
'YTickMode','manual', ...
'ZColor',[0 0 0]);
data.sel_chist_h=h1;
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4960629921259843 -0.08080808080808044 9.160254037844386], ...
'Tag','Axes1Text4', ...
'VerticalAlignment','cap');
set(get(h2,'Parent'),'XLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[-0.0551181102362206 0.4848484848484853 9.160254037844386], ...
'Rotation',90, ...
'Tag','Axes1Text3', ...
'VerticalAlignment','baseline');
set(get(h2,'Parent'),'YLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','right', ...
'Position',[-1.2283 5.7980 9.1603], ...
'Tag','Axes1Text2', ...
'Visible','off');
set(get(h2,'Parent'),'ZLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4960629921259843 1.070707070707071 9.160254037844386], ...
'Tag','Axes1Text1', ...
'VerticalAlignment','bottom');
set(get(h2,'Parent'),'Title',h2);
h1 = axes('Parent',h0, ...
'CameraUpVector',[0 1 0], ...
'CameraUpVectorMode','manual', ...
'Color',[0.7529 0.7529 0.7529], ...
'Position',[0.4950000000000001 0.2567741935483871 0.4766666666666667 0.1290322580645161], ...
'Tag','Axes2', ...
'XColor',[0 0 0], ...
'XTickMode','manual', ...
'YColor',[0 0 0], ...
'YTick',[0 0.5 1], ...
'YTickMode','manual', ...
'ZColor',[0 0 0]);
data.vector_h=h1;
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4982456140350879 -0.08080808080808044 9.160254037844386], ...
'Tag','Axes2Text4', ...
'VerticalAlignment','cap');
set(get(h2,'Parent'),'XLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[-0.1018 0.4848 9.1603], ...
'Rotation',90, ...
'Tag','Axes2Text3', ...
'VerticalAlignment','baseline');
set(get(h2,'Parent'),'YLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','right', ...
'Position',[-1.045614035087719 5.797979797979799 9.160254037844386], ...
'Tag','Axes2Text2', ...
'Visible','off');
set(get(h2,'Parent'),'ZLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4982456140350879 1.070707070707071 9.160254037844386], ...
'Tag','Axes2Text1', ...
'VerticalAlignment','bottom');
set(get(h2,'Parent'),'Title',h2);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.3922580645161291 0.17 0.01806451612903226], ...
'String','STATISTICS', ...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2583333333333334 0.3922580645161291 0.1633333333333333 0.01806451612903226], ...
'String','HISTOGRAM', ...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi',...
'FontSize',6,...
'HorizontalAlignment','left',...
'String',{'LEFT: NEW SELECTION';'RIGHT: ADD TO SELECTION'}, ...
'ListboxTop',0, ...
'Position',[0.5016666666666667 0.38 0.235 0.03741935483870968], ...
'Style','text', ...
'Tag','StaticText6', ...
'UserData','[ ]');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess selall', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.8066666666666668 0.3922580645161291 0.1666666666666667 0.03483870967741935], ...
'String','SELECT ALL', ...
'Tag','Pushbutton2', ...
'UserData','[ ]');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.7529 0.7529 0.7529], ...
'Position',[0.01833333333333333 0.4503225806451613 0.23 0.3225806451612903], ...
'String',' ', ...
'Style','listbox', ...
'Tag','Listbox1', ...
'Value',1);
data.comp_names_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'Position',[0.4950000000000001 0.4503225806451613 0.2333333333333333 0.3225806451612903], ...
'String',' ', ...
'Style','listbox', ...
'Tag','Listbox2', ...
'Value',1);
data.vect_mean_h = h1;
h1 = axes('Parent',h0, ...
'CameraUpVector',[0 1 0], ...
'CameraUpVectorMode','manual', ...
'Color',[1 1 1], ...
'Position',[0.7383333333333334 0.4503225806451613 0.2333333333333333 0.3225806451612903], ...
'Tag','Axes3', ...
'XColor',[0 0 0], ...
'XTickMode','manual', ...
'YColor',[0 0 0], ...
'YTickMode','manual', ...
'ZColor',[0 0 0]);
data.sel_cplot_h = h1;
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4964028776978418 -0.03212851405622486 9.160254037844386], ...
'Tag','Axes3Text4', ...
'VerticalAlignment','cap');
set(get(h2,'Parent'),'XLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[-0.05035971223021596 0.493975903614458 9.160254037844386], ...
'Rotation',90, ...
'Tag','Axes3Text3', ...
'VerticalAlignment','baseline');
set(get(h2,'Parent'),'YLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','right', ...
'Position',[-3.1942 1.7028 9.1603], ...
'Tag','Axes3Text2', ...
'Visible','off');
set(get(h2,'Parent'),'ZLabel',h2);
h2 = text('Parent',h1, ...
'Color',[0 0 0], ...
'HandleVisibility','off', ...
'HorizontalAlignment','center', ...
'Position',[0.4964028776978418 1.028112449799197 9.160254037844386], ...
'Tag','Axes3Text1', ...
'VerticalAlignment','bottom');
set(get(h2,'Parent'),'Title',h2);
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess plxy', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.265 0.4683870967741936 0.125 0.03483870967741935], ...
'String','XY-PLOT', ...
'Tag','Pushbutton3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess hist', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.265 0.5303225806451613 0.125 0.03483870967741935], ...
'String','HISTOGRAM', ...
'Tag','Pushbutton4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess bplo', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.265 0.5922580645161291 0.125 0.03483870967741935], ...
'String','BOX PLOT', ...
'Tag','Pushbutton5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess plot', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.265 0.654195483870968 0.125 0.03483870967741935], ...
'String','PLOT', ...
'Tag','Pushbutton6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.4088888888888889 0.5333333333333333 0.06 0.03268817204301075], ...
'String','30', ...
'Style','edit', ...
'Tag','EditText1');
data.no_of_bins_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.775483870967742 0.2016666666666667 0.01806451612903226], ...
'String','COMPONENT LIST', ...
'Style','text', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4950000000000001 0.775483870967742 0.1966666666666667 0.01806451612903226], ...
'String','AVERAGE', ...
'Style','text', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.7383333333333334 0.775483870967742 0.225 0.01806451612903226], ...
'String','RELATIVE VALUES', ...
'Style','text', ...
'Tag','StaticText9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',10, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.8154838709677419 0.2033333333333333 0.0232258064516129], ...
'String','COMPONENTS', ...
'Style','text', ...
'Tag','StaticText10');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',10, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.4950000000000001 0.8154838709677419 0.2 0.0232258064516129], ...
'String','VECTORS', ...
'Style','text', ...
'Tag','StaticText11');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess sD_management', ...
'FontSize',5, ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.8503225806451613 0.1666666666666667 0.03483870967741935], ...
'String','DATA SET MANAGEMENT', ...
'Tag','Pushbutton7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','preprocess sel_sD', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.8890322580645161 0.1666666666666667 0.03483870967741935], ...
'String',' ', ...
'Style','popupmenu', ...
'Tag','PopupMenu2', ...
'Value',1);
data.sD_set_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.2516666666666667 0.8503225806451613 0.7216666666666667 0.07354838709677419], ...
'Style','text', ...
'Tag','StaticText12');
data.sD_name_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',10, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.01833333333333333 0.9341935483870968 0.1616666666666667 0.02064516129032258], ...
'String','DATA SETS', ...
'Style','text', ...
'Tag','StaticText13');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',10, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2516666666666667 0.9341935483870968 0.2833333333333333 0.02064516129032258], ...
'String','SELECTED DATA SET', ...
'Style','text', ...
'Tag','StaticText14');
if ~isstruct(sData),
sData=som_data_struct(sData);
end
ui_h=uimenu('Label','&Normalization');
uimenu(ui_h,'Label','Scale [0,1]','Callback','preprocess zscale');
uimenu(ui_h,'Label','Scale var=1','Callback','preprocess vscale');
uimenu(ui_h,'Label','HistD','Callback','preprocess histeq');
uimenu(ui_h,'Label','HistC','Callback','preprocess histeq2');
uimenu(ui_h,'Label','Log','Callback','preprocess log');
uimenu(ui_h,'Label','Eval (1-comp)','Callback','preprocess eval1');
ui_h=uimenu('Label','&Components');
uimenu(ui_h,'Label','Move component','Callback','preprocess move');
uimenu(ui_h,'Label','Copy component','Callback','preprocess copy');
uimenu(ui_h,'Label','Add: N binary types','Callback','preprocess oneo');
uimenu(ui_h,'Label','Add: zeros','Callback','preprocess zero');
uimenu(ui_h,'Label','Remove component','Callback','preprocess remove');
uimenu(ui_h,'Label','Remove selected vectors',...
'Callback','preprocess remove_vects');
uimenu(ui_h,'Label','Select all components',...
'Callback','preprocess sel_all_comps');
ui_h=uimenu('Label','&Misc');
ui_h1=uimenu(ui_h,'Label','Calculate');
ui_h2=uimenu(ui_h,'Label','Process');
uimenu(ui_h,'Label','Get LOG-file','Callback','preprocess LOG');
uimenu(ui_h,'Label','Indices of the selected vectors',...
'Callback','preprocess get_inds');
uimenu(ui_h,'Label','Undo','Callback','preprocess undo');
uimenu(ui_h1,'Label','Number of values','Callback','preprocess noof');
uimenu(ui_h1,'Label','Number of selected vectors',...
'Callback','preprocess no_of_sel');
uimenu(ui_h1,'Label','Correlation','Callback','preprocess corr');
uimenu(ui_h2,'Label','Unit length','Callback','preprocess unit');
uimenu(ui_h2,'Label','Eval','Callback','preprocess eval2');
uimenu(ui_h2,'Label','Clipping','Callback','preprocess clipping');
uimenu(ui_h2,'Label','Delay','Callback','preprocess delay');
uimenu(ui_h2,'Label','Windowed','Callback','preprocess window');
uimenu(ui_h2,'Label','Select vectors','Callback','preprocess select');
len=getfield(size(sData(1).data),{1});
data.selected_vects=find(ones(1,len));
data.sD_set=sData;
set(data.vector_h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')');
set(gcf,'UserData',data);
if ~set_sD_stats;
return;
end
sel_sD;
return; %%% Preprocess-window is ready.
else
arg=sData;
if strcmp(arg,'rename')
rename(arg2);
elseif strcmp(arg,'sel_sD')
if isempty(arg2)
sel_sD;
else
sel_sD(arg2);
end
elseif strcmp(arg,'zscale')
if isempty(arg2)
zero2one_scale;
else
zero2one_scale(arg2);
end
elseif strcmp(arg,'vscale');
if isempty(arg2)
var_scale;
else
var_scale(arg2);
end
elseif strcmp(arg,'histeq2')
if isempty(arg2)
hist_eq2;
else
hist_eq2(arg2);
end
elseif strcmp(arg,'log')
if isempty(arg2)
logarithm;
else
logarithm(arg2);
end
elseif strcmp(arg,'eval1')
if isempty(arg2)
eval1;
else
eval1(arg2);
end
elseif strcmp(arg,'eval2')
if isempty(arg2)
eval2;
else
eval2(arg2);
end
elseif strcmp(arg,'histeq');
if isempty(arg2)
hist_eq;
else
hist_eq(arg2);
end
elseif strcmp(arg,'selall')
if isempty(arg2)
select_all;
else
select_all(arg2);
end
elseif strcmp(arg,'sel_button');
if isempty(arg2)
sel_button;
else
sel_button(arg2);
end
elseif strcmp(arg,'clear_button')
if isempty(arg2)
clear_button;
else
clear_button(arg2)
end
elseif(strcmp(arg,'move'))
if isempty(arg2)
move_component;
else
move_component(arg2);
end
elseif(strcmp(arg,'copy'))
if isempty(arg2)
copy_component;
else
copy_component(arg2);
end
elseif strcmp(arg,'oneo')
if isempty(arg2)
one_of_n;
else
one_of_n(arg2);
end
elseif strcmp(arg,'zero')
if isempty(arg2)
add_zeros;
else
add_zeros(arg2);
end
elseif strcmp(arg,'remove')
if isempty(arg2)
remove_component;
else
remove_component(arg2);
end
elseif strcmp(arg,'remove_vects')
if isempty(arg2)
remove_vects;
else
remove_vects(arg2);
end
elseif strcmp(arg,'noof')
if isempty(arg2)
no_of_values;
else
no_of_values(arg2);
end
elseif strcmp(arg,'corr');
if isempty(arg2)
correlation;
else
correlation(arg2);
end
elseif strcmp(arg,'unit')
if isempty(arg2)
unit_length;
else
unit_length(arg2);
end
elseif strcmp(arg,'clip_data')
clip_data(arg2);
elseif strcmp(arg,'copy_delete')
copy_delete(arg2);
elseif strcmp(arg,'and_or_cb')
and_or_cb(arg2);
elseif strcmp(arg,'all_sel_cb')
all_sel_cb(arg2);
elseif strcmp(arg,'clip_exp_cb')
clip_exp_cb(arg2);
elseif strcmp(arg,'window_cb')
window_cb(arg2);
elseif strcmp(arg,'set_state_vals')
set_state_vals(arg2);
elseif strcmp(arg,'vector_bdf')
vector_bdf(arg2);
elseif strcmp(arg,'sD_management');
if isempty(arg2)
sD_management;
else
sD_management(arg2);
end
elseif strcmp(arg,'clipping')
if isempty(arg2)
clipping;
else
clipping(arg2);
end
elseif strcmp(arg,'delay')
if isempty(arg2)
delay;
else
delay(arg2);
end
elseif strcmp(arg,'window');
if isempty(arg2)
window;
else
window(arg2);
end
elseif strcmp(arg,'select');
if isempty(arg2)
select;
else
select(arg2);
end
elseif strcmp(arg,'import')
if isempty(arg2)
import;
else
import(arg2);
end
elseif strcmp(arg,'export')
if isempty(arg2)
export;
else
export(arg2);
end
elseif strcmp(arg,'undo');
if isempty(arg2)
undo;
else
undo(arg2);
end
elseif strcmp(arg,'delay_data')
if isempty(arg2)
delay_data;
else
delay_data(arg2);
end
elseif strcmp(arg,'eval_windowed')
if isempty(arg2)
eval_windowed;
else
eval_windowed(arg2);
end
elseif strcmp(arg,'get_inds')
if isempty(arg2)
get_selected_inds;
else
get_selected_inds(arg2);
end
elseif strcmp(arg,'no_of_sel')
if isempty(arg2)
no_of_selected;
else
no_of_selected(arg2);
end
elseif strcmp(arg,'sel_comp');
if isempty(arg2)
sel_comp;
else
sel_comp(arg2);
end
elseif strcmp(arg,'sel_all_comps')
if isempty(arg2)
select_all_comps;
else
select_all_comps(arg2);
end
elseif strcmp(arg,'refresh')
set_var_names;
elseif any(strcmp(arg,{'close_c','close_d','close_s','close_w','close_sD'}))
if isempty(arg2)
close_func(arg)
else
close_func(arg,arg2);
end
end
switch arg
case 'sD_stats'
sD_stats;
case 'LOG'
log_file;
otherwise
pro_tools(arg);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function set_compnames(sData,h)
%SET_COMPNAMES
%
% set_compnames(sData,h)
%
% ARGUMENTS
%
% sData (struct) som_data_struct
% h (scalar) handle to a list box object
%
%
% This function sets the component names of sData to the list box
% indicated by 'h'.
%
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
error('Figure ''Preprocess'' does not exist. Closing program...');
close_preprocess;
end
udata=get(pre_h,'UserData');
set(h,'Value',[]);
for i=1:length(sData.comp_names)
tmp=sprintf('#%d: ',i);
names{i,1}=cat(2,tmp, sData.comp_names{i});
end
set(h,'String',names,'Max',2);
set(udata.sel_comp_h,'String',names);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function draw_vectors(vectors,h)
%DRAW_VECTORS
%
% draw_vectors(vectors,h)
%
% ARGUMENTS
%
% vectors (vector) vector of 0's and 1's
% h (scalar) handle to an axis object
%
%
% This function draws an horizontal bar of 'vectors' in the axis
% indicated by 'h'.
%
%
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
subplot(h);
hold off;
cla;
set(h,'YLim',[0 1]);
set(h,'YTick',[]);
set(h,'XLim',[0 length(vectors)+1]);
hold on;
comp_no=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value');
comp=getfield(get(pre_h,'UserData'),'sData');
comp=comp.data(:,comp_no);
Max = max(comp);
Min = min(comp);
lims=get(gca,'YLim');
lims(1)=Min;
h=abs(0.1*Max);
lims(2)=Max;
if Max - Min <= eps
tmp=Max;
lims(1)=tmp-1;
lims(2)=tmp+1;
end
lims(2)=lims(2)+h;
if ~all(isnan(lims))
set(gca,'YLim',lims);
end
h=(lims(2)-lims(1))/4;
set(gca,'YTickMode','auto');
t=1:length(vectors);
h=plot(t,comp);
set(h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')');
indices =find(vectors);
vectors(indices)=0.1*(getfield(get(gca,'YLim'),...
{2})-getfield(get(gca,'YLim'),{1}));
plot(indices,vectors(indices)+getfield(get(gca,'YLim'),{1}),...
'ored','MarkerSize',4);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function vect_means(sData,handle,indices)
%VECT_MEANS
%
% vect_means(sData,handle,indices)
%
% ARGUMENTS
%
% sData (struct) som_data_struct
% handle (scalar) handle to the static text box object
% indices (vector) indices of selected vectors
%
%
% This function calculates means of selected vectors' components
% and writes them in the static text box indicated by 'handle'.
%
%
sData= sData.data(indices,:);
for i=1:length(sData(1,:))
names{i}=sprintf('#%d: ',i);
end
for i=1:length(sData(1,:))
tmp=sData(:,i);
tmp=cat(2,names{i},sprintf('%-10.3g',mean(tmp(find(~isnan(tmp))))));
string{i}=tmp;
end
set(handle,'String',string);
set(handle,'HorizontalAlignment','left');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function vector_bdf(arg)
%VECTOR_BDF A button down function.
%
% vector_bdf(arg)
%
% ARGUMENTS
%
% arg (string) 'down' or 'up', tells the mouse button's state.
%
%
% This function selects vectors in the vector-window and plots maxima,
% minima and means of the selected vectors. It also writes means of the
% selected vectors' components in a static text box and takes care of
% changes of the chosen component's data.
%
% See also VECTOR_MEANS, SEL_COMP
%
%
arg2=arg(6:length(arg));
if ~isempty(arg2)
LOG=1;
else
LOG=0;
end
arg=arg(1:4);
%%% arg's first "word" is 4 letters long and it can be:
%%%
%%% 'key '
%%% 'down'
%%% 'drag'
%%% 'up '
if strcmp(arg,'key ') %string is 'key' + 1 space!!!
if ~LOG
key=get(gcf,'CurrentCharacter');
else
key=arg2
end
if ~strcmp(key,'<') & ~strcmp(key,'>')
return;
end
data=get(gcf,'UserData');
sel=data.selected_vects;
if length(sel) == 1
if strcmp(key,'<') & sel ~= 1
data.selected_vects=sel-1;
set(gcf,'UserData',data);
elseif strcmp(key,'>') & sel ~= length(data.sData.data(:,1))
data.selected_vects = sel + 1;
set(gcf,'UserData',data);
end
else
if strcmp(key,'<') & sel(1) ~= 1
data.selected_vects=cat(2,sel(1)-1,sel);
set(gcf,'UserData',data);
elseif strcmp(key,'>') & sel(length(sel)) ~= length(sel)
data.selected_vects=cat(2,sel,sel(length(sel))+1);
set(gcf,'UserData',data);
end
end
cplot_mimema;
pro_tools('plot_hist');
pro_tools('c_stat');
vects=zeros(1,length(data.sData.data(:,1)));
vects(data.selected_vects)=1;
draw_vectors(vects,data.vector_h);
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}=...
sprintf('preprocess(''vector_bdf'',''key %s'');',key);
%string is 'key'+2spaces+%s
set(gcf,'UserData',data);
end
return;
end
switch arg
case 'down'
set(gcf,'WindowButtonUpFcn','preprocess(''vector_bdf'',''up '')');
set(gcf,'WindowButtonMotionFcn','preprocess(''vector_bdf'',''drag'')');
switch get(gcf,'SelectionType')
case 'normal'
data.lims1=round(getfield(get(gca,'CurrentPoint'),{1,1}));
data.lims2=[];
case 'alt'
tmp=round(getfield(get(gca,'CurrentPoint'),{1,1}));
if isempty(get(gca,'UserData'))
data.lims1=tmp;
data.lims2=[];
else
data.lims1=cat(2,getfield(get(gca,'UserData'),'lims1'),tmp);
data.lims2=getfield(get(gca,'UserData'),'lims2');
end
end
coords=get(gca,'CurrentPoint');
h=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor');
set(h,'Color','red');
h2=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor');
set(h2,'Color','red');
data.h=h;
data.h2=h2;
set(gca,'UserData',data);
case 'drag'
coords=get(gca,'CurrentPoint');
lim=get(gca,'XLim');
h2=getfield(get(gca,'UserData'),'h2');
if lim(1) >= coords(1)
set(h2,'XData',[lim(1) lim(1)]);
elseif lim(2) <= coords(2)
set(h2,'XData',[lim(2) lim(2)]);
else
set(h2,'XData',[coords(1) coords(1)]);
end
case 'up ' % string is 'up' + 2 spaces!!!
set(gcf,'WindowButtonUpFcn','');
set(gcf,'WindowButtonMotionFcn','');
if ~LOG
data=get(gca,'UserData');
delete(data.h);
delete(data.h2);
tmp=round(getfield(get(gca,'CurrentPoint'),{1,1}));
data.lims2=cat(2,data.lims2,tmp);
tmp_data=sort(cat(1,data.lims1,data.lims2));
high=getfield(get(gca,'XLim'),{2})-1;
vectors=zeros(1,high);
tmp_data(find(tmp_data<1))=1;
tmp_data(find(tmp_data>high))=high;
for i=1:getfield(size(tmp_data),{2})
vectors(tmp_data(1,i):tmp_data(2,i))=1;
end
selected_vects=find(vectors);
else
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
len=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data'));
vectors=zeros(1,len(1));
i=1;
while i <= length(arg2) & (isspace(arg2(i)) | ~isletter(arg2(i)))
i=i+1;
end
arg3=arg2(i:length(arg2));
selected_vects=str2num(arg2(1:i-1));
if ~isempty(arg3) & ~all(isspace(arg3))
selected_vects=unique(cat(2,selected_vects,...
getfield(get(pre_h,'UserData'),'selected_vects')));
end
vectors(selected_vects)=1;
set(pre_h,'CurrentAxes',getfield(get(pre_h,'UserData'),'vector_h'));
set(0,'CurrentFigure',pre_h);
end
draw_vectors(vectors,gca);
sData=getfield(get(gcf,'UserData'),'sData');
h=getfield(get(gcf,'UserData'),'vect_mean_h');
vect_means(sData,h,selected_vects);
if ~LOG
set(gca,'UserData',data);
end
data=get(gcf,'UserData');
data.undo.sData=data.sData;
data.undo.selected=data.selected_vects;
data.selected_vects=selected_vects;
if ~LOG
data.LOG{length(data.LOG)+1}='% Vector selection by using the mouse...';
tmp=sprintf('preprocess(''vector_bdf'',''up %s'');',...
num2str(data.selected_vects));
if length(tmp) > 500
tmp=textwrap({tmp},500);
data.LOG{length(data.LOG)+1}=cat(2,tmp{1},''');');
for i=2:length(tmp)-1
data.LOG{length(data.LOG)+1}=...
cat(2,sprintf('preprocess(''vector_bdf'',''up %s',...
tmp{i}),'add'');');
end
data.LOG{length(data.LOG)+1}=...
cat(2,sprintf('preprocess(''vector_bdf'',''up %s',...
tmp{length(tmp)}(1:length(tmp{length(tmp)})-3)),' add'');');
else
data.LOG{length(data.LOG)+1}=tmp;
end
end
set(gcf,'UserData',data);
cplot_mimema;
sel_comp;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sel_button(varargin)
%SEL_BUTTON A Callback function. It performs the operations needed
% when vector components are selected.
%
% See also SEL_COMP
%
if nargin == 1
LOG=1;
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
string=getfield(get(pre_h,'UserData'),'comp_names_h');
string=getfield(get(string,'String'),{str2num(varargin{1})});
set(0,'CurrentFigure',pre_h);
else
LOG=0;
val=get(getfield(get(gcf,'UserData'),'comp_names_h'),'Value');
end
sel_button_h=getfield(get(gcf,'UserData'),'sel_button_h');
sel_comps_h=getfield(get(gcf,'UserData'),'sel_comps_h');
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
if ~LOG
string=getfield(get(comp_names_h,'String'),{get(comp_names_h,'Value')});
end
tmp_string=get(sel_comps_h,'String');
if iscell(tmp_string)
for i=1:length(string)
if ~any(strcmp(string{i},tmp_string))
tmp_string=cat(1,tmp_string,string(i));
end
end
string=tmp_string;
end
set(sel_comps_h,'String',string);
set(comp_names_h,'Value',[]);
sel_comp;
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}='% Select components';
data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_button'',''%s'');',...
num2str(val));
set(gcf,'UserData',data);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clear_button(varargin)
%CLEAR_BUTTON Function callback evaluated when a 'Clear'-button is
% pressed. It removes texts from the 'selected components'
% -window and the 'selected component data' -window and
% clears the 'histogram' -axis.
%
%
if nargin==1
LOG=1;
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
else
LOG=0;
end
sel_comp_h=getfield(get(gcf,'UserData'),'sel_comp_h');
sel_cdata_h=getfield(get(gcf,'UserData'),'sel_cdata_h');
sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h');
sel_chist_h=getfield(get(gcf,'UserData'),'sel_chist_h');
vector_h=getfield(get(gcf,'UserData'),'vector_h');
set(sel_comp_h,'Value',1);
set(sel_cdata_h,'String',' ');
subplot(sel_chist_h);
hold off;
cla;
selected=getfield(get(gcf,'UserData'),'selected_vects');
dims=size(getfield(getfield(get(gcf,'UserData'),'sData'),'data'));
vectors=zeros(1,dims(1));
vectors(selected)=1;
subplot(vector_h);
draw_vectors(vectors,vector_h);
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}='% Remove components from the selected list.';
data.LOG{length(data.LOG)+1}='preprocess(''clear_button'',''foo'');';
set(gcf,'UserData',data);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sel_comp(varargin)
%SEL_COMP performs the operations needed when vector components are
% chosen. It writes maxima, minima, mean and standard deviation
% of the chosen component to a text box window and draws a
% histogram of the chosen component of selected vectors'
%
%
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h');
if nargin == 1
set(sel_comp_h,'Value',str2num(varargin{1}));
elseif ~isempty(gcbo)
no=get(sel_comp_h,'Value');
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}='% Select one component';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''sel_comp'',''',...
num2str(no),''');');
set(gcf,'UserData',data);
end
pro_tools('c_stat');
pro_tools('plot_hist');
data=get(gcf,'UserData');
sData=data.sData;
vector_h=data.vector_h;
len=length(sData.data(:,1));
vects=zeros(1,len);
vects(data.selected_vects)=1;
draw_vectors(vects,vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function cplot_mimema
global no_of_sc
sData=getfield(get(gcf,'UserData'),'sData');
sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h');
selected=getfield(get(gcf,'UserData'),'selected_vects');
set(sel_cplot_h,'YLim',[0 length(sData.data(1,:))+1]);
subplot(sel_cplot_h);
hold off;
cla;
hold on;
for i=1:length(sData.data(1,:))
Max=max(sData.data(:,i));
Min=min(sData.data(:,i));
tmp=sData.data(selected,i);
selMax=max(tmp);
selMin=min(tmp);
Mean=abs(mean(tmp(find(~isnan(tmp)))));
Median=abs(median(tmp(find(~isnan(tmp)))));
if Max ~= Min & ~all(isnan(sData.data(:,i)))
if rem(i,no_of_sc) % no_of_sc is defined in the beginning of this file...
line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],...
[i i],'Color','black');
plot(abs(Mean-Min)/(Max-Min),i,'oblack');
plot(abs(Median-Min)/(Max-Min),i,'xblack');
else
line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],...
[i i],'Color','black','LineWidth',2);
plot(abs(Mean-Min)/(Max-Min),i,'oblack','LineWidth',2);
plot(abs(Median-Min)/(Max-Min),i,'xblack','LineWidth',2);
end
else
if rem(i,no_of_sc) % N is defined in the beginning of this file.
plot(mean(get(gca,'XLim')),i,'oblack');
plot(mean(get(gca,'XLim')),i,'xblack');
else
plot(mean(get(gca,'XLim')),i,'oblack','LineWidth',2);
plot(mean(get(gca,'XLim')),i,'xblack','LineWidth',2);
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool=set_sD_stats
%SET_SD_STATS Writes the data set names to popup menu.
%
%
bool=1;
data=get(gcf,'UserData');
for i=1:length(data.sD_set)
% if ~isvalid_var_name({data.sD_set(i).name})
% close_preprocess;
% bool=0;
% return;
% end
string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name);
end
set(data.sD_set_h,'String',string);
data.sData=data.sD_set(get(data.sD_set_h,'Value'));
data.sData.MODIFIED=0;
data.sData.INDEX=1;
set(gcf,'UserData',data);
write_sD_stats;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function write_sD_stats
%WRITE_SD_STATS writes data's name, length and dimension to text box.
%
%
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
sD_name_h=getfield(get(pre_h,'UserData'),'sD_name_h');
sData=getfield(get(pre_h,'UserData'),'sData');
dims=size(sData.data);
string{1}=cat(2,'Name: ',sData.name);
string{2}=cat(2,'Length: ',sprintf('%d',dims(1)));
string{3}=cat(2,'Dim: ',sprintf('%d',dims(2)));
set(sD_name_h,'String',string);
set(sD_name_h,'HorizontalAlignment','left');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sel_sD(varargin)
%SEL_SD sets new data to UserData's 'sData'.
%
%
if nargin==1
LOG=1;
index=str2num(varargin{1});
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
else
LOG=0;
end
sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h');
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
vector_h=getfield(get(gcf,'UserData'),'vector_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
if ~LOG
index=get(sD_set_h,'Value');
end
data=get(gcf,'UserData');
data.undo = [];
INDEX=data.sData.INDEX;
data.sData=rmfield(data.sData,'MODIFIED');
data.sData=rmfield(data.sData,'INDEX');
tmp=data.sD_set(index);
tmp.MODIFIED=0;
tmp.INDEX=index;
data.sD_set(INDEX)=data.sData;
data.sData=tmp;
len=getfield(size(tmp.data),{1});
data.selected_vects=find(ones(1,len));
if ~LOG
data.LOG{length(data.LOG)+1}='% Select a new data set.';
data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_sD'',''%s'');',...
num2str(index));
end
set(gcf,'UserData',data);
write_sD_stats;
set_compnames(tmp,comp_names_h);
draw_vectors(ones(1,len),vector_h);
vect_means(tmp,vect_mean_h,data.selected_vects);
clear_button;
sel_comp;
cplot_mimema;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function indices=get_indices
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
indices = get(comp_names_h,'Value');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sD_management(varargin)
if nargin ~= 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'% Starting the ''Data Set Management'' -window...';
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'preprocess(''sD_management'',''foo'');';
set(pre_h,'UserData',preh_udata);
end
man_h=findobj(get(0,'Children'),'Tag','Management');
if ~isempty(man_h)
figure(man_h);
return;
end
h0 = figure('BackingStore','off', ...
'Color',[0.8 0.8 0.8], ...
'Name','Data Set Management', ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[753 523 324 470], ...
'RendererMode','manual', ...
'Tag','Management');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Max',2, ...
'Position',[0.02777777777777778 0.0723404255319149 0.7716049382716049 0.1914893617021277], ...
'String',' ', ...
'Style','edit', ...
'Tag','EditText1');
data.new_c_name_h = h1;
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess rename comp',...
'Units','normalized', ...
'FontSize',6, ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.8240740740740741 0.2106382978723404 0.154320987654321 0.05319148936170213], ...
'String','RENAME', ...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess close_sD',...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.8240740740740741 0.01914893617021277 0.154320987654321 0.05319148936170213], ...
'String','CLOSE', ...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.02777777777777778 0.2680851063829787 0.345679012345679 0.02978723404255319], ...
'String','COMPONENTS:', ...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'HorizontalAlignment','left', ...
'Position',[0.02777777777777778 0.3170212765957447 0.3549382716049382 0.5319148936170213], ...
'String',' ', ...
'Style','listbox', ...
'Tag','Listbox1', ...
'Value',1);
data.sets_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'HorizontalAlignment','left', ...
'Position',[0.6234567901234568 0.3170212765957447 0.3549382716049382 0.5319148936170213], ...
'String',' ', ...
'Style','listbox', ...
'Tag','Listbox2', ...
'Value',1);
data.variables_h = h1;
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess export',...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.4259259259259259 0.551063829787234 0.154320987654321 0.0425531914893617], ...
'String','->', ...
'Tag','Pushbutton4');
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess import',...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.4259259259259259 0.625531914893617 0.154320987654321 0.0425531914893617], ...
'String','<-', ...
'Tag','Pushbutton3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.02777777777777778 0.8531914893617022 0.2993827160493827 0.02978723404255319], ...
'String','DATA SETS', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.6234567901234568 0.8531914893617022 0.2561728395061728 0.02978723404255319], ...
'String','WORKSPACE', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess rename set',...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1820987654320987 0.9127659574468086 0.7808641975308641 0.0425531914893617], ...
'Style','edit', ...
'Tag','EditText2');
data.new_name_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.02777777777777778 0.9127659574468086 0.1388888888888889 0.02978723404255319], ...
'String','NAME:', ...
'Style','text', ...
'Tag','StaticText4');
ui_h=uimenu('Label','&Tools');
uimenu(ui_h,'Label','Copy','Callback','preprocess copy_delete copy');
uimenu(ui_h,'Label','Delete','Callback','preprocess copy_delete delete');
uimenu(ui_h,'Label','Refresh','Callback','preprocess refresh');
set(gcf,'UserData',data);
set_var_names;
sD_names;
sD_stats;
%%% Subfunction: set_var_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function set_var_names
variables_h=getfield(get(gcf,'UserData'),'variables_h');
value=get(variables_h,'Value');
len=evalin('base','length(who)');
names=cell(len,1);
for i=1:len
string=cat(2,'getfield(who,{',num2str(i),'})');
names(i)=evalin('base',string);
end
set(variables_h,'String',names);
if(value > length(names))
set(variables_h,'Value',1);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: sD_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sD_names
sets_h=getfield(get(gcf,'UserData'),'sets_h');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
sD_set = getfield(get(pre_h,'UserData'),'sD_set');
for i=1:length(sD_set)
names{i,1}=cat(2,sprintf('#%d: ',i),sD_set(i).name);
end
set(sets_h,'String',names);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: sD_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sD_stats
man_h=findobj(get(0,'Children'),'Tag','Management');
c_names_h=getfield(get(man_h,'UserData'),'new_c_name_h');
sD_name_h=getfield(get(man_h,'UserData'),'new_name_h');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
INDEX=getfield(getfield(get(pre_h,'UserData'),'sData'),'INDEX');
MODIFIED=getfield(getfield(get(pre_h,'UserData'),'sData'),'MODIFIED');
value=get(getfield(get(man_h,'UserData'),'sets_h'),'Value');
if value==INDEX
data=get(pre_h,'UserData');
sData=rmfield(data.sData,[{'INDEX'};{'MODIFIED'}]);
data.sD_set(INDEX)=sData;
data.sData.MODIFIED=0;
set(pre_h,'UserData',data);
end
sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{value});
string1=[{sData.name}];
set(sD_name_h,'String',string1);
set(c_names_h,'String',sData.comp_names);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: import %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function import(varargin)
if nargin==1
LOG=1;
man_h=findobj(get(0,'Children'),'Tag','Management');
set(0,'CurrentFigure',man_h);
name=varargin;
else
LOG=0;
end
variables_h=getfield(get(gcf,'UserData'),'variables_h');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if ~LOG
name=getfield(get(variables_h,'String'),{get(variables_h,'Value')});
end
errstr='Data to be imported must be real matrix or ''som_data_struct''.';
new_sD=evalin('base',name{1});
if isempty(pre_h)
errordlg('''Preprocess'' -figure does not exist. Terminating program...');
close_preprocess;
return;
end
if isstr(new_sD) | (~isstruct(new_sD) & ~isreal(new_sD))
errordlg(errstr);
return;
elseif isstruct(new_sD) & length(new_sD) > 1
errordlg(errstr)
return;
elseif ~isstruct(new_sD)
new_sD=som_data_struct(new_sD);
new_sD.name=name{1};
end
new_sD_names=fieldnames(new_sD);
right_names=fieldnames(som_data_struct(1));
for i=1:length(new_sD_names)
if ~any(strcmp(new_sD_names(i),right_names));
errordlg(errstr);
return;
end
end
data=get(pre_h,'UserData');
data.sD_set(length(data.sD_set) + 1)=new_sD;
if ~LOG
data.LOG{length(data.LOG)+1}='% Import a data set from the workspace.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''import'',''',...
name{1},''');');
end
set(pre_h,'UserData',data);
sD_names;
sD_stats;
old =gcf;
set(0,'CurrentFigure',pre_h);
set_sD_stats;
set(0,'CurrentFigure',old);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: export %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function export(varargin)
if nargin == 1
LOG=1;
man_h=findobj(get(0,'Children'),'Tag','Management');
set(0,'CurrentFigure',man_h);
index=str2num(varargin{1});
else
LOG=0;
end
sets_h=getfield(get(gcf,'UserData'),'sets_h');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if ~LOG
index=get(sets_h,'Value');
end
if isempty(pre_h)
errordlg('''Preprocess''-figure does not exist. Terminating program...');
close(findobj(get(0,'Children'),'Tag','Management'));
close(findobj(get(0,'Children'),'Tag','PlotWin'));
return;
end
sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{index});
if ~isvalid_var_name({sData.name})
return;
end
assignin('base',sData.name,sData);
disp(sprintf('Data set ''%s'' is set to the workspace.',sData.name));
if ~LOG
data=get(pre_h,'UserData');
data.LOG{length(data.LOG)+1}='% Export a data set to the workspace.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''export'',''',...
num2str(index),''');');
set(pre_h,'UserData',data);
end
set_var_names;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: rename %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rename(arg)
i=1;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
arg2=arg(i+1:length(arg));
arg=arg(1:i-1);
if ~isempty(arg2)
LOG=1;
i=1;
if arg2(1) ~= '{'
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
index=str2num(arg2(i+1:length(arg2)));
arg2=arg2(1:i-1);
else
while i <= length(arg2) & arg2(i) ~= '}'
i=i+1;
end
index=str2num(arg2(i+1:length(arg2)));
arg2=arg2(1:i);
end
else
LOG=0;
end
new_name_h=getfield(get(gcf,'UserData'),'new_name_h');
new_c_name_h=getfield(get(gcf,'UserData'),'new_c_name_h');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess'' -figure does not exist. Terminating program...');
close_preprocess;
return;
end
switch arg
case 'set'
if LOG
name={arg2};
else
name=get(new_name_h,'String');
end
if ~isempty(name{1}) & ~any(isspace(name{1}))
if ~isvalid_var_name(name)
sD_stats;
return;
end
if ~LOG
index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value');
end
data=get(pre_h,'UserData');
tmp_set.name=name{1};
data.sD_set(index).name=name{1};
if data.sData.INDEX == index
data.sData.name=name{1};
end
if ~LOG
data.LOG{length(data.LOG)+1}='% Rename a data set.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''rename'',''set ',...
name{1},' ',...
num2str(index),...
''');');
end
set(pre_h,'UserData',data);
sD_names;
string=get(data.sD_set_h,'String');
string{index}=cat(2,sprintf('#%d: ',index),name{1});
set(data.sD_set_h,'String',string);
string=get(data.sD_name_h,'String');
string{1}=cat(2,'Name: ',name{1});
if index==data.sData.INDEX
set(data.sD_name_h,'String',string);
end
else
sD_stats;
end
case 'comp'
if ~LOG
names=get(new_c_name_h,'String');
index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value');
else
names=eval(arg2);
end
if check_cell_names(names)
data=get(pre_h,'UserData');
sData=data.sD_set(index);
if length(sData.comp_names)==length(names)
data.sD_set(index).comp_names=names;
if index == data.sData.INDEX
for i=1:length(names)
names{i}=cat(2,sprintf('#%d: ',i),names{i});
end
set(data.comp_names_h,'String',names);
set(data.sel_comp_h,'String',names);
end
if ~LOG
data.LOG{length(data.LOG)+1}='% Rename components.';
str='preprocess(''rename'',''comp {';
for i=1:length(names)-1
str=cat(2,str,'''''',names{i},''''',');
end
str=cat(2,str,'''''',names{length(names)},'''''} ',...
num2str(index),''');');
data.LOG{length(data.LOG)+1}=str;
else
set(new_c_name_h,'String',names);
end
set(pre_h,'UserData',data);
else
errordlg('There are less components in data.');
sD_stats;
return;
end
else
sD_stats;
end
end
%%% Subfunction: check_cell_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool=check_cell_names(names)
bool = 1;
if isempty(names)
bool= 0;
return;
end
for i=1:length(names)
if isempty(names{i}) | isspace(names{i})
bool = 0;
return;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: isvalid_var_name %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool=isvalid_var_name(name)
bool=1;
tmp=name{1};
if ~((tmp(1)>='a' & tmp(1)<='z') | (tmp(1)>='A' & tmp(1)<='Z'))
errordlg('Invalid name.');
bool=0;
return;
end
for j=1:length(tmp)
if ~((tmp(j)>='a' & tmp(j)<='z') | ...
(tmp(j)>='A' & tmp(j)<='Z') | ...
(j>1 & tmp(j) == '_') | ...
(tmp(j)>='0' & tmp(j) <= '9')) | tmp(j) == '.'
errordlg('Invalid name.');
bool=0;
return;
end
if j == length(tmp) & tmp(j) == '_'
errordlg('Invalid name.');
bool=0;
return;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: copy_delete %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function copy_delete(arg)
i=1;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
arg2=arg(i+1:length(arg));
arg=arg(1:i-1);
if ~isempty(arg2)
index=str2num(arg2);
LOG=1;
else
LOG=0;
end
sets_h=getfield(get(gcf,'UserData'),'sets_h');
if ~LOG
index=get(sets_h,'Value');
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess'' -figure does not exist. Terminating program.');
close_preprocess;
return;
end
switch arg
case 'copy'
data=get(pre_h,'UserData');
data.sD_set(length(data.sD_set)+1)=data.sD_set(index);
if ~LOG
data.LOG{length(data.LOG)+1}='% Copy a data set.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',...
'copy ',num2str(index),''');');
end
set(pre_h,'UserData',data);
sD_names;
old=gcf;
set(0,'CurrentFigure',pre_h);
set_sD_stats;
set(0,'CurrentFigure',old);
case 'delete'
if length(get(sets_h,'String')) == 1
msgbox('No data left. Closing program...')
close_preprocess;
return;
end
data=get(pre_h,'UserData');
if ~isempty(data.undo) & any(strcmp('index',fieldnames(data.undo)))
if data.undo.index > index
data.undo.index = data.undo.index-1;
elseif data.undo.index==index;
data.undo=[];
end
end
set1=data.sD_set(1:index-1);
set2=data.sD_set(index+1:length(data.sD_set));
if ~isempty(set1)
data.sD_set=[set1 set2];
else
data.sD_set=set2;
end
if ~LOG
data.LOG{length(data.LOG)+1}='% Delete a data set.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',...
'delete ',num2str(index),''');');
end
set(pre_h,'UserData',data);
set(sets_h,'Value',1);
sD_names;
sD_stats;
old = gcf;
set(0,'CurrentFigure',pre_h);
for i=1:length(data.sD_set)
string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name);
end
set(data.sD_set_h,'String',string);
data.sData=data.sD_set(get(data.sD_set_h,'Value'));
data.sData.MODIFIED=0;
data.sData.INDEX=1;
set(gcf,'UserData',data);
write_sD_stats;
sData=getfield(get(gcf,'UserData'),'sData');
if sData.INDEX > index
value=get(getfield(get(gcf,'UserData'),'sD_set_h'),'Value');
set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',value-1);
sData.INDEX = sData.INDEX -1;
elseif sData.INDEX == index
set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',1);
end
sel_sD;
set(0,'CurrentFigure',old);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clipping(varargin)
if nargin ~= 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'% Starting the ''Clipping'' -window...';
preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''clipping'',''foo'');';
set(pre_h,'UserData',preh_udata);
end
clip_h=findobj(get(0,'Children'),'Tag','Clipping');
if ~isempty(clip_h)
figure(clip_h);
return;
end
h0 = figure('Color',[0.8 0.8 0.8], ...
'PaperPosition',[18 180 575 432], ...
'PaperUnits','points', ...
'Position',[718 389 300 249], ...
'Tag','Clipping');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.03 0.03614457831325301 0.4666666666666667 0.9236947791164658], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.05333333333333334 0.5983935742971887 0.42 0.3333333333333333], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Style','frame', ...
'Position',[0.05333333333333334 0.33 0.42 0.24], ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Style','frame', ...
'Position',[0.05333333333333334 0.06 0.42 0.24],...
'Tag','Frame4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[0.5133333333333334 0.6385542168674698 0.4666666666666667 0.321285140562249], ...
'Style','frame', ...
'Tag','Frame5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.5366666666666667 0.6666666666666666 0.42 0.2650602409638554], ...
'Style','frame', ...
'Tag','Frame6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.31 0.823293172690763 0.15 0.09638554216867469], ...
'Style','edit', ...
'Tag','EditText1');
data.big_val_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.31 0.7148594377510039 0.15 0.09638554216867469], ...
'Style','edit', ...
'Tag','EditText2');
data.small_val_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.31 0.606425702811245 0.15 0.09638554216867469], ...
'Style','edit', ...
'Tag','EditText3');
data.equal_val_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.06000000000000001 0.8473895582329316 0.22 0.05622489959839357], ...
'String','Bigger than', ...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.06000000000000001 0.7389558232931727 0.24 0.04819277108433735], ...
'String','Smaller than', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.06000000000000001 0.610441767068273 0.22 0.07228915662650602], ...
'String','Equal to', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.07000000000000001 0.465863453815261 0.06333333333333334 0.07228915662650602], ...
'Style','radiobutton', ...
'Value',1,...
'Tag','Radiobutton1');
data.and_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.07000000000000001 0.3734939759036144 0.06333333333333334 0.07228915662650602], ...
'Style','radiobutton', ...
'Tag','Radiobutton2');
data.or_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'Position',[0.1466666666666667 0.45 0.2333333333333333 0.07228915662650602], ...
'String','AND', ...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'String','OR', ...
'Position',[0.1466666666666667 0.35 0.2333333333333333 0.07228915662650602], ...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.07000000000000001 0.1967871485943775 0.06333333333333334 0.07228915662650602], ...
'Style','radiobutton', ...
'Value',1,...
'Tag','Radiobutton3');
data.all_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.07000000000000001 0.09236947791164658 0.06333333333333334 0.07228915662650602], ...
'Style','radiobutton', ...
'Tag','Radiobutton4');
data.sel_vects_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1466666666666667 0.1927710843373494 0.2333333333333333 0.07228915662650602], ...
'String','All vectors', ...
'Style','text', ...
'Tag','StaticText6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1466666666666667 0.09638554216867469 0.3133333333333334 0.05622489959839357], ...
'String','Among selected', ...
'Style','text', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.7866666666666667 0.823293172690763 0.1366666666666667 0.09236947791164658], ...
'Style','edit', ...
'Tag','EditText4');
data.replace_val_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',6, ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.5633333333333334 0.8273092369477911 0.2066666666666667 0.07630522088353413], ...
'String','Replace', ...
'Style','text', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.5700000000000001 0.6827309236947791 0.3566666666666667 0.08032128514056225], ...
'String','Replace', ...
'Tag','Pushbutton1');
data.OK_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess close_c',...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.6633333333333333 0.07228915662650602 0.2833333333333333 0.09638554216867469], ...
'String','Close', ...
'Tag','Pushbutton2');
data.state.and=1;
data.state.all=1;
data.state.big=[];
data.state.small=[];
data.state.equal=[];
data.state.replace=[];
set(data.or_button_h,'Callback','preprocess and_or_cb or');
set(data.and_button_h,'Callback','preprocess and_or_cb and');
set(data.and_button_h,'Value',1);
set(data.all_button_h,'Callback','preprocess all_sel_cb all');
set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel');
set(data.big_val_h,'Callback','preprocess set_state_vals big');
set(data.small_val_h,'Callback','preprocess set_state_vals small');
set(data.equal_val_h,'Callback','preprocess set_state_vals equal');
set(data.replace_val_h,'Callback','preprocess set_state_vals replace');
set(data.OK_button_h,'Callback','preprocess clip_data clip');
set(h0,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function select(varargin)
if nargin ~= 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'% Starting the ''Select'' -window...';
preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''select'',''foo'');';
set(pre_h,'UserData',preh_udata);
end
sel_h=findobj(get(0,'Children'),'Tag','Select');
if ~isempty(sel_h)
figure(sel_h);
return;
end
h0 = figure('Color',[0.8 0.8 0.8], ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[750 431 168 365], ...
'Tag','Select');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.05357142857142857 0.2712328767123288 0.8333333333333333 0.6301369863013698], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.05357142857142857 0.1041095890410959 0.8333333333333333 0.1397260273972603], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0,...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.6547945205479452 0.75 0.2273972602739726], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.4794520547945206 0.75 0.1506849315068493], ...
'Style','frame', ...
'Tag','Frame4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.09523809523809523 0.2986301369863014 0.75 0.1506849315068493], ...
'Style','frame', ...
'Tag','Frame5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.5535714285714285 0.8082191780821918 0.2678571428571429 0.06575342465753425], ...
'Style','edit', ...
'Tag','EditText1');
data.big_val_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.5535714285714285 0.7342465753424657 0.2678571428571429 0.06575342465753425], ...
'Style','edit', ...
'Tag','EditText2');
data.small_val_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.5535714285714285 0.6602739726027397 0.2678571428571429 0.06575342465753425], ...
'Style','edit', ...
'Tag','EditText3');
data.equal_val_h=h1;
h1 = uicontrol('Parent',h0, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Units','normalized', ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1071 0.8247 0.3929 0.0384], ...
'String','Bigger than', ...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Units','normalized', ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1071 0.7507 0.4286 0.0329], ...
'String','Smaller than', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1071 0.6630 0.3929 0.0493], ...
'String','Equal to', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.125 0.5643835616438356 0.1130952380952381 0.04931506849315068], ...
'Style','radiobutton', ...
'Tag','Radiobutton1');
data.and_button_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.125 0.5013698630136987 0.1130952380952381 0.04931506849315068], ...
'Style','radiobutton', ...
'Tag','Radiobutton2');
data.or_button_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2619047619047619 0.5561643835616439 0.3809523809523809 0.05205479452054795], ...
'String','AND', ...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2619047619047619 0.4986301369863014 0.3809523809523809 0.04657534246575343], ...
'String','OR', ...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.125 0.3808219178082192 0.1130952380952381 0.04931506849315068], ...
'Style','radiobutton', ...
'Tag','Radiobutton3');
data.all_button_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.125 0.3095890410958904 0.1130952380952381 0.04931506849315068], ...
'Style','radiobutton', ...
'Tag','Radiobutton4');
data.sel_vects_button_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2619047619047619 0.3780821917808219 0.4166666666666666 0.04931506849315068], ...
'String','All vectors', ...
'Style','text', ...
'Tag','StaticText6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.2619047619047619 0.3123287671232877 0.5595238095238095 0.03835616438356165], ...
'String','Among selected', ...
'Style','text', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.0952 0.1178 0.7500 0.1068], ...
'Style','frame', ...
'Tag','Frame6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.5298 0.1342 0.2738 0.0712], ...
'Style','edit', ...
'Tag','EditText4');
data.replace_val_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontSize',8,...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1369047619047619 0.136986301369863 0.3214285714285714 0.06027397260273973], ...
'String','Vectors', ...
'Style','text', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.05357142857142857 0.01917808219178082 0.3869047619047619 0.0684931506849315], ...
'String','OK', ...
'Tag','Pushbutton1');
data.OK_button_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'Callback','preprocess close_s',...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.5 0.01917808219178082 0.3869047619047619 0.0684931506849315], ...
'String','Close', ...
'Tag','Pushbutton2');
data.state.and=1;
data.state.all=1;
data.state.big=[];
data.state.small=[];
data.state.equal=[];
data.state.replace=[];
set(data.or_button_h,'Callback','preprocess and_or_cb or');
set(data.and_button_h,'Callback','preprocess and_or_cb and');
set(data.and_button_h,'Value',1);
set(data.all_button_h,'Callback','preprocess all_sel_cb all');
set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel');
set(data.big_val_h,'Callback','preprocess set_state_vals big');
set(data.small_val_h,'Callback','preprocess set_state_vals small');
set(data.equal_val_h,'Callback','preprocess set_state_vals equal');
set(data.replace_val_h,'Callback','preprocess set_state_vals replace');
set(data.OK_button_h,'Callback','preprocess clip_data sel');
set(h0,'UserData',data);
%%% Subfunction: and_or_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function and_or_cb(arg)
%AND_OR_CB A callback function. Checks that only one of the radiobox
% buttons 'AND' and 'OR' is pressed down.
%
%
and_button_h=getfield(get(gcf,'UserData'),'and_button_h');
or_button_h=getfield(get(gcf,'UserData'),'or_button_h');
data=get(gcf,'UserData');
switch arg
case 'or'
set(and_button_h,'Value',0);
set(or_button_h,'Value',1);
data.state.and=0;
case 'and'
set(or_button_h,'Value',0);
set(and_button_h,'Value',1);
data.state.and=1;
end
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: all_sel_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function all_sel_cb(arg)
all_button_h=getfield(get(gcf,'UserData'),'all_button_h');
sel_vects_button_h=getfield(get(gcf,'UserData'),'sel_vects_button_h');
data=get(gcf,'UserData');
switch arg
case 'all'
set(sel_vects_button_h,'Value',0);
set(all_button_h,'Value',1);
data.state.all=1;
case 'sel'
set(all_button_h,'Value',0);
set(sel_vects_button_h,'Value',1);
data.state.all=0;
end
set(gcf,'UserData',data);
%%% Subfunction: set_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function set_state_vals(arg)
%SET_STATE_VALS sets the values to the UserData's state-struct.
%
%
data=get(gcf,'UserData');
switch arg
case 'big'
big_val_h=getfield(get(gcf,'UserData'),'big_val_h');
val =str2num(get(big_val_h,'String'));
dims=size(val);
if dims(1) ~= 1 | dims(2) ~= 1
errordlg('Argument of the operation must be scalar.');
set(big_val_h,'String','');
return;
end
if isreal(val)
data.state.big=val;
else
errordlg('Limits of the operation must be real.');
set(big_val_h,'String','');
return;
end
case 'small'
small_val_h=getfield(get(gcf,'UserData'),'small_val_h');
val=str2num(get(small_val_h,'String'));
dims=size(val);
if dims(1) ~= 1 | dims(2) ~= 1
errordlg('Argument of the operation must be scalar.')
set(small_val_h,'String','');
return;
end
if isreal(val)
data.state.small=val;
else
errordlg('Limits of the operation must be real.');
set(small_val_h,'String','');
return;
end
case 'equal'
equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h');
val = str2num(get(equal_val_h,'String'));
dims=size(val);
if dims(1) ~= 1 | dims(2) ~= 1
errordlg('Argument of the operation must be scalar.');
set(equal_val_h,'String','');
return;
end
if isreal(val)
data.state.equal=val;
else
errordlg('Limits of the operation must be real.');
set(equal_val_h,'String','');
return;
end
case 'replace'
replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h');
val=str2num(get(replace_val_h,'String'));
dims=size(val);
if (dims(1) ~= 1 | dims(2) ~= 1) & ~strcmp(get(gcf,'Tag'),'Select')
errordlg('Argument of the operation must be scalar.');
set(replace_val_h,'String','');
return;
end
if isreal(val)
data.state.replace=val;
else
errordlg('Limits of the operation must be real.');
set(replace_val_h,'String','');
return;
end
end
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: clip_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clip_data(arg)
%CLIP_DATA A callback function. Filters the data.
%
%
i=1;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
arg2=arg(i+1:length(arg));
arg=arg(1:i-1);
if ~isempty(arg2)
LOG=1;
if strcmp(arg,'sel')
c_h=findobj(get(0,'Children'),'Tag','Select');
else
c_h=findobj(get(0,'Children'),'Tag','Clipping');
end
set(0,'CurrentFigure',c_h);
i=1;
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
BT=str2num(arg2(1:i-1));
i=i+1;
j=i;
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
ST=str2num(arg2(j:i-1));
i=i+1;
j=i;
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
EQ=str2num(arg2(j:i-1));
i=i+1;
j=i;
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
AND_OR=str2num(arg2(j:i-1));
i=i+1;
j=i;
while i <= length(arg2) & arg2(i) ~= ' '
i=i+1;
end
ALL_AMONG=str2num(arg2(j:i-1));
i=i+1;
j=i;
while i <= length(arg2)
i=i+1;
end
VECT_REPL=str2num(arg2(j:i-1));
else
LOG=0;
end
if ~LOG
big_val_h=getfield(get(gcf,'UserData'),'big_val_h');
small_val_h=getfield(get(gcf,'UserData'),'small_val_h');
equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h');
replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h');
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess'' -figure does not exist. Terminating program...');
pro_tools('close');
return;
end
comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
selected=getfield(get(pre_h,'UserData'),'selected_vects');
sData=getfield(get(pre_h,'UserData'),'sData');
undo = sData;
state=getfield(get(gcf,'UserData'),'state');
if LOG
state.big=BT;
state.small=ST;
state.equal=EQ;
state.replace=VECT_REPL;
state.and=AND_OR;
state.all=ALL_AMONG;
end
if isempty(pre_h)
pro_tools('close');
end
if isempty(get(comp_names_h,'Value'))
clear_state_vals;
errordlg('There must be one component chosen for the operation.');
return;
end
n_th_comp=getfield(get_indices,{1});
if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ...
strcmp(arg,'clip')
clear_state_vals;
errordlg('At least one limit must be chosen for the-operation.');
return;
end
if ~isempty(state.replace) & strcmp(arg,'sel')
if ~all(state.replace == round(state.replace)) | any(state.replace < 1)
errordlg('Indices of vectors must be positive integers.');
return;
elseif any(state.replace > length(sData.data(:,1)))
errordlg('Indices of the vectors to be selected are too big.');
return;
end
end
if isempty(state.replace) & strcmp(arg,'clip')
clear_state_vals;
errordlg('Replace value must be determined for Clipping-operation.');
return;
end
if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ...
isempty(state.replace)
clear_state_vals;
return;
end
bt_indices=[];
lt_indices=[];
equal_indices=[];
if ~isempty(state.big)
if state.all
bt_indices=find(sData.data(:,n_th_comp) > state.big);
else
bt_indices=selected(find(sData.data(selected,n_th_comp) > state.big));
end
end
if ~isempty(state.small)
if state.all
lt_indices=find(sData.data(:,n_th_comp) < state.small);
else
lt_indices=selected(find(sData.data(selected,n_th_comp) < state.small));
end
end
if ~isempty(state.equal)
if isnan(state.equal)
if state.all
equal_indices=find(isnan(sData.data(:,n_th_comp)));
else
equal_indices=selected(find(isnan(sData.data(selected,n_th_comp))));
end
elseif state.all
equal_indices=find(sData.data(:,n_th_comp)==state.equal);
else
equal_indices=selected(find(sData.data(selected,n_th_comp)==state.equal));
end
end
if state.and
if ~isempty(bt_indices) | ~isempty(lt_indices) | ~isempty(equal_indices)...
| strcmp(arg,'sel')
if isempty(bt_indices) & isempty(lt_indices) & isempty(equal_indices) &...
isempty(state.replace)
clear_state_vals;
return;
end
if isempty(bt_indices)
if ~state.all
bt_indices=selected;
else
bt_indices=1:getfield(size(sData.data),{1});
end
end
if isempty(lt_indices)
if ~state.all
lt_indices=selected;
else
lt_indices=1:getfield(size(sData.data),{1});
end
end
if isempty(equal_indices)
if ~state.all
equal_indices=selected;
else
equal_indices=1:getfield(size(sData.data),{1});
end
end
indices=intersect(intersect(bt_indices,lt_indices),equal_indices);
if strcmp(arg,'sel')
if ~isempty(indices) | ~isempty(state.replace)
if isempty(state.replace)
NOTEMPTY=0;
if ~state.all
state.replace=selected;
else
state.replace=1:getfield(size(sData.data),{1});
end
else
NOTEMPTY=1;
end
if isempty(indices)
indices=selected;
end
indices=intersect(indices,state.replace);
if isempty(indices)
indices=selected;
end
data=get(pre_h,'UserData');
data.undo.sData=sData;
data.undo.selected=data.selected_vects;
data.selected_vects=indices;
if ~LOG
if ~NOTEMPTY
data.LOG{length(data.LOG)+1}='% Select vectors.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',...
arg,...
' ',num2str(state.big),...
' ',num2str(state.small),...
' ',num2str(state.equal),...
' ',num2str(state.and),...
' ',num2str(state.all),...
''');');
else
code=write_log_code(state.replace,...
arg,...
state.big,...
state.small,...
state.equal,...
state.and,...
state.all);
data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
end
end
set(pre_h,'UserData',data);
old=gcf;
set(0,'CurrentFigure',pre_h);
sel_comp;
cplot_mimema;
vect_means(data.sData,data.vect_mean_h,data.selected_vects);
set(0,'CurrentFigure',old);
end
clear_state_vals;
return;
end
sData.data(indices,n_th_comp) = state.replace;
sData.MODIFIED=1;
end
else
indices=union(union(bt_indices,lt_indices),equal_indices);
if ~isempty(indices) | strcmp(arg,'sel')
if strcmp(arg,'sel')
if ~isempty(indices) | ~isempty(state.replace')
data=get(pre_h,'UserData');
data.undo.sData=sData;
data.undo.selected=data.selected_vects;
data.selected_vects=union(indices,state.replace);
if ~LOG
if isempty(state.replace);
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',...
arg,...
' ',num2str(state.big),...
' ',num2str(state.small),...
' ',num2str(state.equal),...
' ',num2str(state.and),...
' ',num2str(state.all),...
''');');
else
code=write_log_code(state.replace,...
arg,...
state.big,...
state.small,...
state.equal,...
state.and,...
state.all);
data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
end
end
set(pre_h,'UserData',data);
old=gcf;
set(0,'CurrentFigure',pre_h);
sel_comp;
vect_means(data.sData,data.vect_mean_h,data.selected_vects);
cplot_mimema;
set(0,'CurrentFigure',old);
end
clear_state_vals;
return;
end
sData.data(indices,n_th_comp)=state.replace;
sData.MODIFIED=1;
end
end
if sData.MODIFIED
data=get(pre_h,'UserData');
data.sData=sData;
data.undo.sData=undo;
if ~LOG
if strcmp(arg,'sel')
data.LOG{length(data.LOG)+1}='% Select vectors';
else
data.LOG{length(data.LOG)+1}='% Clip values.';
end
if strcmp(arg,'clip') | isempty(state.replace)
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',arg,...
' ',num2str(state.big),...
' ',num2str(state.small),...
' ',num2str(state.equal),...
' ',num2str(state.and),...
' ',num2str(state.all),...
' ',num2str(state.replace),...
''');');
else
code=write_log_code(state.replace,...
arg,...
state.big,...
state.small,...
state.equal,...
state.and,...
state.all);
data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
end
end
set(pre_h,'UserData',data);
old=gcf;
set(0,'CurrentFigure',pre_h)
vector_h=getfield(get(gcf,'UserData'),'vector_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
set(gcf,'CurrentAxes',vector_h);
vect_means(sData,vect_mean_h,selected);
cplot_mimema;
sel_comp;
set(0,'CurrentFigure',old);
end
clear_state_vals;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: clear_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clear_state_vals
%CLEAR_STATE_VALS Sets the fields of the UserData's state-struct empty.
%
%
data=get(gcf,'UserData');
set(data.big_val_h,'String','');
set(data.small_val_h,'String','');
set(data.equal_val_h,'String','');
set(data.replace_val_h,'String','');
data.state.big=[];
data.state.small=[];
data.state.equal=[];
data.state.replace=[];
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function delay(varargin)
delay_h=findobj(get(0,'Children'),'Tag','Delay');
if nargin ~= 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'% Starting the ''Delay'' -window...';
preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''delay'',''foo'');';
set(pre_h,'UserData',preh_udata);
end
if ~isempty(delay_h)
figure(delay_h);
return;
end
h0 = figure('Color',[0.8 0.8 0.8], ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[759 664 162 215], ...
'Tag','Delay');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.05555555555555555 0.2046511627906977 0.8950617283950617 0.7441860465116279], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.08641975308641975 0.6976744186046512 0.8333333333333333 0.2232558139534884], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.08641975308641975 0.227906976744186 0.8333333333333333 0.4418604651162791], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','preprocess delay_data',...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.0556 0.0326 0.4012 0.1163], ...
'String','OK', ...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','preprocess close_d',...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.5494 0.0326 0.4012 0.1163], ...
'String','Close', ...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.4876543209876543 0.7534883720930232 0.3518518518518519 0.1255813953488372], ...
'Style','edit', ...
'Tag','EditText1');
data.delay_val_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.1173 0.7860 0.3086 0.0651], ...
'String','Delay', ...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','preprocess clip_exp_cb c_this',...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.1173 0.5349 0.1173 0.0837], ...
'Style','radiobutton', ...
'Tag','Radiobutton1');
data.c_this_button_h=h1;
data.mode='c_this';
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess clip_exp_cb c_all',...
'ListboxTop',0, ...
'Position',[0.1173 0.4047 0.1173 0.0837], ...
'Style','radiobutton', ...
'Tag','Radiobutton2');
data.c_all_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess clip_exp_cb e_all',...
'ListboxTop',0, ...
'Position',[0.1173 0.2651 0.1173 0.0837], ...
'Style','radiobutton', ...
'Tag','Radiobutton3');
data.e_all_button_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.26 0.5534883720930233 0.4135802469135802 0.06511627906976744], ...
'String','Clip this', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.26 0.413953488372093 0.3765432098765432 0.06511627906976744], ...
'String','Clip all', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.26 0.2744186046511628 0.4197530864197531 0.06511627906976744], ...
'String','Expand all', ...
'Style','text', ...
'Tag','StaticText4');
set(gcf,'UserData',data);
%%% Subfunction clip_exp_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function clip_exp_cb(arg)
c_this_button_h=getfield(get(gcf,'UserData'),'c_this_button_h');
c_all_button_h=getfield(get(gcf,'UserData'),'c_all_button_h');
e_all_button_h=getfield(get(gcf,'UserData'),'e_all_button_h');
data=get(gcf,'UserData');
switch arg
case 'c_this'
set(c_all_button_h,'Value',0);
set(e_all_button_h,'Value',0);
set(c_this_button_h,'Value',1);
data.mode='c_this';
case 'c_all'
set(c_this_button_h,'Value',0);
set(e_all_button_h,'Value',0);
set(c_all_button_h,'Value',1);
data.mode='c_all';
case 'e_all'
set(c_this_button_h,'Value',0);
set(c_all_button_h,'Value',0);
set(e_all_button_h,'Value',1);
data.mode='e_all';
end
set(gcf,'UserData',data);
%%% Subfunction: delay_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function delay_data(varargin)
if nargin == 1
del_h=findobj(get(0,'Children'),'Tag','Delay');
set(0,'CurrentFigure',del_h);
LOG=1;
arg=varargin{1};
i=1;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
delay=str2num(arg(1:i-1));
no=str2num(arg(i+1:length(arg)));
else
LOG=0;
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
q='Delay operation is not evaluated.';
t='Warning';
if isempty(pre_h)
errordlg('''Preprocess'' -figure does not exist. Terminating program...');
pro_tools('close');
return;
end
sData=getfield(get(pre_h,'UserData'),'sData');
undo = sData;
data=get(gcf,'UserData');
if ~LOG
delay=str2num(get(data.delay_val_h,'String'));
if isempty(delay)
errordlg('Value of ''Delay'' must be defined.');
return
end
set(data.delay_val_h,'String','');
if round(delay) ~= delay
errordlg('Value of ''Delay'' must be integer.');
return;
end
end
comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
if isempty(get(comp_names_h,'Value'))
errordlg('There are not components chosen.');
return;
end
n_th_comp=getfield(get_indices,{1});
len=length(sData.data(:,1));
if LOG
switch no
case 1
data.mode='c_this';
preprocess('clip_exp_cb','c_this');
case 2
data.mode='c_all';
preprocess('clip_exp_cb','c_all');
case 3
data.mode='e_all';
preprocess('clip_exp_cb','e_all');
end
end
switch data.mode
case 'c_this'
MODE='1';
if delay > 0
sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay);
if delay >= len
errordlg(q,t);
return;
else
sData.data(1:delay,n_th_comp)=NaN;
end
elseif delay < 0
sData.data(1:len+delay,n_th_comp)=...
sData.data(abs(delay)+1:len,n_th_comp);
if abs(delay) >= len
errordlg(q,t);
return;
else
sData.data(len+delay+1:len,n_th_comp)=NaN;
end
end
if delay ~= 0
data=get(pre_h,'UserData');
sData.MODIFIED=1;
sData.comp_norm(n_th_comp)=[];
data.sData=sData;
data.undo.sData=undo;
set(pre_h,'UserData',data);
old = gcf;
set(0,'CurrentFigure',pre_h);
sel_comp;
cplot_mimema;
set(0,'CurrentFigure',old);
end
case 'c_all'
MODE='2';
if delay > 0
sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay,n_th_comp);
if delay >= len
errordlg(q,t);
return;
else
sData.data=sData.data(delay+1:len,:);
end
elseif delay < 0
sData.data(1:len+delay,n_th_comp)=sData.data(abs(delay)+1:len,n_th_comp);
if abs(delay) >= len
errordlg(q,t);
return;
else
sData.data=sData.data(1:len+delay,:);
end
end
if delay ~= 0
data=get(pre_h,'UserData');
sData.MODIFIED=1;
sData.comp_norm(:,:)={[]};
data.sData=sData;
data.undo.sData=undo;
data.undo.selected=data.selected_vects;
if delay > 0
data.selected_vects=...
data.selected_vects(find(data.selected_vects>delay));
data.selected_vects=data.selected_vects-delay;
elseif nargin == 1
data.selected_vects=...
data.selected_vects(find(data.selected_vects<=len-abs(delay)));
end
set(pre_h,'UserData',data);
old=gcf;
set(0,'CurrentFigure',pre_h);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
write_sD_stats;
draw_vectors(vects,data.vector_h);
sel_comp;
cplot_mimema;
set(0,'CurrentFigure',old);
end
case 'e_all'
MODE='3';
if delay > 0
sData.data(len+1:len+delay,:)=NaN;
sData.data(1+delay:delay+len,n_th_comp)=sData.data(1:len,n_th_comp);
sData.data(1:delay,n_th_comp)=NaN;
elseif delay < 0
delay=abs(delay);
sData.data(delay+1:len+delay,:)=sData.data;
sData.data(1:delay,:)=NaN;
sData.data(1:len,n_th_comp)=sData.data(delay+1:len+delay,n_th_comp);
sData.data(len+1:len+delay,n_th_comp)=NaN;
end
if delay ~= 0
data=get(pre_h,'UserData');
sData.MODIFIED=1;
sData.comp_norm(:,:)={[]};
data.sData=sData;
data.undo.sData=undo;
data.undo.selected=data.selected_vects;
set(pre_h,'UserData',data);
old=gcf;
set(0,'CurrentFigure',pre_h);
write_sD_stats;
pro_tools('selall');
set(0,'CurrentFigure',old);
end
end
if ~LOG
data=get(pre_h,'UserData');
data.LOG{length(data.LOG)+1}='% Delay a component.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''delay_data'',''',...
num2str(delay),' ',MODE,''');');
set(pre_h,'UserData',data);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function window(varargin)
if nargin ~= 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
preh_udata.LOG{length(preh_udata.LOG)+1}=...
'% Starting the ''Windowed'' -window...';
preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''window'',''foo'');';
set(pre_h,'UserData',preh_udata);
end
win_h=findobj(get(0,'Children'),'Tag','Window');
if ~isempty(win_h)
figure(win_h);
return;
end
h0 = figure('Color',[0.8 0.8 0.8], ...
'PaperPosition',[18 180 576 432], ...
'PaperUnits','points', ...
'Position',[513 703 288 219], ...
'Tag','Window');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
'ListboxTop',0, ...
'Position',[0.03125 0.1552511415525114 0.9375 0.7990867579908676], ...
'Style','frame', ...
'Tag','Frame1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.04861111111111111 0.7214611872146118 0.9027777777777777 0.2009132420091324], ...
'Style','frame', ...
'Tag','Frame2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.04861111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
'Style','frame', ...
'Tag','Frame3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.3611111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
'Style','frame', ...
'Tag','Frame4');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.6736111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
'Style','frame', ...
'Tag','Frame5');
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess eval_windowed',...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.03125 0.0319634703196347 0.2256944444444444 0.091324200913242], ...
'String','OK', ...
'Tag','Pushbutton1');
h1 = uicontrol('Parent',h0, ...
'Callback','preprocess close_w', ...
'Units','normalized', ...
'FontWeight','demi', ...
'ListboxTop',0, ...
'Position',[0.7430555555555555 0.0319634703196347 0.2256944444444444 0.091324200913242], ...
'String','Close', ...
'Tag','Pushbutton2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[1 1 1], ...
'ListboxTop',0, ...
'Position',[0.7083333333333333 0.7625570776255708 0.2083333333333333 0.1232876712328767], ...
'Style','edit', ...
'Tag','EditText1');
data.win_len_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.07638888888888888 0.8036529680365296 0.3784722222222222 0.0547945205479452], ...
'String','Window length', ...
'Style','text', ...
'Tag','StaticText1');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb centered',...
'ListboxTop',0, ...
'Position',[0.06597222222222222 0.5616438356164384 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton1');
data.centered_h=h1;
data.position='center';
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb previous',...
'ListboxTop',0, ...
'Position',[0.06597222222222222 0.4018264840182648 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton2');
data.previous_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb next',...
'ListboxTop',0, ...
'Position',[0.06597222222222222 0.2465753424657534 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton3');
data.next_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb mean',...
'ListboxTop',0, ...
'Position',[0.3784722222222222 0.5799086757990868 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton4');
data.mean_h=h1;
data.mode='mean';
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb median',...
'ListboxTop',0, ...
'Position',[0.3784722222222222 0.4611872146118721 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton5');
data.median_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb max',...
'ListboxTop',0, ...
'Position',[0.3784722222222222 0.3515981735159817 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton6');
data.max_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'Callback','preprocess window_cb min',...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',[0.3784722222222222 0.2374429223744292 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton7');
data.min_h = h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb clip',...
'ListboxTop',0, ...
'Position',[0.6909722222222222 0.5525114155251141 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton8');
data.clip_h=h1;
data.eval_mode='clip';
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Callback','preprocess window_cb expand',...
'ListboxTop',0, ...
'Position',[0.6909722222222222 0.2922374429223744 0.06597222222222222 0.0821917808219178], ...
'Style','radiobutton', ...
'Tag','Radiobutton9');
data.expand_h=h1;
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.132 0.5799 0.19 0.0548], ...
'String','Centered', ...
'Style','text', ...
'Tag','StaticText2');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.132 0.4247 0.1667 0.0548], ...
'String','Previous', ...
'Style','text', ...
'Tag','StaticText3');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.132 0.2648 0.1632 0.0548], ...
'String','Next', ...
'Style','text', ...
'Tag','StaticText4');
h1 = uicontrol('Parent',h0, ...,
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.445 0.6027397260273972 0.19 0.0547945205479452], ...
'String','Mean', ...
'Style','text', ...
'Tag','StaticText5');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.445 0.4795 0.1806 0.0548], ...
'String','Median', ...
'Style','text', ...
'Tag','StaticText6');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.445 0.3699 0.1667 0.0548], ...
'String','Max', ...
'Style','text', ...
'Tag','StaticText7');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.445 0.2557077625570776 0.1597222222222222 0.0547945205479452], ...
'String','Min', ...
'Style','text', ...
'Tag','StaticText8');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.7535 0.5753 0.1354 0.054], ...
'String','Clip', ...
'Style','text', ...
'Tag','StaticText9');
h1 = uicontrol('Parent',h0, ...
'Units','normalized', ...
'BackgroundColor',[0.8 0.8 0.8], ...
'FontWeight','demi', ...
'FontSize',8,...
'HorizontalAlignment','left', ...
'ListboxTop',0, ...
'Position',[0.7534722222222222 0.3150684931506849 0.1527777777777778 0.0547945205479452], ...
'String','Expand', ...
'Style','text', ...
'Tag','StaticText10');
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function window_cb(arg)
data=get(gcf,'UserData');
if any(strcmp(arg,[{'centered'},{'previous'},{'next'}]))
switch arg
case 'centered'
data.position='center';
set(data.previous_h,'Value',0);
set(data.next_h,'Value',0);
set(data.centered_h,'Value',1);
case 'previous'
data.position='previous';
set(data.centered_h,'Value',0);
set(data.next_h,'Value',0);
set(data.previous_h,'Value',1);
case 'next'
data.position='next';
set(data.centered_h,'Value',0);
set(data.previous_h,'Value',0);
set(data.next_h,'Value',1);
end
elseif any(strcmp(arg,[{'mean'},{'median'},{'min'},{'max'}]))
switch arg
case 'mean'
data.mode='mean';
set(data.median_h,'Value',0);
set(data.min_h,'Value',0);
set(data.max_h,'Value',0);
set(data.mean_h,'Value',1);
case 'median'
data.mode='median';
set(data.mean_h,'Value',0);
set(data.max_h,'Value',0);
set(data.min_h,'Value',0);
set(data.median_h,'Value',1);
case 'max'
data.mode='max';
set(data.mean_h,'Value',0);
set(data.median_h,'Value',0);
set(data.min_h,'Value',0);
set(data.max_h,'Value',1);
case 'min'
data.mode='min';
set(data.mean_h,'Value',0);
set(data.median_h,'Value',0);
set(data.max_h,'Value',0);
set(data.min_h,'Value',1);
end
elseif any(strcmp(arg,[{'clip','expand'}]))
switch arg
case 'clip'
data.eval_mode='clip';
set(data.expand_h,'Value',0);
set(data.clip_h,'Value',1);
case 'expand'
data.eval_mode='expand';
set(data.clip_h,'Value',0);
set(data.expand_h,'Value',1);
end
end
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function eval_windowed(varargin)
if nargin == 1
LOG=1;
i=1;
arg=varargin{1};
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
value=str2num(arg(1:i-1));
i=i+1;
j=i;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
position=arg(j:i-1);
i=i+1;
j=i;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
mode=arg(j:i-1);
i=i+1;
j=i;
while i <= length(arg) & arg(i) ~= ' '
i=i+1;
end
eval_mode=arg(j:i-1);
else
LOG=0;
end
data=get(gcf,'UserData');
if LOG
data.position=position;
data.eval_mode=eval_mode;
data.mode=mode;
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess''-window does not exist. Terminating program...');
pro_tools('close');
return;
end
comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
sData=getfield(get(pre_h,'UserData'),'sData');
undo=sData;
if isempty(get(comp_names_h,'Value'))
errordlg('There are not components chosen.');
return;
end
if ~LOG
if isempty(get(data.win_len_h,'String'))
errordlg('Window length must be defined');
return;
end
value=str2num(get(data.win_len_h,'String'));
end
set(data.win_len_h,'String','');
if ~LOG
if isempty(value) | value < 0 | value ~= round(value)
errordlg('Window length must be positive integer.');
return;
end
if value > length(sData.data(:,1))
errordlg('Length of window is too big.');
return;
end
end
index=getfield(get_indices,{1});
sData=eval_operation(sData,value,data.mode,data.eval_mode,data.position,index);
sData.comp_norm(index)={[]};
u_data=get(pre_h,'UserData');
u_data.sData=sData;
u_data.undo.sData=undo;
u_data.undo.selected=u_data.selected_vects;
if ~LOG
u_data.LOG{length(u_data.LOG)+1}=...
'% Evaluating the wanted ''windowed'' -operation.';
u_data.LOG{length(u_data.LOG)+1}=cat(2,'preprocess(''eval_windowed'',',...
'''',num2str(value),...
' ',data.position,' ',data.mode,...
' ',data.eval_mode,''');');
end
set(pre_h,'UserData',u_data);
old=gcf;
set(0,'CurrentFigure',pre_h);
if strcmp(data.eval_mode,'expand');
write_sD_stats;
pro_tools('selall');
else
sel_comp;
cplot_mimema;
end
set(0,'CurrentFigure',old);
%%% Subfunction: eval_operation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sData=eval_operation(sData,winlen,mode,evalmode,position,n)
len=length(sData.data(:,1));
dim=length(sData.data(1,:));
switch(position)
case 'center'
prev=round(winlen/2)-1;
next=winlen-round(winlen/2);
case 'previous'
prev=winlen-1;
next=0;
case 'next'
prev=0;
next=winlen-1;
end
switch(evalmode)
case 'clip'
for center=1:len
win=center-prev:center-prev+winlen-1;
win=win(find(win > 0 & win <= len));
str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))');
tmp(center)=eval(str);
end
sData.data(:,n)=tmp;
case 'expand'
for i=1:len+winlen-1
win=i-(winlen-1):i;
win=win(find(win > 0 & win <= len));
str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))');
tmp(i)=eval(str);
end
sData.data=cat(1,repmat(NaN,next,dim),sData.data,repmat(NaN,prev,dim));
sData.data(:,n)=tmp;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function pro_tools(arg)
switch arg
case 'close'
close_preprocess;
case 'c_stat'
write_c_stats;
case 'plot_hist'
plot_hist;
case 'plot'
plot_button;
case 'plxy'
plxy_button;
case 'bplo'
bplo_button;
case 'hist'
hist_button;
end
%%% Subfunction close_preprocess %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function close_preprocess
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
man_h=findobj(get(0,'Children'),'Tag','Management');
clip_h=findobj(get(0,'Children'),'Tag','Clipping');
plot_h=findobj(get(0,'Children'),'Tag','PlotWin');
delay_h=findobj(get(0,'Children'),'Tag','Delay');
window_h=findobj(get(0,'Children'),'Tag','Window');
sel_h=findobj(get(0,'Children'),'Tag','Select');
if ~isempty(man_h)
close(man_h);
end
if ~isempty(clip_h)
close(clip_h);
end
if ~isempty(plot_h)
close(plot_h);
end
if ~isempty(delay_h)
close(delay_h);
end
if ~isempty(window_h)
close(window_h);
end
if ~isempty(sel_h)
close(sel_h);
end
if ~isempty(pre_h)
close(pre_h);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: undo %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function undo(varargin)
if nargin == 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
if ~isempty(data.undo)
if any(strcmp('selected',fieldnames(data.undo)))
data.selected_vects=data.undo.selected;
end
if ~any(strcmp('index',fieldnames(data.undo)))
data.sData=data.undo.sData;
data.undo=[];
if ~LOG
data.LOG{length(data.LOG)+1}='% Undo the most recent operation.';
data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');';
end
set(gcf,'UserData',data);
set_compnames(data.sData,data.comp_names_h);
write_sD_stats;
vect_means(data.sData,data.vect_mean_h,data.selected_vects);
sel_comp;
cplot_mimema;
return;
end
% 'undo.sData' does not exist in sD_set - array
index=data.undo.index;
data.undo.sData=rmfield(data.undo.sData,[{'INDEX'};{'MODIFIED'}]);
if index<=length(data.sD_set)
rest=data.sD_set(index:length(data.sD_set));
else
rest=[];
end
data.sD_set=cat(2,data.sD_set(1:index-1),data.undo.sData,rest);
data.undo=[];
if ~LOG
data.LOG{length(data.LOG)+1}='% Undo the most recent operation.';
data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');';
end
set(gcf,'UserData',data);
set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',index);
set_sD_stats;
sel_sD;
else
msgbox('Can''t do...');
end
%%% Subfunction: write_c_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function write_c_stats(varargin)
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h');
sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h');
if nargin==1
val1=varargin(1);
else
val1=get(sel_comp_h,'String');
end
if ~isempty(val1) & iscell(val1)
selected_vects=getfield(get(pre_h,'UserData'),'selected_vects');
sData=getfield(get(pre_h,'UserData'),'sData');
sel_cdata_h=getfield(get(pre_h,'UserData'),'sel_cdata_h');
name=getfield(get(sel_comp_h,'String'),{get(sel_comp_h,'Value')});
name=name{1};
i=2;
while ~isempty(str2num(name(i)))
value(i-1)=name(i);
i=i+1;
end
value=str2num(value);
data=sData.data(selected_vects,value);
string{1} = cat(2,'Min: ',sprintf('%-10.3g',min(data)));
string{2} = cat(2,'Mean: ',sprintf('%-10.3g',mean(data(find(~isnan(data))))));
string{3} = cat(2,'Max: ',sprintf('%-10.3g',max(data)));
string{4} = cat(2,'Std: ',sprintf('%-10.3g',std(data(find(~isnan(data))))));
string{5} = cat(2,'Number of NaNs: ',sprintf('%-10.3g',sum(isnan(data))));
string{6} = cat(2,'NaN (%):',...
sprintf('%-10.3g',100*sum(isnan(data))/length(data)));
string{7} = cat(2,'Number of values: ',sprintf('%-10.3g',...
length(find(~isnan(unique(data))))));
set(sel_cdata_h,'String',string);
set(sel_cdata_h,'HorizontalAlignment','left');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction plot_hist %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plot_hist
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h');
sData=getfield(get(pre_h,'UserData'),'sData');
selected=getfield(get(pre_h,'UserData'),'selected_vects');
value=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value');
subplot(sel_chist_h);
hold off;
cla;
if all(isnan(sData.data(:,value)));
return;
end
hold on;
lim1=min(sData.data(:,value));
lim2=max(sData.data(:,value));
if lim2 - lim1 >= eps
x=lim1:(lim2-lim1)/(30-1):lim2;
set(sel_chist_h,'XLim',[lim1 lim2]);
elseif lim1 ~= 0
x=(lim1)/2:lim1/(30-1):lim1+(lim1)/2;
set(sel_chist_h,'Xlim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]);
else
x=-1:2/(30-1):1;
set(sel_chist_h,'XLim',[-1 1]);
end
hist(sData.data(selected,value),x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: select_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function select_all(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
data.selected_vects=(1:length(data.sData.data(:,1)));
if ~LOG
data.LOG{length(data.LOG)+1}='% Select all vectors.';
data.LOG{length(data.LOG)+1}='selall(''foo'');';
end
set(gcf,'UserData',data);
tmp=zeros(1,length(data.sData.data(:,1)));
tmp(data.selected_vects)=1;
draw_vectors(tmp,data.vector_h);
cplot_mimema;
vect_means(data.sData,data.vect_mean_h,data.selected_vects);
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: plot_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plot_button
%PLOT_BUTTON A callback function. Plots all the components and marks
% the chosen components.
%
%
sData=getfield(get(gcf,'UserData'),'sData');
selected=getfield(get(gcf,'UserData'),'selected_vects');
indices=get_indices;
if isempty(indices)
return;
end
h=findobj(get(0,'Children'),'Tag','PlotWin');
if isempty(h)
h= figure;
set(h,'Tag','PlotWin');
end
names=sData.comp_names(indices);
data=sData.data(:,indices);
set(0,'CurrentFigure',h);
hold off;
clf;
t=0:1/(getfield(size(data),{1})-1):1;
tmp=setdiff(1:length(data(:,1)),selected);
for i=1:length(names)
subplot(length(names),1,i);
hold on;
if max(data(:,i))- min(data(:,i)) <= eps
set(gca,'YLim',[max(data(:,i))-1 max(data(:,i))+1]);
end
plot(t,data(:,i));
if ~isempty(tmp);
data(tmp,i)=NaN;
end
plot(t,data(:,i),'red');
ylabel(names{i});
set(gca,'XTick',[]);
end
set(gcf,'Name','Plotted Data Components');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: plxy_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plxy_button
%PLXY_BUTTON A callback function. XY-plots the first and the second
% components chosen.
%
%
sData=getfield(get(gcf,'UserData'),'sData');
selected=getfield(get(gcf,'UserData'),'selected_vects');
inds = get_indices;
if length(inds) < 2
errordlg('There must be two components chosen for XY-plot.');
return;
end
inds=inds(1:2);
names=getfield(sData,'comp_names',{inds});
h=findobj(get(0,'Children'),'Tag','PlotWin');
if isempty(h)
h= figure;
set(h,'Tag','PlotWin');
end
set(0,'CurrentFigure',h);
clf;
axes;
if max(sData.data(:,inds(1))) - min(sData.data(:,inds(1))) <= eps
set(gca,'XLim',[max(sData.data(:,inds(1)))-1 max(sData.data(:,inds(1)))+1]);
end
if max(sData.data(:,inds(2))) - min(sData.data(:,inds(2))) <= eps
set(gca,'YLim',[max(sData.data(:,inds(2)))-1 max(sData.data(:,inds(2)))+1]);
end
hold on;
plot(sData.data(:,inds(1)),sData.data(:,inds(2)),'o');
x=sData.data(selected,inds(1));
y=sData.data(selected,inds(2));
plot(x,y,'ored','MarkerSize',4);
xlabel(names(1));
ylabel(names(2));
set(h,'Name','Plotted Data Components');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Sub_function: bplo_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bplo_button
%BPLO_BUTTON A callback function. Box-plots the first component chosen.
sData=getfield(get(gcf,'UserData'),'sData');
selected=getfield(get(gcf,'UserData'),'selected_vects');
if length(selected) == 1
errordlg('There are too few vectors chosen for box-plotting.');
else
indices=get_indices;
if isempty(indices)
return;
end
for i=1:length(indices)
if length(unique(sData.data(selected,indices(i))))==1
errordlg('All the values are the same. Operation can''t be evaluated.');
return;
end
end
names=getfield(sData,'comp_names',{indices});
h= findobj(get(0,'Children'),'Tag','PlotWin');
if isempty(h)
h= figure;
set(h,'Tag','PlotWin');
end
data=sData.data(selected,indices);
set(0,'CurrentFigure',h);
hold off;
clf;
hold on;
for i=1:getfield(size(data),{2})
subplot(getfield(size(data),{2}),1,i);
if ~all(isnan(data(:,i)))
boxplot(data(:,i));
end
name=names{i};
tmp=get(get(gca,'YLabel'),'String');
ylabel(cat(2,sprintf('[%s] ',name),tmp));
end
set(h,'Name','Box-plot');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: hist_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hist_button
no_of_bins_h=getfield(get(gcf,'UserData'),'no_of_bins_h');
selected=getfield(get(gcf,'UserData'),'selected_vects');
sData=getfield(get(gcf,'UserData'),'sData');
n=str2num(get(no_of_bins_h,'String'));
s1='Invalid number of bins.';
s2=sprintf('\nSet new value to the box under the ''Histogram''-button.');
if isempty(n)
errordlg(cat(2,s1,s2));
else
indices=get_indices;
if isempty(indices)
return;
end
n=round(n);
if n < 1
errordlg('Number of bins must be positive integer.');
else
h= findobj(get(0,'Children'),'Tag','PlotWin');
if isempty(h)
h= figure;
set(h,'Tag','PlotWin');
end
set(0,'CurrentFigure',h);
hold off;
clf;
data=sData.data(selected,indices);
names=sData.comp_names(indices);
for i=1:length(names)
subplot(length(names),1,i);
hold on;
lim1=min(sData.data(:,indices(i)));
lim2=max(sData.data(:,indices(i)));
if n > 1
if lim2 - lim1 >= eps
x=lim1:(lim2-lim1)/(n-1):lim2;
set(gca,'XLim',[lim1 lim2]);
elseif lim1 ~= 0
x=lim1/2:lim1/(n-1):lim1/2+lim1;
if ~all(isnan([lim1 lim2]))
set(gca,'XLim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]);
end
else
x=-1:2/(n-1):1;
set(gca,'XLim',[-1 1]);
end
else
x=1;
if lim2 ~= lim1
set(gca,'XLim',[lim1 lim2]);
else
set(gca,'XLim',[lim1/2 lim1/2+lim1]);
end
end
if ~all(isnan(data(:,i)))
hist(data(:,i),x);
end
name=names{i};
xlabel(name);
end
set(h,'Name','Histogram');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: no_of_values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function no_of_values(varargin);
%NO_OF_VALUES A callback function. Calculates the number of different
% values of the chosen components.
%
%
if nargin==1;
LOG=1;
else
LOG=0;
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
results_h=getfield(get(pre_h,'UserData'),'results_h');
sData=getfield(get(pre_h,'UserData'),'sData');
selected=getfield(get(pre_h,'UserData'),'selected_vects');
str1='There must be one component chosen for ''Number of Values''-operation';
if ~LOG & isempty(get_indices)
errordlg(str1);
else
indices=get_indices;
data=sData.data(selected,indices);
string{1} = 'Number of different values:';
for i=1:getfield(size(data),{2})
tmp=data(:,i);
string{i+1}=cat(2,sprintf('#%d:',indices(i)),...
sprintf('%d',length(find(~isnan(unique(data(:,i)))))));
end
set(results_h,'String',string);
set(results_h,'HorizontalAlignment','left');
if ~LOG
data=get(pre_h,'UserData');
data.LOG{length(data.LOG)+1}='% Number of values';
data.LOG{length(data.LOG)+1}='preprocess(''noof'',''foo'');';
set(pre_h,'UserData',data);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: correlation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function correlation(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
results_h=getfield(get(pre_h,'UserData'),'results_h');
selected=getfield(get(pre_h,'UserData'),'selected_vects');
sData=getfield(get(pre_h,'UserData'),'sData');
if length(get_indices) < 2
errordlg('There must be two components chosen for Correlation');
else
indices=getfield(get_indices,{1:2});
data=sData.data(selected,indices);
inds=find(~isnan(data(:,1)) & ~isnan(data(:,2)));
value=getfield(corrcoef(data(inds,1),data(inds,2)),{1,2});
names=sData.comp_names(indices);
string{1}='Correlation between';
string{2}=cat(2,names{1},' and ',names{2},':');
string{3}=sprintf('%-10.3g',value);
set(results_h,'String',string);
set(results_h,'HorizontalAlignment','left');
if ~LOG
data=get(pre_h,'UserData');
data.LOG{length(data.LOG)+1}='% Correlation';
data.LOG{length(data.LOG)+1}='preprocess(''corr'',''foo'');';
set(pre_h,'UserData',data);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: unit_length %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function unit_length(varargin)
%UNIT_LENGTH A callback function Scales all the vectors to the unit
% length.
%
%
if nargin==1
LOG=1;
else
LOG=0;
end
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
sData=getfield(get(gcf,'UserData'),'sData');
sData.MODIFIED=1;
scaled=sData.data;
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
if ~LOG & isempty(get(comp_names_h,'Value'))
errordlg('There must be components chosen for the ''unit length''- operation');
return;
end
inds=get_indices;
for i=1:length(scaled(:,1));
x=find(~isnan(scaled(i,inds)));
scaled(i,inds(x))=(1/sqrt(sum(scaled(i,inds(x)).^2)))*scaled(i,inds(x));
end
data=get(gcf,'UserData');
data.undo.sData = sData;
data.sData.data=scaled;
for i=1:length(inds)
data.sData.comp_norm{inds(i)}=[];
end
if ~LOG
data.LOG{length(data.LOG)+1}='% Unit length';
data.LOG{length(data.LOG)+1}='preprocess(''unit'',''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
draw_vectors(vects,data.vector_h);
vect_means(sData,vect_mean_h,data.selected_vects);
cplot_mimema;
plot_hist;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: one_of_n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function one_of_n(varargin)
if nargin==1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
vector_h=getfield(get(gcf,'Userdata'),'vector_h');
comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
sData=data.sData;
undo=data.sData;
selected=getfield(get(gcf,'UserData'),'selected_vects');
msg='Creating over 10 new components. Stop operation?';
if ~LOG
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be one component chosen for ''Add: N binary types'' -operation');
return;
end
end
index=getfield(get_indices,{1});
tmp=unique(sData.data(:,index));
n=length(tmp);
if ~LOG
if n>10
answer=questdlg(msg,'Question','Yes','No','Yes');
if strcmp(answer,'Yes')
msgbox('Operation stopped.');
return;
end
end
end
dim1=getfield(size(sData.data),{1});
dim2=getfield(size(sData.data),{2});
sData.data=cat(2,sData.data,zeros(dim1,n));
dim=dim2+n;
for i=1:n
sData.data(:,dim-(n-i))=(sData.data(:,index) == tmp(i));
end
INDEX=sData.INDEX;
for i=1:n
sData.comp_names{dim2+i}=sprintf('%dNewVar',dim2+i);
end
tmp_norm=cat(1,sData.comp_norm,cell(n,1));
sData=som_data_struct(sData.data,...
'name',sData.name,...
'labels',sData.labels,...
'comp_names',sData.comp_names);
sData.MODIFIED=1;
sData.INDEX=INDEX;
sData.comp_norm=tmp_norm;
data.undo.sData=undo;
data.sData=sData;
data.selected_vects=1:length(sData.data(:,1));
if ~LOG
data.LOG{length(data.LOG)+1}='% Add: N binary types';
data.LOG{length(data.LOG)+1}='preprocess(''oneo'',''foo'');';
end
set(gcf,'UserData',data);
clear_button;
write_sD_stats;
set_compnames(sData,comp_names_h);
tmp=ones(1,length(sData.data(:,1)));
draw_vectors(tmp,vector_h);
vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
cplot_mimema;
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: add_zeros %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function add_zeros(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
vector_h=getfield(get(gcf,'Userdata'),'vector_h');
comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
sData=data.sData;
undo=sData;
dim1=getfield(size(sData.data),{1});
dim2=getfield(size(sData.data),{2});
sData.data=cat(2,sData.data,zeros(dim1,1));
INDEX=sData.INDEX;
sData.comp_names{dim2+1}=sprintf('%dNewVar',dim2+1);
tmp_norm=cat(1,sData.comp_norm,cell(1,1));
sData=som_data_struct(sData.data,...
'name',sData.name,...
'labels',sData.labels,...
'comp_names',sData.comp_names);
sData.MODIFIED=1;
sData.INDEX=INDEX;
sData.comp_norm=tmp_norm;
data.sData=sData;
data.undo.sData=undo;
data.selected_vects=1:length(sData.data(:,1));
if ~LOG
data.LOG{length(data.LOG)+1}='% Add: zeros';
data.LOG{length(data.LOG)+1}='preprocess(''zero'',''foo'');';
end
set(gcf,'UserData',data);
clear_button;
write_sD_stats;
set_compnames(sData,comp_names_h);
tmp=ones(1,length(sData.data(:,1)));
draw_vectors(tmp,vector_h);
vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
cplot_mimema;
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: move_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function move_component(varargin)
%MOVE_COMPONENT A callback function. Moves one component of vectors to
% the position wanted.
%
%
if nargin == 1
LOG=1;
i=1;
while varargin{1}(i) ~= ' '
value(i)=varargin{1}(i);
i=i+1;
end
value=str2num(value); % the new place
index=str2num(varargin{1}(i:length(varargin{1}))); % index of the chosen
% component
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
prompt='Enter the number of the new component place:';
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be one component chosen for ''Move Component''-operation');
return;
end
if ~LOG
index=getfield(get_indices,{1});
answer=inputdlg(prompt);
if isempty(answer) | (iscell(answer) & isempty(answer{1}))
msgbox('No components moved');
return;
end
value=str2num(answer{1});
dims=size(value);
if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
errordlg('The new component place must be positive integer.')
return;
end
if value <= 0 | round(value) ~= value
errordlg('The new component place must be positive integer.');
return;
end
if value > getfield(size(sData.data),{2})
errordlg('Too big value for the new component place.');
return;
end
end
sData.MODIFIED=1;
if index < value
indices1=setdiff(1:value,index);
indices2=setdiff(value+1:length(sData.data(1,:)),index);
elseif index > value
indices1=setdiff(1:value-1,index);
indices2=setdiff(value:length(sData.data(1,:)),index);
else
data.sData=sData;
data.undo.sData=undo;
set(gcf,'UserData',data);
return;
end
tmp1=sData.data(:,indices1);
tmp2=sData.data(:,indices2);
sData.data=cat(2,tmp1,sData.data(:,index),tmp2);
tmp1=sData.comp_names(indices1);
tmp2=sData.comp_names(indices2);
sData.comp_names=cat(1,tmp1,sData.comp_names(index),tmp2);
tmp1=sData.comp_norm(indices1);
tmp2=sData.comp_norm(indices2);
sData.comp_norm=cat(1,tmp1,sData.comp_norm(index),tmp2);
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Move component.';
data.LOG{length(data.LOG)+1}=sprintf('preprocess(''move'',''%s %s'');',...
num2str(value),num2str(index));
end
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
vector_h=getfield(get(gcf,'UserData'),'vector_h');
data.selected_vects=1:length(sData.data(:,1));
set(gcf,'UserData',data);
clear_button;
set_compnames(sData,comp_names_h);
draw_vectors(ones(1,length(sData.data(:,1))),vector_h);
vect_means(sData,vect_mean_h,data.selected_vects);
cplot_mimema;
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: copy_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function copy_component(varargin)
%COPY_COMPONENT Copies one component of vectors to the position wanted.
%
%
if nargin == 1
LOG=1;
i=1;
while varargin{1}(i) ~= ' '
value(i)=varargin{1}(i);
i=i+1;
end
value=str2num(value); % the new place
index=str2num(varargin{1}(i:length(varargin{1}))); % index of the chosen
% component
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
if ~LOG
prompt='Enter the number of the new component place:';
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be one component chosen for ''Copy Component''-operation');
return;
end
index=getfield(get_indices,{1});
answer=inputdlg(prompt);
if isempty(answer) | (iscell(answer) & isempty(answer{1}))
msgbox('No components moved');
return
end
value=str2num(answer{1});
dims=size(value);
if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
errordlg('The new component place must be positive integer.')
return;
end
if value <= 0 | round(value) ~= value
errordlg('The new component place must be positive integer.');
return;
end
if value > getfield(size(sData.data),{2}) + 1
errordlg('Too big value for the new component place.');
return;
end
end
sData.MODIFIED=1;
indices1=1:value-1;
indices2=value:length(sData.data(1,:));
tmp1=sData.data(:,indices1);
tmp2=sData.data(:,indices2);
sData.data=cat(2,tmp1,sData.data(:,index),tmp2);
tmp1=sData.comp_names(indices1);
tmp2=sData.comp_names(indices2);
name=cell(1,1);
name{1}=cat(2,'Copied',sData.comp_names{index});
sData.comp_names=cat(1,tmp1,name,tmp2);
tmp1=sData.comp_norm(indices1);
tmp2=sData.comp_norm(indices2);
norm=cell(1,1);
norm{1}=sData.comp_norm{index};
sData.comp_norm=cat(1,tmp1,norm,tmp2);
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Copy component';
data.LOG{length(data.LOG)+1}=sprintf('preprocess(''copy'',''%s %s'');',...
num2str(value),num2str(index));
end
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
vector_h=getfield(get(gcf,'UserData'),'vector_h');
data.selected_vects=1:length(sData.data(:,1));
set(gcf,'UserData',data);
clear_button;
write_sD_stats;
set_compnames(sData,comp_names_h);
draw_vectors(ones(1,length(sData.data(:,1))),vector_h);
vect_means(sData,vect_mean_h,data.selected_vects);
cplot_mimema;
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: remove_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function remove_component(varargin)
if nargin == 1
LOG=1;
value=str2num(varargin{1});
else
LOG=0;
end
data=get(gcf,'UserData');
vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
vector_h=getfield(get(gcf,'UserData'),'vector_h');
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
sData=data.sData;
undo=sData;
prompt='Enter the number of component to be removed.';
dim=length(sData.data(1,:));
if ~LOG
answer=inputdlg(prompt);
if isempty(answer) | (iscell(answer) & isempty(answer{1}))
msgbox('Components not removed.');
return;
end
value=str2num(answer{1});
dims=size(value);
if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
errordlg('Number of the component to be removed must be positive integer.')
return;
end
if value <= 0 | round(value) ~= value
errordlg('Number of the component to be removed must be positive integer.');
return;
end
if value > getfield(size(sData.data),{2})
errordlg('There are less components.');
return;
end
end
sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h');
index=get(sD_set_h,'Value');
if value == 1 & getfield(size(sData.data),{2}) == 1
if length(get(sD_set_h,'String')) == 1
msgbox('No data left. Closing program...')
pro_tools('close');
return;
end
set1=data.sD_set(1:index-1);
set2=data.sD_set(index+1:length(data.sD_set));
data.sD_set=[set1 set2];
set(gcf,'UserData',data);
set_sD_stats;
sel_sD;
data=get(gcf,'UserData');
data.undo.sData=undo;
data.undo.index=index;
set(gcf,'UserData',data);
return;
end
dims=size(sData.data);
tmp_data=cat(2,sData.data(:,1:value-1),sData.data(:,value+1:dims(2)));
tmp_norm=cat(1,sData.comp_norm(1:value-1),sData.comp_norm(value+1:dims(2)));
names=cat(1,sData.comp_names(1:value-1),sData.comp_names(value+1:dims(2)));
INDEX=sData.INDEX;
comp_norm=sData.comp_norm;
sData=som_data_struct(tmp_data,...
'name',sData.name,...
'labels',sData.labels,...
'comp_names',names);
sData.comp_norm=tmp_norm;
sData.MODIFIED=1;
sData.INDEX=INDEX;
data=get(gcf,'UserData');
data.sData=sData;
data.undo.sData=undo;
data.selected_vects=1:length(sData.data(:,1));
if ~LOG
data.LOG{length(data.LOG)+1}='% Remove component';
data.LOG{length(data.LOG)+1}=sprintf('preprocess(''remove'',''%s'');',...
answer{1});
end
set(gcf,'UserData',data);
clear_button;
write_sD_stats;
set_compnames(sData,comp_names_h);
tmp=ones(1,length(sData.data(:,1)));
draw_vectors(tmp,vector_h);
vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
cplot_mimema;
sel_comp;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: remove_vects %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function remove_vects(varargin)
if nargin==1
LOG=1;
tmp_str=varargin{1};
else
LOG=0;
tmp_str='_foo';
end
data=get(gcf,'UserData');
vect_mean_h=data.vect_mean_h;
vector_h=data.vector_h;
sData=data.sData;
undo=sData;
if length(data.selected_vects) == getfield(size(sData.data),{1})
if LOG
answer='Yes';
else
answer=questdlg('Do you want to delete this data set?');
end
if strcmp(answer,'No')
return;
else
index=get(data.sD_set_h,'Value');
if length(get(data.sD_set_h,'String')) == 1
msgbox('No data left. Closing program...')
pro_tools('close');
return;
end
set1=data.sD_set(1:index-1);
set2=data.sD_set(index+1:length(data.sD_set));
data.sD_set=[set1 set2];
set(gcf,'UserData',data);
set(data.sD_set_h,'Value',1);
set_sD_stats;
sel_sD;
data=get(gcf,'UserData');
data.undo.sData=undo;
data.undo.index=index;
if ~LOG
data.LOG{length(data.LOG)+1}='% Remove selected vectors';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',...
tmp_str,''');');
end
set(gcf,'UserData',data);
return;
end
end
tmp=sData.data(data.selected_vects,:);
if ~LOG
answer=questdlg('Do you want to save removed values to workspace?');
else
if ~strcmp(tmp_str,'_foo')
answer='Yes';
else
answer='No';
end
end
old=gcf;
if strcmp(answer,'Yes')
if ~LOG
answer=inputdlg('Give the name of the output -variable.');
else
answer={tmp_str};
end
if isvalid_var_name(answer)
assignin('base',answer{1},tmp);
disp(sprintf('Removed values are set to workspace as''%s''.',answer{1}));
tmp_str=answer{1};
end
end
set(0,'CurrentFigure',old);
sData.data(data.selected_vects,:)=[];
sData.labels(data.selected_vects,:)=[];
sData.MODIFIED=1;
data.sData=sData;
data.selected=1:length(sData.data(:,1));
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)}='% Remove selected vectors';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',...
tmp_str,''');');
end
set(gcf,'UserData',data);
draw_vectors(ones(1,length(data.selected)),data.vector_h);
write_sD_stats;
select_all('foo');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: eval1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function eval1(varargin);
if nargin == 1
answer=varargin
LOG=1;
else
LOG=0;
end
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess''-figure does not exist. Terminating program...');
pro_tools('close');
return;
end
undo=getfield(get(pre_h,'UserData'),'sData');
if ~LOG
prompt={'Enter the expression to be evaluated.',...
'Enter the inverse normalization method (optional).'};
title='Single component eval.';
answer= inputdlg(prompt,title,1);
end
if ~isempty(answer)
tmp=[];
if ~isempty(answer{1})
[tmp,method]=build_expr(answer{1},'single');
if ~isstr(tmp)
sData=getfield(get(gcf,'UserData'),'sData');
tmp='Done.';
%if ~isempty(answer{2})
% sN=som_norm_struct('eval',{method,answer{2}});
%else
% sN=som_norm_struct('eval',{method});
%end
%sN=som_set(sN,'status','done');
params={answer{1};answer{2}};
ind=getfield(get_indices,{1});
x.type='';
x.method='eval';
x.params={answer{1};answer{2}};
x.status='';
sData.comp_norm{ind}=x;
data=get(gcf,'UserData');
data.undo.sData=undo;
data.sData=sData;
if ~LOG
data.LOG{length(data.LOG)+1}='% Eval (1-comp)';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess eval1 ',...
sprintf('{''%s'' ''%s''};',answer{1},answer{2}));
end
set(pre_h,'UserData',data);
end
end
set(getfield(get(pre_h,'UserData'),'results_h'),'String',tmp);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: eval2
function eval2(varargin)
if nargin == 1
answer=varargin{1};
LOG=1;
else
LOG=0;
end
undo=getfield(get(gcf,'UserData'),'sData');
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
errordlg('''Preprocess''-figure does not exist. Terminating program.');
pro_tools('close');
return;
end
if ~LOG
prompt='Enter the expression to be evaluated.';
title ='Eval';
answer=inputdlg(prompt,title,1);
end
if ~isempty(answer) & ~isempty(answer{1})
str=answer{1};
[answer,foo]=build_expr(answer{1},'multiple');
if ~isstr(answer)
answer='Done.';
data=get(gcf,'UserData');
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Eval';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''eval2'',',...
sprintf('{''%s''});',str));
end
set(gcf,'UserData',data);
end
end
set(getfield(get(pre_h,'UserData'),'results_h'),'String',answer);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: zero2one_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function zero2one_scale(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
INDEX=sData.INDEX;
sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be components chosen for scaling.');
return;
end
sData=som_normalize(sData,'range',get_indices);
sData.MODIFIED=1;
sData.INDEX=INDEX;
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Scale [0,1]';
data.LOG{length(data.LOG)+1}='preprocess(''zscale'', ''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
cplot_mimema;
plot_hist;
vect_means(sData,data.vect_mean_h,data.selected_vects);
draw_vectors(vects,data.vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: var_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function var_scale(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
INDEX=sData.INDEX;
sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be components chosen for scaling.');
return;
end
sData=som_normalize(sData,'var',get_indices);
sData.INDEX=INDEX;
sData.MODIFIED=1;
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Scale var=1';
data.LOG{length(data.LOG)+1}='preprocess(''vscale'', ''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
cplot_mimema;
plot_hist;
vect_means(sData,data.vect_mean_h,data.selected_vects);
draw_vectors(vects,data.vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: hist_eq %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hist_eq(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
INDEX=sData.INDEX;
sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]);
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be components chosen for ''Histogram eq''.');
return;
end
sData=som_normalize(sData,'histD',get_indices);
sData.INDEX=INDEX;
sData.MODIFIED=1;
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Histogram eq';
data.LOG{length(data.LOG)+1}='preprocess(''histeq'', ''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
cplot_mimema;
plot_hist;
vect_means(sData,data.vect_mean_h,data.selected_vects);
draw_vectors(vects,data.vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: hist_eq2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hist_eq2(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
INDEX=sData.INDEX;
sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be components chosen for ''Histogram eq2''.');
return;
end
inds=get_indices;
%%%[sData,ok]=som_normalize(sData,inds,'histC');
sData=som_normalize(sData,'histC',inds);
sData.INDEX=INDEX;
sData.MODIFIED=1;
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Histogram eq2';
data.LOG{length(data.LOG)+1}='preprocess(''histeq2'', ''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
cplot_mimema;
plot_hist;
vect_means(sData,data.vect_mean_h,data.selected_vects);
draw_vectors(vects,data.vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: logarithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function logarithm(varargin)
if nargin == 1
LOG=1;
else
LOG=0;
end
data=get(gcf,'UserData');
sData=data.sData;
undo=sData;
INDEX=sData.INDEX;
sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]);
if isempty(get(data.comp_names_h,'Value'))
errordlg('There must be components chosen for ''Log''.');
return;
end
Data=som_normalize(sData,'log',get_indices);
sData.INDEX=INDEX;
sData.MODIFIED=1;
data.sData=sData;
data.undo.sData=undo;
if ~LOG
data.LOG{length(data.LOG)+1}='% Log';
data.LOG{length(data.LOG)+1}='preprocess(''log'', ''foo'');';
end
set(gcf,'UserData',data);
vects=zeros(1,length(sData.data(:,1)));
vects(data.selected_vects)=1;
cplot_mimema;
plot_hist;
vect_means(sData,data.vect_mean_h,data.selected_vects);
draw_vectors(vects,data.vector_h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [answer,method]=build_expr(string,evaltype)
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
method=[];
if isempty(pre_h)
close_preprocess;
errordlg('''Preprocess'' -figure does not exist. Terminating program...');
return;
end
if isempty(string)
str = '[]';
return;
end
tmp=[];
[name,assign,skip]=check_assign(string,evaltype);
if ~strcmp(assign,'NOTASSIGN') & ~strcmp(assign,'error')
string=string(skip:length(string));
end
if ~strcmp(assign,'error')
if isempty(string)
answer='Illegal expression.';
return;
end
[str,skip]=check_token(string,evaltype);
method=string;
while ~strcmp(str,'error') & ~strcmp(tmp,'error') & skip < length(string)
if ~strcmp(tmp,')')
str=cat(2,str,tmp);
end
[tmp,skip2]=check_token(string(skip+1:length(string)),evaltype);
skip=skip+skip2;
end
if ~strcmp(tmp,')') & ~strcmp(tmp,'error')
str=cat(2,str,tmp);
elseif strcmp(tmp,'error')
str='error';
end
end
if ~strcmp(assign,'error') & ~strcmp(str,'error');
answer=evalin('caller',str,'lasterr');
else
answer='??? Illegal expression.';
end
data=get(pre_h,'UserData');
sData=data.sData;
if strcmp(assign,'NOTASSIGN') & strcmp(evaltype,'single') & ~isstr(answer)
if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
errordlg('There are not components chosen.');
answer='??? Illegal expression.';
return;
end
index=getfield(get_indices,{1});
if strcmp(assign,'NOTASSIGN')
if length(sData.data(:,index)) ~=length(answer) & ~isscalar(answer)
answer='??? Illegal assignment.';
else
sData.data(:,index)=answer;
sData.MODIFIED=1;
data.sData=sData;
set(pre_h,'UserData',data);
end
else
if length(sData.data(str2num(assign),index)) ~=length(answer) & ~isscalar(answer)
answer='??? Illegal assignment.';
else
sData.data(str2num(assign),index)=answer;
sData.MODIFIED=1;
data.sData=sData;
set(pre_h,'UserData',data);
end
end
elseif ~strcmp(assign,'error') & ~isstr(answer) & ~strcmp(assign,'NOTASSIGN')
switch name
case 'x'
if isempty(get(data.comp_names_h,'Value'))
return;
end
index = getfield(get_indices,{1});
if isempty(assign)
if length(sData.data(:,index)) ~= length(answer) & ~isscalar(answer)
answer='??? Illegal assignment.';
else
sData.data(:,index)=answer;
sData.MODIFIED=1;
data.sData=sData;
if strcmp(evaltype,'multiple')
data.sData.comp_norm(index)={[]};
end
set(pre_h,'UserData',data);
end
else
args=create_args(assign,'x');
if length(args) == 1
len=max(str2num(args{1}));
if ~isscalar(len)
answer='??? Illegal assignment.';
return;
elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
answer='??? Illegal assignment.';
return;
elseif ~all(size(sData.data(str2num(args{1}),index))) == size(answer) & ~isscalar(answer)
answer='??? Illegal assignment.';
return;
else
sData.data(str2num(args{1}),index)=answer;
sData.MODIFIED=1;
data.sData=sData;
if strcmp(evaltype,'multiple')
data.sData.comp_norm(index)={[]};
end
set(pre_h,'UserData',data);
end
else
len=max(str2num(args{1}));
dim=max(str2num(args{2}));
asize=size(answer);
msize=size(sData.data);
if ~isscalar(len) | ~isscalar(dim)
answer='??? Illegal assignment.';
return;
elseif len > length(sData.data(:,1)) | len < 1
answer='??? Illegal assignment.';
return;
elseif dim > 1 | dim > msize(2) | min(str2num(args{2})) < 1
answer='??? Illegal assignment.';
return;
end
len=length(str2num(args{1}));
dim=length(str2num(args{1}));
if ~all([len dim] == asize) & ~isscalar(answer)
answer='??? Illegal assignment.';
return;
else
tmp=sData.data(:,index);
tmp([str2num(args{1})],[str2num(args{2})])=answer;
sData.data(:,index)=tmp;
sData.MODIFIED=1;
data.sData=sData;
if strcmp(evaltype,'multiple')
data.sData.comp_norm(index)={[]};
end
set(pre_h,'UserData',data);
end
end
end
case 'xs'
if isempty(get(data.comp_names_h,'Value'))
return;
end
indices=get_indices;
if isempty(assign)
if ~all(size(answer) == size(sData.data(:,indices))) & ~isscalar(answer)
answer='??? Illegal assignment.';
else
sData.data(:,indices) = answer;
sData.MODIFIED=1;
data.sData=sData;
data.sData.comp_norm(indices)={[]};
set(pre_h,'UserData',data);
end
else
args=create_args(assign,'xs');
if length(args) == 1
len=max(str2num(args{1}));
if ~isscalar(len)
answer='??? Illegal assignment.';
return;
elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
answer='??? Illegal assignment.';
return;
end
if ~all(size(answer) == size(sData.data(str2num(args{1})))) &...
~isscalar(answer)
answer='??? Illegal assignment.';
return;
else
tmp=sData.data(:,indices);
tmp(str2num(args{1}))=answer;
sData.data(:,indices)=tmp;
sData.MODIFIED=1;
sData.comp_norm{indices}={[]};
data.sData=sData;
set(pre_h,'UserData',data);
end
else
len=max(str2num(args{1}));
dim=max(str2num(args{2}));
asize=size(answer);
msize=size(sData.data(:,indices));
if ~isscalar(len) | ~isscalar(dim)
answer='??? Illegal assignment.';
return;
elseif len > msize(1) | min(str2num(args{1})) < 1
answer='??? Illegal assignment.';
return;
elseif dim > msize(2) | min(str2num(args{2})) < 1
answer='??? Illegal assignment.';
return;
end
len=length(str2num(args{1}));
dim=length(str2num(args{2}));
if ~all([len dim] == asize) & ~isscalar(answer)
answer='??? Illegal assignment';
return;
else
tmp=sData.data(:,indices);
tmp([str2num(args{1})],[str2num(args{2})])=answer;
sData.MODIFIED=1;
sData.data(:,indices)=tmp;
data.sData=sData;
data.sData.comp_norm(indices)={[]};
set(pre_h,'UserData',data);
end
end
end
case 'D'
if isempty(assign)
if ~all(size(answer) == size(sData.data)) & ~isscalar(answer)
answer='??? Illegal assignment.';
else
if isscalar(answer)
sData.data(:,:)=answer;
else
sData.data=answer;
end
sData.MODIFIED=1;
data.sData=sData;
data.sData.comp_norm(1:length(sData.data(1,:)))={[]};
set(pre_h,'UserData',data);
end
else
args=create_args(assign,'D');
if length(args) == 1
len=max(str2num(args{1}));
if ~isscalar(len)
answer='??? Illegal assignment.';
return;
elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
answer='??? Illegal assignment.';
return;
end
if ~all(size(answer) == size(sData.data(str2num(args{1})))) &...
~isscalar(answer)
answer='??? Illegal assignment.';
else
sData.data(str2num(args{1}))=answer;
sData.MODIFIED=1;
data.sData=sData;
[i,j]=ind2sub(size(sData.data),str2num(args{1}));
data.sData.comp_norm(j)={[]};
set(pre_h,'UserData',data);
end
else
len=max(str2num(args{1}));
dim=max(str2num(args{2}));
asize=size(answer);
msize=size(sData.data);
if ~isscalar(len) | ~isscalar(dim)
answer='??? Illegal assignment.';
return;
elseif len > msize(1) | min(str2num(args{1})) < 1
answer='??? Illegal assignment.';
return;
elseif dim > msize(2) | min(str2num(args{2})) < 1
answer= '??? Illegal assignment.';
return;
end
len = length(str2num(args{1}));
dim = length(str2num(args{2}));
if ~all([len dim] == asize) & ~isscalar(answer)
answer='??? Illegal assignment.';
return;
else
sData.data([str2num(args{1})],[str2num(args{2})])=answer;
sData.MODIFIED=1;
data.sData=sData;
data.sData.comp_norm(str2num(args{2}))={[]};
set(pre_h,'UserData',data);
end
end
end
end
end
if sData.MODIFIED
selected=getfield(get(pre_h,'UserData'),'selected_vects');
vector_h=getfield(get(pre_h,'UserData'),'vector_h');
vect_mean_h=getfield(get(pre_h,'UserData'),'vect_mean_h');
vects=zeros(length(sData.data(:,1)));
vects(selected)=1;
draw_vectors(vects,vector_h);
vect_means(sData,vect_mean_h,selected);
pro_tools('plot_hist');
pro_tools('c_stat');
cplot_mimema;
end
%%% Subfunction: check_assign %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [name,string,skip]=check_assign(string,evaltype)
reswords=[{'D'};{'x'};{'xs'}];
flag=0;
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
if isempty(pre_h)
man_h=findobj(get(0,'Children'),'Tag','Management');
clip_h=findobj(get(0,'Children'),'Tag','Clipping');
errordlg('''Preprocess'' -window does not exist. Terminating program.');
if ~isempty(man_h)
close man_h;
end
if ~isempty(clip_h)
close clip_h;
end
return;
end
EMPTY=isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'));
[name,s]=give_token(string,evaltype);
skip=length(s);
if strcmp(evaltype,'single') & ~strcmp(name,'x')
string='NOTASSIGN';
return;
end
if strcmp(name,'other') & ~strcmp(s,'x')
string = 'error';
return;
end
if strcmp(name,[{'x'};{'xs'}])
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
if isempty(get(comp_names_h,'Value'))
errordlg('There are not components chosen.');
string='error';
return;
end
end
if skip == length(string) | ~strcmp(name,reswords)
string = 'NOTASSIGN';
return;
end
if (strcmp(name,'x') | strcmp(name,'xs')) & EMPTY
errordlg('There are not components chosen.');
string = 'error';
return;
end
[t,s]=give_token(string(length(name)+1),evaltype);
if strcmp(t,'(')
flag=1;
end
[foo,skip]=check_token(string,evaltype);
if length(name) ~= skip-1
skip=skip-1;
tmp=string(length(name)+1:skip);
else
tmp = [];
end
if flag & tmp(length(tmp)) ~= ')'
tmp(length(tmp)+1)=')';
end
if skip==length(string)
return;
end
skip=skip+1;
if length(string) ~= skip
[t,s]=give_token(string(skip+1:length(string)),evaltype);
else
string='NOTASSIGN';
return;
end
if ~strcmp(t,'=')
string = 'NOTASSIGN';
return;
end
string=tmp;
skip = skip+2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: isscalar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool = isscalar(x)
m= size(x);
bool = m(1) == 1 & m(2) == 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: create_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function args=create_args(string,type)
arg2='';
i=2;
j=1;
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
msize=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data'));
if string(i) == ':'
arg1=num2str(cat(2,'1:',num2str(msize(1))));
i=i+1;
j=j+length(arg1);
end
while string(i) ~=',' & string(i) ~=')'
arg1(j)=string(i);
i=i+1;
j=j+1;
end
if string(i) ==','
j=1;
i=i+1;
if string(i)==':'
switch type
case 'x'
arg2='1';
case 'cs'
arg2=num2str(get_indices);
case 'D'
arg2=num2str(cat(2,'1:',num2str(msize(2))));
end
i=i+1;
j=j+length(arg2);
end
while string(i) ~= ')'
arg2(j)=string(i);
j=j+1;
i=i+1;
end
end
args{1}=arg1;
if ~isempty(arg2)
args{2} = arg2;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [str,skip] = check_token(string,evaltype)
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
tmp_string=string;
[t,s]=give_token(tmp_string,evaltype);
skip=length(s);
if strcmp(t,'c')
if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
errordlg('There are no components chosen.');
str='error';
return;
end
index=getfield(get_indices,{1});
str=cat(2,'[',num2str(index),']');
if skip == length(tmp_string)
return;
end
tmp_string=tmp_string(skip+1:length(tmp_string));
[t,s] = give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
end
[args,skip2] = get_args(tmp_string(length(s)+1:length(tmp_string)),'c',...
evaltype);
skip=skip+skip2+2;
if strcmp(args,'error')
str = 'error'
return;
elseif ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
else
str=cat(2,'getfield(',str,',{[1]})');
end
elseif strcmp(t,'cs')
if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
errordlg('There are no components chosen.');
str='error';
return;
end
str =cat(2,'[',num2str(get_indices),']');
if length(s) == length(string)
return;
end
tmp_string=tmp_string(1+length(s):length(string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
else
[args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'cs',...
evaltype);
skip=2+skip+skip2;
if strcmp(args,'error')
str='error';
return;
elseif ~strcmp(args,'all')
str = cat(2,'getfield(',str,',',args,')');
else
tmp_str=str;
str=cat(2,'[getfield(',str,',','{1})');
for i=2:length(get_indices)
str=cat(2,str,';getfield(',tmp_str,',',sprintf('{%d})',i));
end
str=cat(2,str,']');
end
end
elseif strcmp(t,'dim')
ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{2});
str=cat(2,'[',num2str(ind1),']');
if length(s)==length(string)
return;
end
tmp_string=string(1+length(s):length(string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
end
skip=1+skip+length(s);
[args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dim',...
evaltype);
if strcmp(args,'error')
str = 'error';
return;
else
skip=skip+skip2;
if ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
end
end
elseif strcmp(t,'dlen')
ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{1});
str=cat(2,'[',num2str(ind1),']');
if length(s)==length(string)
return;
end
tmp_string=string(1+length(s):length(string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
end
skip=skip+length(s);
[args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dlen',...
evaltype);
if strcmp(args,'error')
str='error';
return;
else
skip=1+skip+skip2;
if ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
end
end
elseif strcmp(t,'x')
if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
errordlg('There are not components chosen.');
str='error';
return;
end
len=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
'sData'),'data')),{1});
index=num2str(getfield(get_indices,{1}));
h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')');
get_str=cat(2,'getfield(',get_str,',''data'')');
str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})');
if length(s) == length(string)
return;
end
tmp_string=string(1+length(s):length(string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(');
return;
end
skip=skip+length(s);
[args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'x',...
evaltype);
if strcmp(args,'error')
str = 'error';
return;
else
skip=1+skip+skip2;
if ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
end
end
elseif strcmp(t,'xs')
if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
errordlg('There are not components chosen.');
str='error';
return;
end
len=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
'sData'),'data')),{1});
index=get_indices;
index=cat(2,'[',num2str(index),']');
h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')');
get_str=cat(2,'getfield(',get_str,',''data'')');
str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})');
if length(s) == length(string)
return;
end
tmp_string=string(1+length(s):length(string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
end
skip=1+skip+length(s);
[args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'xs',...
evaltype);
if strcmp(args,'error')
str = 'error';
return;
elseif ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
skip=skip+skip2;
else
skip=skip+skip2;
[dlen,dim]=size(eval(str));
tmp_str=str;
str=cat(2,'[','getfield(',tmp_str,sprintf(',{1:%d,1})',dlen));
for i=2:dim
tmp=sprintf(',{1:%d,%d})',dlen,dim);
str=cat(2,str,';','getfield(',tmp_str,tmp);
end
str=cat(2,str,']');
end
elseif strcmp(t,'D')
get_h='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
str=cat(2,'getfield(getfield(get(',get_h,',''UserData''),''sData''),''data'')');
if length(s) >= length(tmp_string)
return;
end
tmp_string=tmp_string(1+length(s):length(tmp_string));
[t,s]=give_token(tmp_string,evaltype);
if ~strcmp(t,'(')
return;
else
tmp_string=tmp_string(1+length(s):length(tmp_string));
skip = skip+length(s);
[args, skip2]=get_args(tmp_string,'D',evaltype);
if strcmp(args,'error')
str='error';
return;
elseif ~strcmp(args,'all')
str=cat(2,'getfield(',str,',',args,')');
skip=1+skip+skip2;
else
skip=1+skip+skip2;
[dlen,dim]=size(eval(str));
tmp_str=str;
str=cat(2,'[getfield(',str,sprintf(',{1:%d,1})',dlen));
for i=2:dim
tmp=sprintf(',{1:%d,%d}',dlen,i);
str=cat(2,str,';getfield(',tmp_str,tmp,')');
end
str=cat(2,str,']');
end
end
else
if strcmp(t,'(')
str = t;
str2='';
tmp_string=tmp_string(1+length(s):length(tmp_string));
while ~strcmp(str2,')') & ~isempty(tmp_string)
[str2,skip2]=check_token(tmp_string,evaltype);
if strcmp(str2,'error')
str='error';
return;
end
skip=skip+skip2;
tmp_string=tmp_string(skip2+1:length(tmp_string));
str=cat(2,str,str2);
end
if ~strcmp(str2,')')
str = 'error';
end
else
str = s;
end
end
%%% Subfunction: get_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [str,skip] = get_args(string,flag,evaltype)
res_words=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'}];
NOTALL=1;
if isempty(string)
str='error'
skip=[];
return;
end
[t,s] = give_token(string,evaltype);
skip=length(s);
if any(strcmp(t,res_words));
[str,skip2] = check_token(string,evaltype);
string=string(1+length(s):length(string));
str=cat(2,'{[',str);
[t,s]=give_token(string,evaltype);
elseif t==')' | t==','
str = 'error';
return;
elseif strcmp(t,':');
if length(s) == length(string)
str='error';
return;
end
[t,s]=give_token(string(1+length(s):length(string)),evaltype);
if t == ')'
str = 'all';
return;
end
switch flag
case {'c','cs','dim','dlen'}
str= '{[1';
otherwise
str=cat(2,'{[',get_all('vect'));
end
NOTALL=0;
string=string(1+length(s):length(string));
[t,s]=give_token(string,evaltype);
skip=skip+1;
else
str = cat(2,'{[',s);
end
str2 =[];
if ~strcmp(t,',') & ~strcmp(t,')')
skip=skip-length(s);
end
while ~strcmp(t,',') & ~strcmp(t,')') & NOTALL;
str=cat(2,str,str2);
[t,s] = give_token(string,evaltype);
if length(s) == length(string)
str = 'error';
return;
end
string=string(1+length(s):length(string));
skip=skip+length(s);
[t,s]=give_token(string,evaltype);
if length(s) == length(string) & ~strcmp(t,')')
str = 'error';
return;
end
[str2,foo]=check_token(string,evaltype);
end
if NOTALL & ~strcmp(t,')')
skip=skip+1;
end
if strcmp(t,')')
str=cat(2,str,']}');
return
end
str=cat(2,str,']',',','[');
str2 = [];
[t,s] = give_token(string,evaltype);
if strcmp(t,')')
str = 'error'
return;
end
NOTALL=1;
string=string(1+length(s):length(string));
[t,s]=give_token(string,evaltype);
if strcmp(t,':');
switch flag
case {'c','dim','dlen','x'}
str=cat(2,str,'1');
case 'D'
str=cat(2,str,get_all('comp'));
case {'cs','xs'}
str=cat(2,str,'1:',num2str(length(get_indices)));
end
NOTALL=0;
if length(s) == length(string)
str='error';
return;
end
string=string(1+length(s):length(string));
[t,s]=give_token(string,evaltype);
end
if ~strcmp(t,')') & NOTALL
skip=skip-1;
end
while ~strcmp(t,')') & NOTALL
str=cat(2,str,str2);
skip=skip+length(s);
if length(s) == length(string) & ~strcmp(t,')')
str='error';
return;
end
[str2,foo]=check_token(string,evaltype);
string=string(1+length(s):length(string));
[t,s]=give_token(string,evaltype);
end
if ~strcmp(t,')')
str='error';
return;
end
str=cat(2,str,str2,']}');
skip=skip+length(s);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: get_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function str=get_all(vect_or_comp)
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
switch vect_or_comp
case 'vect'
dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
'sData'),'data')),{1});
str=cat(2,'1:',num2str(dim));
case 'comp'
dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
'sData'),'data')),{2});
str=cat(2,'1:',num2str(dim));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [token,str]=give_token(string,evaltype)
n=length(string);
i=1;
char=string(i);
switch analyze_char(string(i));
case 'num'
token='num';
while i <= n & strcmp('num',analyze_char(string(i)))
str(i)=string(i);
i=i+1;
end
case 'other'
switch string(i)
case ':'
token = ':';
case ','
token = ',';
case '('
token = '(';
case ')'
token = ')';
case '='
token = '=';
otherwise
token='other';
end
str=string(i);
case 'alpha'
while i <= n & strcmp('alpha',analyze_char(string(i)))
str(i)=string(i);
i=i+1;
end
token = find_res_word(str,evaltype);
end
%%% Subfunction: analyze_char %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function type=analyze_char(char)
if ((char-0) >= ('0'-0) & (char-0) <= ('9'-0))
type='num';
elseif ((char-0) >= ('a'-0) & (char-0) <= ('z'-0)) ...
| ((char-0) >= ('A'-0) & (char-0) <= ('Z'-0))
type='alpha';
else
type='other';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Subfunction: find_res_word %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function token = find_res_word(string,evaltype)
reswords=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'};{'other'}];
for i=1:length(reswords);
token=reswords{i};
if strcmp(string,reswords{i})
if strcmp(evaltype,'single') & ~strcmp(string,'x')
token = 'other';
end
return;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function close_func(varargin)
switch varargin{1}
case 'close_c'
str='% Closing the ''Clipping'' -window...';
clip_h=findobj(get(0,'Children'),'Tag','Clipping');
close(clip_h);
case 'close_sD'
str='% Closing the ''Data Set Management'' -window...';
sD_h=findobj(get(0,'Children'),'Tag','Management');
close(sD_h);
case 'close_w'
str='% Closing the ''Windowed'' -window...';
win_h=findobj(get(0,'Children'),'Tag','Window');
close(win_h);
case 'close_s'
str='% Closing the ''Select'' -window...';
sel_h=findobj(get(0,'Children'),'Tag','Select');
close(sel_h);
case 'close_d'
str='% Closing the ''Delay'' -window...';
del_h=findobj(get(0,'Children'),'Tag','Delay');
close(del_h);
end
if nargin ~= 2
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
preh_udata=get(pre_h,'UserData');
str2=cat(2,'preprocess(''',varargin{1},''',''foo'');');
preh_udata.LOG{length(preh_udata.LOG)+1}=str;
preh_udata.LOG{length(preh_udata.LOG)+1}=str2;
set(pre_h,'UserData',preh_udata);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function log_file
answer=inputdlg('Give the name of the outputfile:','LOG function',1,...
{'log_function'});
if isempty(answer)
return;
end
tmp=clock;
str =cat(2,'% Created: ',...
date,...
' ',sprintf('%d:%d\n%\n\n',tmp(4),tmp(5)));
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
LOG=getfield(get(pre_h,'UserData'),'LOG');
file=cat(2,pwd,'/',answer{1},'.m');
fid =fopen(file,'w');
arg=LOG{2}(12:length(LOG{2})-2);
fprintf(fid,'%s\n \n',cat(2,'function ',answer{1},'(',arg,')'));
fprintf(fid,'%s\n',str);
for i=1:length(LOG)
fprintf(fid,'%s\n',LOG{i});
end
fclose(fid);
disp(sprintf('LOG-file ''%s'' is done.',file));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function get_selected_inds(varargin)
if nargin == 1
LOG=1;
answer = {varargin{1}};
else
LOG=0;
end
selected=getfield(get(gcf,'UserData'),'selected_vects');
if ~LOG
answer=inputdlg('Give the name of the output variable:',...
'',1,{'indices'});
end
if isempty(answer) | isempty(answer{1})
return;
else
assignin('base',answer{1},selected);
disp(cat(2,'Indices of the selected vectors are set to the workspace ',...
sprintf(' as ''%s''.',answer{1})));
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}=...
'% Saving indices of the selected vectors to the workspace.';
data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''get_inds'',',...
'''',answer{1},''');');
set(gcf,'UserData',data);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function no_of_selected(varargin)
if nargin == 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
LOG = 1;
else
LOG = 0;
end
results_h=getfield(get(gcf,'UserData'),'results_h');
no=length(getfield(get(gcf,'UserData'),'selected_vects'));
str={sprintf('Number of selected vectors: %d\n', no)};
set(results_h,'String',str,'HorizontalAlignment','left');
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}='% Number of selected vectors';
data.LOG{length(data.LOG)+1}='preprocess(''no_of_sel'',''foo'');';
set(gcf,'UserData',data);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function select_all_comps(varargin)
if nargin == 1
pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
set(0,'CurrentFigure',pre_h);
LOG=1;
else
LOG=0;
end
comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
set(comp_names_h,'Value',[1:length(get(comp_names_h,'String'))]);
sel_comp;
if ~LOG
data=get(gcf,'UserData');
data.LOG{length(data.LOG)+1}='% Select all components';
data.LOG{length(data.LOG)+1}='preprocess(''sel_all_comps'',''foo'');';
set(gcf,'UserData',data);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function code=write_log_code(indices,arg1,arg2,arg3,arg4,arg5,arg6);
str=textwrap({num2str(indices)},500);
code{1}=sprintf('inds=[];');
for i=1:length(str);
code{i+1}=sprintf(' inds=cat(2,inds,[%s]);',str{i});
end
str=cat(2,'preprocess(''''clip_data'''',''''',arg1,' ',num2str(arg2),' ',...
num2str(arg3),' ',num2str(arg4),...
' ',num2str(arg5),' ',num2str(arg6),' ');
code{length(code)+1}=cat(2,'eval(cat(2,',...
'''',str,'''',...
',num2str(inds),'''''');''));');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_kmeans.m
|
.m
|
mirtooloct-master/somtoolbox/som_kmeans.m
| 4,244 |
utf_8
|
dca5b32dd99e19a186277df2a168dcf2
|
function [codes,clusters,err] = som_kmeans(method, D, k, epochs, verbose)
% SOM_KMEANS K-means algorithm.
%
% [codes,clusters,err] = som_kmeans(method, D, k, [epochs], [verbose])
%
% Input and output arguments ([]'s are optional):
% method (string) k-means algorithm type: 'batch' or 'seq'
% D (matrix) data matrix
% (struct) data or map struct
% k (scalar) number of centroids
% [epochs] (scalar) number of training epochs
% [verbose] (scalar) if <> 0 display additonal information
%
% codes (matrix) codebook vectors
% clusters (vector) cluster number for each sample
% err (scalar) total quantization error for the data set
%
% See also KMEANS_CLUSTERS, SOM_MAKE, SOM_BATCHTRAIN, SOM_SEQTRAIN.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Function has been renamed by Kimmo Raivio, because matlab65 also have
% kmeans function 1.10.02
%% input arguments
if isstruct(D),
switch D.type,
case 'som_map', data = D.codebook;
case 'som_data', data = D.data;
end
else
data = D;
end
[l dim] = size(data);
if nargin < 4 | isempty(epochs) | isnan(epochs), epochs = 100; end
if nargin < 5, verbose = 0; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
rand('state', sum(100*clock)); % init rand generator
lr = 0.5; % learning rate for sequential k-means
temp = randperm(l);
centroids = data(temp(1:k),:);
res = zeros(k,l);
clusters = zeros(1, l);
if dim==1,
[codes,clusters,err] = scalar_kmeans(data,k,epochs);
return;
end
switch method
case 'seq',
len = epochs * l;
l_rate = linspace(lr,0,len);
order = randperm(l);
for iter = 1:len
x = D(order(rem(iter,l)+1),:);
dx = x(ones(k,1),:) - centroids;
[dist nearest] = min(sum(dx.^2,2));
centroids(nearest,:) = centroids(nearest,:) + l_rate(iter)*dx(nearest,:);
end
[dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
ones(l, 1) * sum((centroids.^2)',1) - ...
2.*(data*(centroids')))');
case 'batch',
iter = 0;
old_clusters = zeros(k, 1);
while iter<epochs
[dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
ones(l, 1) * sum((centroids.^2)',1) - ...
2.*(data*(centroids')))');
for i = 1:k
f = find(clusters==i);
s = length(f);
if s, centroids(i,:) = sum(data(f,:)) / s; end
end
if iter
if sum(old_clusters==clusters)==0
if verbose, fprintf(1, 'Convergence in %d iterations\n', iter); end
break;
end
end
old_clusters = clusters;
iter = iter + 1;
end
[dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
ones(l, 1) * sum((centroids.^2)',1) - ...
2.*(data*(centroids')))');
otherwise,
fprintf(2, 'Unknown method\n');
end
err = 0;
for i = 1:k
f = find(clusters==i);
s = length(f);
if s, err = err + sum(sum((data(f,:)-ones(s,1)*centroids(i,:)).^2,2)); end
end
codes = centroids;
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [y,bm,qe] = scalar_kmeans(x,k,maxepochs)
nans = ~isfinite(x);
x(nans) = [];
n = length(x);
mi = min(x); ma = max(x)
y = linspace(mi,ma,k)';
bm = ones(n,1);
bmold = zeros(n,1);
i = 0;
while ~all(bm==bmold) & i<maxepochs,
bmold = bm;
[c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]);
y = full(sum(sparse(bm,1:n,x,k,n),2));
zh = (c(1:end-1)==0);
y(~zh) = y(~zh)./c(~zh);
inds = find(zh)';
for j=inds, if j==1, y(j) = mi; else y(j) = y(j-1) + eps; end, end
i=i+1;
end
if i==maxepochs, [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]); end
if nargout>2, qe = sum(abs(x-y(bm)))/n; end
if any(nans),
notnan = find(~nans); n = length(nans);
y = full(sparse(notnan,1,y ,n,1)); y(nans) = NaN;
bm = full(sparse(notnan,1,bm,n,1)); bm(nans) = NaN;
if nargout>2, qe = full(sparse(notnan,1,qe,n,1)); qe(nans) = NaN; end
end
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
sompak_rb_control.m
|
.m
|
mirtooloct-master/somtoolbox/sompak_rb_control.m
| 7,240 |
utf_8
|
bcfb4ce0fc8edd340c4a5b73afa57746
|
function varargout=sompak_rb_control(str)
%SOMPAK_RB_CONTROL An auxiliary function for SOMPAK_*_GUI functions.
%
% This is an auxiliary function for SOMPAK_GUI, SOMPAK_INIT_GUI,
% SOMPAK_SAMMON_GUI and SOMPAK_TRAIN_GUI functions. It controls the
% radio buttons in the GUIs.
%
% See also SOMPAK_GUI, SOMPAK_INIT_GUI, SOMPAK_SAMMON_GUI, SOMPAK_TRAIN_GUI.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
data=get(gcf,'UserData');
switch str
case {'rand','linear'}
h=cat(2,findobj(get(gcf,'Children'),'Tag','RANDOM'),...
findobj(get(gcf,'Children'),'Tag','LINEAR'));
set(h,'Value',0);
set(gcbo,'Value',1);
data.inittype=str;
case {'bubble','gaussian'}
h=cat(2,findobj(get(gcf,'Children'),'Tag','BUBBLE'),...
findobj(get(gcf,'Children'),'Tag','GAUSSIAN'));
set(h,'Value',0);
set(gcbo,'Value',1);
data.neigh=str;
case {'hexa','rect'}
h=cat(2,findobj(get(gcf,'Children'),'Tag','HEXA'),...
findobj(get(gcf,'Children'),'Tag','RECT'));
set(h,'Value',0);
set(gcbo,'Value',1);
data.topol=str;
case {'out_ft'}
value=get(gcbo,'Value');
switch value
case 1
h=findobj(get(gcf,'Children'),'Tag','OUT_FILE');
data.out_file_type='';
set(h,'String','');
case 2
data.out_file_type='box';
case 3
data.out_file_type='pak';
end
case {'input_ft'}
value=get(gcbo,'Value');
switch value
case 1
data.input_file_type='';
case 2
data.input_file_type='box';
case 3
data.input_file_type='pak';
end
case {'map_ft'}
value=get(gcbo,'Value');
switch value
case 1
data.map_type='';
case 2
data.map_type='box';
case 3
data.map_type='pak';
end
case {'out_file'}
if isempty(data.out_file_type)
data.out_file='';
h=findobj(get(gcf,'Children'),'Tag','OUT_FILE');
set(h,'String','');
else
data.out_file=get(findobj(get(gcf,'Children'),'Tag','OUT_FILE'),'String');
if isempty(data.out_file)
h=findobj(get(gcf,'Children'),'Tag','OUT_FILE_TYPE');
set(h,'Value',1);
end
end
case {'out_var'}
h=findobj(get(gcf,'Children'),'Tag','OUT_VAR');
if ~isempty(get(h,'String'))
data.out_var=get(h,'String');
else
data.out_var=[];
set(h,'String','''ans''');
end
case {'xdim'}
h=findobj(get(gcf,'Children'),'Tag','XDIM');
data.xdim=str2num(get(h,'String'));
case {'ydim'}
h=findobj(get(gcf,'Children'),'Tag','YDIM');
data.ydim=str2num(get(h,'String'));
case {'radius'}
h=findobj(get(gcf,'Children'),'Tag','RADIUS');
data.radius=str2num(get(h,'String'));
case {'data'}
h=findobj(get(gcf,'Children'),'Tag','DATA');
data.data=get(h,'String');
case {'rlen'}
h=findobj(get(gcf,'Children'),'Tag','RLEN');
data.rlen=str2num(get(h,'String'));
case {'alpha'}
h=findobj(get(gcf,'Children'),'Tag','ALPHA');
data.alpha=str2num(get(h,'String'));
case {'map'}
h=findobj(get(gcf,'Children'),'Tag','MAP');
data.map=get(h,'String');
case 'init_ok'
if isempty(data.xdim) | ~is_positive_integer(data.xdim)
errordlg('Argument ''xdim'' must be positive integer.');
return;
end
if isempty(data.ydim) | ~is_positive_integer(data.ydim)
errordlg('Argument ''ydim'' must be positive integer.');
return;
end
if isempty(data.data)
errordlg('Argument ''Workspace data'' must be a string.');
return;
end
if isempty(data.input_file_type)
sData=evalin('base',data.data);
else
sData=data.data;
end
if isempty(data.out_file)
if ~isempty(data.out_file_type)
errordlg('Argument ''Output file'' is not defined.');
return;
end
data.out_file=[];
end
answer=sompak_init(sData,...
data.input_file_type,...
data.inittype,...
data.out_file,...
data.out_file_type,...
data.xdim,...
data.ydim,...
data.topol,...
data.neigh);
if any(strcmp(data.out_var,{'ans','''ans'''})) | isstr(answer)
varargout{1}=answer;
else
assignin('base',data.out_var,answer);
disp(sprintf('Map is set to workspace as ''%s''.',data.out_var));
end
close(findobj(get(0,'Children'),'Tag','InitGUI'));
return;
case 'train_ok'
if isempty(data.rlen) | ~is_positive_integer(data.rlen)
errordlg('Argument ''Running Length'' must be positive integer.');
return;
end
if isempty(data.alpha) | data.alpha <= 0
errordlg('Argument ''Initial Alpha Value'' must be a positive float.');
return;
end
if isempty(data.radius) | data.radius <= 0
errordlg('Argument ''Neighborhood Radius'' must be a positive float.');
return;
end
if isempty(data.data)
errordlg('Argument ''Teaching Data'' must be a string.');
return;
end
if isempty(data.input_file_type)
sData=evalin('base',data.data);
else
sData=data.data;
end
if isempty(data.out_file);
data.outfile = [];
end
if isempty(data.map)
errordlg('Argument ''Workspace Map'' must be a string.');
return;
end
if isempty(data.map_type)
sMap=evalin('base',data.map);
else
sMap=data.map;
end
answer=sompak_train(sMap,...
data.map_type,...
data.out_file,...
data.out_file_type,...
data.data,...
data.input_file_type,...
data.rlen,...
data.alpha,...
data.radius);
if any(strcmp(data.out_var,{'''ans''','ans'})) | isstr(answer)
varargout{1}=answer;
else
assignin('base',data.out_var,answer);
disp(sprintf('Map is set to workspace as ''%s''.',data.out_var));
end
close(findobj(get(0,'Children'),'Tag','TrainGUI'));
return;
case 'sammon_ok'
if isempty(data.map)
errordlg('Argument ''Workspace Map'' must be a string.');
return;
end
if isempty(data.map_type)
sMap=evalin('base',data.map);
else
sMap=data.map;
end
if isempty(data.out_file);
data.outfile = [];
end
answer=sompak_sammon(sMap,...
data.map_type,...
data.out_file,...
data.out_file_type,...
data.rlen);
if strcmp(data.out_var,'''ans''')|strcmp(data.out_var,'ans')|isstr(answer)
varargout{1}=answer;
else
assignin('base',data.out_var,answer);
disp(sprintf('Codebook is set to workspace as ''%s''.',data.out_var));
end
close(findobj(get(0,'Children'),'Tag','SammonGUI'));
return;
end
set(gcf,'UserData',data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function bool = is_positive_integer(x)
bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0;
if ~isempty(bool)
if bool & x~=round(x)
bool = 0;
end
else
bool = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
lvq1.m
|
.m
|
mirtooloct-master/somtoolbox/lvq1.m
| 5,022 |
utf_8
|
86a6a5093c040aededf200f82b599cc0
|
function codebook=lvq1(codebook, data, rlen, alpha);
%LVQ1 Trains a codebook with the LVQ1 -algorithm.
%
% sM = lvq1(sM, D, rlen, alpha)
%
% sM = lvq1(sM,sD,30*length(sM.codebook),0.08);
%
% Input and output arguments:
% sM (struct) map struct, the class information must be
% present on the first column of .labels field
% D (struct) data struct, the class information must
% be present on the first column of .labels field
% rlen (scalar) running length
% alpha (scalar) learning parameter
%
% sM (struct) map struct, the trained codebook
%
% NOTE: does not take mask into account.
%
% For more help, try 'type lvq1', or check out online documentation.
% See also LVQ3, SOM_SUPERVISED, SOM_SEQTRAIN.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% lvq1
%
% PURPOSE
%
% Trains codebook with the LVQ1 -algorithm (described below).
%
% SYNTAX
%
% sM = lvq1(sM, D, rlen, alpha)
%
% DESCRIPTION
%
% Trains codebook with the LVQ1 -algorithm. Codebook contains a number
% of vectors (mi, i=1,2,...,n) and so does data (vectors xj,
% j=1,2,...,k). Both vector sets are classified: vectors may have a
% class (classes are set to the first column of data or map -structs'
% .labels -field). For each xj there is defined the nearest codebook
% -vector index c by searching the minimum of the euclidean distances
% between the current xj and codebook -vectors:
%
% c = min{ ||xj - mi|| }, i=[1,..,n], for fixed xj
% i
% If xj and mc belong to the same class, mc is updated as follows:
% mc(t+1) = mc(t) + alpha * (xj(t) - mc(t))
% If xj and mc belong to different classes, mc is updated as follows:
% mc(t+1) = mc(t) - alpha * (xj(t) - mc(t))
% Otherwise updating is not performed.
%
% Argument 'rlen' tells how many times training sequence is performed.
% LVQ1 -algorithm may be stopped after a number of steps, that is
% 30-50 times the number of codebook vectors.
%
% Argument 'alpha' is the learning rate, recommended to be smaller
% than 0.1.
%
% NOTE: does not take mask into account.
%
% REFERENCES
%
% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
% Berlin, 1995, pp. 176-179.
%
% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml
%
% REQUIRED INPUT ARGUMENTS
%
% sM The data to be trained.
% (struct) A map struct.
%
% D The data to use in training.
% (struct) A data struct.
%
% rlen (integer) Running length of LVQ1 -algorithm.
%
% alpha (float) Learning rate used in training.
%
% OUTPUT ARGUMENTS
%
% codebook Trained data.
% (struct) A map struct.
%
% EXAMPLE
%
% lab = unique(sD.labels(:,1)); % different classes
% mu = length(lab)*5; % 5 prototypes for each
% sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes
% sM.labels = [lab;lab;lab;lab;lab]; % their classes
% sM = lvq1(sM,sD,50*mu,0.05); % use LVQ1 to adjust
% % the prototypes
% sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3); % then use LVQ3
%
% SEE ALSO
%
% lvq3 Use LVQ3 algorithm for training.
% som_supervised Train SOM using supervised training.
% som_seqtrain Train SOM with sequential algorithm.
% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
% Copyright (c) Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 310100 juuso 020200
cod = codebook.codebook;
c_class = class2num(codebook.labels(:,1));
dat = data.data;
d_class = class2num(data.labels(:,1));
x=size(dat,1);
y=size(cod,2);
ONES=ones(size(cod,1),1);
for t=1:rlen
fprintf(1,'\rTraining round: %d',t);
tmp=NaN*ones(x,y);
for j=1:x
no_NaN=find(~isnan(dat(j,:)));
di = sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2));
[foo,ind] = min(di);
if d_class(j) & d_class(j) == c_class(ind) % 0 is for unclassified vectors
tmp(ind,:) = cod(ind,:) + alpha * (dat(j,:) - cod(ind,:));
elseif d_class(j)
tmp(ind,:) = cod(ind,:) - alpha*(dat(j,:) - cod(ind,:));
end
end
inds = find(~isnan(sum(tmp,2)));
cod(inds,:) = tmp(inds,:);
end
codebook.codebook = cod;
sTrain = som_set('som_train','algorithm','lvq1',...
'data_name',data.name,...
'neigh','',...
'mask',ones(y,1),...
'radius_ini',NaN,...
'radius_fin',NaN,...
'alpha_ini',alpha,...
'alpha_type','constant',...
'trainlen',rlen,...
'time',datestr(now,0));
codebook.trainhist(end+1) = sTrain;
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function nos = class2num(class)
names = {};
nos = zeros(length(class),1);
for i=1:length(class)
if ~isempty(class{i}) & ~any(strcmp(class{i},names))
names=cat(1,names,class(i));
end
end
tmp_nos = (1:length(names))';
for i=1:length(class)
if ~isempty(class{i})
nos(i,1) = find(strcmp(class{i},names));
end
end
|
github
|
martinarielhartmann/mirtooloct-master
|
som_colorcode.m
|
.m
|
mirtooloct-master/somtoolbox/som_colorcode.m
| 7,983 |
utf_8
|
e110e71452756946ea1943fa0b627791
|
function colors=som_colorcode(m, colorcode, scaling)
%SOM_COLORCODE Calculates a heuristic color coding for the SOM grid
%
% colors = som_colorcode(m, colorcode, scaling)
%
% Input and output arguments ([]'s are optional):
% m (struct) map or topol struct
% (cell array) of form {str,[m1 m2]} where
% str = 'hexa' or 'rect' and [m1 m2] = msize
% (matrix) size N x 2, unit coordinates
% [colorcode] (string) 'rgb1' (default),'rgb2','rgb3','rgb4','hsv'
% [scaling] (scalar) 1=on (default), 0=off. Has effect only
% if m is a Nx2 matrix of coordinates:
% controls whether these are scaled to
% range [0,1] or not.
%
% colors (matrix) size N x 3, RGB colors for each unit (or point)
%
% The function gives a color coding by location for the map grid
% (or arbitrary set of points). Map grid coordinates are always linearly
% normalized to a unit square (x and y coordinates between [0,1]), except
% if m is a Nx2 matrix and scaling=0. In that case too, the coordinates
% must be in range [0,1].
%
% Following heuristic color codings are available:
%
% 'rgb1' slice of RGB-cube so that green - yellow
% the corners have colors: | |
% blue - magenta
%
% 'rgb2' slice of RGB-cube so that red - yellow
% the corners have colors: | |
% blue - cyan
%
% 'rgb3' slice of RGB-cube so that mixed_green - orange
% the corners have colors: | |
% light_blue - pink
%
% 'rgb4' has 'rgb1' on the diagonal + additional colors in corners
% (more resolution but visually strongly discontinuous)
%
% 'hsv' angle and radius from map centre are coded by hue and
% intensity (more resoluton but visually discontinuous)
%
% See also SOM_CPLANE, SOM_SHOW, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR,
% SOM_BMUCOLOR.
% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
% Copyright (c) by Johan Himberg
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0 Johan 140799
%%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error(nargchk(1, 3, nargin)); % check no. of input args is correct
%% Check m: map, topol, cell or data?
if vis_valuetype(m,{'nx2'}),
p=m; % explicit coordinates
else
% map, topol, cell
[tmp,ok,tmp]=som_set(m);
if isstruct(m) & all(ok)
switch m.type
case 'som_topol' % topol
msize=m.msize;
lattice=m.lattice;
case 'som_map'
msize=m.topol.msize; % map
lattice=m.topol.lattice;
otherwise
error('Invalid map or topol struct.');
end
% cell
elseif iscell(m) & vis_valuetype(size(m),{[1 2]}),
if vis_valuetype(m{2},{[1 2]}) & vis_valuetype(m{1},{'string'}),
lattice=m{1};
msize=m{2};
else
error('Invalid map size information.');
end
end
%% Check map parameters
switch lattice % lattice
case 'hexa'
;
case 'rect'
;
otherwise
error('Unknown lattice type');
end
if length(msize)>2 % dimension
error('Only 2D maps allowed!');
end
% Calculate coordinates
p=som_unit_coords(msize,lattice,'sheet');
% Set scaling to 1 as it is done always in this case
scaling=1;
end
% Check colorcode
if nargin < 2 | isempty(colorcode),
colorcode='rgb1';
end
if ~ischar(colorcode)
error('String value for colorcode mode expected.');
else
switch colorcode
case { 'rgb1', 'rgb2', 'rgb3' , 'rgb4' ,'hsv'}
otherwise
error([ 'Colorcode mode ' colorcode ' not implemented.']);
end
end
% Check scaling
if nargin < 3 | isempty(scaling)
scaling=1;
end
if ~vis_valuetype(scaling,{'1x1'})
error('Scaling should be 0 (off) or 1 (on).');
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% scale coordintes between [0,1]
if scaling
n=size(p,1);
mn=min(p);
e=max(p)-mn;
p=(p-repmat(mn,n,1))./repmat(e,n,1);
elseif sum(p(:,1)>1+p(:,1)<0+p(:,2)>1+p(:,2)<0),
error('Coordinates out of range [0,1].');
end
switch colorcode
case 'rgb1'
h(:,1)=p(:,1);
h(:,2)=1-p(:,2);
h(:,3)=p(:,2);
case 'rgb2'
h(:,1)=p(:,1);
h(:,2)=1-p(:,2);
h(:,3)=1-p(:,1);
case 'rgb3'
h(:,1)=p(:,1);
h(:,2)=.5;
h(:,3)=p(:,2);
case 'rgb4'
p=rgb4(p);
h(:,1)=p(:,1);
h(:,2)=1-p(:,2);
h(:,3)=p(:,3);
case 'hsv'
munits = n;
Hsv = zeros(munits,3);
for i=1:n,
dx = .5-p(i,1);
dy = .5-p(i,2);
r = sqrt(dx^2+dy^2);
if r==0,
h=1;
elseif dx==0,
h=.5; %h=ay;
elseif dy==0,
h=.5; %h=ax;
else
h = min(abs(.5/(dx/r)),abs(.5/(dy/r)));
end
if r==0,
angle = 0;
else
angle = acos(dx/r);
if dy<0,
angle = 2*pi-angle;
end
end
Hsv(i,1) = 1-sin(angle/4);
Hsv(i,2) = 1;
Hsv(i,3) = r/h;
h = hsv2rgb(Hsv);
end
end
%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
colors=h;
%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% juha %%%%
function p=rgb4(coord)
for i=1:size(coord,1);
p(i,:)=get_coords(coord(i,:))';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function coords=get_coords(coords)
%GET_COORDS
%
% get_coords(coords)
%
% ARGUMENTS
%
% coords (1x2 or 2x1 vector) coords(1) is an x-coordinate and coords(2)
% y-coordinate.
%
%
% RETURNS
%
% coords (3x1 vector) x,y and z-coordinates.
%
if ~(all(size(coords) == [1 2]) | all(size(coords) == [2 1]))
error('Argument ''coords'' must be an 2x1 or 1x2 vector.');
end
if all(size(coords) == [1 2])
coords=coords';
end
if any(coords > 1) any(coords < 0)
error('Coordinates must lay inside the interval [0,1].');
end
if coords(1) <= 1/(sqrt(2)+1),
if coords(2) <= line3(coords(1))
coords=coords_in_base(4,coords);
elseif coords(2) <= line2(coords(1))
coords=coords_in_base(1,coords);
else
coords=coords_in_base(2,coords);
end
elseif coords(1) <= sqrt(2)/(sqrt(2)+1)
if coords(2) <= line1(coords(1))
coords=coords_in_base(3,coords);
elseif coords(2) <= line2(coords(1))
coords=coords_in_base(1,coords);
else
coords=coords_in_base(2,coords);
end
else
if coords(2) <= line1(coords(1)),
coords=coords_in_base(3,coords);
elseif coords(2) <= line4(coords(1))
coords=coords_in_base(1,coords);
else
coords=coords_in_base(5,coords);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function coords=coords_in_base(base_no,coords)
A=[0;1/(sqrt(2)+1)];
E=[1;1];
F=[0;0];
G=[1;0];
H=[0;1];
const=1+1/sqrt(2);
switch base_no
case 1
x=(coords-A)*const;
coords=[(1/sqrt(2))*(x(1)-x(2));0.5*(x(1)+x(2));0.5*(x(1)+x(2))];
case 2
x=(coords-H)*const;
coords=[0;x(1);1+x(2)];
case 3
x=(coords-G)*const;
coords=[1;1+x(1);x(2)];
case 4
x=(coords-F)*const;
coords=[0.5+(1/sqrt(2))*(x(1)-x(2));...
0.5-(1/sqrt(2))*(x(1)+x(2));...
0];
case 5
x=(coords-E)*const;
coords=[0.5+(1/sqrt(2))*(x(1)-x(2));...
0.5-(1/sqrt(2))*(x(1)+x(2));...
1];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y=line1(x)
y = x-1/(sqrt(2)+1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y=line2(x)
y = x+1/(sqrt(2)+1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y=line3(x)
y = -x+1/(sqrt(2)+1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y= line4(x)
y = -x+(2*sqrt(2)+1)/(sqrt(2)+1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github
|
martinarielhartmann/mirtooloct-master
|
som_pieplane.m
|
.m
|
mirtooloct-master/somtoolbox/som_pieplane.m
| 9,892 |
utf_8
|
7d00431bbaad7c3344ddb3a827ada08b
|
function h=som_pieplane(varargin)
%SOM_PIEPLANE Visualize the map prototype vectors as pie charts
%
% h=som_pieplane(lattice, msize, data, [color], [s], [pos])
% h=som_pieplane(topol, data, [color], [s], [pos])
%
% som_pieplane('hexa',[5 5], rand(25,4), jet(4), rand(25,1))
% som_pieplane(sM, sM.codebook);
%
% Input and output arguments ([]'s are optional):
% lattice (string) grid 'hexa' or 'rect'
% msize (vector) size 1x2, defines the grid, M=msize(1)*msize(2)
% (matrix) size Mx2, gives explicit coordinates for each node: in
% this case the lattice does not matter.
% topol (struct) map or topology struct
% data (matrix) size Mxd, Mth row is the data for Mth pie. The
% values will be normalized to have unit sum in each row.
% [color] (matrix) size dx3, RGB triples. The first row is the
% color of the first slice in each pie etc. Default is hsv(d).
% (string) ColorSpec or 'none' gives the same color for each slice.
% [s] (matrix) size Mx1, gives an individual size scaling for each node.
% (scalar) gives the same size for each node. Default is 0.8.
% [pos] (vectors) a 1x2 vector that determines position for the
% origin, i.e. upper left corner. Default is no translation.
%
% h (scalar) the object handle to the PATCH object
%
% The data will be linearly scaled so that its sum is 1 in each unit.
% Negative values are invalid. Axis are set as in som_cplane.
%
% For more help, try 'type som_pieplane' or check out online documentation.
% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_BARPLANE
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_pieplane
%
% PURPOSE
%
% Visualizes the map prototype vectors as pie charts.
%
% SYNTAX
%
% h = som_pieplane(topol, data)
% h = som_pieplane(lattice, msize, data)
% h = som_pieplane(..., color)
% h = som_pieplane(..., color, s)
% h = som_pieplane(..., color, s, pos)
%
% DESCRIPTION
%
% Visualizes the map prototype vectors as pie charts.
%
% KNOWN BUGS
%
% It is not possible to specify explicit coordinates for map
% consisting of just one unit as then the msize is interpreted as
% map size.
%
% FEATURES
%
% - negative values in data cause an error
%
% - the colors are fixed: changing colormap in the figure (see help
% colormap) will not affect the coloring of the slices.
%
% - if input variable s has size Nxd it gives each slice an individual
% scaling factor. This may be used to create a glyph where
% the radius of the slice, not the angle, shows the variable
% try, e.g., som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4));
%
% REQUIRED INPUT ARGUMENTS
%
% lattice The basic shape of the map units
%
% (string) 'hexa' or 'rect' positions the pies according to hexagonal or
% rectangular map lattice.
%
% msize The size of the map grid
%
% (vector) [n1 n2] vector defines the map size (height n1 units,
% width n2 units, total M=n1xn2 units). The units will
% be placed to their topological locations to form a
% uniform hexagonal or rectangular grid.
% (matrix) Mx2 matrix defines arbitary coordinates for the M units. In
% this case the argument 'lattice' has no effect.
%
% topol Topology of the map grid
%
% (struct) map or topology struct from which the topology is taken
%
% data The data to be visualized
%
% (matrix) Mxd matrix of data vectors. Negative values are invalid.
%
% OPTIONAL INPUT ARGUMENTS
%
% If value is unspecified or empty ([] or ''), the default values
% are used for optional input arguments.
%
% s The size scaling factors for the units
%
% (scalar) gives each unit the same size scaling:
% 0 unit disappears (edges can be seen as a dot)
% ... default size is 0.8
% >1 unit overlaps others
% (matrix) Mx1 double: each unit gets individual size scaling
%
% color The color of the slices in each pie
%
% (string) ColorSpec or 'none' gives the same color for each slice
% (matrix) dx3 matrix assigns an RGB color determined by the dth row of
% the matrix to the dth slice (variable) in each pie plot
%
% pos Position of origin
%
% (vector) size 1x2: this is meant for drawing the plane in arbitary
% location in a figure. Note the operation: if this argument is
% given, the axis limits setting part in the routine is skipped and
% the limits setting will be left to be done by
% MATLAB's defaults. Default is no translation.
%
% OUTPUT ARGUMENTS
%
% h (scalar) Handle to the created patch object.
%
% OBJECT TAGS
%
% One object handle is returned: field Tag is set to 'planePie'
%
% EXAMPLES
%
% %%% Create the data and make a map
%
% data=rand(100,5); map=som_make(data);
%
% %%% Create a 'jet' colormap that has as many rows as the data has variables
%
% colors=jet(5);
%
% %%% Draw pies
%
% som_pieplane(map, map.codebook, colors);
%
% %%% Calculate the hits of data on the map and normalize them between [0,1]
%
% hit=som_hits(map,data); hit=hit./max(max(hit));
%
% %%% Draw the pies so that their size tells the hit count
%
% som_pieplane(map, map.codebook, colors, hit);
%
% %%% Try this! (see section FEATURES)
%
% som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4));
%
% SEE ALSO
%
% som_cplane Visualize a 2D component plane, u-matrix or color plane
% som_barplane Visualize the map prototype vectors as bar diagrams
% som_plotplane Visualize the map prototype vectors as line graphs
% Copyright (c) 1999-2000 by the SOM toolbox programming team.
% http://www.cis.hut.fi/projects/somtoolbox/
% Version 2.0beta Johan 140799 juuso 310300 070600
%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[nargin, lattice, msize, data, color, s, pos] = vis_planeGetArgs(varargin{:});
error(nargchk(3, 6, nargin)); % check no. of input args is correct
% check pos
if nargin < 6 | isempty(pos)
pos=NaN; % default value for pos (no translation)
elseif ~vis_valuetype(pos,{'1x2'})
error('Position of origin has to be given as an 1x2 vector');
end
% check msize
if ~vis_valuetype(msize,{'1x2','nx2'}),
error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
end
% check data
if ~isnumeric(data),
error('Data matrix must be numeric.');
elseif length(size((data)))>2
error('Data matrix has too many dimensions!');
else
d=size(data,2);
N=size(data,1);
end
if any(data(:)<0)
error('Negative data values not allowed in pie plots!');
end
% Check lattice
if ~ischar(lattice) | ~any(strcmp(lattice,{'hexa','rect'})),
error('Invalid lattice.');
end
%% Calculate patch coordinates for slices
for i=1:N,
[nx,ny]=vis_piepatch(data(i,:));
piesx(:,(1+(i-1)*d):(i*d))=nx;
piesy(:,(1+(i-1)*d):(i*d))=ny;
end
l=size(piesx,1);
if size(msize,1) == 1,
if prod(msize) ~= N
error('Data matrix has wrong size.');
else
coord=som_vis_coords(lattice, msize);
end
else
if N ~= size(msize,1),
error('Data matrix has wrong size.');
end
coord=msize;
% This turns the axis tightening off,
% as now we don't now the limits (no fixed grid)
if isnan(pos); pos=[0 0]; end
end
x=reshape(repmat(coord(:,1),1,l*d)',l,d*N);
y=reshape(repmat(coord(:,2),1,l*d)',l,d*N);
% Check size
if nargin < 5 | isempty(s),
s=0.8; % default value for scaling
elseif ~vis_valuetype(s, {'1x1', [N 1], [N d]}),
error('Size matrix does not match with the data matrix.');
elseif size(s) == [N 1],
s=reshape(repmat(s,1,l*d)',l,d*N);
elseif all(size(s) ~= [1 1]),
s=reshape(repmat(reshape(s',d*N,1),1,l)',l,d*N);
end
% Check color
% C_FLAG is a flag for color 'none'
if nargin < 4 | isempty(color)
color=hsv(d); C_FLAG=0; % default n hsv colors
end
if ~(vis_valuetype(color, {[d 3], 'nx3rgb'},'all')) & ...
~vis_valuetype(color,{'colorstyle','1x3rgb'}),
error('The color matrix has wrong size or contains invalid values.');
elseif ischar(color) & strcmp(color,'none'),
C_FLAG=1; % check for color 'none'
color='w';
else
C_FLAG=0; % valid color string or colormap
end
%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Size zero would cause division by zero. eps is as good (node disappears)
% The edge may be visible, though. (NaN causes some other problems)
s(s==0)=eps;
%% 1. Scaling
x=(x./s+piesx).*s; y=(y./s+piesy).*s;
%% 2. Translation
if ~isnan(pos)
x=x+pos(1);y=y+pos(2);
end
%% 3. Rearrange dx3 color matrix
if ~isstr(color) & size(color,1)~=1,
color=reshape(repmat(color,N,1),[1 N*d 3]);
end
%% Set axes properties
ax=newplot; % get current axis
vis_PlaneAxisProperties(ax,lattice, msize, pos);
%% Draw the plane!
h_=patch(x,y,color);
if C_FLAG
set(h_,'FaceColor','none');
end
set(h_,'Tag','planePie'); % tag the object
%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>0, h=h_; end % Set h only if
% there really is output
%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [x,y]=vis_piepatch(v)
% Do a pie (see e.g. the MathWorks function PIE).
% Origin is at (0,0) and the radius is .5.
N=25;
if sum(v)==0, v_is_zero = 1; v(1) = 1; else v_is_zero = 0; end
v(v==0) = eps; % Matlab 5.2 version of linspace doesn't work otherwise
phi=[0 2*pi*cumsum(v./sum(v))];
for i=2:length(phi),
[xi,yi]=pol2cart(linspace(phi(i-1),phi(i),N),0.5);
x(:,i-1)=[0 xi 0]';
y(:,i-1)=[0 yi 0]';
end
if v_is_zero, x = x*0; y = y*0; end
|
github
|
martinarielhartmann/mirtooloct-master
|
metrop.m
|
.m
|
mirtooloct-master/netlab/metrop.m
| 4,976 |
utf_8
|
53e05637fbfd2fcd95efaadd86e97ce9
|
function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
% sample from the distribution P ~ EXP(-F), where F is the first
% argument to METROP. The Markov chain starts at the point X and each
% candidate state is picked from a Gaussian proposal distribution and
% accepted or rejected according to the Metropolis criterion.
%
% SAMPLES = METROP(F, X, OPTIONS, [], P1, P2, ...) allows additional
% arguments to be passed to F(). The fourth argument is ignored, but
% is included for compatibility with HMC and the optimisers.
%
% [SAMPLES, ENERGIES, DIAGN] = METROP(F, X, OPTIONS) also returns a log
% of the energy values (i.e. negative log probabilities) for the
% samples in ENERGIES and DIAGN, a structure containing diagnostic
% information (position and acceptance threshold) for each step of the
% chain in DIAGN.POS and DIAGN.ACC respectively. All candidate states
% (including rejected ones) are stored in DIAGN.POS.
%
% S = METROP('STATE') returns a state structure that contains the state
% of the two random number generators RAND and RANDN. These are
% contained in fields randstate, randnstate.
%
% METROP('STATE', S) resets the state to S. If S is an integer, then
% it is passed to RAND and RANDN. If S is a structure returned by
% METROP('STATE') then it resets the generator to exactly the same
% state.
%
% The optional parameters in the OPTIONS vector have the following
% interpretations.
%
% OPTIONS(1) is set to 1 to display the energy values and rejection
% threshold at each step of the Markov chain. If the value is 2, then
% the position vectors at each step are also displayed.
%
% OPTIONS(14) is the number of samples retained from the Markov chain;
% default 100.
%
% OPTIONS(15) is the number of samples omitted from the start of the
% chain; default 0.
%
% OPTIONS(18) is the variance of the proposal distribution; default 1.
%
% See also
% HMC
%
% Copyright (c) Ian T Nabney (1996-2001)
if nargin <= 2
if ~strcmp(f, 'state')
error('Unknown argument to metrop');
end
switch nargin
case 1
% Return state of sampler
samples = get_state(f); % Function defined in this module
return;
case 2
% Set the state of the sampler
set_state(f, x); % Function defined in this module
return;
end
end
display = options(1);
if options(14) > 0
nsamples = options(14);
else
nsamples = 100;
end
if options(15) >= 0
nomit = options(15);
else
nomit = 0;
end
if options(18) > 0.0
std_dev = sqrt(options(18));
else
std_dev = 1.0; % default
end
nparams = length(x);
% Set up string for evaluating potential function.
f = fcnchk(f, length(varargin));
samples = zeros(nsamples, nparams); % Matrix of returned samples.
if nargout >= 2
en_save = 1;
energies = zeros(nsamples, 1);
else
en_save = 0;
end
if nargout >= 3
diagnostics = 1;
diagn_pos = zeros(nsamples, nparams);
diagn_acc = zeros(nsamples, 1);
else
diagnostics = 0;
end
% Main loop.
n = - nomit + 1;
Eold = feval(f, x, varargin{:}); % Evaluate starting energy.
nreject = 0; % Initialise count of rejected states.
while n <= nsamples
xold = x;
% Sample a new point from the proposal distribution
x = xold + randn(1, nparams)*std_dev;
% Now apply Metropolis algorithm.
Enew = feval(f, x, varargin{:}); % Evaluate new energy.
a = exp(Eold - Enew); % Acceptance threshold.
if (diagnostics & n > 0)
diagn_pos(n,:) = x;
diagn_acc(n,:) = a;
end
if (display > 1)
fprintf(1, 'New position is\n');
disp(x);
end
if a > rand(1) % Accept the new state.
Eold = Enew;
if (display > 0)
fprintf(1, 'Finished step %4d Threshold: %g\n', n, a);
end
else % Reject the new state
if n > 0
nreject = nreject + 1;
end
x = xold; % Reset position
if (display > 0)
fprintf(1, ' Sample rejected %4d. Threshold: %g\n', n, a);
end
end
if n > 0
samples(n,:) = x; % Store sample.
if en_save
energies(n) = Eold; % Store energy.
end
end
n = n + 1;
end
if (display > 0)
fprintf(1, '\nFraction of samples rejected: %g\n', ...
nreject/(nsamples));
end
if diagnostics
diagn.pos = diagn_pos;
diagn.acc = diagn_acc;
end
% Return complete state of the sampler.
function state = get_state(f)
state.randstate = rand('state');
state.randnstate = randn('state');
return
% Set state of sampler, either from full state, or with an integer
function set_state(f, x)
if isnumeric(x)
rand('state', x);
randn('state', x);
else
if ~isstruct(x)
error('Second argument to metrop must be number or state structure');
end
if (~isfield(x, 'randstate') | ~isfield(x, 'randnstate'))
error('Second argument to metrop must contain correct fields')
end
rand('state', x.randstate);
randn('state', x.randnstate);
end
return
|
github
|
martinarielhartmann/mirtooloct-master
|
hmc.m
|
.m
|
mirtooloct-master/netlab/hmc.m
| 7,683 |
utf_8
|
64c15e958297afe69787b8617dc1a56a
|
function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin)
%HMC Hybrid Monte Carlo sampling.
%
% Description
% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
% algorithm to sample from the distribution P ~ EXP(-F), where F is the
% first argument to HMC. The Markov chain starts at the point X, and
% the function GRADF is the gradient of the `energy' function F.
%
% HMC(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional arguments to
% be passed to F() and GRADF().
%
% [SAMPLES, ENERGIES, DIAGN] = HMC(F, X, OPTIONS, GRADF) also returns a
% log of the energy values (i.e. negative log probabilities) for the
% samples in ENERGIES and DIAGN, a structure containing diagnostic
% information (position, momentum and acceptance threshold) for each
% step of the chain in DIAGN.POS, DIAGN.MOM and DIAGN.ACC respectively.
% All candidate states (including rejected ones) are stored in
% DIAGN.POS.
%
% [SAMPLES, ENERGIES, DIAGN] = HMC(F, X, OPTIONS, GRADF) also returns
% the ENERGIES (i.e. negative log probabilities) corresponding to the
% samples. The DIAGN structure contains three fields:
%
% POS the position vectors of the dynamic process.
%
% MOM the momentum vectors of the dynamic process.
%
% ACC the acceptance thresholds.
%
% S = HMC('STATE') returns a state structure that contains the state of
% the two random number generators RAND and RANDN and the momentum of
% the dynamic process. These are contained in fields randstate,
% randnstate and mom respectively. The momentum state is only used for
% a persistent momentum update.
%
% HMC('STATE', S) resets the state to S. If S is an integer, then it
% is passed to RAND and RANDN and the momentum variable is randomised.
% If S is a structure returned by HMC('STATE') then it resets the
% generator to exactly the same state.
%
% The optional parameters in the OPTIONS vector have the following
% interpretations.
%
% OPTIONS(1) is set to 1 to display the energy values and rejection
% threshold at each step of the Markov chain. If the value is 2, then
% the position vectors at each step are also displayed.
%
% OPTIONS(5) is set to 1 if momentum persistence is used; default 0,
% for complete replacement of momentum variables.
%
% OPTIONS(7) defines the trajectory length (i.e. the number of leap-
% frog steps at each iteration). Minimum value 1.
%
% OPTIONS(9) is set to 1 to check the user defined gradient function.
%
% OPTIONS(14) is the number of samples retained from the Markov chain;
% default 100.
%
% OPTIONS(15) is the number of samples omitted from the start of the
% chain; default 0.
%
% OPTIONS(17) defines the momentum used when a persistent update of
% (leap-frog) momentum is used. This is bounded to the interval [0,
% 1).
%
% OPTIONS(18) is the step size used in leap-frogs; default 1/trajectory
% length.
%
% See also
% METROP
%
% Copyright (c) Ian T Nabney (1996-2001)
% Global variable to store state of momentum variables: set by set_state
% Used to initialise variable if set
global HMC_MOM
if nargin <= 2
if ~strcmp(f, 'state')
error('Unknown argument to hmc');
end
switch nargin
case 1
samples = get_state(f);
return;
case 2
set_state(f, x);
return;
end
end
display = options(1);
if (round(options(5) == 1))
persistence = 1;
% Set alpha to lie in [0, 1)
alpha = max(0, options(17));
alpha = min(1, alpha);
salpha = sqrt(1-alpha*alpha);
else
persistence = 0;
end
L = max(1, options(7)); % At least one step in leap-frogging
if options(14) > 0
nsamples = options(14);
else
nsamples = 100; % Default
end
if options(15) >= 0
nomit = options(15);
else
nomit = 0;
end
if options(18) > 0
step_size = options(18); % Step size.
else
step_size = 1/L; % Default
end
x = x(:)'; % Force x to be a row vector
nparams = length(x);
% Set up strings for evaluating potential function and its gradient.
f = fcnchk(f, length(varargin));
gradf = fcnchk(gradf, length(varargin));
% Check the gradient evaluation.
if (options(9))
% Check gradients
feval('gradchek', x, f, gradf, varargin{:});
end
samples = zeros(nsamples, nparams); % Matrix of returned samples.
if nargout >= 2
en_save = 1;
energies = zeros(nsamples, 1);
else
en_save = 0;
end
if nargout >= 3
diagnostics = 1;
diagn_pos = zeros(nsamples, nparams);
diagn_mom = zeros(nsamples, nparams);
diagn_acc = zeros(nsamples, 1);
else
diagnostics = 0;
end
n = - nomit + 1;
Eold = feval(f, x, varargin{:}); % Evaluate starting energy.
nreject = 0;
if (~persistence | isempty(HMC_MOM))
p = randn(1, nparams); % Initialise momenta at random
else
p = HMC_MOM; % Initialise momenta from stored state
end
lambda = 1;
% Main loop.
while n <= nsamples
xold = x; % Store starting position.
pold = p; % Store starting momenta
Hold = Eold + 0.5*(p*p'); % Recalculate Hamiltonian as momenta have changed
if ~persistence
% Choose a direction at random
if (rand < 0.5)
lambda = -1;
else
lambda = 1;
end
end
% Perturb step length.
epsilon = lambda*step_size*(1.0 + 0.1*randn(1));
% First half-step of leapfrog.
p = p - 0.5*epsilon*feval(gradf, x, varargin{:});
x = x + epsilon*p;
% Full leapfrog steps.
for m = 1 : L - 1
p = p - epsilon*feval(gradf, x, varargin{:});
x = x + epsilon*p;
end
% Final half-step of leapfrog.
p = p - 0.5*epsilon*feval(gradf, x, varargin{:});
% Now apply Metropolis algorithm.
Enew = feval(f, x, varargin{:}); % Evaluate new energy.
p = -p; % Negate momentum
Hnew = Enew + 0.5*p*p'; % Evaluate new Hamiltonian.
a = exp(Hold - Hnew); % Acceptance threshold.
if (diagnostics & n > 0)
diagn_pos(n,:) = x;
diagn_mom(n,:) = p;
diagn_acc(n,:) = a;
end
if (display > 1)
fprintf(1, 'New position is\n');
disp(x);
end
if a > rand(1) % Accept the new state.
Eold = Enew; % Update energy
if (display > 0)
fprintf(1, 'Finished step %4d Threshold: %g\n', n, a);
end
else % Reject the new state.
if n > 0
nreject = nreject + 1;
end
x = xold; % Reset position
p = pold; % Reset momenta
if (display > 0)
fprintf(1, ' Sample rejected %4d. Threshold: %g\n', n, a);
end
end
if n > 0
samples(n,:) = x; % Store sample.
if en_save
energies(n) = Eold; % Store energy.
end
end
% Set momenta for next iteration
if persistence
p = -p;
% Adjust momenta by a small random amount.
p = alpha.*p + salpha.*randn(1, nparams);
else
p = randn(1, nparams); % Replace all momenta.
end
n = n + 1;
end
if (display > 0)
fprintf(1, '\nFraction of samples rejected: %g\n', ...
nreject/(nsamples));
end
if diagnostics
diagn.pos = diagn_pos;
diagn.mom = diagn_mom;
diagn.acc = diagn_acc;
end
% Store final momentum value in global so that it can be retrieved later
HMC_MOM = p;
return
% Return complete state of sampler (including momentum)
function state = get_state(f)
global HMC_MOM
state.randstate = rand('state');
state.randnstate = randn('state');
state.mom = HMC_MOM;
return
% Set complete state of sampler (including momentum) or just set randn
% and rand with integer argument.
function set_state(f, x)
global HMC_MOM
if isnumeric(x)
rand('state', x);
randn('state', x);
HMC_MOM = [];
else
if ~isstruct(x)
error('Second argument to hmc must be number or state structure');
end
if (~isfield(x, 'randstate') | ~isfield(x, 'randnstate') ...
| ~isfield(x, 'mom'))
error('Second argument to hmc must contain correct fields')
end
rand('state', x.randstate);
randn('state', x.randnstate);
HMC_MOM = x.mom;
end
return
|
github
|
martinarielhartmann/mirtooloct-master
|
gtminit.m
|
.m
|
mirtooloct-master/netlab/gtminit.m
| 5,204 |
utf_8
|
ab76f6114a7e85375ade5e5889d5f6a7
|
function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% If the SAMPTYPE is 'REGULAR', then regular grids of latent data
% points and RBF centres are created. The dimension of the latent data
% space must be 1 or 2. For one-dimensional latent space, the
% LSAMPSIZE parameter gives the number of latent points and the
% RBFSAMPSIZE parameter gives the number of RBF centres. For a two-
% dimensional latent space, these parameters must be vectors of length
% 2 with the number of points in each of the x and y directions to
% create a rectangular grid. The widths of the RBF basis functions are
% set by a call to RBFSETFW passing OPTIONS(7) as the scaling
% parameter.
%
% If the SAMPTYPE is 'UNIFORM' or 'GAUSSIAN' then the latent data is
% found by sampling from a uniform or Gaussian distribution
% correspondingly. The RBF basis function parameters are set by a call
% to RBFSETBF with the DATA parameter as dataset and the OPTIONS
% vector.
%
% Finally, the output layer weights of the RBF are initialised by
% mapping the mean of the latent variable to the mean of the target
% variable, and the L-dimensional latent variale variance to the
% variance of the targets along the first L principal components.
%
% See also
% GTM, GTMEM, PCA, RBFSETBF, RBFSETFW
%
% Copyright (c) Ian T Nabney (1996-2001)
% Check for consistency
errstring = consist(net, 'gtm', data);
if ~isempty(errstring)
error(errstring);
end
% Check type of sample
stypes = {'regular', 'uniform', 'gaussian'};
if (strcmp(samp_type, stypes)) == 0
error('Undefined sample type.')
end
if net.dim_latent > size(data, 2)
error('Latent space dimension must not be greater than data dimension')
end
nlatent = net.gmmnet.ncentres;
nhidden = net.rbfnet.nhidden;
% Create latent data sample and set RBF centres
switch samp_type
case 'regular'
if nargin ~= 6
error('Regular type must specify latent and RBF shapes');
end
l_samp_size = varargin{1};
rbf_samp_size = varargin{2};
if round(l_samp_size) ~= l_samp_size
error('Latent sample specification must contain integers')
end
% Check existence and size of rbf specification
if any(size(rbf_samp_size) ~= [1 net.dim_latent]) | ...
prod(rbf_samp_size) ~= nhidden
error('Incorrect specification of RBF centres')
end
% Check dimension and type of latent data specification
if any(size(l_samp_size) ~= [1 net.dim_latent]) | ...
prod(l_samp_size) ~= nlatent
error('Incorrect dimension of latent sample spec.')
end
if net.dim_latent == 1
net.X = [-1:2/(l_samp_size-1):1]';
net.rbfnet.c = [-1:2/(rbf_samp_size-1):1]';
net.rbfnet = rbfsetfw(net.rbfnet, options(7));
elseif net.dim_latent == 2
net.X = gtm_rctg(l_samp_size);
net.rbfnet.c = gtm_rctg(rbf_samp_size);
net.rbfnet = rbfsetfw(net.rbfnet, options(7));
else
error('For regular sample, input dimension must be 1 or 2.')
end
case {'uniform', 'gaussian'}
if strcmp(samp_type, 'uniform')
net.X = 2 * (rand(nlatent, net.dim_latent) - 0.5);
else
% Sample from N(0, 0.25) distribution to ensure most latent
% data is inside square
net.X = randn(nlatent, net.dim_latent)/2;
end
net.rbfnet = rbfsetbf(net.rbfnet, options, net.X);
otherwise
% Shouldn't get here
error('Invalid sample type');
end
% Latent data sample and basis function parameters chosen.
% Now set output weights
[PCcoeff, PCvec] = pca(data);
% Scale PCs by eigenvalues
A = PCvec(:, 1:net.dim_latent)*diag(sqrt(PCcoeff(1:net.dim_latent)));
[temp, Phi] = rbffwd(net.rbfnet, net.X);
% Normalise X to ensure 1:1 mapping of variances and calculate weights
% as solution of Phi*W = normX*A'
normX = (net.X - ones(size(net.X))*diag(mean(net.X)))*diag(1./std(net.X));
net.rbfnet.w2 = Phi \ (normX*A');
% Bias is mean of target data
net.rbfnet.b2 = mean(data);
% Must also set initial value of variance
% Find average distance between nearest centres
% Ensure that distance of centre to itself is excluded by setting diagonal
% entries to realmax
net.gmmnet.centres = rbffwd(net.rbfnet, net.X);
d = dist2(net.gmmnet.centres, net.gmmnet.centres) + ...
diag(ones(net.gmmnet.ncentres, 1)*realmax);
sigma = mean(min(d))/2;
% Now set covariance to minimum of this and next largest eigenvalue
if net.dim_latent < size(data, 2)
sigma = min(sigma, PCcoeff(net.dim_latent+1));
end
net.gmmnet.covars = sigma*ones(1, net.gmmnet.ncentres);
% Sub-function to create the sample data in 2d
function sample = gtm_rctg(samp_size)
xDim = samp_size(1);
yDim = samp_size(2);
% Produce a grid with the right number of rows and columns
[X, Y] = meshgrid([0:1:(xDim-1)], [(yDim-1):-1:0]);
% Change grid representation
sample = [X(:), Y(:)];
% Shift grid to correct position and scale it
maxXY= max(sample);
sample(:,1) = 2*(sample(:,1) - maxXY(1)/2)./maxXY(1);
sample(:,2) = 2*(sample(:,2) - maxXY(2)/2)./maxXY(2);
return;
|
github
|
martinarielhartmann/mirtooloct-master
|
mlphess.m
|
.m
|
mirtooloct-master/netlab/mlphess.m
| 1,633 |
utf_8
|
b91a15ca11b4886de6c1671c33a735d3
|
function [h, hdata] = mlphess(net, x, t, hdata)
%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
%
% Description
% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second derivatives of
% the negative log posterior distribution, evaluated for the current
% weight and bias values as defined by NET.
%
% [H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and
% the contribution HDATA arising from the data dependent term in the
% Hessian.
%
% H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a
% matrix X of input values, and a matrix T of target values, together
% with the contribution HDATA arising from the data dependent term in
% the Hessian, and returns the full Hessian matrix H corresponding to
% the second derivatives of the negative log posterior distribution.
% This version saves computation time if HDATA has already been
% evaluated for the current weight and bias values.
%
% See also
% MLP, HESSCHEK, MLPHDOTV, EVIDENCE
%
% Copyright (c) Ian T Nabney (1996-2001)
% Check arguments for consistency
errstring = consist(net, 'mlp', x, t);
if ~isempty(errstring);
error(errstring);
end
if nargin == 3
% Data term in Hessian needs to be computed
hdata = datahess(net, x, t);
end
[h, hdata] = hbayes(net, hdata);
% Sub-function to compute data part of Hessian
function hdata = datahess(net, x, t)
hdata = zeros(net.nwts, net.nwts);
for v = eye(net.nwts);
hdata(find(v),:) = mlphdotv(net, x, t, v);
end
return
|
github
|
martinarielhartmann/mirtooloct-master
|
glmhess.m
|
.m
|
mirtooloct-master/netlab/glmhess.m
| 4,024 |
utf_8
|
2d706b82d25cb35ff9467fe8837ef26f
|
function [h, hdata] = glmhess(net, x, t, hdata)
%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
%
% Description
% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second derivatives of
% the negative log posterior distribution, evaluated for the current
% weight and bias values as defined by NET. Note that the target data
% is not required in the calculation, but is included to make the
% interface uniform with NETHESS. For linear and logistic outputs, the
% computation is very simple and is done (in effect) in one line in
% GLMTRAIN.
%
% [H, HDATA] = GLMHESS(NET, X, T) returns both the Hessian matrix H and
% the contribution HDATA arising from the data dependent term in the
% Hessian.
%
% H = GLMHESS(NET, X, T, HDATA) takes a network data structure NET, a
% matrix X of input values, and a matrix T of target values, together
% with the contribution HDATA arising from the data dependent term in
% the Hessian, and returns the full Hessian matrix H corresponding to
% the second derivatives of the negative log posterior distribution.
% This version saves computation time if HDATA has already been
% evaluated for the current weight and bias values.
%
% See also
% GLM, GLMTRAIN, HESSCHEK, NETHESS
%
% Copyright (c) Ian T Nabney (1996-2001)
% Check arguments for consistency
errstring = consist(net, 'glm', x, t);
if ~isempty(errstring);
error(errstring);
end
ndata = size(x, 1);
nparams = net.nwts;
nout = net.nout;
p = glmfwd(net, x);
inputs = [x ones(ndata, 1)];
if nargin == 3
hdata = zeros(nparams); % Full Hessian matrix
% Calculate data component of Hessian
switch net.outfn
case 'linear'
% No weighting function here
out_hess = [x ones(ndata, 1)]'*[x ones(ndata, 1)];
for j = 1:nout
hdata = rearrange_hess(net, j, out_hess, hdata);
end
case 'logistic'
% Each output is independent
e = ones(1, net.nin+1);
link_deriv = p.*(1-p);
out_hess = zeros(net.nin+1);
for j = 1:nout
inputs = [x ones(ndata, 1)].*(sqrt(link_deriv(:,j))*e);
out_hess = inputs'*inputs; % Hessian for this output
hdata = rearrange_hess(net, j, out_hess, hdata);
end
case 'softmax'
bb_start = nparams - nout + 1; % Start of bias weights block
ex_hess = zeros(nparams); % Contribution to Hessian from single example
for m = 1:ndata
X = x(m,:)'*x(m,:);
a = diag(p(m,:))-((p(m,:)')*p(m,:));
ex_hess(1:nparams-nout,1:nparams-nout) = kron(a, X);
ex_hess(bb_start:nparams, bb_start:nparams) = a.*ones(net.nout, net.nout);
temp = kron(a, x(m,:));
ex_hess(bb_start:nparams, 1:nparams-nout) = temp;
ex_hess(1:nparams-nout, bb_start:nparams) = temp';
hdata = hdata + ex_hess;
end
otherwise
error(['Unknown activation function ', net.outfn]);
end
end
[h, hdata] = hbayes(net, hdata);
function hdata = rearrange_hess(net, j, out_hess, hdata)
% Because all the biases come after all the input weights,
% we have to rearrange the blocks that make up the network Hessian.
% This function assumes that we are on the jth output and that all outputs
% are independent.
bb_start = net.nwts - net.nout + 1; % Start of bias weights block
ob_start = 1+(j-1)*net.nin; % Start of weight block for jth output
ob_end = j*net.nin; % End of weight block for jth output
b_index = bb_start+(j-1); % Index of bias weight
% Put input weight block in right place
hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nin, 1:net.nin);
% Put second derivative of bias weight in right place
hdata(b_index, b_index) = out_hess(net.nin+1, net.nin+1);
% Put cross terms (input weight v bias weight) in right place
hdata(b_index, ob_start:ob_end) = out_hess(net.nin+1,1:net.nin);
hdata(ob_start:ob_end, b_index) = out_hess(1:net.nin, net.nin+1);
return
|
github
|
martinarielhartmann/mirtooloct-master
|
rbfhess.m
|
.m
|
mirtooloct-master/netlab/rbfhess.m
| 3,138 |
utf_8
|
0a6ef29c8be32e9991cacfe42bdfa0b3
|
function [h, hdata] = rbfhess(net, x, t, hdata)
%RBFHESS Evaluate the Hessian matrix for RBF network.
%
% Description
% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second derivatives of
% the negative log posterior distribution, evaluated for the current
% weight and bias values as defined by NET. Currently, the
% implementation only computes the Hessian for the output layer
% weights.
%
% [H, HDATA] = RBFHESS(NET, X, T) returns both the Hessian matrix H and
% the contribution HDATA arising from the data dependent term in the
% Hessian.
%
% H = RBFHESS(NET, X, T, HDATA) takes a network data structure NET, a
% matrix X of input values, and a matrix T of target values, together
% with the contribution HDATA arising from the data dependent term in
% the Hessian, and returns the full Hessian matrix H corresponding to
% the second derivatives of the negative log posterior distribution.
% This version saves computation time if HDATA has already been
% evaluated for the current weight and bias values.
%
% See also
% MLPHESS, HESSCHEK, EVIDENCE
%
% Copyright (c) Ian T Nabney (1996-2001)
% Check arguments for consistency
errstring = consist(net, 'rbf', x, t);
if ~isempty(errstring);
error(errstring);
end
if nargin == 3
% Data term in Hessian needs to be computed
[a, z] = rbffwd(net, x);
hdata = datahess(net, z, t);
end
% Add in effect of regularisation
[h, hdata] = hbayes(net, hdata);
% Sub-function to compute data part of Hessian
function hdata = datahess(net, z, t)
% Only works for output layer Hessian currently
if (isfield(net, 'mask') & ~any(net.mask(...
1:(net.nwts - net.nout*(net.nhidden+1)))))
hdata = zeros(net.nwts);
ndata = size(z, 1);
out_hess = [z ones(ndata, 1)]'*[z ones(ndata, 1)];
for j = 1:net.nout
hdata = rearrange_hess(net, j, out_hess, hdata);
end
else
error('Output layer Hessian only.');
end
return
% Sub-function to rearrange Hessian matrix
function hdata = rearrange_hess(net, j, out_hess, hdata)
% Because all the biases come after all the input weights,
% we have to rearrange the blocks that make up the network Hessian.
% This function assumes that we are on the jth output and that all outputs
% are independent.
% Start of bias weights block
bb_start = net.nwts - net.nout + 1;
% Start of weight block for jth output
ob_start = net.nwts - net.nout*(net.nhidden+1) + (j-1)*net.nhidden...
+ 1;
% End of weight block for jth output
ob_end = ob_start + net.nhidden - 1;
% Index of bias weight
b_index = bb_start+(j-1);
% Put input weight block in right place
hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nhidden, ...
1:net.nhidden);
% Put second derivative of bias weight in right place
hdata(b_index, b_index) = out_hess(net.nhidden+1, net.nhidden+1);
% Put cross terms (input weight v bias weight) in right place
hdata(b_index, ob_start:ob_end) = out_hess(net.nhidden+1, ...
1:net.nhidden);
hdata(ob_start:ob_end, b_index) = out_hess(1:net.nhidden, ...
net.nhidden+1);
return
|
github
|
JoHof/semantic-profiles-master
|
plotTrainingData.m
|
.m
|
semantic-profiles-master/testData/plotTrainingData.m
| 1,013 |
utf_8
|
64d0eb06ad1195992191e94809702544
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [ ] = plotTrainingData(data, weakLabels, trueLabels )
%% plotting the training data
figure;
subplot(2,2,1);
scatter(data(1,weakLabels(:,1)),data(2,weakLabels(:,1)),'blue');
title('records with weak label blue')
xlabel('feature1');
ylabel('feature2');
subplot(2,2,2);
scatter(data(1,weakLabels(:,2)),data(2,weakLabels(:,2)),'red');
title('records with weak label red')
xlabel('feature1');
ylabel('feature2');
subplot(2,2,3);
scatter(data(1,weakLabels(:,3)),data(2,weakLabels(:,3)),'green');
title('records with weak label green')
xlabel('feature1');
ylabel('feature2');
subplot(2,2,4);
scatter(data(1,trueLabels==1),data(2,trueLabels==1),'blue');
hold on;
scatter(data(1,trueLabels==2),data(2,trueLabels==2),'red');
scatter(data(1,trueLabels==3),data(2,trueLabels==3),'green');
title('true labeling of records')
xlabel('feature1');
ylabel('feature2');
end
|
github
|
JoHof/semantic-profiles-master
|
semSynthWeakTrainingData.m
|
.m
|
semantic-profiles-master/testData/semSynthWeakTrainingData.m
| 600 |
utf_8
|
6e81b0bda8c665bba8190c8fdb846553
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [ data, weakLabels, trueLabels ] = semSynthWeakTrainingData()
[data, trueLabels] = semSynthTestData();
classes = unique(trueLabels);
numClasses = length(classes);
tClassLabels = zeros(size(trueLabels,1),length(unique(trueLabels)));
randTrue = randperm(numel(tClassLabels));
tClassLabels(randTrue(1:ceil(length(randTrue)/2))) = 1;
for i = 1:numClasses
tClassLabels(trueLabels==classes(i),i) = 1;
end
weakLabels = logical(tClassLabels);
end
|
github
|
JoHof/semantic-profiles-master
|
preRecall.m
|
.m
|
semantic-profiles-master/testData/preRecall.m
| 1,559 |
utf_8
|
d7829c75a484a69707cce9697b83588c
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [ mprecision MAP base] = preRecall( trainingVectors,testVectors,trainingLabels,testLabels, queryInDatabase )
M = pdist2(trainingVectors',testVectors');
[~, indices] = sort(M);
clear M
indices = uint32(indices);
if queryInDatabase
indices = indices(2:end,:);
end
labels = trainingLabels(indices);
clear indices
classes = unique(testLabels);
for j = 1:length(classes)
class = classes(j);
classQueries = find(testLabels==class);
pr = zeros(length(classQueries),20);
parfor kk = 1:length(classQueries)
queryResults = labels(:,classQueries(kk));
classhit = queryResults==class;
[pr(kk,:) mp(kk)] = precRecallAP(classhit);
end
mprecision(:,j) = mean(pr); %#ok<AGROW>
MAP(j) = mean(mp); %#ok<AGROW>
base(j) = sum(trainingLabels==class)/length(trainingLabels); %#ok<AGROW>
end
end
function [ precision AP ] = precRecallAP( hits )
hitPos = find(hits); %ceil(recallLevels*nClass);
parfor jj = 1:length(hitPos)
k = hitPos(jj);
Allprecision(jj) = jj/k;
end
AP = mean(Allprecision);
recallLevels = 0.05:0.05:1;
kForRecall = ceil(recallLevels*length(hitPos));
precision = Allprecision(kForRecall);
end
|
github
|
JoHof/semantic-profiles-master
|
semSynthTestData.m
|
.m
|
semantic-profiles-master/testData/semSynthTestData.m
| 998 |
utf_8
|
c2bcd9636ff4921a96bffa06a904bdb8
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [data labels] = semSynthTestData()
CL1 = 200;
CL2 = 200;
CL3 = 300;
data1 = zeros(CL1,2);
data2 = zeros(CL2,2);
data3 = zeros(CL3,2);
data32 = zeros(CL3,2);
for i=1:CL1
% rand value for x and y axis
data1(i,1) = 1.5+randn*0.7; data1(i,2) = 1+randn;
end
for i = 1 : CL2
% rand value for x and y axis
data2(i,1) = 3 + randn*0.3; data2(i,2) = 2 + randn*3;
end
for i = 1:CL3
% rand value for x and y axis
data32(i,1) = rand*5.2-1; data32(i,2) = 1 + randn*0.4;
end
[tdatay, tadayidx] = sort(data32(:,1));
for i = 1:CL3
data3(tadayidx(i),1) = data32(tadayidx(i),1); data3(tadayidx(i),2) = real(data32(tadayidx(i),2) + 3*sqrt((mean(data32(:,1))-tdatay(1)).^2-abs(data32(tadayidx(i),1)-mean(data32(:,1))).^2)-1);
end
data = [data1; data2; data3]';
labels = [ones(CL1,1); ones(CL2,1)*2; ones(CL3,1)*3 ];
end
|
github
|
JoHof/semantic-profiles-master
|
spgetprofiles.m
|
.m
|
semantic-profiles-master/semProf/spgetprofiles.m
| 1,782 |
utf_8
|
591269a51cfc50e22f35a0fad3ed1754
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
%% [ semProfiles ] = spgetprofiles(records, model)
%
% calculates the semantic profiles for a novel set of records given trained
% model
%
% Input:
%
% records: a set of vecors in the from dxn
% model: model returned by function sptrainmodel
% model = sptrainmodel(trainingData,weakLabels,p);
%
% Output:
%
% semProfiles: dxn a set of sem Profiles where d=number of classes
%
function [ semProfiles ] = spgetprofiles(records, model)
numClasses = length(model.sIdx);
t1 = tic;
[fernsVec] = getFernsResponse(records', model.ferns);
tfernsResp = toc(t1);
disp(['Ferns response in ... ' num2str(tfernsResp) 's']);
semHist = single(zeros(size(fernsVec,1),numClasses));
t2 = tic;
for i = 1:numClasses
disp([num2str(i) '...Processing: ' num2str(i)]);
[num fern] = ind2sub([2^model.ferns_depth model.num_ferns],model.sIdx{i});
num=uint8(num)-1;
runs = ceil(length(num)/10000); % splitting data for saving some memory
very = zeros(size(fernsVec,1),1);
itemsPerIteration = 2e6;
N = size(fernsVec,1);
tv = zeros(N,1);
for run = 1:runs
subset = ((run-1)*10000)+1:min(run*10000,length(num));
Nruns = ceil(size(fernsVec,1)/itemsPerIteration);
for Nrun = 1:Nruns
Nsubset = ((Nrun-1)*itemsPerIteration)+1:min(Nrun*itemsPerIteration,N);
tv(Nsubset) = sum(bsxfun(@eq,fernsVec(Nsubset,fern(subset)),num(subset)'),2);
end
very = very+tv;
end
veryProp = very/model.maxCount(i);
semHist(:,i) = veryProp;
end
tProfiles = toc(t2);
disp(['Profiles generation in ... ' num2str(tProfiles) 's']);
semProfiles = semHist';
end
|
github
|
JoHof/semantic-profiles-master
|
sptrainmodel.m
|
.m
|
semantic-profiles-master/semProf/sptrainmodel.m
| 4,115 |
utf_8
|
2e41e116bceaf8f144b2a80f93ed9830
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
%% [ r ] = sptrainmodel(records, classLabels, p)
%
% calculates the semantic profiles for a novel set of records given trained
% model
%
% Input:
%
% records: a set of vecors in the from dxn
% classLabels: weakLabels in the form nxc in [0,1] where c = number of
% Classes. Each record is labeled with one to c classes
% where only one is the true class.
% p = struct holding parameters. Default parameters are:
%
% dp.num_ferns = 1200; % number of ferns to be generated
% dp.ferns_depth = 8; % depth of one fern (e.g. 2^8 partitions per fern)
% dp.sub_dims = 9; % number of sub-dimensions used on each split (usually <12)
% dp.partitionRes = 5000; % parameter K in the Paper
% dp.classSmoothing = 20; % parameter gamma in the paper (prevents overfitting)
%
% Output:
%
% r: struct holding all the model information needed to infer semantic
% profiles for a novel record (e.g. like random ferns and relative class
% frequencies in the partitions selected partitions
%
function [ r ] = sptrainmodel(records, classLabels, p)
dp.num_ferns = 1200; % number of ferns to be generated
dp.ferns_depth = 8; % depth of one fern (e.g. 2^8 partitions per fern)
dp.sub_dims = 9; % number of sub-dimensions used on each split (usually <12)
dp.partitionRes = 5000; % parameter K in the Paper
dp.classSmoothing = 20; % parameter gamma in the paper (prevents overfitting)
p = defaultParams(p,dp);
if (size(classLabels,2)==1 && length(unique(classLabels))>2)
classes = unique(classLabels);
numClasses = length(classes);
tClassLabels = zeros(size(classLabels,1),length(unique(classLabels)));
for i = 1:numClasses
tClassLabels(classLabels==classes(i),i) = 1;
end
classLabels = tClassLabels;
end
numClasses = size(classLabels,2);
disp('Train the ferns...');
t1 = tic;
[fernsVec r.ferns] = createFerns(records', p.num_ferns, p.ferns_depth, p.sub_dims);
fernsTime = toc(t1);
disp(['Feature space partitioning in ... ' num2str(fernsTime) 's']);
disp('Building class distribution models...');
t2 = tic;
ii = 0:(2^p.ferns_depth)-1;
jj = p.num_ferns;
histClass = zeros(length(ii),jj);
fernsVecT = fernsVec'; %for efficiency
classN = zeros(2^p.ferns_depth,jj);
parfor j = 1:jj
classN(:,j) = hist(double(fernsVec(:,j)),ii);
end
classN = classN(:);
for i = 1:numClasses
%actClass = classes(i);
disp([num2str(i) '...Processing: ' num2str(i)]);
classHit = logical(classLabels(:,i));
fVclassHit = single(fernsVecT(:,classHit))'; %slicing for parfor
%fVnclassHit = double(fernsVecT(:,nclassHit))'; %slicing for parfor
parfor j = 1:jj
%histnClass(:,j) = hist(fVnclassHit(:,j),ii);
histClass(:,j) = hist(fVclassHit(:,j),ii);
end
relTermFrequency = (histClass(:)+1)./(classN(:)+p.classSmoothing); %dirichlet prior
relTermFrequency(isnan(relTermFrequency)) = 0;
relTermFrequency(relTermFrequency<0) = 0;
[~, scIdx] = sort(relTermFrequency,1,'descend');
sIdx{i} = scIdx(1:p.partitionRes,:);
[num fern] = ind2sub([2^p.ferns_depth p.num_ferns],sIdx{i});
num=uint8(num)-1;
runs = ceil(length(num)/10000);
very = zeros(size(fernsVec,1),1);
itemsPerIteration = 3e6;
N = size(fernsVec,1);
tv = zeros(N,1);
for run = 1:runs
subset = ((run-1)*10000)+1:min(run*10000,length(num));
Nruns = ceil(size(fernsVec,1)/itemsPerIteration);
for Nrun = 1:Nruns
Nsubset = ((Nrun-1)*itemsPerIteration)+1:min(Nrun*itemsPerIteration,N);
tv(Nsubset) = sum(bsxfun(@eq,fernsVec(Nsubset,fern(subset)),num(subset)'),2);
end
very = very+tv;
end
r.maxCount(i) = max(very);
end
clear fVclassHit
clear fVnclassHit
modelTime = toc(t2);
disp(['Distribution model learning in ... ' num2str(modelTime) 's']);
r.sIdx = sIdx;
r.num_ferns = p.num_ferns;
r.ferns_depth = p.ferns_depth;
end
|
github
|
JoHof/semantic-profiles-master
|
defaultParams.m
|
.m
|
semantic-profiles-master/semProf/utilFunctions/defaultParams.m
| 522 |
utf_8
|
388caf785537f6f53256b4f8201f1a20
|
% Functional Matlab Library
% (c) 2013 Rene Donner, [email protected]
% For academic research / private use only, commercial use prohibited
%% function p = defaultParams(p,defaultp)
%
% Compare fields of "p" with "defaultp".
% If field from "defaultp" is not existent in "p", add it.
function p = defaultParams(p,defaultp)
names = fieldnames(defaultp);
for n = 1:length(names)
name = names{n};
if not(isfield(p,name))
p = setfield(p,name,getfield(defaultp,name)); %#ok<GFLD,SFLD>
end
end
|
github
|
JoHof/semantic-profiles-master
|
d2b.m
|
.m
|
semantic-profiles-master/semProf/utilFunctions/d2b.m
| 482 |
utf_8
|
515c221dc19dfff23e689bc46e828679
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function y = d2b(x,nBits)
% Convert a decimanl number into a binary array
%
% Similar to dec2bin but yields a numerical array instead of a string and is found to
% be rather faster
y =zeros([length(x) nBits],'single');
for iBit = 1:nBits % Loop over the bits
y(:,iBit) = bitget(x,iBit); % Get the bit values
end
|
github
|
JoHof/semantic-profiles-master
|
b2d.m
|
.m
|
semantic-profiles-master/semProf/utilFunctions/b2d.m
| 409 |
utf_8
|
4ea979cc601122efc8af700fdf979da7
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function y = b2d(x)
% Convert a binary array to a decimal number
%
% Similar to bin2dec but works with arrays instead of strings and is found to be
% rather faster
z = single(2.^(0:1:size(x,2)-1));
y = single(x)*z';
y = cast(y,class(x));
%y = bsxfun(@times,x,z')
|
github
|
JoHof/semantic-profiles-master
|
createFerns.m
|
.m
|
semantic-profiles-master/semProf/randomFerns/createFerns.m
| 1,959 |
utf_8
|
1bf77a9732ac27cc920c3ec0aa7da732
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [ wordVector, ferns ] = createFerns( featureVector, num_ferns, num_nodes, dim )
%CREATE_FERNS Trains ferns out of featureVectore provided
vector_dim = size(featureVector,2);
sub = size(featureVector,1);
if(dim>vector_dim)
error('createFern:chkInput', 'featureVector dimension lower than split dimensions!');
end
if(sub<vector_dim)
fprintf('nDim < N, Input should be provided as column vectors!');
end
switch (ceil(num_nodes/8))
case 1
type = 'uint8';
case 2
type = 'uint16';
case {3,4}
type = 'uint32';
otherwise
error('createFern:chkInput', 'Max fern depth is 32!');
end
%allocating memory
binaryVector = zeros([sub num_nodes], type);
wordVector = zeros([sub num_ferns],type);
ferns.dims = zeros(dim,num_ferns,num_nodes);
ferns.proj_vector = zeros(dim,num_ferns,num_nodes);
ferns.threshold = zeros(num_ferns,num_nodes);
for fern=1:1:num_ferns
for node=1:1:num_nodes
x = randperm(vector_dim);
rand_dims = x(1:dim);
subspace = featureVector(:,rand_dims); %produce a random subspace
rand_proj_vector =randn([size(subspace,2) 1]); %generate random peojection vector
projection_values = subspace * rand_proj_vector; %project the subvectors
ridx = randi(length(projection_values)); %random value with distribution on projection
threshold = projection_values(ridx); %choose the value
binaryVector(:,node) = projection_values>threshold; %thresholding of values
%save fern in data structure
ferns.dims(:,fern,node) = rand_dims;
ferns.proj_vector(:,fern,node) = rand_proj_vector;
ferns.threshold(fern,node) = threshold;
end
wordVector(:,fern) = b2d(binaryVector); %save the outcome
end
end
|
github
|
JoHof/semantic-profiles-master
|
getFernsResponse.m
|
.m
|
semantic-profiles-master/semProf/randomFerns/getFernsResponse.m
| 1,382 |
utf_8
|
c064bca01e8de189931c41bf41c5f578
|
% (c) 2015 Johannes Hofmanninger, [email protected]
% For academic research / private use only, commercial use prohibited
function [ leafIndizes ] = getFernsResponse( queryVector, ferns )
% gets the fern response for provided vector and fern
num_ferns = size(ferns.dims,2);
num_nodes = size(ferns.dims,3);
switch (ceil(num_nodes/8))
case 1
type = 'uint8';
case 2
type = 'uint16';
case {3,4}
type = 'uint32';
otherwise
fprintf('Max fern depth is 32!\n');
return;
end
sub = size(queryVector,1);
%memory pre-allocation
binaryVector = zeros([sub num_nodes], type);
leafIndizes = zeros([sub num_ferns],type);
for fern=1:num_ferns
for node=1:num_nodes
rand_dims = ferns.dims(:,fern,node);
subspace = queryVector(:,rand_dims); %produce a random subspace
rand_proj_vector = ferns.proj_vector(:,fern,node);
projection_values = subspace * rand_proj_vector; %project the subvectors
threshold = ferns.threshold(fern,node);
binaryVector(:,node) = projection_values>(threshold+(1e-10)); %save the outcome and floating point error correction
%leafIndizes(:,fern) = leafIndizes(:,fern) + cast((projection_values>(threshold+(1e-10)))*2^(node-1),type);
end
leafIndizes(:,fern) = b2d(binaryVector);
end
end
|
github
|
maeager/Agilent2Dicom-master
|
call_mci.m
|
.m
|
Agilent2Dicom-master/matlab/call_mci.m
| 4,352 |
utf_8
|
5af977ebde5cecfe6cf1052bc018648f
|
function call_mci(in1,in2,out,saveRI)
% Calling MCI - max contrast imaging
%
% - (C) 2015 Michael Eager ([email protected])
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'matlab'))
addpath(fullfile(root_path,'matlab/NIFTI'))
addpath(fullfile(root_path, 'matlab/Agilent/'))
display('Calling MCI')
if nargin == 3
saveRI=0;
end
%% Clean input strings
in1 = regexprep(in1,'["\[\]]','');
if ~isempty(in2)
if isstr(in2)
in2 = regexprep(in2,'["\[\]]','');
else
in2=[];
end
end
out = regexprep(out,'["\[\]]','');
voxelsize=[];
ksp1=[];ksp2=[];
if exist(in1,'file')==2 && ~isempty(strfind(in1,'.nii'))
nii1_in=load_nii(in1);
img=nii1_in.img;
ksp1=fftn(img);
voxelsize1=nii1_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in1,'.img')) && isdir(in1)
[img hdr] =readfdf(in1);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp1=fftn(img);
% voxelsize1=hdr.roi*10/hdr.matrix;
voxelsize1 = hdr.voxelsize*10;
elseif ~isempty(strfind(in1,'.fid')) && isdir(in1)
[img, hdr, ksp1, RE, IM] = readfid(in1);
voxelsize1=hdr.voxelmm;
% voxelsize=hdr.FOVcm*10/size(img);
else
display(['Cannot find ' in1])
return
end
if ~isempty(in2)
if exist(in2,'file')==2 && ~isempty(strfind(in2,'.nii'))
nii2_in=load_nii(in2);
img=nii2_in.img;
ksp2=fftn(img);
voxelsize2=nii2_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in2,'.img')) && isdir(in2)
[img hdr] =readfdf(in2);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp2=fftn(img);
%voxelsize2=hdr.roi*10/hdr.matrix;
voxelsize2 = hdr.voxelsize*10;
elseif ~isempty(strfind(in2,'.fid')) && isdir(in2)
[img, hdr, ksp2, RE, IM] = readfid(in2);
% voxelsize2=hdr.FOVcm*10/size(img);
voxelsize2=hdr.voxelmm;
else
display(['Cannot find ' in2])
% return
end
end
if ~isempty(in2) && sum(voxelsize1) ~= sum(voxelsize2)
display(['Voxelsizes don''t match: ' str2num(voxelsize1) ' ' ...
str2num(voxelsize2)])
return
end
voxelsize=voxelsize1;
[pha1, swi_n1, swi_p1, mag1] = phaserecon_v1(ksp1,ksp1,0.4,1,0.05);
% Necessary translations to match FDF images
mag1=flipdim(flipdim(flipdim(mag1,1),2),3);
mag1=circshift(mag1,[1,1,1]);
pha1=flipdim(flipdim(flipdim(pha1,1),2),3);
pha1=circshift(pha1,[1,1,1]);
if ~isempty(ksp2)
[pha2, swi_n2, swi_p2, mag2] = phaserecon_v1(ksp2,ksp2,0.4,1, ...
0.05);
mag2=flipdim(flipdim(flipdim(mag2,1),2),3);
mag2=circshift(mag2,[1,1,1]);
pha2=flipdim(flipdim(flipdim(pha2,1),2),3);
pha2=circshift(pha2,[1,1,1]);
end
stdmask=stdfilt(mag1);
stdphmask=stdfilt(pha1);
gbmask =getbiggestobject(stdphmask<0.013 | stdmask>2000);
mask=(gbmask.*(pha1>0));
r_mag = mean(mag1(mask==1));
r_pha = mean(pha1(mask==1));
mci_mag1 = mci(mag1,pha1,[r_mag, r_pha]);
if exist(out,'file')~=2 && ~isdir(out)
%if not a file or a dir, create dir
mkdir (out)
end
if isdir(out)
out=[out '/mci_magn.nii.gz'];
end
if exist(out,'file')
delete(out)
end
if isempty(ksp2)
save_nii(make_nii(abs(mci_mag1),voxelsize,[],16),out)
else
mci_mag2 = mci(mag2,pha2,[r_mag,rpha]);
save_nii(make_nii(abs((mci_mag1+mci_mag2)/2.0),voxelsize,[],16),out)
end
if saveRI
if isempty(ksp2)
save_nii(make_nii(real(mci_mag1),voxelsize,[],16),regexprep(out,'magn','real'))
save_nii(make_nii(imag(mci_mag1),voxelsize,[],16),regexprep(out,'magn','imag'))
else
save_nii(make_nii(real((mci_mag1+mci_mag2)/2.0),voxelsize, ...
[],16),regexprep(out,'magn','real'))
save_nii(make_nii(imag((mci_mag1+mci_mag2)/2.0),voxelsize, ...
[],16),regexprep(out,'magn','imag'))
end
end
function output = mci(magnitude,phase,reference)
% MCI create Maximum Contrast Image
%
% OUTPUT = MCI(MAGNITUDE,PHASE,REFERENCE)
%
% MAGNITUDE = magnitude image
% PHASE = phase image
% REFERENCE = reference point
%
% Created by Zhaolin Chen
% Adapted by Amanda Ng on 11 March 2009
% Updated by Michael Eager July 2015
phasereg=phase.*0;
phasereg(phase>0)=phase(phase>0)-reference(2);
phasereg(phase<0)=phase(phase<0)+reference(2);
output = sqrt((magnitude-reference(1)).^2 + (phasereg).^2);
|
github
|
maeager/Agilent2Dicom-master
|
call_swi.m
|
.m
|
Agilent2Dicom-master/matlab/call_swi.m
| 5,198 |
utf_8
|
f4bde8fd7e63390cac2b6b4cfe3c7669
|
function call_swi(in1,in2,out,order,preprocess,saveRI,swineg,swipos)
% Calling susceptibility weighted imaging filter
%
% - (C) 2015 Michael Eager ([email protected])
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'matlab'))
addpath(fullfile(root_path,'matlab/NIFTI'))
addpath(fullfile(root_path, 'matlab/Agilent/'))
%% Clean input strings
in1 = regexprep(in1,'["\[\]]','');
if ~isempty(in2)
if isstr(in2)
in2 = regexprep(in2,'["\[\]]','');
else
in2=[];
end
end
out = regexprep(out,'["\[\]]',''); %"
display('Calling SWI')
display(in1)
display (in2)
display (out)
if nargin < 8
swipos=0;
end
if nargin < 7
swipneg=0;
end
if nargin < 6
saveRI=0;
end
if nargin < 5
preprocess=0;
end
if nargin < 4
order=0;
end
voxelsize=[];
ksp1=[];ksp2=[];
if exist(in1,'file')==2 && ~isempty(strfind(in1,'.nii'))
nii1_in=load_nii(in1);
img=nii1_in.img;
ksp1=fftn(img);
voxelsize1=nii1_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in1,'.img')) && isdir(in1)
[img hdr] =readfdf(in1);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp1=fftn(img);
% voxelsize1=hdr.roi*10/hdr.matrix;
voxelsize1 = hdr.voxelsize*10;
elseif ~isempty(strfind(in1,'.fid')) && isdir(in1)
[img, hdr, ksp1, RE, IM] = readfid(in1);
voxelsize1=hdr.voxelmm;
% voxelsize=hdr.FOVcm*10/size(img);
else
display(['Cannot find ' in1])
return
end
if ~isempty(in2)
if exist(in2,'file')==2 && ~isempty(strfind(in2,'.nii'))
nii2_in=load_nii(in2);
img=nii2_in.img;
ksp2=fftn(img);
voxelsize2=nii2_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in2,'.img')) && isdir(in2)
[img hdr] =readfdf(in2);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp2=fftn(img);
%voxelsize2=hdr.roi*10/hdr.matrix;
voxelsize2 = hdr.voxelsize*10;
elseif ~isempty(strfind(in2,'.fid')) && isdir(in2)
[img, hdr, ksp2, RE, IM] = readfid(in2);
% voxelsize2=hdr.FOVcm*10/size(img);
voxelsize2=hdr.voxelmm;
else
display(['Cannot find second image ' in2])
% return
end
end
if ~isempty(in2) && sum(voxelsize1) ~= sum(voxelsize2)
display(['Voxelsizes don''t match: ' str2num(voxelsize1) ' ' ...
str2num(voxelsize2)])
return
end
voxelsize=voxelsize1;
ksp1=squeeze(ksp1);
if length(size(ksp1)) == 3
[pha, swi_n, swi_p, mag] = phaserecon_v1(ksp1,ksp1,0.4,1,0.05);
% Necessary translations to match FDF images
swi_n=flipdim(flipdim(flipdim(swi_n,1),2),3);
swi_n=circshift(swi_n,[1,1,1]);
img=mag.*exp(1i*pha);
elseif length(size(ksp1)) == 4
for echo=1:size(ksp1,4)
[pha(:,:,:,echo), swi_n(:,:,:,echo), swi_p(:,:,:,echo), mag(:,:,:,echo)] = phaserecon_v1(ksp1(:,:,:,echo),ksp1(:,:,:,echo),0.4,1,0.05);
% Necessary translations to match FDF images
swi_n(:,:,:,echo)=flipdim(flipdim(flipdim(swi_n(:,:,:,echo),1),2),3);
swi_n(:,:,:,echo)=circshift(swi_n(:,:,:,echo),[1,1,1]);
img(:,:,:,echo)=mag(:,:,:,echo).*exp(1i*pha(:,:,:,echo));
end
end
if ~isempty(ksp2)
if length(size(ksp2)) == 3
[pha2, swi_n2, swi_p2, mag2] = phaserecon_v1(ksp2,ksp2,0.4,1, ...
0.05);
swi_n2=flipdim(flipdim(flipdim(swi_n2,1),2),3);
swi_n2=circshift(swi_n2,[1,1,1]);
elseif length(size(ksp2)) == 4
pha2=zeros(size(ksp2));
mag2=zeros(size(ksp2));
swi_n2=zeros(size(ksp2));
swi_p2=zeros(size(ksp2));
img2=zeros(size(ksp2));
for echo=1:size(ksp2,4)
[pha2(:,:,:,echo), swi_n2(:,:,:,echo), swi_p2(:,:,:,echo), mag2(:,:,:,echo)] = phaserecon_v1(ksp2(:,:,:,echo),ksp2(:,:,:,echo),0.4,1,0.05);
% Necessary translations to match FDF images
swi_n2(:,:,:,echo)=flipdim(flipdim(flipdim(swi_n2(:,:,:,echo),1),2),3);
swi_n2(:,:,:,echo)=circshift(swi_n2(:,:,:,echo),[1,1,1]);
img2(:,:,:,echo)=mag2(:,:,:,echo).*exp(1i*pha2(:,:,:,echo));
end
end
% Combine input 1 and 2
swi_n = (swi_n+swi_n2)/2;
swi_p = (swi_p+swi_p2)/2;
img = (img+ (mag2.*exp(1i*pha2)))/2;
end
%% Write output
if exist(out,'file')~=2 && ~isdir(out)
%if not a file or a dir, create dir
mkdir (out)
end
if isdir(out)
out=[out '/swi_neg.nii.gz'];
end
if exist(out,'file')
delete(out)
end
savetonii(swi_n,voxelsize,out)
if swipos
swi_p=flipdim(flipdim(flipdim(swi_p,1),2),3);
swi_p=circshift(swi_p,[1,1,1]);
outp = regexprep(out,'neg','pos');
savetonii(swi_p,voxelsize,outp)
end
if saveRI
savetonii(real(img),voxelsize,regexprep(out,'neg','real'))
savetonii(imag(img),voxelsize,regexprep(out,'neg','imag'))
end
function savetonii(vol,vsize,fname)
if length(size(ksp1)) ==3
save_nii(make_nii(single(vol),vsize,[0,0,0],16),fname)
elseif length(size(ksp1)) == 4
for echo=1:size(vol,4)
save_nii(make_nii(single(vol(:,:,:,1,echo)),vsize,[0,0,0],16),regexprep(fname,'.nii.gz',['echo' num2str(echo) '.nii.gz']))
end
end
|
github
|
maeager/Agilent2Dicom-master
|
call_mee.m
|
.m
|
Agilent2Dicom-master/matlab/call_mee.m
| 4,619 |
utf_8
|
b8b8430d463afad48497ddc86880095b
|
function call_mee(in1,in2,out,porder,preprocess,saveRI,useswi)
% Calling MEE - multi-echo enhancement
%
% - (C) 2015 Michael Eager ([email protected])
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'./matlab'))
addpath(fullfile(root_path,'./matlab/NIFTI'))
addpath(fullfile(root_path, './matlab/Agilent/'))
display('Calling MCI')
if nargin < 4
porder=3;
end
if nargin < 5
preprocess=0;
end
if nargin < 6
saveRI=0;
end
if nargin < 7
useswi=0;
end
%% Clean input strings
in1 = regexprep(in1,'["\[\]]','');
if ~isempty(in2)
if isstr(in2)
in2 = regexprep(in2,'["\[\]]','');
else
in2=[];
end
end
out = regexprep(out,'["\[\]]','');
voxelsize=[];
ksp1=[];ksp2=[];
if exist(in1,'file')==2 && ~isempty(strfind(in1,'.nii'))
nii1_in=load_nii(in1);
img=nii1_in.img;
ksp1=fftn(img);
voxelsize1=nii1_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in1,'.img')) && isdir(in1)
[img hdr] =readfdf(in1);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp1=fftn(img);
% voxelsize1=hdr.roi*10/hdr.matrix;
voxelsize1 = hdr.voxelsize*10;
elseif ~isempty(strfind(in1,'.fid')) && isdir(in1)
[img, hdr, ksp1, RE, IM] = readfid(in1);
voxelsize1=hdr.voxelmm;
% voxelsize=hdr.FOVcm*10/size(img);
else
display(['Cannot find ' in1])
return
end
if ~isempty(in2)
if exist(in2,'file')==2 && ~isempty(strfind(in2,'.nii'))
nii2_in=load_nii(in2);
img=nii2_in.img;
ksp2=fftn(img);
voxelsize2=nii2_in.dime.pixdim(2:4);
elseif ~isempty(strfind(in2,'.img')) && isdir(in2)
[img hdr] =readfdf(in2);
% voxelsize=hdr.FOVcm/size(img)*10;
ksp2=fftn(img);
%voxelsize2=hdr.roi*10/hdr.matrix;
voxelsize2 = hdr.voxelsize*10;
elseif ~isempty(strfind(in2,'.fid')) && isdir(in2)
[img, hdr, ksp2, RE, IM] = readfid(in2);
% voxelsize2=hdr.FOVcm*10/size(img);
voxelsize2=hdr.voxelmm;
else
display(['Cannot find ' in2])
% return
end
end
if ~isempty(in2) && sum(voxelsize1) ~= sum(voxelsize2)
display(['Voxelsizes don''t match: ' str2num(voxelsize1) ' ' ...
str2num(voxelsize2)])
return
end
voxelsize=voxelsize1;
%% If using preprocessing
% add content
%% Homodyne filter
[pha1, swi_n1, swi_p1, mag1] = phaserecon_v1(ksp1,ksp1,0.4,1,0.05);
% Necessary translations to match FDF images
if ~useswi
mag1=flipdim(flipdim(flipdim(mag1,1),2),3);
mag1=circshift(mag1,[1,1,1]);
pha1=flipdim(flipdim(flipdim(pha1,1),2),3);
pha1=circshift(pha1,[1,1,1]);
if ~isempty(ksp2)
[pha2, swi_n2, swi_p2, mag2] = phaserecon_v1(ksp2,ksp2,0.4,1, 0.05);
mag2=flipdim(flipdim(flipdim(mag2,1),2),3);
mag2=circshift(mag2,[1,1,1]);
pha2=flipdim(flipdim(flipdim(pha2,1),2),3);
pha2=circshift(pha2,[1,1,1]);
end
mee_mag1 = mee((mag1.*exp(1i*pha1)),porder);
else
swi1=flipdim(flipdim(flipdim(swi_n1,1),2),3);
swi1=circshift(swi1,[1,1,1]);
mee_mag1 = mee(swi1,porder);
end
if exist(out,'file')~=2 && ~isdir(out)
%if not a file or a dir, create dir
mkdir (out)
end
if isdir(out)
out=[out '/mee_magn.nii.gz'];
end
if exist(out,'file')
delete(out)
end
if isempty(ksp2)
save_nii(make_nii(abs(mee_mag1),voxelsize,[],16),out)
else
if ~useswi
mee_mag2 = mee((mag2.*exp(1i*pha2)),porder);
else
swi2=flipdim(flipdim(flipdim(swi_n2,1),2),3);
swi2=circshift(swi2,[1,1,1]);
mee_mag2 = mee(swi2,porder);
end
save_nii(make_nii(abs((mee_mag1+mee_mag2)/2.0),voxelsize,[],16),out)
end
if saveRI && ~useswi
if isempty(ksp2)
save_nii(make_nii(real(mee_mag1),voxelsize,[],16),regexprep(out,'magn','real'))
save_nii(make_nii(imag(mee_mag1),voxelsize,[],16),regexprep(out,'magn','imag'))
else
save_nii(make_nii(real((mee_mag1+mee_mag2)/2.0),voxelsize, ...
[],16),regexprep(out,'magn','real'))
save_nii(make_nii(imag((mee_mag1+mee_mag2)/2.0),voxelsize, ...
[],16),regexprep(out,'magn','imag'))
end
end
function output = mee(img,order)
% Multi-echo enhancement
%
% img = 5D magnitude image
%
% Created by Michael Eager July 2015
sz=size(img);output=[];
if len(sz)==4
sz(5)=sz(4);sz(4)=1;
img = reshape(img,sz);
elseif len(sz)~=5
return
end
if sz(4)~=1 && sz(5) == 1
output=img;
return
end
img = (abs(img));
if nargin==1
p=3.0;
else
p=order;
end
output = (sum(img.^-p,5)./sz).^(-1/p);
|
github
|
maeager/Agilent2Dicom-master
|
ReadProcpar.m
|
.m
|
Agilent2Dicom-master/matlab/ReadProcpar.m
| 1,951 |
utf_8
|
da490bc01ee896267897830ecffd509e
|
function vals = ReadProcpar( ppName, ppPath )
% Get the values for parameter name in procpar file path
% Usage: vals = getPPV( ppName, ppPath )
% fn = 'I_t.fid/procpar'
ppPath;
fp = fopen( ppPath, 'r');
done = 0;
vals = [];
while( done == 0 )
line = fgetl(fp);
if (line == -1)
done = 1;
elseif isempty(line)
disp('Warning,there is an empty space in the procpar file')
line = fgetl(fp);
else
% if ~isletter(line)
% disp(line);
% error( 'bad format')
% else
if (strcmp(line(1),ppName(1)))
[name, attr] = strtok(line);
if (strcmp(name, ppName))
attr = str2num(attr);
% Read in the values
line = fgetl(fp);
%disp(line);
[cnt, parm] = strtok(line);
cnt = str2num(cnt);
% REAL_VALS
if (attr(2) == 1)
vals = str2num( parm );
% STRING_VALS
else
vals = dbl_quote_extract( parm );
while( size(vals,1) ~= cnt )
line = fgetl(fp);
vals = char(vals, dbl_quote_extract( line ) );
end
end
if (strcmp(name, ppName))
break;
else
vals=[];
end
% Read in the enums
enum_line = fgetl(fp);
end
end
% end
end
end
fclose( fp );
function outStr = dbl_quote_extract( inStr )
%dbl_quote_extract - Extract String from between pair of Double Quotes
dqIdx = findstr(inStr, '"');
dqCnt = size(dqIdx,2);
if ((dqCnt == 0) | (1 == mod(dqCnt,2)))
error( 'Bad string double quote balance')
end
for idx = 1:2:dqCnt
off = [dqIdx(idx) + 1, dqIdx(idx+1) - 1];
if (idx == 1)
outStr = inStr(off(1):off(2));
else
outStr = char(outStr, inStr(off(1):off(2)));
end
end
|
github
|
michtesar/asymmetry_toolbox-master
|
eegplugin_faa.m
|
.m
|
asymmetry_toolbox-master/faa/eegplugin_faa.m
| 445 |
utf_8
|
83c4cda851f338a5163d7300dde14946
|
% This book/study is a result of the research funded by the project
% Nr. LO1611 with a financial support from the MEYS under the NPU I program.
function eegplugin_faa(fig, try_strings, catch_strings)
% Create menu
toolsmenu = findobj(fig, 'tag', 'tools');
submenu = uimenu( toolsmenu, 'label', 'Compute FAA');
% Compute FAA index
uimenu( submenu, 'label', 'Compute FAA index', 'callback',...
'faa_index');
end
|
github
|
OrangeOwlSolutions/Optimization-master
|
dbrent.m
|
.m
|
Optimization-master/Polak-Ribiere/Matlab/dbrent.m
| 7,146 |
utf_8
|
b79e27e7cc8bb2f0c8c9687e2a3ec230
|
% Given a function costfunctional and its derivative function grad_costfunctional, and given a bracketing triplet of abscissas ax,
% bx, cx [such that ax < bx < cx, and f(bx) < f(ax) and f(bx) < f(cx), tipically the output of mnbrak], this routine isolates the
% minimum to a fractional precision of about tol using a modification of Brent’s method that uses derivatives. The abscissa of the
% minimum is returned as lambdamin, and the minimum function value is returned as out.
% --- The function f(lambda) is a contraction for f(x + lambda * p)
function [lambdamin out] = dbrent(ax, bx, cx, tol, x, p, itmax, costfunctional, grad_costfunctional)
% --- itmax number of iterations
zeps = 1.e-10;
% --- a and b are fixed as the extremes of the bracketing interval so that
% a < b
a = min([ax cx]);
b = max([ax cx]);
% --- lambda, lambda_1 and lambda_2 are the estimates of the minimum at the current iteration, at two iterations
% before, and at the last iteration, respectively. They are all initialized at initial estimate of the minimum
lambda = bx;
lambda_1 = lambda;
lambda_2 = lambda;
% --- xt = x + lambda * p
% --- flambda = f(x + lambda * p) is the functional value at the current
% estimate of the minimum
[flambda xt] = f1dim(lambda, x, p, costfunctional);
flambda_1 = flambda;
flambda_2 = flambda;
% --- dotlambda = <Gradf_xt,p>
dotlambda = df1dim(xt, p, grad_costfunctional);
dotlambda_2 = dotlambda;
dotlambda_1 = dotlambda;
e = 0.;
for iter=1:itmax
% --- Bisection step
xm = 0.5 * (a + b);
tol1 = tol * abs(lambda) + zeps;
tol2 = 2. * tol1;
% --- Convergence check. If the current minimum estimate lambda is
% sufficiently close to the bisected point, then the minimum is
% considered to be reached.
if (abs(lambda - xm) <= (tol2 - 0.5 * (b - a)))
lambdamin = lambda;
out = flambda;
return;
end
if (abs(e) > tol1)
% --- Initialize these increment d’s to an out-of-bracket value
d1 = 2. * (b - a);
d2 = d1;
% --- Secant method with one point
if (dotlambda_1 ~= dotlambda)
d1 = (lambda_2 - lambda) * dotlambda / (dotlambda - dotlambda_1);
end
if (dotlambda_2 ~= dotlambda)
d2 = (lambda_1 - lambda) * dotlambda / (dotlambda - dotlambda_2);
end
% --- Which of these two estimates of d shall we take? We will insist that they be within
% the bracket, and on the side pointed to by the derivative at x:
u1 = lambda + d1;
u2 = lambda + d2;
ok1 = ((a - u1) * (u1 - b) > 0.) && (dotlambda * d1 <= 0.);
ok2 = ((a - u2) * (u2 - b) > 0.) && (dotlambda * d2 <= 0.);
olde = e;
e = d;
% --- Take only an acceptable d, and if both are acceptable, then take the smallest one.
if (ok1 || ok2)
if (ok1 && ok2)
if (abs(d1) < abs(d2))
d = d1;
else
d = d2;
end
else
if (ok1)
d = d1;
else
d = d2;
end
end
if (abs(d) <= abs(0.5 * olde))
u = lambda + d;
if (((u - a) < tol2) || ((b - u) < tol2))
d = abs(tol1) * sign(xm - lambda);
end
else
if (dotlambda >= 0.)
e = a - lambda;
else
e = b - lambda;
end
d = 0.5 * e;
end
else
if (dotlambda >= 0.)
e = a - lambda;
else
e = b - lambda;
end
d = 0.5 * e;
end
else
% --- If the scalar product between the gradient at the current
% point and the search direction > 0, then set the bisection step d to bisect (a, lambda)
% otherwise set the bisection step d to bisect (lambda, b)
if (dotlambda >= 0.)
e = a - lambda;
else
e = b - lambda;
end
d = 0.5 * e;
end
if (abs(d) >= tol1)
% --- If the bisection step d is not too small, use d to perform a real bisection
u = lambda + d;
[fu xt] = f1dim(u, x, p, costfunctional);
else
% --- If the bisection step d is not too small, use d to perform a real bisection
u = lambda + abs(tol1) * sign(d);
[fu xt] = f1dim(u, x, p, costfunctional);
% --- If the minimum step in the downhill direction takes us uphill, then we are done.
if (fu > flambda)
lambdamin = lambda;
out = flambda;
return;
end
end
du = df1dim(xt, p, grad_costfunctional);
if (fu < flambda)
% --- Enters here if the new trial minimum produces a lower
% functional value
if (u >= lambda)
% --- Advance a to lambda if the current trial minimum is
% larger than lambda
a = lambda;
else
% --- Decrease b to lambda if the current trial minimim is
% lower than lambda
b = lambda;
end
% --- Saves the estimate of the minimum, its functional and the dot
% product at two iterations before
lambda_2 = lambda_1;
flambda_2 = flambda_1;
dotlambda_2 = dotlambda_1;
% --- Saves the estimate of the minimum, its functional and the dot
% product at the previous iteration
lambda_1 = lambda;
flambda_1 = flambda;
dotlambda_1 = dotlambda;
% --- Saves the estimate of the minimum, its functional and the dot
% product at the current iteration
lambda = u;
flambda = fu;
dotlambda = du;
else
% --- Here fu > flambda
if (u < lambda)
a = u;
else
b = u;
end
if ((fu <= flambda_1) || (lambda_1 == lambda))
% --- If the bisection point does not improve on the current
% minimum estimate, but improves on the previous estimate (step -1), then
% shift lambda_1 -> lambda_2 and u -> lambda_1
lambda_2 = lambda_1;
flambda_2 = flambda_1;
dotlambda_2 = dotlambda_1;
lambda_1 = u;
flambda_1 = fu;
dotlambda_1 = du;
else
if ((fu <= flambda_2) || (lambda_2 == lambda) || (lambda_2 == lambda_1))
% --- If the bisection point does not improve on the current
% minimum estimate, but improves on the previous estimate (step -1), then
% shift u -> lambda_1
lambda_2 = u;
flambda_2 = fu;
dotlambda_2 = du;
end
end
end
end
disp('Too many iterations in dbrent')
lambdamin = lambda;
out = flambda;
|
github
|
OrangeOwlSolutions/Optimization-master
|
mnbrak.m
|
.m
|
Optimization-master/Polak-Ribiere/Matlab/mnbrak.m
| 3,496 |
utf_8
|
8c9911a490d4309896419e613ecbe22e
|
% Given a function costfunctional, and given distinct initial points ax and bx, this routine searches in
% the downhill direction (defined by the function as evaluated at the initial points) and returns
% new points ax, bx, cx that bracket a minimum of the function. The points ax, bx and cx are such that
% the minimum is within ax and cx and in the proximity of bx. In other words, ax < bx < cx
function [ax, bx, cx] = mnbrak(ax, bx, cx, x, p, costfunctional)
% --- p Search direction
gold = 1.618034;
glimit = 100.;
tiny = 1.e-20;
% --- It is assumed that fb < fa. If not, swap ax and bx
fa = f1dim(ax, x, p, costfunctional);
fb = f1dim(bx, x, p, costfunctional);
if (fb > fa)
[ax bx] = swap(ax, bx);
[fa fb] = swap(fa, fb);
end
% --- First guess for cx
cx = bx + gold * (bx - ax);
fc = f1dim(cx, x, p, costfunctional);
% --- Keep runnning until we bracket
while (fb > fc)
% --- Compute u by parabolic extrapolation from ax, bx, cx. TINY is used to prevent any possible division by zero
r = (bx - ax) * (fb - fc);
q = (bx - cx) * (fb - fa);
u = bx - ((bx - cx) * q - (bx - ax) * r) / (2. * (abs(max([abs(q - r), tiny])) * sign(q - r)));
ulim = bx + glimit * (cx - bx);
if (((bx - u) * (u - cx)) > 0.)
% --- Enters this branch if u is in between bx and cx, that is, cx < u < bx or bx < u < cx
fu = f1dim(u, x, p, costfunctional);
if (fu < fc)
% --- Enters here if u is in between bx and cx and fu < fc < fb
% (the last inequality is due to the while loop) => we have a
% minimum between bx and cx
ax = bx;
fa = fb;
bx = u;
fb = fu;
return;
else
if (fu > fb)
% --- Enters here if fb < fu and fb < fa (the last
% inequality is due to the first swap) => we have a minimum
% between ax and u
cx = u;
fc = fu;
return;
end
end
% --- No minimum found yet. Use default magnification.
u = cx + gold * (cx - bx);
fu=f1dim(u, x, p, costfunctional);
else
if ((cx - u) * (u - ulim) > 0.)
% --- Enters this branch if u is in between cx and its allowed limit ulim
fu=f1dim(u, x, p, costfunctional);
if (fu < fc)
% --- fa > fb > fc > fu => the function is decreasing
% towards fu => shift everything towards u
bx = cx;
cx = u;
u = cx + gold * (cx - bx);
fb = fc;
fc = fu;
fu = f1dim(u, x, p, costfunctional);
end
else
if ((u - ulim) * (ulim - cx) >= 0.)
% --- ulim is between u and cx which means that u is beyond
% its maximum allowed value => limit u to its maximum value
% ulim
u = ulim;
fu = f1dim(u, x, p, costfunctional);
else
% --- Move u ahead with default magnification
u = cx + gold * (cx - bx);
fu = f1dim(u, x, p, costfunctional);
end
end
end
% --- Update the points
ax = bx;
bx = cx;
cx = u;
fa = fb;
fb = fc;
fc = fu;
end
|
github
|
OrangeOwlSolutions/Optimization-master
|
linmin.m
|
.m
|
Optimization-master/Polak-Ribiere/Matlab/linmin.m
| 690 |
utf_8
|
a6bebec04d1c563720ecad807edcdca0
|
% --- Line minimization ... see Numerical Recipes
function [x p] = linmin(x, p, itmax, costfunctional, grad_costfunctional)
% --- p Search direction
% --- x Unknowns (input - output)
% --- itmax Maximum number of iterations
% --- Bracketing tolerance
tol = 1.e-8;
lsm = max(abs(p));
disp('Prima di mnbrak')
ax = 0.;
xx = .1e-2/(lsm+1.e-10);
bx = .2e-2/(lsm+1.e-10);
% xx = 0;
% bx = 1;
[ax bx cx] = mnbrak(ax, xx, bx, x, p, costfunctional);
lambdamin = dbrent(ax, cx, bx, tol, x, p, itmax, costfunctional, grad_costfunctional);
p = lambdamin * p;
x = x + p;
|
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