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github
|
khanhnamle1994/machine-learning-master
|
submitWithConfiguration.m
|
.m
|
machine-learning-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m
| 5,562 |
utf_8
|
4ac719ea6570ac228ea6c7a9c919e3f5
|
function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github
|
khanhnamle1994/machine-learning-master
|
savejson.m
|
.m
|
machine-learning-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m
| 17,462 |
utf_8
|
861b534fc35ffe982b53ca3ca83143bf
|
function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadjson.m
|
.m
|
machine-learning-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m
| 18,732 |
ibm852
|
ab98cf173af2d50bbe8da4d6db252a20
|
function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadubjson.m
|
.m
|
machine-learning-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m
| 15,574 |
utf_8
|
5974e78e71b81b1e0f76123784b951a4
|
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
saveubjson.m
|
.m
|
machine-learning-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m
| 16,123 |
utf_8
|
61d4f51010aedbf97753396f5d2d9ec0
|
function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';
|
github
|
khanhnamle1994/machine-learning-master
|
submit.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/submit.m
| 1,567 |
utf_8
|
1dba733a05282b2db9f2284548483b81
|
function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ...
{ ...
'2', ...
{ 'oneVsAll.m' }, ...
'One-vs-All Classifier Training', ...
}, ...
{ ...
'3', ...
{ 'predictOneVsAll.m' }, ...
'One-vs-All Classifier Prediction', ...
}, ...
{ ...
'4', ...
{ 'predict.m' }, ...
'Neural Network Prediction Function' ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxdata)
% Random Test Cases
X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
y = sin(X(:,1) + X(:,2)) > 0;
Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
1 1 ; 1 2 ; 2 1 ; 2 2 ; ...
-1 1 ; -1 2 ; -2 1 ; -2 2 ; ...
1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ];
ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
t1 = sin(reshape(1:2:24, 4, 3));
t2 = cos(reshape(1:2:40, 4, 5));
if partId == '1'
[J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
out = sprintf('%0.5f ', J);
out = [out sprintf('%0.5f ', grad)];
elseif partId == '2'
out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
elseif partId == '3'
out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
elseif partId == '4'
out = sprintf('%0.5f ', predict(t1, t2, Xm));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
submitWithConfiguration.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m
| 5,562 |
utf_8
|
4ac719ea6570ac228ea6c7a9c919e3f5
|
function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github
|
khanhnamle1994/machine-learning-master
|
savejson.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m
| 17,462 |
utf_8
|
861b534fc35ffe982b53ca3ca83143bf
|
function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadjson.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m
| 18,732 |
ibm852
|
ab98cf173af2d50bbe8da4d6db252a20
|
function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadubjson.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m
| 15,574 |
utf_8
|
5974e78e71b81b1e0f76123784b951a4
|
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
saveubjson.m
|
.m
|
machine-learning-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m
| 16,123 |
utf_8
|
61d4f51010aedbf97753396f5d2d9ec0
|
function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';
|
github
|
khanhnamle1994/machine-learning-master
|
submit.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/submit.m
| 2,135 |
utf_8
|
eebb8c0a1db5a4df20b4c858603efad6
|
function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...
'2', ...
{ 'selectThreshold.m' }, ...
'Select Threshold', ...
}, ...
{ ...
'3', ...
{ 'cofiCostFunc.m' }, ...
'Collaborative Filtering Cost', ...
}, ...
{ ...
'4', ...
{ 'cofiCostFunc.m' }, ...
'Collaborative Filtering Gradient', ...
}, ...
{ ...
'5', ...
{ 'cofiCostFunc.m' }, ...
'Regularized Cost', ...
}, ...
{ ...
'6', ...
{ 'cofiCostFunc.m' }, ...
'Regularized Gradient', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
n_u = 3; n_m = 4; n = 5;
X = reshape(sin(1:n_m*n), n_m, n);
Theta = reshape(cos(1:n_u*n), n_u, n);
Y = reshape(sin(1:2:2*n_m*n_u), n_m, n_u);
R = Y > 0.5;
pval = [abs(Y(:)) ; 0.001; 1];
Y = (Y .* double(R)); % set 'Y' values to 0 for movies not reviewed
yval = [R(:) ; 1; 0];
params = [X(:); Theta(:)];
if partId == '1'
[mu sigma2] = estimateGaussian(X);
out = sprintf('%0.5f ', [mu(:); sigma2(:)]);
elseif partId == '2'
[bestEpsilon bestF1] = selectThreshold(yval, pval);
out = sprintf('%0.5f ', [bestEpsilon(:); bestF1(:)]);
elseif partId == '3'
[J] = cofiCostFunc(params, Y, R, n_u, n_m, ...
n, 0);
out = sprintf('%0.5f ', J(:));
elseif partId == '4'
[J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...
n, 0);
out = sprintf('%0.5f ', grad(:));
elseif partId == '5'
[J] = cofiCostFunc(params, Y, R, n_u, n_m, ...
n, 1.5);
out = sprintf('%0.5f ', J(:));
elseif partId == '6'
[J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...
n, 1.5);
out = sprintf('%0.5f ', grad(:));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
submitWithConfiguration.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m
| 5,562 |
utf_8
|
4ac719ea6570ac228ea6c7a9c919e3f5
|
function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github
|
khanhnamle1994/machine-learning-master
|
savejson.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/lib/jsonlab/savejson.m
| 17,462 |
utf_8
|
861b534fc35ffe982b53ca3ca83143bf
|
function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadjson.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m
| 18,732 |
ibm852
|
ab98cf173af2d50bbe8da4d6db252a20
|
function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadubjson.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m
| 15,574 |
utf_8
|
5974e78e71b81b1e0f76123784b951a4
|
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
saveubjson.m
|
.m
|
machine-learning-master/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m
| 16,123 |
utf_8
|
61d4f51010aedbf97753396f5d2d9ec0
|
function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';
|
github
|
khanhnamle1994/machine-learning-master
|
submit.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/submit.m
| 1,876 |
utf_8
|
8d1c467b830a89c187c05b121cb8fbfd
|
function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m' }, ...
'Computing Cost (for One Variable)', ...
}, ...
{ ...
'3', ...
{ 'gradientDescent.m' }, ...
'Gradient Descent (for One Variable)', ...
}, ...
{ ...
'4', ...
{ 'featureNormalize.m' }, ...
'Feature Normalization', ...
}, ...
{ ...
'5', ...
{ 'computeCostMulti.m' }, ...
'Computing Cost (for Multiple Variables)', ...
}, ...
{ ...
'6', ...
{ 'gradientDescentMulti.m' }, ...
'Gradient Descent (for Multiple Variables)', ...
}, ...
{ ...
'7', ...
{ 'normalEqn.m' }, ...
'Normal Equations', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId)
% Random Test Cases
X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];
Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));
X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];
Y2 = Y1.^0.5 + Y1;
if partId == '1'
out = sprintf('%0.5f ', warmUpExercise());
elseif partId == '2'
out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));
elseif partId == '3'
out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));
elseif partId == '4'
out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));
elseif partId == '5'
out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));
elseif partId == '6'
out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));
elseif partId == '7'
out = sprintf('%0.5f ', normalEqn(X2, Y2));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
submitWithConfiguration.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m
| 5,562 |
utf_8
|
4ac719ea6570ac228ea6c7a9c919e3f5
|
function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github
|
khanhnamle1994/machine-learning-master
|
savejson.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m
| 17,462 |
utf_8
|
861b534fc35ffe982b53ca3ca83143bf
|
function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadjson.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m
| 18,732 |
ibm852
|
ab98cf173af2d50bbe8da4d6db252a20
|
function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
loadubjson.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m
| 15,574 |
utf_8
|
5974e78e71b81b1e0f76123784b951a4
|
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end
|
github
|
khanhnamle1994/machine-learning-master
|
saveubjson.m
|
.m
|
machine-learning-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m
| 16,123 |
utf_8
|
61d4f51010aedbf97753396f5d2d9ec0
|
function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';
|
github
|
JoseBarreiros/CellDecomposition-master
|
Cell_Decomposition_v2.m
|
.m
|
CellDecomposition-master/Cell_Decomposition_v2.m
| 11,970 |
utf_8
|
3e144c774b1c2719cdd0f1b246d5b744
|
%Exact Cell Decomposition [ECD]%
%Implementation of Path Planning Algorith using ECD%
%The algorithm identify the cells and C-objects from an XML file that include a C-workspace (.cmap), then find
%the distance between the centroids of each cell and calculate the shortest
%path. The actual trajectory for the robot is a set of lines from the midpoint of
%every cell included in the path.
%Created by Jose Barreiros, Oct. 2017.
%PhD student.
%Cornell University.
%The XML script and GUI structure are based on the work of Ahmad Abbadi, Brno University of Technology (VUT), Czech Republic
function varargout = Cell_Decomposition_v2(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Cell_Decomposition_v2_OpeningFcn, ...
'gui_OutputFcn', @Cell_Decomposition_v2_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
end
function Cell_Decomposition_v2_OpeningFcn(hObject, eventdata, handles, varargin)
handles.drawingFigHandle=handles.axes1;
handles.obstacles=[];
handles.cells=[];
handles.pois=[];
handles.currentobstacleIndex=[];
handles.file.FileName=[];
handles.file.PathName=[];
handles.changesaved=1;
handles.axesAttribute.xlim=[0 10];
handles.axesAttribute.ylim=[0 10];
adjacent=[];
w_adj=[];
coord=[];
path_vertex=[];
handles.SPosition.center=[];
handles.SPosition.radius=[];
handles.SPosition.h=[];
handles.SPosition.type=[];
handles.GPosition.center=[];
handles.GPosition.radius=[];
handles.GPosition.h=[];
handles.GPosition.type=[];
handles.cellDecom.graph=[];
handles.cellDecom.cells=[];
set(handles.drawingFigHandle,'xlim',handles.axesAttribute.xlim,'ylim',handles.axesAttribute.ylim);
hold on;
% Choose default command line output for planner_GUI
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes planner_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
end
function LoadCSpace_pushbutton_Callback(hObject, eventdata, handles)
% hObject handle to LoadCSpace_pushbutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[FileName,PathName,FilterIndex] = uigetfile('*.cmap','Open File');
if (FileName~=0)
handles.file.FileName=FileName;
handles.file.PathName=PathName;
if size(handles.obstacles,1) >0
delete(handles.obstacles(:).linehandles);
handles.obstacles=[];
handles.currentobstacleIndex=[];
end
ObstacleXML=xmlread(strcat(PathName,FileName));
scpaceNode=ObstacleXML.getDocumentElement;
dimension= str2double(scpaceNode.getAttribute('dim'));
if dimension~=2
h=warndlg('Can not open dimention oher than 2');
uiwait(h);
else
% retrieve obstacles
obstacles=scpaceNode.getElementsByTagName('obstacle');
cells=scpaceNode.getElementsByTagName('cell');
pois=scpaceNode.getElementsByTagName('poi');
obsLength=obstacles.getLength - 1;
cellLength=cells.getLength - 1;
poiLength=pois.getLength - 1;
for i= 0 : obsLength
obs=obstacles.item(i);
newobstacle=[];
newobstacle.type=char(obs.getAttribute('type'));
vertices=obs.getElementsByTagName('vertex');
verLength=vertices.getLength-1;
for j=0:verLength
newobstacle.vertex(j+1,1)=str2double(vertices.item(j).getAttribute('x'));
newobstacle.vertex(j+1,2)=str2double(vertices.item(j).getAttribute('y'));
end
newobstacle.ID=i+1;
handles.obstacles=[handles.obstacles;newobstacle];
end
% retrieve cells
for i= 0 : cellLength
cel=cells.item(i);
newcell=[];
newcell.type=char(cel.getAttribute('type'));
vertices=cel.getElementsByTagName('vertex');
verLength=vertices.getLength-1;
for j=0:verLength
newcell.vertex(j+1,1)=str2double(vertices.item(j).getAttribute('x'));
newcell.vertex(j+1,2)=str2double(vertices.item(j).getAttribute('y'));
end
newcell.ID=i+1;
handles.cells=[handles.cells;newcell];
end
% retrieve points of interest POI: start and end condition
for i= 0 : poiLength
poi=pois.item(i);
newpoi=[];
newpoi.type=char(poi.getAttribute('type'));
vertices=poi.getElementsByTagName('point');
verLength=vertices.getLength-1;
for j=0:verLength
newpoi.vertex(j+1,1)=str2double(vertices.item(j).getAttribute('x'));
newpoi.vertex(j+1,2)=str2double(vertices.item(j).getAttribute('y'));
end
newpoi.ID=i+1;
handles.pois=[handles.pois;newpoi];
end
% retrieve Axes sitting
AxesNode=scpaceNode.getElementsByTagName('Axes');
handles.axesAttribute.xlim = str2num(AxesNode.item(0).getAttribute('xlim'));
handles.axesAttribute.ylim = str2num(AxesNode.item(0).getAttribute('xlim'));
set(handles.drawingFigHandle,'xlim',handles.axesAttribute.xlim,'ylim',handles.axesAttribute.ylim);
hold on;
%draw obstacle
for i=1:size(handles.obstacles,1)
handles.obstacles(i).linehandles=fill([handles.obstacles(i).vertex(:,1);handles.obstacles(i).vertex(1,1)],[handles.obstacles(i).vertex(:,2);handles.obstacles(i).vertex(1,2)],'b');
alpha(handles.obstacles(i).linehandles,0.3);% color transparency
set(handles.obstacles(i).linehandles,'EdgeColor','none');% edge color of obstacle
end
%draw cells
for i=1:size(handles.cells,1)
handles.cells(i).linehandles=fill([handles.cells(i).vertex(:,1);handles.cells(i).vertex(1,1)],[handles.cells(i).vertex(:,2);handles.cells(i).vertex(1,2)],'g');
alpha(handles.cells(i).linehandles,0.2);% color transparency
set(handles.cells(i).linehandles,'EdgeColor','r');% edge color of obstacle
[ geom, iner, cpmo ] = polygeom([handles.cells(i).vertex(:,1);handles.cells(i).vertex(1,1)],[handles.cells(i).vertex(:,2);handles.cells(i).vertex(1,2)]);
area = geom(1);
handles.cells(i).centroid(1) = geom(2);
handles.cells(i).centroid(2) = geom(3);
x_cen=handles.cells(i).centroid(1)
y_cen=handles.cells(i).centroid(2)
txt=int2str(i);
% text(x_cen,y_cen,txt);
coord(i,:)=[x_cen y_cen]; %create coordinates for connectivity graph
handles.coord(i,:)=[x_cen y_cen];
end
tic;
end
end
hObject.UserData = handles;
end
function Path_pushbutton_Callback(hObject, eventdata, handles)
% hObject handle to LoadCSpace_pushbutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
h = findobj('Tag','LoadCSpace_pushbutton');
handles = h.UserData
coord = handles.coord
%test for adjacency
adjacent=zeros(size(handles.cells,1));
w_adj=zeros(size(handles.cells,1));
x=zeros(size(handles.cells,1),1)
y=zeros(size(handles.cells,1),1)
v_count=0;
for i=1:size(handles.cells,1)
for j=1:size(handles.cells,1)
v_count=0;
for k=1:subsref(size(handles.cells(i).vertex),struct('type','()','subs',{{1,1}}))
for m=1:subsref(size(handles.cells(j).vertex),struct('type','()','subs',{{1,1}}))
if isequal(handles.cells(i).vertex(k,:),handles.cells(j).vertex(m,:))==1
v_count=v_count+1; %count common vertices
if v_count>1 %if cells share at least 2 common vertices then the cells are adjacent
adjacent(i,j)=1;
end
end
end
end
end
end
adjacent;
%plot adjacent matrix
gplot(adjacent,coord,'-*')
%build connectivity graph and find shortest path
for i=1:size(handles.cells,1)
xi=handles.cells(i).centroid(1)
x(i)=xi
yi=handles.cells(i).centroid(2)
y(i)=yi
for j=i+1:size(handles.cells,1)
xj=handles.cells(j).centroid(1)
yj=handles.cells(j).centroid(2)
if adjacent(i,j)==1
distance=pdist([xi,yi;xj,yj],'euclidean')
w_adj(i,j)=distance; %build a weighted adjacency matrix
mid_x=(xi+xj)/2
mid_y=(yi+yj)/2
txt=num2str(distance,4);
text(mid_x,mid_y,txt);
end
end
end
w_adj
G=graph(w_adj,'upper')
plot(G, 'XData', x, 'YData', y,'LineWidth',2, 'EdgeColor',[0,0.7,0.9])
P = shortestpath(G,3,8)
%draw POIs
for i=1:size(handles.pois,1)
%handles.pois(i).linehandles=fill([handles.pois(i).vertex(:,1);handles.pois(i).vertex(1,1)],[handles.pois(i).vertex(:,2);handles.pois(i).vertex(1,2)],'c');
plot(handles.pois(i).vertex(1,1),handles.pois(i).vertex(1,2),'ks')
txt=handles.pois(i).type
tex=text(handles.pois(i).vertex(1,1),handles.pois(i).vertex(1,2),txt)
tex.FontSize=11
tex.FontWeight = 'bold'
% alpha(handles.pois(i).linehandles,0.4);% color transparency
% set(handles.pois(i).linehandles,'EdgeColor','k');% edge color of obstacle
end
%find the matrix of the shortest path line using the midpoints of the edges
for i=1:size(P,2)
i
for j=1:subsref(size(handles.cells(P(i)).vertex),struct('type','()','subs',{{1,1}}))
j
if j==subsref(size(handles.cells(P(i)).vertex),struct('type','()','subs',{{1,1}}))
k=1
else
k=j+1
end
lx=[handles.cells(P(i)).centroid(1) handles.cells(P(i)+1).centroid(1) handles.cells(P(i)).vertex(j,1) handles.cells(P(i)).vertex(k,1)]
ly=[handles.cells(P(i)).centroid(2) handles.cells(P(i)+1).centroid(2) handles.cells(P(i)).vertex(j,2) handles.cells(P(i)).vertex(k,2)]
dt1=det([1,1,1;lx(1),lx(2),lx(3);ly(1),ly(2),ly(3)])*det([1,1,1;lx(1),lx(2),lx(4);ly(1),ly(2),ly(4)])
dt2=det([1,1,1;lx(1),lx(3),lx(4);ly(1),ly(3),ly(4)])*det([1,1,1;lx(2),lx(3),lx(4);ly(2),ly(3),ly(4)])
if(dt1<=0 & dt2<=0)
path_vertex(i,:)=[(lx(3)+lx(4))/2 (ly(3)+ly(4))/2]
end
end
end
path_vertex
path_vertex=cat(1,handles.pois(1).vertex,path_vertex)
path_vertex(i+1,:)=handles.pois(2).vertex
%plot minimum path from start to end point
plot(path_vertex(:,1),path_vertex(:,2),'k','LineWidth',3)
toc
% new load; no change
handles.changesaved=1;
% update handles data
guidata(hObject, handles);
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
test_model.m
|
.m
|
CNN-without-BN-master/test_model.m
| 13,664 |
utf_8
|
886d977fcccf7561fd2cfb2ae616eb21
|
function [net,stats] = test_model(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.expDir = fullfile('data','exp') ;
opts.continue = true ;
opts.batchSize = 256 ;
opts.numSubBatches = 1 ;
opts.train = [] ;
opts.val = [] ;
opts.gpus = [] ;
opts.prefetch = false ;
opts.numEpochs = 300 ;
opts.learningRate = 0.001 ;
opts.weightDecay = 0.0005 ;
opts.momentum = 0.9 ;
opts.randomSeed = 0 ;
opts.memoryMapFile = fullfile(tempdir, 'matconvnet.bin') ;
opts.profile = false ;
opts.derOutputs = {'objective', 1} ;
opts.extractStatsFn = @extractStats ;
opts.checkpointFn = []; % will be called after every epoch
opts.plotStatistics = false;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
% -------------------------------------------------------------------------
% Initialization
% -------------------------------------------------------------------------
evaluateMode = isempty(opts.train) ;
if ~evaluateMode
if isempty(opts.derOutputs)
error('DEROUTPUTS must be specified when training.\n') ;
end
end
state.getBatch = getBatch ;
stats = [] ;
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
start = opts.continue * findLastCheckpoint(opts.expDir) ;
if start >= 1
fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ;
[net, stats] = loadState(modelPath(start)) ;
end
for epoch=start+1:opts.numEpochs
% Set the random seed based on the epoch and opts.randomSeed.
% This is important for reproducibility, including when training
% is restarted from a checkpoint.
rng(epoch + opts.randomSeed) ;
prepareGPUs(opts, epoch == start+1) ;
% Train for one epoch.
state.epoch = epoch ;
state.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ;
state.train = opts.train(randperm(numel(opts.train))) ; % shuffle
state.val = opts.val(randperm(numel(opts.val))) ;
state.imdb = imdb ;
if numel(opts.gpus) <= 1
stats.val(epoch) = process_epoch(net, state, opts, 'val') ;
if opts.profile
profview(0,prof) ;
keyboard ;
end
else
savedNet = net.saveobj() ;
spmd
net_ = dagnn.DagNN.loadobj(savedNet) ;
[stats_.train, prof_] = process_epoch(net_, state, opts, 'train') ;
stats_.val = process_epoch(net_, state, opts, 'val') ;
if labindex == 1, savedNet_ = net_.saveobj() ; end
end
net = dagnn.DagNN.loadobj(savedNet_{1}) ;
stats__ = accumulateStats(stats_) ;
stats.train(epoch) = stats__.train ;
stats.val(epoch) = stats__.val ;
if opts.profile
mpiprofile('viewer', [prof_{:,1}]) ;
keyboard ;
end
clear net_ stats_ stats__ savedNet savedNet_ ;
end
% save
if ~evaluateMode
saveState(modelPath(epoch), net, stats) ;
end
if opts.plotStatistics
switchFigure(1) ; clf ;
plots = setdiff(...
cat(2,...
fieldnames(stats.train)', ...
fieldnames(stats.val)'), {'num', 'time'}) ;
for p = plots
p = char(p) ;
values = zeros(0, epoch) ;
leg = {} ;
for f = {'train', 'val'}
f = char(f) ;
if isfield(stats.(f), p)
tmp = [stats.(f).(p)] ;
values(end+1,:) = tmp(1,:)' ;
leg{end+1} = f ;
end
end
subplot(1,numel(plots),find(strcmp(p,plots))) ;
plot(1:epoch, values','o-') ;
xlabel('epoch') ;
title(p) ;
legend(leg{:}) ;
grid on ;
end
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
if ~isempty(opts.checkpointFn),
opts.checkpointFn();
end
end
% -------------------------------------------------------------------------
function [stats, prof] = process_epoch(net, state, opts, mode)
% -------------------------------------------------------------------------
% initialize empty momentum
if strcmp(mode,'train')
state.momentum = num2cell(zeros(1, numel(net.params))) ;
end
% move CNN to GPU as needed
numGpus = numel(opts.gpus) ;
if numGpus >= 1
net.move('gpu') ;
if strcmp(mode,'train')
state.momentum = cellfun(@gpuArray,state.momentum,'UniformOutput',false) ;
end
end
if numGpus > 1
mmap = map_gradients(opts.memoryMapFile, net, numGpus) ;
else
mmap = [] ;
end
% profile
if opts.profile
if numGpus <= 1
profile clear ;
profile on ;
else
mpiprofile reset ;
mpiprofile on ;
end
end
subset = state.(mode) ;
num = 0 ;
stats.num = 0 ; % return something even if subset = []
stats.time = 0 ;
adjustTime = 0 ;
start = tic ;
for t=1:opts.batchSize:numel(subset)
fprintf('%s: epoch %02d: %3d/%3d:', mode, state.epoch, ...
fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ;
batchSize = min(opts.batchSize, numel(subset) - t + 1) ;
for s=1:opts.numSubBatches
% get this image batch and prefetch the next
batchStart = t + (labindex-1) + (s-1) * numlabs ;
batchEnd = min(t+opts.batchSize-1, numel(subset)) ;
batch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
num = num + numel(batch) ;
if numel(batch) == 0, continue ; end
inputs = state.getBatch(state.imdb, batch) ;
if opts.prefetch
if s == opts.numSubBatches
batchStart = t + (labindex-1) + opts.batchSize ;
batchEnd = min(t+2*opts.batchSize-1, numel(subset)) ;
else
batchStart = batchStart + numlabs ;
end
nextBatch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
state.getBatch(state.imdb, nextBatch) ;
end
if strcmp(mode, 'train')
net.mode = 'normal' ;
net.accumulateParamDers = (s ~= 1) ;
net.eval(inputs, opts.derOutputs) ;
else
net.mode = 'test' ;
net.eval(inputs) ;
end
end
% accumulate gradient
if strcmp(mode, 'train')
if ~isempty(mmap)
write_gradients(mmap, net) ;
labBarrier() ;
end
state = accumulate_gradients(state, net, opts, batchSize, mmap, payload) ;
end
% get statistics
time = toc(start) + adjustTime ;
batchTime = time - stats.time ;
stats = opts.extractStatsFn(net) ;
stats.num = num ;
stats.time = time ;
currentSpeed = batchSize / batchTime ;
averageSpeed = (t + batchSize - 1) / time ;
if t == opts.batchSize + 1
% compensate for the first iteration, which is an outlier
adjustTime = 2*batchTime - time ;
stats.time = time + adjustTime ;
end
fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ;
% we do not report the top-5 error here
count = 0;
for f = setdiff(fieldnames(stats)', {'num', 'time'})
count = count + 1;
if count ~= 2
f = char(f) ;
fprintf(' %s:', f) ;
fprintf(' %.3f', stats.(f)) ;
end
end
fprintf('\n') ;
end
if ~isempty(mmap)
unmap_gradients(mmap) ;
end
if opts.profile
if numGpus <= 1
prof = profile('info') ;
profile off ;
else
prof = mpiprofile('info');
mpiprofile off ;
end
else
prof = [] ;
end
net.reset() ;
net.move('cpu') ;
% -------------------------------------------------------------------------
function state = accumulate_gradients(state, net, opts, batchSize, mmap)
% -------------------------------------------------------------------------
numGpus = numel(opts.gpus) ;
otherGpus = setdiff(1:numGpus, labindex) ;
for p=1:numel(net.params)
% accumualte gradients from multiple labs (GPUs) if needed
if numGpus > 1
tag = net.params(p).name ;
for g = otherGpus
tmp = gpuArray(mmap.Data(g).(tag)) ;
net.params(p).der = net.params(p).der + tmp ;
end
end
switch net.params(p).trainMethod
case 'average' % mainly for batch normalization
thisLR = net.params(p).learningRate ;
net.params(p).value = ...
(1 - thisLR) * net.params(p).value + ...
(thisLR/batchSize/net.params(p).fanout) * net.params(p).der ;
case 'gradient'
thisDecay = opts.weightDecay * net.params(p).weightDecay ;
thisLR = state.learningRate * net.params(p).learningRate ;
state.momentum{p} = opts.momentum * state.momentum{p} ...
- thisDecay * net.params(p).value ...
- (1 / batchSize) * net.params(p).der ;
net.params(p).value = net.params(p).value + thisLR * state.momentum{p} ;
case 'otherwise'
error('Unknown training method ''%s'' for parameter ''%s''.', ...
net.params(p).trainMethod, ...
net.params(p).name) ;
end
end
% -------------------------------------------------------------------------
function mmap = map_gradients(fname, net, numGpus)
% -------------------------------------------------------------------------
format = {} ;
for i=1:numel(net.params)
format(end+1,1:3) = {'single', size(net.params(i).value), net.params(i).name} ;
end
format(end+1,1:3) = {'double', [3 1], 'errors'} ;
if ~exist(fname) && (labindex == 1)
f = fopen(fname,'wb') ;
for g=1:numGpus
for i=1:size(format,1)
fwrite(f,zeros(format{i,2},format{i,1}),format{i,1}) ;
end
end
fclose(f) ;
end
labBarrier() ;
mmap = memmapfile(fname, ...
'Format', format, ...
'Repeat', numGpus, ...
'Writable', true) ;
% -------------------------------------------------------------------------
function write_gradients(mmap, net)
% -------------------------------------------------------------------------
for i=1:numel(net.params)
mmap.Data(labindex).(net.params(i).name) = gather(net.params(i).der) ;
end
% -------------------------------------------------------------------------
function unmap_gradients(mmap)
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
function stats = accumulateStats(stats_)
% -------------------------------------------------------------------------
for s = {'train', 'val'}
s = char(s) ;
total = 0 ;
% initialize stats stucture with same fields and same order as
% stats_{1}
stats__ = stats_{1} ;
names = fieldnames(stats__.(s))' ;
values = zeros(1, numel(names)) ;
fields = cat(1, names, num2cell(values)) ;
stats.(s) = struct(fields{:}) ;
for g = 1:numel(stats_)
stats__ = stats_{g} ;
num__ = stats__.(s).num ;
total = total + num__ ;
for f = setdiff(fieldnames(stats__.(s))', 'num')
f = char(f) ;
stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;
if g == numel(stats_)
stats.(s).(f) = stats.(s).(f) / total ;
end
end
end
stats.(s).num = total ;
end
% -------------------------------------------------------------------------
function stats = extractStats(net)
% -------------------------------------------------------------------------
sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ;
stats = struct() ;
for i = 1:numel(sel)
stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ;
end
% -------------------------------------------------------------------------
function saveState(fileName, net, stats)
% -------------------------------------------------------------------------
net_ = net ;
net = net_.saveobj() ;
save(fileName, 'net', 'stats') ;
% -------------------------------------------------------------------------
function [net, stats] = loadState(fileName)
% -------------------------------------------------------------------------
load(fileName, 'net', 'stats') ;
net = dagnn.DagNN.loadobj(net) ;
% -------------------------------------------------------------------------
function epoch = findLastCheckpoint(modelDir)
% -------------------------------------------------------------------------
list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ;
epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
epoch = max([epoch 0]) ;
% -------------------------------------------------------------------------
function switchFigure(n)
% -------------------------------------------------------------------------
if get(0,'CurrentFigure') ~= n
try
set(0,'CurrentFigure',n) ;
catch
figure(n) ;
end
end
% -------------------------------------------------------------------------
function prepareGPUs(opts, cold)
% -------------------------------------------------------------------------
numGpus = numel(opts.gpus) ;
if numGpus > 1
% check parallel pool integrity as it could have timed out
pool = gcp('nocreate') ;
if ~isempty(pool) && pool.NumWorkers ~= numGpus
delete(pool) ;
end
pool = gcp('nocreate') ;
if isempty(pool)
parpool('local', numGpus) ;
cold = true ;
end
if exist(opts.memoryMapFile)
delete(opts.memoryMapFile) ;
end
end
if numGpus >= 1 && cold
fprintf('%s: resetting GPU\n', mfilename)
if numGpus == 1
gpuDevice(opts.gpus)
else
spmd, gpuDevice(opts.gpus(labindex)), end
end
end
%end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_compile.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/vl_compile.m
| 5,060 |
utf_8
|
978f5189bb9b2a16db3368891f79aaa6
|
function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefiles.
%
% VL_COMPILE() only compiles the MEX files and assumes that the
% VLFeat DLL (i.e. the file VLFEATROOT/bin/win{32,64}/vl.dll) has
% already been built. This file is built by the Makefiles.
%
% By default VL_COMPILE() assumes that Visual C++ is the active
% MATLAB compiler. VL_COMPILE('lcc') assumes that the active
% compiler is LCC instead (see MEX -SETUP). Unfortunately LCC does
% not seem to be able to compile the latest versions of VLFeat due
% to bugs in the support of 64-bit integers. Therefore it is
% recommended to use Visual C++ instead.
%
% See also: VL_NOPREFIX(), VL_HELP().
% Authors: Andrea Vedadli, Jonghyun Choi
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if nargin < 1, compiler = 'visualc' ; end
switch lower(compiler)
case 'visualc'
fprintf('%s: assuming that Visual C++ is the active compiler\n', mfilename) ;
useLcc = false ;
case 'lcc'
fprintf('%s: assuming that LCC is the active compiler\n', mfilename) ;
warning('LCC may fail to compile VLFeat. See help vl_compile.') ;
useLcc = true ;
otherwise
error('Unknown compiler ''%s''.', compiler)
end
vlDir = vl_root ;
toolboxDir = fullfile(vlDir, 'toolbox') ;
switch computer
case 'PCWIN'
fprintf('%s: compiling for PCWIN (32 bit)\n', mfilename);
mexwDir = fullfile(toolboxDir, 'mex', 'mexw32') ;
binwDir = fullfile(vlDir, 'bin', 'win32') ;
case 'PCWIN64'
fprintf('%s: compiling for PCWIN64 (64 bit)\n', mfilename);
mexwDir = fullfile(toolboxDir, 'mex', 'mexw64') ;
binwDir = fullfile(vlDir, 'bin', 'win64') ;
otherwise
error('The architecture is neither PCWIN nor PCWIN64. See help vl_compile.') ;
end
impLibPath = fullfile(binwDir, 'vl.lib') ;
libDir = fullfile(binwDir, 'vl.dll') ;
mkd(mexwDir) ;
% find the subdirectories of toolbox that we should process
subDirs = dir(toolboxDir) ;
subDirs = subDirs([subDirs.isdir]) ;
discard = regexp({subDirs.name}, '^(.|..|noprefix|mex.*)$', 'start') ;
keep = cellfun('isempty', discard) ;
subDirs = subDirs(keep) ;
subDirs = {subDirs.name} ;
% Copy support files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if ~exist(fullfile(binwDir, 'vl.dll'))
error('The VLFeat DLL (%s) could not be found. See help vl_compile.', ...
fullfile(binwDir, 'vl.dll')) ;
end
tmp = dir(fullfile(binwDir, '*.dll')) ;
supportFileNames = {tmp.name} ;
for fi = 1:length(supportFileNames)
name = supportFileNames{fi} ;
cp(fullfile(binwDir, name), ...
fullfile(mexwDir, name) ) ;
end
% Ensure implib for LCC ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if useLcc
lccImpLibDir = fullfile(mexwDir, 'lcc') ;
lccImpLibPath = fullfile(lccImpLibDir, 'VL.lib') ;
lccRoot = fullfile(matlabroot, 'sys', 'lcc', 'bin') ;
lccImpExePath = fullfile(lccRoot, 'lcc_implib.exe') ;
mkd(lccImpLibDir) ;
cp(fullfile(binwDir, 'vl.dll'), fullfile(lccImpLibDir, 'vl.dll')) ;
cmd = ['"' lccImpExePath '"', ' -u ', '"' fullfile(lccImpLibDir, 'vl.dll') '"'] ;
fprintf('Running:\n> %s\n', cmd) ;
curPath = pwd ;
try
cd(lccImpLibDir) ;
[d,w] = system(cmd) ;
if d, error(w); end
cd(curPath) ;
catch
cd(curPath) ;
error(lasterr) ;
end
end
% Compile each mex file ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
for i = 1:length(subDirs)
thisDir = fullfile(toolboxDir, subDirs{i}) ;
fileNames = ls(fullfile(thisDir, '*.c'));
for f = 1:size(fileNames,1)
fileName = fileNames(f, :) ;
sp = strfind(fileName, ' ');
if length(sp) > 0, fileName = fileName(1:sp-1); end
filePath = fullfile(thisDir, fileName);
fprintf('MEX %s\n', filePath);
dot = strfind(fileName, '.');
mexFile = fullfile(mexwDir, [fileName(1:dot) 'dll']);
if exist(mexFile)
delete(mexFile)
end
cmd = {['-I' toolboxDir], ...
['-I' vlDir], ...
'-O', ...
'-outdir', mexwDir, ...
filePath } ;
if useLcc
cmd{end+1} = lccImpLibPath ;
else
cmd{end+1} = impLibPath ;
end
mex(cmd{:}) ;
end
end
% --------------------------------------------------------------------
function cp(src,dst)
% --------------------------------------------------------------------
if ~exist(dst,'file')
fprintf('Copying ''%s'' to ''%s''.\n', src,dst) ;
copyfile(src,dst) ;
end
% --------------------------------------------------------------------
function mkd(dst)
% --------------------------------------------------------------------
if ~exist(dst, 'dir')
fprintf('Creating directory ''%s''.', dst) ;
mkdir(dst) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_noprefix.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/vl_noprefix.m
| 1,875 |
utf_8
|
97d8755f0ba139ac1304bc423d3d86d3
|
function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stubs are generally constructed by the
% Makefile.
%
% See also: VL_COMPILE(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
root = fileparts(which(mfilename)) ;
list = listMFilesX(root);
outDir = fullfile(root, 'noprefix') ;
if ~exist(outDir, 'dir')
mkdir(outDir) ;
end
for li = 1:length(list)
name = list(li).name(1:end-2) ; % remove .m
nname = name(4:end) ; % remove vl_
stubPath = fullfile(outDir, [nname '.m']) ;
fout = fopen(stubPath, 'w') ;
fprintf('Creating stub %s for %s\n', stubPath, nname) ;
fprintf(fout, 'function varargout = %s(varargin)\n', nname) ;
fprintf(fout, '%% %s Stub for %s\n', upper(nname), upper(name)) ;
fprintf(fout, '[varargout{1:nargout}] = %s(varargin{:})\n', name) ;
fclose(fout) ;
end
end
function list = listMFilesX(root)
list = struct('name', {}, 'path', {}) ;
files = dir(root) ;
for fi = 1:length(files)
name = files(fi).name ;
if files(fi).isdir
if any(regexp(name, '^(\.|\.\.|noprefix)$'))
continue ;
else
tmp = listMFilesX(fullfile(root, name)) ;
list = [list, tmp] ;
end
end
if any(regexp(name, '^vl_(demo|test).*m$'))
continue ;
elseif any(regexp(name, '^vl_(demo|setup|compile|help|root|noprefix)\.m$'))
continue ;
elseif any(regexp(name, '\.m$'))
list(end+1) = struct(...
'name', {name}, ...
'path', {fullfile(root, name)}) ;
end
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_pegasos.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/misc/vl_pegasos.m
| 2,837 |
utf_8
|
d5e0915c439ece94eb5597a07090b67d
|
% VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
end
% DiagnosticCallRef not supported
if (sum(strcmpi('DiagnosticCallRef',varargin)))
varargin(find(strcmpi('DiagnosticCallRef',varargin),1)+1)=[];
varargin(find(strcmpi('DiagnosticCallRef',varargin),1))=[];
fprintf('Option DIAGNOSTICCALLREF is no longer supported.\n Please follow the VLFeat tutorial on SVMs for more information on diagnostics\n');
end
% different default value for MaxIterations
if (sum(strcmpi('MaxIterations',varargin)) == 0)
varargin{end+1} = 'MaxIterations';
varargin{end+1} = ceil(10/LAMBDA);
end
% different default value for BiasMultiplier
if (sum(strcmpi('BiasMultiplier',varargin)) == 0)
varargin{end+1} = 'BiasMultiplier';
varargin{end+1} = 0;
end
% parameters for vl_maketrainingset
setvarargin = {};
if (sum(strcmpi('HOMKERMAP',varargin)))
setvarargin{end+1} = 'HOMKERMAP';
setvarargin{end+1} = varargin{find(strcmpi('HOMKERMAP',varargin),1)+1};
varargin(find(strcmpi('HOMKERMAP',varargin),1)+1)=[];
varargin(find(strcmpi('HOMKERMAP',varargin),1))=[];
end
if (sum(strcmpi('KChi2',varargin)))
setvarargin{end+1} = 'KChi2';
varargin(find(strcmpi('KChi2',varargin),1))=[];
end
if (sum(strcmpi('KINTERS',varargin)))
setvarargin{end+1} = 'KINTERS';
varargin(find(strcmpi('KINTERS',varargin),1))=[];
end
if (sum(strcmpi('KL1',varargin)))
setvarargin{end+1} = 'KL1';
varargin(find(strcmpi('KL1',varargin),1))=[];
end
if (sum(strcmpi('KJS',varargin)))
setvarargin{end+1} = 'KJS';
varargin(find(strcmpi('KJS',varargin),1))=[];
end
if (sum(strcmpi('Period',varargin)))
setvarargin{end+1} = 'Period';
setvarargin{end+1} = varargin{find(strcmpi('Period',varargin),1)+1};
varargin(find(strcmpi('Period',varargin),1)+1)=[];
varargin(find(strcmpi('Period',varargin),1))=[];
end
if (sum(strcmpi('Window',varargin)))
setvarargin{end+1} = 'Window';
setvarargin{end+1} = varargin{find(strcmpi('Window',varargin),1)+1};
varargin(find(strcmpi('Window',varargin),1)+1)=[];
varargin(find(strcmpi('Window',varargin),1))=[];
end
if (sum(strcmpi('Gamma',varargin)))
setvarargin{end+1} = 'Gamma';
setvarargin{end+1} = varargin{find(strcmpi('Gamma',varargin),1)+1};
varargin(find(strcmpi('Gamma',varargin),1)+1)=[];
varargin(find(strcmpi('Gamma',varargin),1))=[];
end
setvarargin{:}
DATA = vl_maketrainingset(double(X),int8(Y),setvarargin{:});
DATA
[w b info] = vl_svmtrain(DATA,LAMBDA,varargin{:});
fprintf('\n vl_pegasos is DEPRECATED. Please use vl_svmtrain instead. \n\n');
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_svmpegasos.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/misc/vl_svmpegasos.m
| 1,178 |
utf_8
|
009c2a2b87a375d529ed1a4dbe3af59f
|
% VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
end
% DiagnosticCallRef not supported
if (sum(strcmpi('DiagnosticCallRef',varargin)))
varargin(find(strcmpi('DiagnosticCallRef',varargin),1)+1)=[];
varargin(find(strcmpi('DiagnosticCallRef',varargin),1))=[];
fprintf('Option DIAGNOSTICCALLREF is no longer supported.\n Please follow the VLFeat tutorial on SVMs for more information on diagnostics\n');
end
% different default value for MaxIterations
if (sum(strcmpi('MaxIterations',varargin)) == 0)
varargin{end+1} = 'MaxIterations';
varargin{end+1} = ceil(10/LAMBDA);
end
% different default value for BiasMultiplier
if (sum(strcmpi('BiasMultiplier',varargin)) == 0)
varargin{end+1} = 'BiasMultiplier';
varargin{end+1} = 0;
end
[w b info] = vl_svmtrain(DATA,LAMBDA,varargin{:});
fprintf('\n vl_svmpegasos is DEPRECATED. Please use vl_svmtrain instead. \n\n');
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_override.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/misc/vl_override.m
| 4,654 |
utf_8
|
e233d2ecaeb68f56034a976060c594c5
|
function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
% default values and UPDATE as a list of new paramete values.
%
% VL_OVERRIDE(..., 'Warn') prints a warning message whenever: (i)
% UPDATE has a field not found in CONFIG, or (ii) non-leaf values of
% CONFIG are overwritten.
%
% VL_OVERRIDE(..., 'Skip') skips fields of UPDATE that are not found
% in CONFIG instead of copying them.
%
% VL_OVERRIDE(..., 'CaseI') matches field names in a
% case-insensitive manner.
%
% Remark::
% Fields are copied at the deepest possible level. For instance,
% if CONFIG has fields A.B.C1=1 and A.B.C2=2, and if UPDATE is the
% structure A.B.C1=3, then VL_OVERRIDE() returns a strucuture with
% fields A.B.C1=3, A.B.C2=2. By contrast, if UPDATE is the
% structure A.B=4, then the field A.B is copied, and VL_OVERRIDE()
% returns the structure A.B=4 (specifying 'Warn' would warn about
% the fact that the substructure B.C1, B.C2 is being deleted).
%
% Remark::
% Two fields are matched if they correspond exactly. Specifically,
% two fileds A(IA).(FA) and B(IA).FB of two struct arrays A and B
% match if, and only if, (i) A and B have the same dimensions,
% (ii) IA == IB, and (iii) FA == FB.
%
% See also: VL_ARGPARSE(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
warn = false ;
skip = false ;
err = false ;
casei = false ;
if length(varargin) == 1 & ~ischar(varargin{1})
% legacy
warn = 1 ;
end
if ~warn & length(varargin) > 0
for i=1:length(varargin)
switch lower(varargin{i})
case 'warn'
warn = true ;
case 'skip'
skip = true ;
case 'err'
err = true ;
case 'argparse'
argparse = true ;
case 'casei'
casei = true ;
otherwise
error(sprintf('Unknown option ''%s''.',varargin{i})) ;
end
end
end
% if CONFIG is not a struct array just copy UPDATE verbatim
if ~isstruct(config)
config = update ;
return ;
end
% if CONFIG is a struct array but UPDATE is not, no match can be
% established and we simply copy UPDATE verbatim
if ~isstruct(update)
config = update ;
return ;
end
% if CONFIG and UPDATE are both struct arrays, but have different
% dimensions then nom atch can be established and we simply copy
% UPDATE verbatim
if numel(update) ~= numel(config)
config = update ;
return ;
end
% if CONFIG and UPDATE are both struct arrays of the same
% dimension, we override recursively each field
for idx=1:numel(update)
fields = fieldnames(update) ;
for i = 1:length(fields)
updateFieldName = fields{i} ;
if casei
configFieldName = findFieldI(config, updateFieldName) ;
else
configFieldName = findField(config, updateFieldName) ;
end
if ~isempty(configFieldName)
config(idx).(configFieldName) = ...
vl_override(config(idx).(configFieldName), ...
update(idx).(updateFieldName)) ;
else
if warn
warning(sprintf('copied field ''%s'' which is in UPDATE but not in CONFIG', ...
updateFieldName)) ;
end
if err
error(sprintf('The field ''%s'' is in UPDATE but not in CONFIG', ...
updateFieldName)) ;
end
if skip
if warn
warning(sprintf('skipping field ''%s'' which is in UPDATE but not in CONFIG', ...
updateFieldName)) ;
end
continue ;
end
config(idx).(updateFieldName) = update(idx).(updateFieldName) ;
end
end
end
% --------------------------------------------------------------------
function field = findFieldI(S, matchField)
% --------------------------------------------------------------------
field = '' ;
fieldNames = fieldnames(S) ;
for fi=1:length(fieldNames)
if strcmpi(fieldNames{fi}, matchField)
field = fieldNames{fi} ;
end
end
% --------------------------------------------------------------------
function field = findField(S, matchField)
% --------------------------------------------------------------------
field = '' ;
fieldNames = fieldnames(S) ;
for fi=1:length(fieldNames)
if strcmp(fieldNames{fi}, matchField)
field = fieldNames{fi} ;
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_quickvis.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/quickshift/vl_quickvis.m
| 3,696 |
utf_8
|
27f199dad4c5b9c192a5dd3abc59f9da
|
function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% between color consistency and spatial consistency (See VL_QUICKSEG) and
% KERNELSIZE controls the bandwidth of the density estimator (See VL_QUICKSEG,
% VL_QUICKSHIFT). MAXDIST is the maximum distance between neighbors which
% increase the density.
%
% VL_QUICKVIS takes at most MAXCUTS thresholds less than MAXDIST, forming at
% most MAXCUTS segmentations. The edges between regions in each of these
% segmentations are labeled in IEDGE, where the label corresponds to the
% largest DIST which preserves the edge.
%
% [IEDGE,DISTS] = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) also
% returns the DIST thresholds that were chosen.
%
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, DISTS) will use the DISTS
% specified
%
% [IEDGE,DISTS,MAP,GAPS] = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS)
% also returns the MAP and GAPS from VL_QUICKSHIFT.
%
% See Also: VL_QUICKSHIFT(), VL_QUICKSEG(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if nargin == 4
dists = maxdist;
maxdist = max(dists);
[Iseg labels map gaps E] = vl_quickseg(I, ratio, kernelsize, maxdist);
else
[Iseg labels map gaps E] = vl_quickseg(I, ratio, kernelsize, maxdist);
dists = unique(floor(gaps(:)));
dists = dists(2:end-1); % remove the inf thresh and the lowest level thresh
if length(dists) > maxcuts
ind = round(linspace(1,length(dists), maxcuts));
dists = dists(ind);
end
end
[Iedge dists] = mapvis(map, gaps, dists);
function [Iedge dists] = mapvis(map, gaps, maxdist, maxcuts)
% MAPVIS Create an edge image from a Quickshift segmentation.
% IEDGE = MAPVIS(MAP, GAPS, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. MAXDIST is the maximum
% distance between neighbors which increase the density.
%
% MAPVIS takes at most MAXCUTS thresholds less than MAXDIST, forming at most
% MAXCUTS segmentations. The edges between regions in each of these
% segmentations are labeled in IEDGE, where the label corresponds to the
% largest DIST which preserves the edge.
%
% [IEDGE,DISTS] = MAPVIS(MAP, GAPS, MAXDIST, MAXCUTS) also returns the DIST
% thresholds that were chosen.
%
% IEDGE = MAPVIS(MAP, GAPS, DISTS) will use the DISTS specified
%
% See Also: VL_QUICKVIS, VL_QUICKSHIFT, VL_QUICKSEG
if nargin == 3
dists = maxdist;
maxdist = max(dists);
else
dists = unique(floor(gaps(:)));
dists = dists(2:end-1); % remove the inf thresh and the lowest level thresh
% throw away min region size instead of maxdist?
ind = find(dists < maxdist);
dists = dists(ind);
if length(dists) > maxcuts
ind = round(linspace(1,length(dists), maxcuts));
dists = dists(ind);
end
end
Iedge = zeros(size(map));
for i = 1:length(dists)
s = find(gaps >= dists(i));
mapdist = map;
mapdist(s) = s;
[mapped labels] = vl_flatmap(mapdist);
fprintf('%d/%d %d regions\n', i, length(dists), length(unique(mapped)))
borders = getborders(mapped);
Iedge(borders) = dists(i);
%Iedge(borders) = Iedge(borders) + 1;
%Iedge(borders) = i;
end
%%%%%%%%% GETBORDERS
function borders = getborders(map)
dx = conv2(map, [-1 1], 'same');
dy = conv2(map, [-1 1]', 'same');
borders = find(dx ~= 0 | dy ~= 0);
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_demo_aib.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/demo/vl_demo_aib.m
| 2,928 |
utf_8
|
590c6db09451ea608d87bfd094662cac
|
function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X1,'color','r') ;
vl_plotframe(X2,'color','g') ;
vl_plotframe(X3,'color','b') ;
axis equal ;
xlim([-4 4]);
ylim([-4 4]);
axis off ;
rectangle('position',D*[-1 -1 2 2])
vl_demo_print('aib_basic_data', .6) ;
C = 1:K*K ;
Pcx = zeros(3,K*K) ;
f1 = quantize(X1,D,K) ;
f2 = quantize(X2,D,K) ;
f3 = quantize(X3,D,K) ;
Pcx(1,:) = vl_binsum(Pcx(1,:), ones(size(f1)), f1) ;
Pcx(2,:) = vl_binsum(Pcx(2,:), ones(size(f2)), f2) ;
Pcx(3,:) = vl_binsum(Pcx(3,:), ones(size(f3)), f3) ;
Pcx = Pcx / sum(Pcx(:)) ;
[parents, cost] = vl_aib(Pcx) ;
cutsize = [K*K, 10, 3, 2, 1] ;
for i=1:length(cutsize)
[cut,map,short] = vl_aibcut(parents, cutsize(i)) ;
parents_cut(short > 0) = parents(short(short > 0)) ;
C = short(1:K*K+1) ; [drop1,drop2,C] = unique(C) ;
figure(i+1) ; clf ;
plotquantization(D,K,C) ; hold on ;
%plottree(D,K,parents_cut) ;
axis equal ;
axis off ;
title(sprintf('%d clusters', cutsize(i))) ;
vl_demo_print(sprintf('aib_basic_clust_%d',i),.6) ;
end
% --------------------------------------------------------------------
function f = quantize(X,D,K)
% --------------------------------------------------------------------
d = 2*D / K ;
j = round((X(1,:) + D) / d) ;
i = round((X(2,:) + D) / d) ;
j = max(min(j,K),1) ;
i = max(min(i,K),1) ;
f = sub2ind([K K],i,j) ;
% --------------------------------------------------------------------
function [i,j] = plotquantization(D,K,C)
% --------------------------------------------------------------------
hold on ;
cl = [[.3 .3 .3] ; .5*hsv(max(C)-1)+.5] ;
d = 2*D / K ;
for i=0:K-1
for j=0:K-1
patch(d*(j+[0 1 1 0])-D, ...
d*(i+[0 0 1 1])-D, ...
cl(C(j*K+i+1),:)) ;
end
end
% --------------------------------------------------------------------
function h = plottree(D,K,parents)
% --------------------------------------------------------------------
d = 2*D / K ;
C = zeros(2,2*K*K-1)+NaN ;
N = zeros(1,2*K*K-1) ;
for i=0:K-1
for j=0:K-1
C(:,j*K+i+1) = [d*j-D; d*i-D]+d/2 ;
N(:,j*K+i+1) = 1 ;
end
end
for i=1:length(parents)
p = parents(i) ;
if p==0, continue ; end;
if all(isnan(C(:,i))), continue; end
if all(isnan(C(:,p)))
C(:,p) = C(:,i) / N(i) ;
else
C(:,p) = C(:,p) + C(:,i) / N(i) ;
end
N(p) = N(p) + 1 ;
end
C(1,:) = C(1,:) ./ N ;
C(2,:) = C(2,:) ./ N ;
xt = zeros(3, 2*length(parents)-1)+NaN ;
yt = zeros(3, 2*length(parents)-1)+NaN ;
for i=1:length(parents)
p = parents(i) ;
if p==0, continue ; end;
xt(1,i) = C(1,i) ; xt(2,i) = C(1,p) ;
yt(1,i) = C(2,i) ; yt(2,i) = C(2,p) ;
end
h=line(xt(:),yt(:),'linestyle','-','marker','.','linewidth',3) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_demo_alldist.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/demo/vl_demo_alldist.m
| 5,460 |
utf_8
|
6d008a64d93445b9d7199b55d58db7eb
|
function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 'single', 'l2', }, ...
{'alldist', 'single', 'l2', 'nosimd'}, ...
{'alldist', 'single', 'l2' }, ...
{'alldist2', 'double', 'l1', }, ...
{'alldist', 'double', 'l1', 'nosimd'}, ...
{'alldist', 'double', 'l1' }, ...
{'alldist2', 'single', 'l1', }, ...
{'alldist', 'single', 'l1', 'nosimd'}, ...
{'alldist', 'single', 'l1' }, ...
{'alldist2', 'double', 'chi2', }, ...
{'alldist', 'double', 'chi2', 'nosimd'}, ...
{'alldist', 'double', 'chi2' }, ...
{'alldist2', 'single', 'chi2', }, ...
{'alldist', 'single', 'chi2', 'nosimd'}, ...
{'alldist', 'single', 'chi2' }, ...
{'alldist2', 'double', 'hell', }, ...
{'alldist', 'double', 'hell', 'nosimd'}, ...
{'alldist', 'double', 'hell' }, ...
{'alldist2', 'single', 'hell', }, ...
{'alldist', 'single', 'hell', 'nosimd'}, ...
{'alldist', 'single', 'hell' }, ...
{'alldist2', 'double', 'kl2', }, ...
{'alldist', 'double', 'kl2', 'nosimd'}, ...
{'alldist', 'double', 'kl2' }, ...
{'alldist2', 'single', 'kl2', }, ...
{'alldist', 'single', 'kl2', 'nosimd'}, ...
{'alldist', 'single', 'kl2' }, ...
{'alldist2', 'double', 'kl1', }, ...
{'alldist', 'double', 'kl1', 'nosimd'}, ...
{'alldist', 'double', 'kl1' }, ...
{'alldist2', 'single', 'kl1', }, ...
{'alldist', 'single', 'kl1', 'nosimd'}, ...
{'alldist', 'single', 'kl1' }, ...
{'alldist2', 'double', 'kchi2', }, ...
{'alldist', 'double', 'kchi2', 'nosimd'}, ...
{'alldist', 'double', 'kchi2' }, ...
{'alldist2', 'single', 'kchi2', }, ...
{'alldist', 'single', 'kchi2', 'nosimd'}, ...
{'alldist', 'single', 'kchi2' }, ...
{'alldist2', 'double', 'khell', }, ...
{'alldist', 'double', 'khell', 'nosimd'}, ...
{'alldist', 'double', 'khell' }, ...
{'alldist2', 'single', 'khell', }, ...
{'alldist', 'single', 'khell', 'nosimd'}, ...
{'alldist', 'single', 'khell' }, ...
} ;
%settingsRange = settingsRange(end-5:end) ;
styles = {} ;
for marker={'x','+','.','*','o'}
for color={'r','g','b','k','y'}
styles{end+1} = {'color', char(color), 'marker', char(marker)} ;
end
end
for ni=1:length(numSamplesRange)
for ti=1:length(settingsRange)
tocs = [] ;
for ri=1:numRepetitions
rand('state',ri) ;
randn('state',ri) ;
numSamples = numSamplesRange(ni) ;
settings = settingsRange{ti} ;
[tocs(end+1), D] = run_experiment(numDimensions, ...
numSamples, ...
settings) ;
end
means(ni,ti) = mean(tocs) ;
stds(ni,ti) = std(tocs) ;
if mod(ti-1,3) == 0
D0 = D ;
else
err = max(abs(D(:)-D0(:))) ;
fprintf('err %f\n', err) ;
if err > 1, keyboard ; end
end
end
end
if 0
figure(1) ; clf ; hold on ;
numStyles = length(styles) ;
for ti=1:length(settingsRange)
si = mod(ti - 1, numStyles) + 1 ;
h(ti) = plot(numSamplesRange, means(:,ti), styles{si}{:}) ;
leg{ti} = sprintf('%s ', settingsRange{ti}{:}) ;
errorbar(numSamplesRange, means(:,ti), stds(:,ti), 'linestyle', 'none') ;
end
end
for ti=1:length(settingsRange)
leg{ti} = sprintf('%s ', settingsRange{ti}{:}) ;
end
figure(1) ; clf ;
barh(means(end,:)) ;
set(gca,'ytick', 1:length(leg), 'yticklabel', leg,'ydir','reverse') ;
xlabel('Time [s]') ;
function [elaps, D] = run_experiment(numDimensions, numSamples, settings)
distType = 'l2' ;
algType = 'alldist' ;
classType = 'double' ;
useSimd = true ;
for si=1:length(settings)
arg = settings{si} ;
switch arg
case {'l1', 'l2', 'chi2', 'hell', 'kl2', 'kl1', 'kchi2', 'khell'}
distType = arg ;
case {'alldist', 'alldist2'}
algType = arg ;
case {'single', 'double'}
classType = arg ;
case 'simd'
useSimd = true ;
case 'nosimd'
useSimd = false ;
otherwise
assert(false) ;
end
end
X = rand(numDimensions, numSamples) ;
X(X < .3) = 0 ;
switch classType
case 'double'
case 'single'
X = single(X) ;
end
vl_simdctrl(double(useSimd)) ;
switch algType
case 'alldist'
tic ; D = vl_alldist(X, distType) ; elaps = toc ;
case 'alldist2'
tic ; D = vl_alldist2(X, distType) ; elaps = toc ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_demo_ikmeans.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/demo/vl_demo_ikmeans.m
| 774 |
utf_8
|
17ff0bb7259d390fb4f91ea937ba7de0
|
function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assignments] = vl_hikmeans(data,3,numClusters) ;
figure(2) ; clf ; axis off ;
plotClusters(data, [], [4 2 1] * double(assignments)) ;
vl_demo_print('hikmeans_2d',0.6);
function plotClusters(data, centers, assignments)
hold on ;
cc=jet(double(max(assignments(:))));
for i=1:max(assignments(:))
plot(data(1,assignments == i),data(2,assignments == i),'.','color',cc(i,:));
end
if ~isempty(centers)
plot(centers(1,:),centers(2,:),'k.','MarkerSize',20)
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_demo_svm.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/demo/vl_demo_svm.m
| 1,235 |
utf_8
|
7cf6b3504e4fc2cbd10ff3fec6e331a7
|
% VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda = 0.01 ; % Regularization parameter
maxIter = 1000 ; % Maximum number of iterations
energy = [] ;
% Diagnostic function
function diagnostics(svm)
energy = [energy [svm.objective ; svm.dualObjective ; svm.dualityGap ] ] ;
end
% Training the SVM
energy = [] ;
[w b info] = vl_svmtrain(X, y, lambda,...
'MaxNumIterations',maxIter,...
'DiagnosticFunction',@diagnostics,...
'DiagnosticFrequency',1)
% Visualisation
eq = [num2str(w(1)) '*x+' num2str(w(2)) '*y+' num2str(b)];
line = ezplot(eq, [-0.9 0.9 -0.9 0.9]);
set(line, 'Color', [0 0.8 0],'linewidth', 2);
vl_demo_print('svm_training_result') ;
figure
hold on
plot(energy(1,:),'--b') ;
plot(energy(2,:),'-.g') ;
plot(energy(3,:),'r') ;
legend('Primal objective','Dual objective','Duality gap')
xlabel('Diagnostics iteration')
ylabel('Energy')
vl_demo_print('svm_energy') ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_demo_kdtree_sift.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/demo/vl_demo_kdtree_sift.m
| 6,832 |
utf_8
|
e676f80ac330a351f0110533c6ebba89
|
function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search', 'file')
if exist(fullfile(vl_root, 'opt', 'flann', 'build', 'matlab'))
addpath(fullfile(vl_root, 'opt', 'flann', 'build', 'matlab')) ;
end
end
do_flann = exist('nearest_neighbors') == 3 ;
if ~do_flann
warning('FLANN not found. Comparison disabled.') ;
end
maxNumComparisonsRange = [1 10 50 100 200 300 400] ;
numTreesRange = [1 2 5 10] ;
% get data (SIFT features)
im1 = imread(fullfile(vl_root, 'data', 'roofs1.jpg')) ;
im2 = imread(fullfile(vl_root, 'data', 'roofs2.jpg')) ;
im1 = single(rgb2gray(im1)) ;
im2 = single(rgb2gray(im2)) ;
[f1,d1] = vl_sift(im1,'firstoctave',-1,'floatdescriptors','verbose') ;
[f2,d2] = vl_sift(im2,'firstoctave',-1,'floatdescriptors','verbose') ;
% add some noise to make matches unique
d1 = single(d1) + rand(size(d1)) ;
d2 = single(d2) + rand(size(d2)) ;
% match exhaustively to get the ground truth
elapsedDirect = tic ;
D = vl_alldist(d1,d2) ;
[drop, best] = min(D, [], 1) ;
elapsedDirect = toc(elapsedDirect) ;
for ti=1:length(numTreesRange)
for vi=1:length(maxNumComparisonsRange)
v = maxNumComparisonsRange(vi) ;
t = numTreesRange(ti) ;
if do_median
tic ;
kdtree = vl_kdtreebuild(d1, ...
'verbose', ...
'thresholdmethod', 'median', ...
'numtrees', t) ;
[i, d] = vl_kdtreequery(kdtree, d1, d2, ...
'verbose', ...
'maxcomparisons',v) ;
elapsedKD_median(vi,ti) = toc ;
errors_median(vi,ti) = sum(double(i) ~= best) / length(best) ;
errorsD_median(vi,ti) = mean(abs(d - drop) ./ drop) ;
end
if do_mean
tic ;
kdtree = vl_kdtreebuild(d1, ...
'verbose', ...
'thresholdmethod', 'mean', ...
'numtrees', t) ;
%kdtree = readflann(kdtree, '/tmp/flann.txt') ;
%checkx(kdtree, d1, 1, 1) ;
[i, d] = vl_kdtreequery(kdtree, d1, d2, ...
'verbose', ...
'maxcomparisons', v) ;
elapsedKD_mean(vi,ti) = toc ;
errors_mean(vi,ti) = sum(double(i) ~= best) / length(best) ;
errorsD_mean(vi,ti) = mean(abs(d - drop) ./ drop) ;
end
if do_flann
tic ;
[i, d] = flann_search(d1, d2, 1, struct('algorithm','kdtree', ...
'trees', t, ...
'checks', v));
ifla = i ;
elapsedKD_flann(vi,ti) = toc;
errors_flann(vi,ti) = sum(i ~= best) / length(best) ;
errorsD_flann(vi,ti) = mean(abs(d - drop) ./ drop) ;
end
end
end
figure(1) ; clf ;
leg = {} ;
hnd = [] ;
sty = {{'color','r'},{'color','g'},...
{'color','b'},{'color','c'},...
{'color','k'}} ;
for ti=1:length(numTreesRange)
s = sty{mod(ti,length(sty))+1} ;
if do_median
h1=loglog(elapsedDirect ./ elapsedKD_median(:,ti),100*errors_median(:,ti),'-*',s{:}) ; hold on ;
leg{end+1} = sprintf('VLFeat median (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h1 ;
end
if do_mean
h2=loglog(elapsedDirect ./ elapsedKD_mean(:,ti), 100*errors_mean(:,ti), '-o',s{:}) ; hold on ;
leg{end+1} = sprintf('VLFeat (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h2 ;
end
if do_flann
h3=loglog(elapsedDirect ./ elapsedKD_flann(:,ti), 100*errors_flann(:,ti), '+--',s{:}) ; hold on ;
leg{end+1} = sprintf('FLANN (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h3 ;
end
end
set([hnd], 'linewidth', 2) ;
xlabel('speedup over linear search (log times)') ;
ylabel('percentage of incorrect matches (%)') ;
h=legend(hnd, leg{:}, 'location', 'southeast') ;
set(h,'fontsize',8) ;
grid on ;
axis square ;
vl_demo_print('kdtree_sift_incorrect',.6) ;
figure(2) ; clf ;
leg = {} ;
hnd = [] ;
for ti=1:length(numTreesRange)
s = sty{mod(ti,length(sty))+1} ;
if do_median
h1=loglog(elapsedDirect ./ elapsedKD_median(:,ti),100*errorsD_median(:,ti),'*-',s{:}) ; hold on ;
leg{end+1} = sprintf('VLFeat median (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h1 ;
end
if do_mean
h2=loglog(elapsedDirect ./ elapsedKD_mean(:,ti), 100*errorsD_mean(:,ti), 'o-',s{:}) ; hold on ;
leg{end+1} = sprintf('VLFeat (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h2 ;
end
if do_flann
h3=loglog(elapsedDirect ./ elapsedKD_flann(:,ti), 100*errorsD_flann(:,ti), '+--',s{:}) ; hold on ;
leg{end+1} = sprintf('FLANN (%d tr.)', numTreesRange(ti)) ;
hnd(end+1) = h3 ;
end
end
set([hnd], 'linewidth', 2) ;
xlabel('speedup over linear search (log times)') ;
ylabel('relative overestimation of minmium distannce (%)') ;
h=legend(hnd, leg{:}, 'location', 'southeast') ;
set(h,'fontsize',8) ;
grid on ;
axis square ;
vl_demo_print('kdtree_sift_distortion',.6) ;
% --------------------------------------------------------------------
function checkx(kdtree, X, t, n, mib, mab)
% --------------------------------------------------------------------
if nargin <= 4
mib = -inf * ones(size(X,1),1) ;
mab = +inf * ones(size(X,1),1) ;
end
lc = kdtree.trees(t).nodes.lowerChild(n) ;
uc = kdtree.trees(t).nodes.upperChild(n) ;
if lc < 0
for i=-lc:-uc-1
di = kdtree.trees(t).dataIndex(i) ;
if any(X(:,di) > mab)
error('a') ;
end
if any(X(:,di) < mib)
error('b') ;
end
end
return
end
i = kdtree.trees(t).nodes.splitDimension(n) ;
v = kdtree.trees(t).nodes.splitThreshold(n) ;
mab_ = mab ;
mab_(i) = min(mab(i), v) ;
checkx(kdtree, X, t, lc, mib, mab_) ;
mib_ = mib ;
mib_(i) = max(mib(i), v) ;
checkx(kdtree, X, t, uc, mib_, mab) ;
% --------------------------------------------------------------------
function kdtree = readflann(kdtree, path)
% --------------------------------------------------------------------
data = textread(path)' ;
for i=1:size(data,2)
nodeIds = data(1,:) ;
ni = find(nodeIds == data(1,i)) ;
if ~isnan(data(2,i))
% internal node
li = find(nodeIds == data(4,i)) ;
ri = find(nodeIds == data(5,i)) ;
kdtree.trees(1).nodes.lowerChild(ni) = int32(li) ;
kdtree.trees(1).nodes.upperChild(ni) = int32(ri) ;
kdtree.trees(1).nodes.splitThreshold(ni) = single(data(2,i)) ;
kdtree.trees(1).nodes.splitDimension(ni) = single(data(3,i)+1) ;
else
di = data(3,i) + 1 ;
kdtree.trees(1).nodes.lowerChild(ni) = int32(- di) ;
kdtree.trees(1).nodes.upperChild(ni) = int32(- di - 1) ;
end
kdtree.trees(1).dataIndex = uint32(1:kdtree.numData) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_impattern.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/imop/vl_impattern.m
| 6,876 |
utf_8
|
1716a4d107f0186be3d11c647bc628ce
|
function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colour images have range in [0,1].
%
% VL_IMPATTERN() without arguments shows a gallery of the stock
% patterns. The following patterns are supported:
%
% Wedge::
% The image of a wedge.
%
% Cone::
% The image of a cone.
%
% SmoothChecker::
% A checkerboard with Gaussian filtering on top. Use the
% option-value pair 'sigma', SIGMA to specify the standard
% deviation of the smoothing and the pair 'step', STEP to specfity
% the checker size in pixels.
%
% ThreeDotsSquare::
% A pattern with three small dots and two squares.
%
% UniformNoise::
% Random i.i.d. noise.
%
% Blobs:
% Gaussian blobs of various sizes and anisotropies.
%
% Blobs1:
% Gaussian blobs of various orientations and anisotropies.
%
% Blob:
% One Gaussian blob. Use the option-value pairs 'sigma',
% 'orientation', and 'anisotropy' to specify the respective
% parameters. 'sigma' is the scalar standard deviation of an
% isotropic blob (the image domain is the rectangle
% [-1,1]^2). 'orientation' is the clockwise rotation (as the Y
% axis points downards). 'anisotropy' (>= 1) is the ratio of the
% the largest over the smallest axis of the blob (the smallest
% axis length is set by 'sigma'). Set 'cut' to TRUE to cut half
% half of the blob.
%
% A stock image::
% Any of 'box', 'roofs1', 'roofs2', 'river1', 'river2', 'spotted'.
%
% All pattern accept a SIZE parameter [WIDTH,HEIGHT]. For all but
% the stock images, the default size is [128,128].
% Author: Andrea Vedaldi
% Copyright (C) 2012 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if nargin > 0
pattern=varargin{1} ;
varargin=varargin(2:end) ;
else
pattern = 'gallery' ;
end
patterns = {'wedge','cone','smoothChecker','threeDotsSquare', ...
'blob', 'blobs', 'blobs1', ...
'box', 'roofs1', 'roofs2', 'river1', 'river2'} ;
% spooling
switch lower(pattern)
case 'wedge', im = wedge(varargin) ;
case 'cone', im = cone(varargin) ;
case 'smoothchecker', im = smoothChecker(varargin) ;
case 'threedotssquare', im = threeDotSquare(varargin) ;
case 'uniformnoise', im = uniformNoise(varargin) ;
case 'blob', im = blob(varargin) ;
case 'blobs', im = blobs(varargin) ;
case 'blobs1', im = blobs1(varargin) ;
case {'box','roofs1','roofs2','river1','river2','spots'}
im = stockImage(pattern, varargin) ;
case 'gallery'
clf ;
num = numel(patterns) ;
for p = 1:num
vl_tightsubplot(num,p,'box','outer') ;
imagesc(vl_impattern(patterns{p}),[0 1]) ;
axis image off ;
title(patterns{p}) ;
end
colormap gray ;
return ;
otherwise
error('Unknown patter ''%s''.', pattern) ;
end
if nargout == 0
clf ; imagesc(im) ; hold on ;
colormap gray ; axis image off ;
title(pattern) ;
clear im ;
end
function [u,v,opts,args] = commonOpts(args)
opts.size = [128 128] ;
[opts,args] = vl_argparse(opts, args) ;
ur = linspace(-1,1,opts.size(2)) ;
vr = linspace(-1,1,opts.size(1)) ;
[u,v] = meshgrid(ur,vr);
function im = wedge(args)
[u,v,opts,args] = commonOpts(args) ;
im = abs(u) + abs(v) > (1/4) ;
im(v < 0) = 0 ;
function im = cone(args)
[u,v,opts,args] = commonOpts(args) ;
im = sqrt(u.^2+v.^2) ;
im = im / max(im(:)) ;
function im = smoothChecker(args)
opts.size = [128 128] ;
opts.step = 16 ;
opts.sigma = 2 ;
opts = vl_argparse(opts, args) ;
[u,v] = meshgrid(0:opts.size(1)-1, 0:opts.size(2)-1) ;
im = xor((mod(u,opts.step*2) < opts.step),...
(mod(v,opts.step*2) < opts.step)) ;
im = double(im) ;
im = vl_imsmooth(im, opts.sigma) ;
function im = threeDotSquare(args)
[u,v,opts,args] = commonOpts(args) ;
im = ones(size(u)) ;
im(-2/3<u & u<2/3 & -2/3<v & v<2/3) = .75 ;
im(-1/3<u & u<1/3 & -1/3<v & v<1/3) = .50 ;
[drop,i] = min(abs(v(:,1))) ;
[drop,j1] = min(abs(u(1,:)-1/6)) ;
[drop,j2] = min(abs(u(1,:))) ;
[drop,j3] = min(abs(u(1,:)+1/6)) ;
im(i,j1) = 0 ;
im(i,j2) = 0 ;
im(i,j3) = 0 ;
function im = blobs(args)
[u,v,opts,args] = commonOpts(args) ;
im = zeros(size(u)) ;
num = 5 ;
square = 2 / num ;
sigma = square / 2 / 3 ;
scales = logspace(log10(0.5), log10(1), num) ;
skews = linspace(1,2,num) ;
for i=1:num
for j=1:num
cy = (i-1) * square + square/2 - 1;
cx = (j-1) * square + square/2 - 1;
A = sigma * diag([scales(i) scales(i)/skews(j)]) * [1 -1 ; 1 1] / sqrt(2) ;
C = inv(A'*A) ;
x = u - cx ;
y = v - cy ;
im = im + exp(-0.5 *(x.*x*C(1,1) + y.*y*C(2,2) + 2*x.*y*C(1,2))) ;
end
end
im = im / max(im(:)) ;
function im = blob(args)
[u,v,opts,args] = commonOpts(args) ;
opts.sigma = 0.15 ;
opts.anisotropy = .5 ;
opts.orientation = 2/3 * pi ;
opts.cut = false ;
opts = vl_argparse(opts, args) ;
im = zeros(size(u)) ;
th = opts.orientation ;
R = [cos(th) -sin(th) ; sin(th) cos(th)] ;
A = opts.sigma * R * diag([opts.anisotropy 1]) ;
T = [0;0] ;
[x,y] = vl_waffine(inv(A),-inv(A)*T,u,v) ;
im = exp(-0.5 *(x.^2 + y.^2)) ;
if opts.cut
im = im .* double(x > 0) ;
end
function im = blobs1(args)
[u,v,opts,args] = commonOpts(args) ;
opts.number = 5 ;
opts.sigma = [] ;
opts = vl_argparse(opts, args) ;
im = zeros(size(u)) ;
square = 2 / opts.number ;
num = opts.number ;
if isempty(opts.sigma)
sigma = 1/6 * square ;
else
sigma = opts.sigma * square ;
end
rotations = linspace(0,pi,num+1) ;
rotations(end) = [] ;
skews = linspace(1,2,num) ;
for i=1:num
for j=1:num
cy = (i-1) * square + square/2 - 1;
cx = (j-1) * square + square/2 - 1;
th = rotations(i) ;
R = [cos(th) -sin(th); sin(th) cos(th)] ;
A = sigma * R * diag([1 1/skews(j)]) ;
C = inv(A*A') ;
x = u - cx ;
y = v - cy ;
im = im + exp(-0.5 *(x.*x*C(1,1) + y.*y*C(2,2) + 2*x.*y*C(1,2))) ;
end
end
im = im / max(im(:)) ;
function im = uniformNoise(args)
opts.size = [128 128] ;
opts.seed = 1 ;
opts = vl_argparse(opts, args) ;
state = vl_twister('state') ;
vl_twister('state',opts.seed) ;
im = vl_twister(opts.size([2 1])) ;
vl_twister('state',state) ;
function im = stockImage(pattern,args)
opts.size = [] ;
opts = vl_argparse(opts, args) ;
switch pattern
case 'river1', path='river1.jpg' ;
case 'river2', path='river2.jpg' ;
case 'roofs1', path='roofs1.jpg' ;
case 'roofs2', path='roofs2.jpg' ;
case 'box', path='box.pgm' ;
case 'spots', path='spots.jpg' ;
end
im = imread(fullfile(vl_root,'data',path)) ;
im = im2double(im) ;
if ~isempty(opts.size)
im = imresize(im, opts.size) ;
im = max(im,0) ;
im = min(im,1) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_tpsu.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/imop/vl_tpsu.m
| 1,755 |
utf_8
|
09f36e1a707c069b375eb2817d0e5f13
|
function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X is a 2xM matrix and Y a 2xN matrix of points and U(r) is
% the opposite -r^2 log(r^2) of the radial basis function of the
% thin plate spline specified by X and Y.
%
% [U,dU]=vl_tpsu(x,y) returns the derivatives of the columns of U with
% respect to the parameters Y. The derivatives are arranged in a
% Mx2xN array, one layer per column of U.
%
% See also: VL_TPS(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if exist('tpsumx')
U = tpsumx(X,Y) ;
else
M=size(X,2) ;
N=size(Y,2) ;
% Faster than repmat, but still fairly slow
r2 = ...
(X( ones(N,1), :)' - Y( ones(1,M), :)).^2 + ...
(X( 1+ones(N,1), :)' - Y(1+ones(1,M), :)).^2 ;
U = - rb(r2) ;
end
if nargout > 1
M=size(X,2) ;
N=size(Y,2) ;
dx = X( ones(N,1), :)' - Y( ones(1,M), :) ;
dy = X(1+ones(N,1), :)' - Y(1+ones(1,M), :) ;
r2 = (dx.^2 + dy.^2) ;
r = sqrt(r2) ;
coeff = drb(r)./(r+eps) ;
dU = reshape( [coeff .* dx ; coeff .* dy], M, 2, N) ;
end
% The radial basis function
function y = rb(r2)
y = zeros(size(r2)) ;
sel = find(r2 ~= 0) ;
y(sel) = - r2(sel) .* log(r2(sel)) ;
% The derivative of the radial basis function
function y = drb(r)
y = zeros(size(r)) ;
sel = find(r ~= 0) ;
y(sel) = - 4 * r(sel) .* log(r(sel)) - 2 * r(sel) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_xyz2lab.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/imop/vl_xyz2lab.m
| 1,570 |
utf_8
|
09f95a6f9ae19c22486ec1157357f0e3
|
function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if nargin < 2
il='E' ;
end
switch lower(il)
case 'a'
xw = 0.4476 ;
yw = 0.4074 ;
case 'b'
xw = 0.3324 ;
yw = 0.3474 ;
case 'c'
xw = 0.3101 ;
yw = 0.3162 ;
case 'e'
xw = 1/3 ;
yw = 1/3 ;
case 'd50'
xw = 0.3457 ;
yw = 0.3585 ;
case 'd55'
xw = 0.3324 ;
yw = 0.3474 ;
case 'd65'
xw = 0.312713 ;
yw = 0.329016 ;
case 'd75'
xw = 0.299 ;
yw = 0.3149 ;
case 'd93'
xw = 0.2848 ;
yw = 0.2932 ;
end
J=zeros(size(I)) ;
% Reference white
Yw = 1.0 ;
Xw = xw/yw ;
Zw = (1-xw-yw)/yw * Yw ;
% XYZ components
X = I(:,:,1) ;
Y = I(:,:,2) ;
Z = I(:,:,3) ;
x = X/Xw ;
y = Y/Yw ;
z = Z/Zw ;
L = 116 * f(y) - 16 ;
a = 500*(f(x) - f(y)) ;
b = 200*(f(y) - f(z)) ;
J = cat(3,L,a,b) ;
% --------------------------------------------------------------------
function b=f(a)
% --------------------------------------------------------------------
sp = find(a > 0.00856) ;
sm = find(a <= 0.00856) ;
k = 903.3 ;
b=zeros(size(a)) ;
b(sp) = a(sp).^(1/3) ;
b(sm) = (k*a(sm) + 16)/116 ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_gmm.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_gmm.m
| 1,332 |
utf_8
|
76782cae6c98781c6c38d4cbf5549d94
|
function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
end
function s = setup()
randn('state',0) ;
s.X = randn(128, 1000) ;
end
function test_multithreading(s)
dataTypes = {'single','double'} ;
for dataType = dataTypes
conversion = str2func(char(dataType)) ;
X = conversion(s.X) ;
vl_twister('state',0) ;
vl_threads(0) ;
[means, covariances, priors, ll, posteriors] = ...
vl_gmm(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Initialization', 'rand') ;
vl_twister('state',0) ;
vl_threads(1) ;
[means_, covariances_, priors_, ll_, posteriors_] = ...
vl_gmm(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Initialization', 'rand') ;
vl_assert_almost_equal(means, means_, 1e-2) ;
vl_assert_almost_equal(covariances, covariances_, 1e-2) ;
vl_assert_almost_equal(priors, priors_, 1e-2) ;
vl_assert_almost_equal(ll, ll_, 1e-2 * abs(ll)) ;
vl_assert_almost_equal(posteriors, posteriors_, 1e-2) ;
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_twister.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_twister.m
| 1,251 |
utf_8
|
2bfb5a30cbd6df6ac80c66b73f8646da
|
function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function test_seed_by_scalar()
rand('twister',1) ; a = rand ;
vl_twister('state',1) ; b = vl_twister ;
vl_assert_equal(a,b,'seed by scalar + VL_TWISTER()') ;
function test_get_set_state()
rand('twister',1) ; a = rand('twister') ;
vl_twister('state',1) ; b = vl_twister('state') ;
vl_assert_equal(a,b,'read state') ;
a(1) = a(1) + 1 ;
vl_twister('state',a) ; b = vl_twister('state') ;
vl_assert_equal(a,b,'set state') ;
function test_multi_dimensions()
b = rand('twister') ;
rand('twister',b) ;
vl_twister('state',b) ;
a=rand([1 2 3 4 5]) ;
b=vl_twister([1 2 3 4 5]) ;
vl_assert_equal(a,b,'VL_TWISTER([M N P ...])') ;
function test_multi_multi_args()
rand('twister',1) ; a=rand(1, 2, 3, 4, 5) ;
vl_twister('state',1) ; b=vl_twister(1, 2, 3, 4, 5) ;
vl_assert_equal(a,b,'VL_TWISTER(M, N, P, ...)') ;
function test_square()
rand('twister',1) ; a=rand(10) ;
vl_twister('state',1) ; b=vl_twister(10) ;
vl_assert_equal(a,b,'VL_TWISTER(N)') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_kdtree.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_kdtree.m
| 2,449 |
utf_8
|
9d7ad2b435a88c22084b38e5eb5f9eb9
|
function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;
X = conv(s.X) ;
Q = conv(s.Q) ;
tree = vl_kdtreebuild(X,'ThresholdMethod', tmethod) ;
[nn, d2] = vl_kdtreequery(tree, X, Q) ;
D2 = vl_alldist2(X, Q, 'l2') ;
[d2_, nn_] = min(D2) ;
vl_assert_equal(...
nn,uint32(nn_),...
'incorrect nns: type=%s th. method=%s', func2str(conv), tmethod) ;
vl_assert_almost_equal(...
d2,d2_,...
'incorrect distances: type=%s th. method=%s', func2str(conv), tmethod) ;
end
end
function test_nearests(s)
numNeighbors = 7 ;
tree = vl_kdtreebuild(s.X) ;
[nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ...
'numNeighbors', numNeighbors) ;
D2 = vl_alldist2(s.X, s.Q, 'l2') ;
[d2_, nn_] = sort(D2) ;
d2_ = d2_(1:numNeighbors, :) ;
nn_ = nn_(1:numNeighbors, :) ;
vl_assert_equal(nn,uint32(nn_)) ;
vl_assert_almost_equal(d2,d2_) ;
function test_ann(s)
vl_twister('state', 1) ;
numNeighbors = 7 ;
maxComparisons = numNeighbors * 50 ;
tree = vl_kdtreebuild(s.X) ;
[nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ...
'numNeighbors', numNeighbors, ...
'maxComparisons', maxComparisons) ;
D2 = vl_alldist2(s.X, s.Q, 'l2') ;
[d2_, nn_] = sort(D2) ;
d2_ = d2_(1:numNeighbors, :) ;
nn_ = nn_(1:numNeighbors, :) ;
for i=1:size(s.Q,2)
overlap = numel(intersect(nn(:,i), nn_(:,i))) / ...
numel(union(nn(:,i), nn_(:,i))) ;
assert(overlap > 0.6, 'ANN did not return enough correct nearest neighbors') ;
end
function test_ann_forest(s)
vl_twister('state', 1) ;
numNeighbors = 7 ;
maxComparisons = numNeighbors * 25 ;
numTrees = 5 ;
tree = vl_kdtreebuild(s.X, 'numTrees', 5) ;
[nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ...
'numNeighbors', numNeighbors, ...
'maxComparisons', maxComparisons) ;
D2 = vl_alldist2(s.X, s.Q, 'l2') ;
[d2_, nn_] = sort(D2) ;
d2_ = d2_(1:numNeighbors, :) ;
nn_ = nn_(1:numNeighbors, :) ;
for i=1:size(s.Q,2)
overlap = numel(intersect(nn(:,i), nn_(:,i))) / ...
numel(union(nn(:,i), nn_(:,i))) ;
assert(overlap > 0.6, 'ANN did not return enough correct nearest neighbors') ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_imwbackward.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_imwbackward.m
| 514 |
utf_8
|
33baa0784c8f6f785a2951d7f1b49199
|
function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,x,y)) ;
function test_invalid_args(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_exception(@() vl_imwbackward(xr,yr,single(s.I),x,y), 'vl:invalidArgument') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_alphanum.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_alphanum.m
| 1,624 |
utf_8
|
2da2b768c2d0f86d699b8f31614aa424
|
function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Allegia 51 Clasteron','Allegia 6R Clasteron','Alpha 100','Alpha 2','Alpha 200','Alpha 2A','Alpha 2A-8000','Alpha 2A-900','Callisto Morphamax','Callisto Morphamax 500','Callisto Morphamax 5000','Callisto Morphamax 600','Callisto Morphamax 6000 SE','Callisto Morphamax 6000 SE2','Callisto Morphamax 700','Callisto Morphamax 7000','Xiph Xlater 10000','Xiph Xlater 2000','Xiph Xlater 300','Xiph Xlater 40','Xiph Xlater 5','Xiph Xlater 50','Xiph Xlater 500','Xiph Xlater 5000','Xiph Xlater 58'} ;
s.sortedStrings = ...
{'10X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','200X Radonius','1000X Radonius Maximus','Allegia 6R Clasteron','Allegia 50 Clasteron','Allegia 50B Clasteron','Allegia 51 Clasteron','Allegia 500 Clasteron','Alpha 2','Alpha 2A','Alpha 2A-900','Alpha 2A-8000','Alpha 100','Alpha 200','Callisto Morphamax','Callisto Morphamax 500','Callisto Morphamax 600','Callisto Morphamax 700','Callisto Morphamax 5000','Callisto Morphamax 6000 SE','Callisto Morphamax 6000 SE2','Callisto Morphamax 7000','Xiph Xlater 5','Xiph Xlater 40','Xiph Xlater 50','Xiph Xlater 58','Xiph Xlater 300','Xiph Xlater 500','Xiph Xlater 2000','Xiph Xlater 5000','Xiph Xlater 10000'} ;
function test_basic(s)
sorted = vl_alphanum(s.strings) ;
assert(isequal(sorted,s.sortedStrings)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_printsize.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_printsize.m
| 1,447 |
utf_8
|
0f0b6437c648b7a2e1310900262bd765
|
function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, sigma) ;
set(1, 'PaperUnits', 'inches') ;
siz = get(1, 'PaperSize') ;
pos = get(1, 'PaperPosition') ;
vl_assert_almost_equal(siz(1), sigma*s.usletter(1), 1e-4) ;
vl_assert_almost_equal(pos(1), 0, 1e-4) ;
vl_assert_almost_equal(pos(3), sigma*s.usletter(1), 1e-4) ;
end
function test_papertype(s)
vl_printsize(s.fig, 1, 'papertype', 'a4') ;
set(1, 'PaperUnits', 'inches') ;
siz = get(1, 'PaperSize') ;
pos = get(1, 'PaperPosition') ;
vl_assert_almost_equal(siz(1), s.a4(1), 1e-4) ;
function test_margin(s)
m = 0.5 ;
vl_printsize(s.fig, 1, 'margin', m) ;
set(1, 'PaperUnits', 'inches') ;
siz = get(1, 'PaperSize') ;
pos = get(1, 'PaperPosition') ;
vl_assert_almost_equal(siz(1), s.usletter(1) * (1 + 2*m), 1e-4) ;
vl_assert_almost_equal(pos(1), s.usletter(1) * m, 1e-4) ;
function test_reference(s)
sigma = 1 ;
vl_printsize(s.fig, 1, 'reference', 'vertical') ;
set(1, 'PaperUnits', 'inches') ;
siz = get(1, 'PaperSize') ;
pos = get(1, 'PaperPosition') ;
vl_assert_almost_equal(siz(2), sigma*s.usletter(2), 1e-4) ;
vl_assert_almost_equal(pos(2), 0, 1e-4) ;
vl_assert_almost_equal(pos(4), sigma*s.usletter(2), 1e-4) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_cummax.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_cummax.m
| 838 |
utf_8
|
5e98ee1681d4823f32ecc4feaa218611
|
function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dims(k) = numel(a) ;
a = reshape(a, dims) ;
b = reshape(b, dims) ;
vl_assert_almost_equal(...
vl_cummax(a, k), b) ;
end
function test_storage_classes()
types = {@double, @single, ...
@int32, @uint32, ...
@int16, @uint16, ...
@int8, @uint8} ;
if vl_matlabversion() > 71000
types = horzcat(types, {@int64, @uint64}) ;
end
for a = types
a = a{1} ;
for b = types
b = b{1} ;
vl_assert_almost_equal(...
vl_cummax(a(eye(3))), a(toeplitz([1 1 1], [1 0 0 ]))) ;
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_imintegral.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_imintegral.m
| 1,429 |
utf_8
|
4750f04ab0ac9fc4f55df2c8583e5498
|
function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 16 20 24 ;
5 10 15 20 25 30 ; ] ;
function test_matlab_equivalent(s)
vl_assert_equal(slow_imintegral(s.I), s.correct) ;
function test_basic(s)
vl_assert_equal(vl_imintegral(s.I), s.correct) ;
function test_multi_dimensional(s)
vl_assert_equal(vl_imintegral(repmat(s.I, [1 1 3])), ...
repmat(s.correct, [1 1 3])) ;
function test_random(s)
numTests = 50 ;
for i = 1:numTests
I = rand(5) ;
vl_assert_almost_equal(vl_imintegral(s.I), ...
slow_imintegral(s.I)) ;
end
function test_datatypes(s)
vl_assert_equal(single(vl_imintegral(s.I)), single(s.correct)) ;
vl_assert_equal(double(vl_imintegral(s.I)), double(s.correct)) ;
vl_assert_equal(uint32(vl_imintegral(s.I)), uint32(s.correct)) ;
vl_assert_equal(int32(vl_imintegral(s.I)), int32(s.correct)) ;
vl_assert_equal(int32(vl_imintegral(-s.I)), -int32(s.correct)) ;
function integral = slow_imintegral(I)
integral = zeros(size(I));
for k = 1:size(I,3)
for r = 1:size(I,1)
for c = 1:size(I,2)
integral(r,c,k) = sum(sum(I(1:r,1:c,k)));
end
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_sift.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_sift.m
| 1,318 |
utf_8
|
806c61f9db9f2ebb1d649c9bfcf3dc0a
|
function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
'firstoctave', -1, ...
'frames', s.ubc.f) ;
D2 = vl_alldist(f, s.ubc.f) ;
[drop, perm] = min(D2) ;
f = f(:,perm) ;
d = d(:,perm) ;
error = mean(sqrt(sum((single(s.ubc.d) - single(d)).^2))) ...
/ mean(sqrt(sum(single(s.ubc.d).^2))) ;
assert(error < 0.1, ...
'sift descriptor did not produce desctiptors similar to UBC ones') ;
function test_ubc_detector(s)
[f, d] = vl_sift(s.I,...
'firstoctave', -1, ...
'peakthresh', .01, ...
'edgethresh', 10) ;
s.ubc.f(4,:) = mod(s.ubc.f(4,:), 2*pi) ;
f(4,:) = mod(f(4,:), 2*pi) ;
% scale the components so that 1 pixel erro in x,y,z is equal to a
% 10-th of angle.
S = diag([1 1 1 20/pi]);
D2 = vl_alldist(S * s.ubc.f, S * f) ;
[d2,perm] = sort(min(D2)) ;
error = sqrt(d2) ;
quant80 = round(.8 * size(f,2)) ;
% check for less than one pixel error at 80% quantile
assert(error(quant80) < 1, ...
'sift detector did not produce enough keypoints similar to UBC ones') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_binsum.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_binsum.m
| 1,377 |
utf_8
|
f07f0f29ba6afe0111c967ab0b353a9d
|
function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function test_four_args()
vl_assert_almost_equal(...
vl_binsum(eye(3), [1 1 1], [1 2 3], 1), 2*eye(3)) ;
vl_assert_almost_equal(...
vl_binsum(eye(3), [1 1 1]', [1 2 3]', 2), 2*eye(3)) ;
vl_assert_almost_equal(...
vl_binsum(eye(3), 1, [1 2 3], 1), 2*eye(3)) ;
vl_assert_almost_equal(...
vl_binsum(eye(3), 1, [1 2 3]', 2), 2*eye(3)) ;
function test_3d_one()
Z = zeros(3,3,3) ;
B = 3*ones(3,1,3) ;
R = Z ; R(:,3,:) = 17 ;
vl_assert_almost_equal(...
vl_binsum(Z, 17, B, 2), R) ;
function test_3d_two()
Z = zeros(3,3,3) ;
B = 3*ones(3,3,1) ;
X = zeros(3,3,1) ; X(:,:,1) = 17 ;
R = Z ; R(:,:,3) = 17 ;
vl_assert_almost_equal(...
vl_binsum(Z, X, B, 3), R) ;
function test_storage_classes()
types = {@double, @single, ...
@int32, @uint32, ...
@int16, @uint16, ...
@int8, @uint8} ;
if vl_matlabversion() > 71000
types = horzcat(types, {@int64, @uint64}) ;
end
for a = types
a = a{1} ;
for b = types
b = b{1} ;
vl_assert_almost_equal(...
vl_binsum(a(eye(3)), a([1 1 1]), b([1 2 3]), 1), a(2*eye(3))) ;
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_lbp.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_lbp.m
| 892 |
utf_8
|
a79c0ce0c85e25c0b1657f3a0b499538
|
function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f), q) ;
end
end
% constant lbps
I = [1 1 1 ; 1 0 1 ; 1 1 1] ;
f = vl_lbp(single(I), 3) ;
vl_assert_equal(find(f), 57) ;
I = [1 1 1 ; 1 1 1 ; 1 1 1] ;
f = vl_lbp(single(I), 3) ;
vl_assert_equal(find(f), 57) ;
% other lbps
I = [1 0 1 ; 0 0 0 ; 1 0 1] ;
f = vl_lbp(single(I), 3) ;
vl_assert_equal(find(f), 58) ;
function test_fliplr(s)
randn('state',0) ;
I = randn(256,256,1,'single') ;
f = vl_lbp(fliplr(I), 8) ;
f_ = vl_lbpfliplr(vl_lbp(I, 8)) ;
vl_assert_almost_equal(f,f_,1e-3) ;
function s = setup()
s.pixels = [5 6 7 ;
4 NaN 0 ;
3 2 1] ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_colsubset.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_colsubset.m
| 828 |
utf_8
|
be0c080007445b36333b863326fb0f15
|
function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equal(6:10, vl_colsubset(1:10, 5, 'ending')) ;
vl_assert_equal(6:10, vl_colsubset(1:10, .5, 'ending')) ;
function test_largest(s)
vl_assert_equal([5 6 7 9 8], vl_colsubset(s.x, 5, 'largest')) ;
vl_assert_equal([5 6 7 9 8], vl_colsubset(s.x, .5, 'largest')) ;
function test_smallest(s)
vl_assert_equal([2 3 4 1 0], vl_colsubset(s.x, 5, 'smallest')) ;
vl_assert_equal([2 3 4 1 0], vl_colsubset(s.x, .5, 'smallest')) ;
function test_random(s)
assert(numel(intersect(s.x, vl_colsubset(s.x, 5, 'random'))) == 5) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_alldist.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_alldist.m
| 2,373 |
utf_8
|
9ea1a36c97fe715dfa2b8693876808ff
|
function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equal(...
vl_alldist(zeros(15,0), zeros(15,0), 'kl2'), ...
zeros(0,0)) ;
vl_assert_equal(...
vl_alldist(zeros(15,0), zeros(15,12), 'kl2'), ...
zeros(0,12)) ;
vl_assert_equal(...
vl_alldist(zeros(0,15), zeros(0,12), 'kl2'), ...
zeros(15,12)) ;
function test_self(s)
vl_assert_almost_equal(...
vl_alldist(s.X, 'kl2'), ...
makedist(@(x,y) x*y, s.X, s.X), ...
1e-6) ;
function test_distances(s)
dists = {'chi2', 'l2', 'l1', 'hell', 'js', ...
'kchi2', 'kl2', 'kl1', 'khell', 'kjs'} ;
distsEquiv = { ...
@(x,y) (x-y)^2 / (x + y), ...
@(x,y) (x-y)^2, ...
@(x,y) abs(x-y), ...
@(x,y) (sqrt(x) - sqrt(y))^2, ...
@(x,y) x - x .* log2(1 + y/x) + y - y .* log2(1 + x/y), ...
@(x,y) 2 * (x*y) / (x + y), ...
@(x,y) x*y, ...
@(x,y) min(x,y), ...
@(x,y) sqrt(x.*y), ...
@(x,y) .5 * (x .* log2(1 + y/x) + y .* log2(1 + x/y))} ;
types = {'single', 'double'} ;
for simd = [0 1]
for d = 1:length(dists)
for t = 1:length(types)
vl_simdctrl(simd) ;
X = feval(str2func(types{t}), s.X) ;
Y = feval(str2func(types{t}), s.Y) ;
vl_assert_almost_equal(...
vl_alldist(X,Y,dists{d}), ...
makedist(distsEquiv{d},X,Y), ...
1e-4, ...
'alldist failed for dist=%s type=%s simd=%d', ...
dists{d}, ...
types{t}, ...
simd) ;
end
end
end
function test_distance_kernel_pairs(s)
dists = {'chi2', 'l2', 'l1', 'hell', 'js'} ;
for d = 1:length(dists)
dist = char(dists{d}) ;
X = s.X ;
Y = s.Y ;
ker = ['k' dist] ;
kxx = vl_alldist(X,X,ker) ;
kyy = vl_alldist(Y,Y,ker) ;
kxy = vl_alldist(X,Y,ker) ;
kxx = repmat(diag(kxx), 1, size(s.Y,2)) ;
kyy = repmat(diag(kyy), 1, size(s.X,1))' ;
d2 = vl_alldist(X,Y,dist) ;
vl_assert_almost_equal(d2, kxx + kyy - 2 * kxy, '1e-6') ;
end
function D = makedist(cmp,X,Y)
[d,m] = size(X) ;
[d,n] = size(Y) ;
D = zeros(m,n) ;
for i = 1:m
for j = 1:n
acc = 0 ;
for k = 1:d
acc = acc + cmp(X(k,i),Y(k,j)) ;
end
D(i,j) = acc ;
end
end
conv = str2func(class(X)) ;
D = conv(D) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_ihashsum.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_ihashsum.m
| 581 |
utf_8
|
edc283062469af62056b0782b171f5fc
|
function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zeros(1,K,'uint32') ;
[h,id,next] = vl_ihashsum(h,id,next,K,s.data) ;
sel = vl_ihashfind(id,next,K,s.data) ;
count = double(h(sel)) ;
[drop,i,j] = unique(s.data','rows') ;
for k=1:size(s.data,2)
count_(k) = sum(j == j(k)) ;
end
vl_assert_equal(count,count_) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_grad.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_grad.m
| 434 |
utf_8
|
4d03eb33a6a4f68659f868da95930ffb
|
function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv_forward(s)
Ix = diff(s.I,2,1) ;
Iy = diff(s.I,2,1) ;
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_whistc.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_whistc.m
| 1,384 |
utf_8
|
81c446d35c82957659840ab2a579ec2c
|
function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o = ones(size(x)) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
function test_multidim()
x = rand(10, 20, 30) ;
e = linspace(0,1,10) ;
o = ones(size(x)) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
vl_assert_equal(histc(x, e, 1), vl_whistc(x, o, e, 1)) ;
vl_assert_equal(histc(x, e, 2), vl_whistc(x, o, e, 2)) ;
vl_assert_equal(histc(x, e, 3), vl_whistc(x, o, e, 3)) ;
function test_nan()
x = rand(10, 20, 30) ;
e = linspace(0,1,10) ;
o = ones(size(x)) ;
x(1:7:end) = NaN ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
vl_assert_equal(histc(x, e, 1), vl_whistc(x, o, e, 1)) ;
vl_assert_equal(histc(x, e, 2), vl_whistc(x, o, e, 2)) ;
vl_assert_equal(histc(x, e, 3), vl_whistc(x, o, e, 3)) ;
function test_no_edges()
x = rand(10, 20, 30) ;
o = ones(size(x)) ;
vl_assert_equal(histc(1, []), vl_whistc(1, 1, [])) ;
vl_assert_equal(histc(x, []), vl_whistc(x, o, [])) ;
vl_assert_equal(histc(x, [], 1), vl_whistc(x, o, [], 1)) ;
vl_assert_equal(histc(x, [], 2), vl_whistc(x, o, [], 2)) ;
vl_assert_equal(histc(x, [], 3), vl_whistc(x, o, [], 3)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_roc.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_roc.m
| 1,019 |
utf_8
|
9b2ae71c9dc3eda0fc54c65d55054d0c
|
function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr, [3 3 3 2 1 0] / 3) ;
function test_perfect_metrics(s)
[tpr,tnr,info] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(info.eer, 0) ;
vl_assert_almost_equal(info.auc, 1) ;
function test_swap1_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores1) ;
vl_assert_almost_equal(tpr, [0 1 1 2 2 2] / 2) ;
vl_assert_almost_equal(tnr, [3 3 2 2 1 0] / 3) ;
function test_swap1_tptn_stable(s)
[tpr,tnr] = vl_roc(s.labels,s.scores1,'stable',true) ;
vl_assert_almost_equal(tpr, [1 2 1 2 2] / 2) ;
vl_assert_almost_equal(tnr, [3 2 2 1 0] / 3) ;
function test_swap1_metrics(s)
[tpr,tnr,info] = vl_roc(s.labels,s.scores1) ;
vl_assert_almost_equal(info.eer, 1/3) ;
vl_assert_almost_equal(info.auc, 1 - 1/(2*3)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_dsift.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_dsift.m
| 2,048 |
utf_8
|
fbbfb16d5a21936c1862d9551f657ccc
|
function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize / magnif ;
windowSize = 5 ;
[f, d] = vl_dsift(vl_imsmooth(s.I, sqrt(scale.^2 - .25)), ...
'size', binSize, ...
'step', 10, ...
'bounds', [20,20,210,140], ...
'windowsize', windowSize, ...
'floatdescriptors') ;
[f_, d_] = vl_dsift(vl_imsmooth(s.I, sqrt(scale.^2 - .25)), ...
'size', binSize, ...
'step', 10, ...
'bounds', [20,20,210,140], ...
'windowsize', windowSize, ...
'floatdescriptors', ...
'fast') ;
error = std(d_(:) - d(:)) / std(d(:)) ;
assert(error < 0.1, 'dsift fast approximation not close') ;
function test_sift(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize / magnif ;
windowSizeRange = [1 1.2 5] ;
for wi = 1:length(windowSizeRange)
windowSize = windowSizeRange(wi) ;
[f, d] = vl_dsift(vl_imsmooth(s.I, sqrt(scale.^2 - .25)), ...
'size', binSize, ...
'step', 10, ...
'bounds', [20,20,210,140], ...
'windowsize', windowSize, ...
'floatdescriptors') ;
numKeys = size(f, 2) ;
f_ = [f ; ones(1, numKeys) * scale ; zeros(1, numKeys)] ;
[f_, d_] = vl_sift(s.I, ...
'magnif', magnif, ...
'frames', f_, ...
'firstoctave', -1, ...
'levels', 5, ...
'floatdescriptors', ...
'windowsize', windowSize) ;
error = std(d_(:) - d(:)) / std(d(:)) ;
assert(error < 0.1, 'dsift and sift equivalence') ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_alldist2.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_alldist2.m
| 2,284 |
utf_8
|
89a787e3d83516653ae8d99c808b9d67
|
function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equal(...
vl_alldist2(zeros(15,0), zeros(15,0), 'kl2'), ...
zeros(0,0)) ;
vl_assert_equal(...
vl_alldist2(zeros(15,0), zeros(15,12), 'kl2'), ...
zeros(0,12)) ;
vl_assert_equal(...
vl_alldist2(zeros(0,15), zeros(0,12), 'kl2'), ...
zeros(15,12)) ;
function test_self(s)
vl_assert_almost_equal(...
vl_alldist2(s.X, 'kl2'), ...
makedist(@(x,y) x*y, s.X, s.X), ...
1e-6) ;
function test_distances(s)
dists = {'chi2', 'l2', 'l1', 'hell', ...
'kchi2', 'kl2', 'kl1', 'khell'} ;
distsEquiv = { ...
@(x,y) (x-y)^2 / (x + y), ...
@(x,y) (x-y)^2, ...
@(x,y) abs(x-y), ...
@(x,y) (sqrt(x) - sqrt(y))^2, ...
@(x,y) 2 * (x*y) / (x + y), ...
@(x,y) x*y, ...
@(x,y) min(x,y), ...
@(x,y) sqrt(x.*y)};
types = {'single', 'double', 'sparse'} ;
for simd = [0 1]
for d = 1:length(dists)
for t = 1:length(types)
vl_simdctrl(simd) ;
X = feval(str2func(types{t}), s.X) ;
Y = feval(str2func(types{t}), s.Y) ;
a = vl_alldist2(X,Y,dists{d}) ;
b = makedist(distsEquiv{d},X,Y) ;
vl_assert_almost_equal(a,b, ...
1e-4, ...
'alldist failed for dist=%s type=%s simd=%d', ...
dists{d}, ...
types{t}, ...
simd) ;
end
end
end
function test_distance_kernel_pairs(s)
dists = {'chi2', 'l2', 'l1', 'hell'} ;
for d = 1:length(dists)
dist = char(dists{d}) ;
X = s.X ;
Y = s.Y ;
ker = ['k' dist] ;
kxx = vl_alldist2(X,X,ker) ;
kyy = vl_alldist2(Y,Y,ker) ;
kxy = vl_alldist2(X,Y,ker) ;
kxx = repmat(diag(kxx), 1, size(s.Y,2)) ;
kyy = repmat(diag(kyy), 1, size(s.X,1))' ;
d2 = vl_alldist2(X,Y,dist) ;
vl_assert_almost_equal(d2, kxx + kyy - 2 * kxy, '1e-6') ;
end
function D = makedist(cmp,X,Y)
[d,m] = size(X) ;
[d,n] = size(Y) ;
D = zeros(m,n) ;
for i = 1:m
for j = 1:n
acc = 0 ;
for k = 1:d
acc = acc + cmp(X(k,i),Y(k,j)) ;
end
D(i,j) = acc ;
end
end
conv = str2func(class(X)) ;
D = conv(D) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_fisher.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_fisher.m
| 2,097 |
utf_8
|
c9afd9ab635bd412cbf8be3c2d235f6b
|
function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s.prior = s.prior / sum(s.prior) ;
function test_basic(s)
phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ;
phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior) ;
vl_assert_almost_equal(phi, phi_, 1e-10) ;
function test_norm(s)
phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ;
phi_ = phi_ / norm(phi_) ;
phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'normalized') ;
vl_assert_almost_equal(phi, phi_, 1e-10) ;
function test_sqrt(s)
phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ;
phi_ = sign(phi_) .* sqrt(abs(phi_)) ;
phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'squareroot') ;
vl_assert_almost_equal(phi, phi_, 1e-10) ;
function test_improved(s)
phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ;
phi_ = sign(phi_) .* sqrt(abs(phi_)) ;
phi_ = phi_ / norm(phi_) ;
phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'improved') ;
vl_assert_almost_equal(phi, phi_, 1e-10) ;
function test_fast(s)
phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior, true) ;
phi_ = sign(phi_) .* sqrt(abs(phi_)) ;
phi_ = phi_ / norm(phi_) ;
phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'improved', 'fast') ;
vl_assert_almost_equal(phi, phi_, 1e-10) ;
function enc = simple_fisher(x, mu, sigma2, pri, fast)
if nargin < 5, fast = false ; end
sigma = sqrt(sigma2) ;
for k = 1:size(mu,2)
delta{k} = bsxfun(@times, bsxfun(@minus, x, mu(:,k)), 1./sigma(:,k)) ;
q(k,:) = log(pri(k)) - 0.5 * sum(log(sigma2(:,k))) - 0.5 * sum(delta{k}.^2,1) ;
end
q = exp(bsxfun(@minus, q, max(q,[],1))) ;
q = bsxfun(@times, q, 1 ./ sum(q,1)) ;
n = size(x,2) ;
if fast
[~,i] = max(q) ;
q = zeros(size(q)) ;
q(sub2ind(size(q),i,1:n)) = 1 ;
end
for k = 1:size(mu,2)
u{k} = delta{k} * q(k,:)' / n / sqrt(pri(k)) ;
v{k} = (delta{k}.^2 - 1) * q(k,:)' / n / sqrt(2*pri(k)) ;
end
enc = cat(1, u{:}, v{:}) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_imsmooth.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_imsmooth.m
| 1,837 |
utf_8
|
718235242cad61c9804ba5e881c22f59
|
function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change
% the signal.
I = ones(3) ;
for ker = {'triangular', 'gaussian'}
ker = char(ker) ;
J = vl_imsmooth(I, 2, ...
'kernel', ker, ...
'padding', 'continuity') ;
vl_assert_almost_equal(J, I, 1e-4, ...
'padding by continutiy with kernel = %s', ker) ;
end
function test_kernels(s)
for ker = {'triangular', 'gaussian'}
ker = char(ker) ;
for type = {@single, @double}
for simd = [0 1]
for sigma = [1 2 7]
for step = [1 2 3]
vl_simdctrl(simd) ;
conv = type{1} ;
g = equivalent_kernel(ker, sigma) ;
J = vl_imsmooth(conv(s.I), sigma, ...
'kernel', ker, ...
'padding', 'zero', ...
'subsample', step) ;
J_ = conv(convolve(s.I, g, step)) ;
vl_assert_almost_equal(J, J_, 1e-4, ...
'kernel=%s sigma=%f step=%d simd=%d', ...
ker, sigma, step, simd) ;
end
end
end
end
end
function g = equivalent_kernel(ker, sigma)
switch ker
case 'gaussian'
W = ceil(4*sigma) ;
g = exp(-.5*((-W:W)/(sigma+eps)).^2) ;
case 'triangular'
W = max(round(sigma),1) ;
g = W - abs(-W+1:W-1) ;
end
g = g / sum(g) ;
function I = convolve(I, g, step)
if strcmp(class(I),'single')
g = single(g) ;
else
g = double(g) ;
end
for k=1:size(I,3)
I(:,:,k) = conv2(g,g,I(:,:,k),'same');
end
I = I(1:step:end,1:step:end,:) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_svmtrain.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_svmtrain.m
| 4,277 |
utf_8
|
071b7c66191a22e8236fda16752b27aa
|
function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) -ones(1,Nn)] ;
s.lambda = 0.01 ;
s.biasMultiplier = 10 ;
if 0
figure(1) ; clf;
vl_plotframe(xp, 'g') ; hold on ;
vl_plotframe(xn, 'r') ;
axis equal ; grid on ;
end
% Run LibSVM as an accuate solver to compare results with. Note that
% LibSVM optimizes a slightly different cost function due to the way
% the bias is handled.
% [s.w, s.b] = accurate_solver(s.x, s.y, s.lambda, s.biasMultiplier) ;
s.w = [1.180762951236242; 0.098366470721632] ;
s.b = -1.540018443946204 ;
s.obj = obj(s, s.w, s.b) ;
end
function test_sgd_basic(s)
for conv = {@single, @double}
conv = conv{1} ;
vl_twister('state',0) ;
[w b info] = vl_svmtrain(s.x, s.y, s.lambda, ...
'Solver', 'sgd', ...
'BiasMultiplier', s.biasMultiplier, ...
'BiasLearningRate', 1/s.biasMultiplier, ...
'MaxNumIterations', 1e5, ...
'Epsilon', 1e-3) ;
% there are no absolute guarantees on the objective gap, but
% the heuristic SGD uses as stopping criterion seems reasonable
% within a factor 10 at least.
o = obj(s, w, b) ;
gap = o - s.obj ;
vl_assert_almost_equal(conv([w; b]), conv([s.w; s.b]), 0.1) ;
assert(gap <= 1e-2) ;
end
end
function test_sdca_basic(s)
for conv = {@single, @double}
conv = conv{1} ;
vl_twister('state',0) ;
[w b info] = vl_svmtrain(s.x, s.y, s.lambda, ...
'Solver', 'sdca', ...
'BiasMultiplier', s.biasMultiplier, ...
'MaxNumIterations', 1e5, ...
'Epsilon', 1e-3) ;
% the gap with the accurate solver cannot be
% greater than the duality gap.
o = obj(s, w, b) ;
gap = o - s.obj ;
vl_assert_almost_equal(conv([w; b]), conv([s.w; s.b]), 0.1) ;
assert(gap <= 1e-3) ;
end
end
function test_weights(s)
for algo = {'sgd', 'sdca'}
for conv = {@single, @double}
conv = conv{1} ;
vl_twister('state',0) ;
numRepeats = 10 ;
pos = find(s.y > 0) ;
neg = find(s.y < 0) ;
weights = ones(1, numel(s.y)) ;
weights(pos) = numRepeats ;
% simulate weighting by repeating positives
[w b info] = vl_svmtrain(...
s.x(:, [repmat(pos,1,numRepeats) neg]), ...
s.y(:, [repmat(pos,1,numRepeats) neg]), ...
s.lambda / (numel(pos) *numRepeats + numel(neg)) / (numel(pos) + numel(neg)), ...
'Solver', 'sdca', ...
'BiasMultiplier', s.biasMultiplier, ...
'MaxNumIterations', 1e6, ...
'Epsilon', 1e-4) ;
% apply weigthing
[w_ b_ info_] = vl_svmtrain(...
s.x, ...
s.y, ...
s.lambda, ...
'Solver', char(algo), ...
'BiasMultiplier', s.biasMultiplier, ...
'MaxNumIterations', 1e6, ...
'Epsilon', 1e-4, ...
'Weights', weights) ;
vl_assert_almost_equal(conv([w; b]), conv([w_; b_]), 0.05) ;
end
end
end
function test_homkermap(s)
for solver = {'sgd', 'sdca'}
for conv = {@single,@double}
conv = conv{1} ;
dataset = vl_svmdataset(conv(s.x), 'homkermap', struct('order',1)) ;
vl_twister('state',0) ;
[w_ b_] = vl_svmtrain(dataset, s.y, s.lambda) ;
x_hom = vl_homkermap(conv(s.x), 1) ;
vl_twister('state',0) ;
[w b] = vl_svmtrain(x_hom, s.y, s.lambda) ;
vl_assert_almost_equal([w; b],[w_; b_], 1e-7) ;
end
end
end
function [w,b] = accurate_solver(X, y, lambda, biasMultiplier)
addpath opt/libsvm/matlab/
N = size(X,2) ;
model = svmtrain(y', [(1:N)' X'*X], sprintf(' -c %f -t 4 -e 0.00001 ', 1/(lambda*N))) ;
w = X(:,model.SVs) * model.sv_coef ;
b = - model.rho ;
format long ;
disp('model w:')
disp(w)
disp('bias b:')
disp(b)
end
function o = obj(s, w, b)
o = (sum(w.*w) + b*b) * s.lambda / 2 + mean(max(0, 1 - s.y .* (w'*s.x + b))) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_phow.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_phow.m
| 549 |
utf_8
|
f761a3bb218af855986263c67b2da411
|
function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color', 'rgb') ;
assert(size(d,1) == 128*3) ;
function test_hsv(s)
[f,d] = vl_phow(s.I, 'color', 'hsv') ;
assert(size(d,1) == 128*3) ;
function test_opponent(s)
[f,d] = vl_phow(s.I, 'color', 'opponent') ;
assert(size(d,1) == 128*3) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_kmeans.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_kmeans.m
| 3,632 |
utf_8
|
0e1d6f4f8101c8982a0e743e0980c65a
|
function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = randn(128, 100) ;
function test_basic(s)
[centers, assignments, en] = vl_kmeans(s.X, 10, 'NumRepetitions', 10) ;
[centers_, assignments_, en_] = simpleKMeans(s.X, 10) ;
assert(en_ <= 1.1 * en, 'vl_kmeans did not optimize enough') ;
function test_algorithms(s)
distances = {'l1', 'l2'} ;
dataTypes = {'single','double'} ;
for dataType = dataTypes
for distance = distances
distance = char(distance) ;
conversion = str2func(char(dataType)) ;
X = conversion(s.X) ;
vl_twister('state',0) ;
[centers, assignments, en] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'Lloyd', ...
'Distance', distance) ;
vl_twister('state',0) ;
[centers_, assignments_, en_] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'Elkan', ...
'Distance', distance) ;
vl_twister('state',0) ;
[centers__, assignments__, en__] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'ANN', ...
'Distance', distance, ...
'NumTrees', 3, ...
'MaxNumComparisons',0) ;
vl_assert_almost_equal(centers, centers_, 1e-5) ;
vl_assert_almost_equal(assignments, assignments_, 1e-5) ;
vl_assert_almost_equal(en, en_, 1e-4) ;
vl_assert_almost_equal(centers, centers__, 1e-5) ;
vl_assert_almost_equal(assignments, assignments__, 1e-5) ;
vl_assert_almost_equal(en, en__, 1e-4) ;
vl_assert_almost_equal(centers_, centers__, 1e-5) ;
vl_assert_almost_equal(assignments_, assignments__, 1e-5) ;
vl_assert_almost_equal(en_, en__, 1e-4) ;
end
end
function test_patterns(s)
distances = {'l1', 'l2'} ;
dataTypes = {'single','double'} ;
for dataType = dataTypes
for distance = distances
distance = char(distance) ;
conversion = str2func(char(dataType)) ;
data = [1 1 0 0 ;
1 0 1 0] ;
data = conversion(data) ;
[centers, assignments, en] = vl_kmeans(data, 4, ...
'NumRepetitions', 100, ...
'Distance', distance) ;
assert(isempty(setdiff(data', centers', 'rows'))) ;
end
end
function [centers, assignments, en] = simpleKMeans(X, numCenters)
[dimension, numData] = size(X) ;
centers = randn(dimension, numCenters) ;
for iter = 1:10
[dists, assignments] = min(vl_alldist(centers, X)) ;
en = sum(dists) ;
centers = [zeros(dimension, numCenters) ; ones(1, numCenters)] ;
centers = vl_binsum(centers, ...
[X ; ones(1,numData)], ...
repmat(assignments, dimension+1, 1), 2) ;
centers = centers(1:end-1, :) ./ repmat(centers(end,:), dimension, 1) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_hikmeans.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_hikmeans.m
| 463 |
utf_8
|
dc3b493646e66316184e86ff4e6138ab
|
function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elkan(s)
[tree, assign] = vl_hikmeans(s.data,3,100,'method','elkan') ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_aib.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_aib.m
| 1,277 |
utf_8
|
78978ae54e7ebe991d136336ba4bf9c6
|
function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 5 6 6 7 1] (1 denotes the root).
[parents,cost] = vl_aib(Pcx) ;
vl_assert_equal(parents, [5 5 6 6 7 7 1]) ;
vl_assert_almost_equal(mi(Pcx)*[1 1 1], cost(1:3), 1e-3) ;
[cut,map,short] = vl_aibcut(parents,2) ;
vl_assert_equal(cut, [5 6]) ;
vl_assert_equal(map, [1 1 2 2 1 2 0]) ;
vl_assert_equal(short, [5 5 6 6 5 6 7]) ;
function test_cluster_null(s)
Pcx = [.5 .5 0 0
0 0 0 0] ;
% This results in the AIB tree
%
% 1 - \
% 5
% 2 - /
%
% 3 x
%
% 4 x
%
% If ClusterNull is specified, the values 3 and 4
% which have zero probability are merged first
%
% 1 ----------\
% 7
% 2 ----- \ /
% 6-/
% 3 -\ /
% 5 -/
% 4 -/
parents1 = vl_aib(Pcx) ;
parents2 = vl_aib(Pcx,'ClusterNull') ;
vl_assert_equal(parents1, [5 5 0 0 1 0 0]) ;
vl_assert_equal(parents2(3), parents2(4)) ;
function x = mi(P)
% mutual information
P1 = sum(P,1) ;
P2 = sum(P,2) ;
x = sum(sum(P .* log(max(P,1e-10) ./ (P2*P1)))) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_plotbox.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_plotbox.m
| 414 |
utf_8
|
aa06ce4932a213fb933bbede6072b029
|
function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10), 'r-.', 'LineWidth', 3) ;
close(1) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_imarray.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_imarray.m
| 795 |
utf_8
|
c5e6a5aa8c2e63e248814f5bd89832a8
|
function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_gray_indexed(s)
A = uint8(255*rand(23,15,4)) ;
B = vl_imarray(A,'movie',true,'cmap',gray(256)) ;
for k=1:size(A,3)
vl_assert_equal(squeeze(A(:,:,k)), ...
frame2im(B(k))) ;
end
function test_basic(s)
M = 3 ;
N = 4 ;
width = 32 ;
height = 15 ;
for i=1:M
for j=1:N
A{i,j} = rand(width,height) ;
end
end
A1 = A';
A1 = cat(3,A1{:}) ;
A2 = cell2mat(A) ;
B = vl_imarray(A1, 'layout', [M N]) ;
vl_assert_equal(A2,B) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_homkermap.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_homkermap.m
| 1,903 |
utf_8
|
c157052bf4213793a961bde1f73fb307
|
function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;
y = feval(conv{1}, y) ;
sx = sign(x) ;
sy = sign(y) ;
psix = vl_homkermap(x, args{:}) ;
psiy = vl_homkermap(y, args{:}) ;
k = vl_alldist(psix,psiy,'kl2') ;
k_ = (sx'*sy) .* vl_alldist(sx.*x,sy.*y,ker) ;
vl_assert_almost_equal(k, k_, 2e-2) ;
end
function test_uniform_kchi2(), check_ker('kchi2', 3, 'uniform', 15) ;
function test_uniform_kjs(), check_ker('kjs', 3, 'uniform', 15) ;
function test_uniform_kl1(), check_ker('kl1', 29, 'uniform', 15) ;
function test_rect_kchi2(), check_ker('kchi2', 3, 'rectangular', 15) ;
function test_rect_kjs(), check_ker('kjs', 3, 'rectangular', 15) ;
function test_rect_kl1(), check_ker('kl1', 29, 'rectangular', 10) ;
function test_auto_uniform_kchi2(),check_ker('kchi2', 3, 'uniform') ;
function test_auto_uniform_kjs(), check_ker('kjs', 3, 'uniform') ;
function test_auto_uniform_kl1(), check_ker('kl1', 25, 'uniform') ;
function test_auto_rect_kchi2(), check_ker('kchi2', 3, 'rectangular') ;
function test_auto_rect_kjs(), check_ker('kjs', 3, 'rectangular') ;
function test_auto_rect_kl1(), check_ker('kl1', 25, 'rectangular') ;
function test_gamma()
x = linspace(0,1,20) ;
for gamma = linspace(.2,2,10)
k = vl_alldist(x, 'kchi2') .* (x'*x + 1e-12).^((gamma-1)/2) ;
psix = vl_homkermap(x, 3, 'kchi2', 'gamma', gamma) ;
assert(norm(k - psix'*psix) < 1e-2) ;
end
function test_negative()
x = linspace(-1,1,20) ;
k = vl_alldist(abs(x), 'kchi2') .* (sign(x)'*sign(x)) ;
psix = vl_homkermap(x, 3, 'kchi2') ;
assert(norm(k - psix'*psix) < 1e-2) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_slic.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_slic.m
| 200 |
utf_8
|
12a6465e3ef5b4bcfd7303cd8a9229d4
|
function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_ikmeans.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_ikmeans.m
| 466 |
utf_8
|
1ee2f647ac0035ed0d704a0cd615b040
|
function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elkan(s)
[centers, assign] = vl_ikmeans(s.data,100,'method','elkan') ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_mser.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_mser.m
| 242 |
utf_8
|
1ad33563b0c86542a2978ee94e0f4a39
|
function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_inthist.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_inthist.m
| 811 |
utf_8
|
459027d0c54d8f197563a02ab66ef45d
|
function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function test_sample(s)
rand('state',0) ;
boxes = 10 * rand(4,20) + .5 ;
boxes(3:4,:) = boxes(3:4,:) + boxes(1:2,:) ;
boxes = min(boxes, 10) ;
boxes = uint32(boxes) ;
inthist = vl_inthist(s.labels) ;
hist = vl_sampleinthist(inthist, boxes) ;
function hist = inthist_slow(labels, numLabels)
m = size(labels,1) ;
n = size(labels,2) ;
l = numLabels ;
b = zeros(m*n,l) ;
b = vl_binsum(b, 1, reshape(labels,m*n,[]), 2) ;
b = reshape(b,m,n,l) ;
for k=1:l
hist(:,:,k) = cumsum(cumsum(b(:,:,k)')') ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_imdisttf.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_imdisttf.m
| 1,885 |
utf_8
|
ae921197988abeb984cbcdf9eaf80e77
|
function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
assert(isequal(D, conv(-1 - [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
assert(isequal(map, 5 * ones(3))) ;
end
function test_1x1()
assert(isequal(1, vl_imdisttf(1))) ;
function test_rand()
I = rand(13,31) ;
for t=1:4
param = [rand randn rand randn] ;
[D0,map0] = imdisttf_equiv(I,param) ;
[D,map] = vl_imdisttf(I,param) ;
vl_assert_almost_equal(D,D0,1e-10)
assert(isequal(map,map0)) ;
end
function test_param()
I = zeros(3,4) ;
I(1,1) = -1 ;
[D,map] = vl_imdisttf(I,[1 0 1 0]);
assert(isequal(-[1 0 0 0 ;
0 0 0 0 ;
0 0 0 0 ;], D)) ;
D0 = -[1 .9 .6 .1 ;
0 0 0 0 ;
0 0 0 0 ;] ;
[D,map] = vl_imdisttf(I,[.1 0 1 0]);
vl_assert_almost_equal(D,D0,1e-10);
D0 = -[1 .9 .6 .1 ;
.9 .8 .5 0 ;
.6 .5 .2 0 ;] ;
[D,map] = vl_imdisttf(I,[.1 0 .1 0]);
vl_assert_almost_equal(D,D0,1e-10);
D0 = -[.9 1 .9 .6 ;
.8 .9 .8 .5 ;
.5 .6 .5 .2 ; ] ;
[D,map] = vl_imdisttf(I,[.1 1 .1 0]);
vl_assert_almost_equal(D,D0,1e-10);
function test_special()
I = rand(13,31) -.5 ;
D = vl_imdisttf(I, [0 0 1e5 0]) ;
vl_assert_almost_equal(D(:,1),min(I,[],2),1e-10);
D = vl_imdisttf(I, [1e5 0 0 0]) ;
vl_assert_almost_equal(D(1,:),min(I,[],1),1e-10);
function [D,map]=imdisttf_equiv(I,param)
D = inf + zeros(size(I)) ;
map = zeros(size(I)) ;
ur = 1:size(D,2) ;
vr = 1:size(D,1) ;
[u,v] = meshgrid(ur,vr) ;
for v_=vr
for u_=ur
E = I(v_,u_) + ...
param(1) * (u - u_ - param(2)).^2 + ...
param(3) * (v - v_ - param(4)).^2 ;
map(E < D) = sub2ind(size(I),v_,u_) ;
D = min(D,E) ;
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_vlad.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_vlad.m
| 1,977 |
utf_8
|
d3797288d6edb1d445b890db3780c8ce
|
function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
assignments = eye(3) ;
phi = vl_vlad(x, mu, assignments, 'unnormalized') ;
vl_assert_equal(phi, [1 2 3]') ;
mu = [0, 1, 2] ;
phi = vl_vlad(x, mu, assignments, 'unnormalized') ;
vl_assert_equal(phi, [1 1 1]') ;
phi = vl_vlad([x x], mu, [assignments assignments], 'unnormalized') ;
vl_assert_equal(phi, [2 2 2]') ;
function test_rand (s)
phi_ = simple_vlad(s.x, s.mu, s.assignments) ;
phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized') ;
vl_assert_equal(phi, phi_) ;
function test_norm (s)
phi_ = simple_vlad(s.x, s.mu, s.assignments) ;
phi_ = phi_ / norm(phi_) ;
phi = vl_vlad(s.x, s.mu, s.assignments) ;
vl_assert_almost_equal(phi, phi_, 1e-4) ;
function test_sqrt (s)
phi_ = simple_vlad(s.x, s.mu, s.assignments) ;
phi_ = sign(phi_) .* sqrt(abs(phi_)) ;
phi_ = phi_ / norm(phi_) ;
phi = vl_vlad(s.x, s.mu, s.assignments, 'squareroot') ;
vl_assert_almost_equal(phi, phi_, 1e-4) ;
function test_individual (s)
phi_ = simple_vlad(s.x, s.mu, s.assignments) ;
phi_ = reshape(phi_, size(s.x,1), []) ;
phi_ = bsxfun(@times, phi_, 1 ./ sqrt(sum(phi_.^2))) ;
phi_ = phi_(:) ;
phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized', 'normalizecomponents') ;
vl_assert_almost_equal(phi, phi_, 1e-4) ;
function test_mass (s)
phi_ = simple_vlad(s.x, s.mu, s.assignments) ;
phi_ = reshape(phi_, size(s.x,1), []) ;
phi_ = bsxfun(@times, phi_, 1 ./ sum(s.assignments,2)') ;
phi_ = phi_(:) ;
phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized', 'normalizemass') ;
vl_assert_almost_equal(phi, phi_, 1e-4) ;
function enc = simple_vlad(x, mu, assign)
for i = 1:size(assign,1)
enc{i} = x * assign(i,:)' - sum(assign(i,:)) * mu(:,i) ;
end
enc = cat(1, enc{:}) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_pr.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_pr.m
| 3,763 |
utf_8
|
4d1da5ccda1a7df2bec35b8f12fdd620
|
function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, [0 1 2 2 2 2] / 2) ;
function test_perfect_metrics(s)
[rc,pr,info] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(info.auc, 1) ;
vl_assert_almost_equal(info.ap, 1) ;
vl_assert_almost_equal(info.ap_interp_11, 1) ;
function test_swap1_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores1) ;
vl_assert_almost_equal(pr, [1 1/1 1/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, [0 1 1 2 2 2] / 2) ;
function test_swap1_tptn_stable(s)
[rc,pr] = vl_pr(s.labels,s.scores1,'stable',true) ;
vl_assert_almost_equal(pr, [1/1 2/3 1/2 2/4 2/5]) ;
vl_assert_almost_equal(rc, [1 2 1 2 2] / 2) ;
function test_swap1_metrics(s)
[rc,pr,info] = vl_pr(s.labels,s.scores1) ;
clf; vl_pr(s.labels,s.scores1) ;
vl_assert_almost_equal(info.auc, [.5 + .5 * (.5 + 2/3)/2]) ;
vl_assert_almost_equal(info.ap, [1/1 + 2/3]/2) ;
vl_assert_almost_equal(info.ap_interp_11, mean([1 1 1 1 1 1 2/3 2/3 2/3 2/3 2/3])) ;
function test_inf(s)
scores = [1 -inf -1 -1 -1 -1] ;
labels = [1 1 -1 -1 -1 -1] ;
[rc1,pr1,info1] = vl_pr(labels, scores, 'includeInf', true) ;
[rc2,pr2,info2] = vl_pr(labels, scores, 'includeInf', false) ;
vl_assert_equal(numel(rc1), numel(rc2) + 1) ;
vl_assert_almost_equal(info1.auc, [1 * .5 + (1/5 + 2/6)/2 * .5]) ;
vl_assert_almost_equal(info1.ap, [1 * .5 + 2/6 * .5]) ;
vl_assert_almost_equal(info1.ap_interp_11, [1 * 6/11 + 2/6 * 5/11]) ;
vl_assert_almost_equal(info2.auc, 0.5) ;
vl_assert_almost_equal(info2.ap, 0.5) ;
vl_assert_almost_equal(info2.ap_interp_11, 1 * 6 / 11) ;
function test_inf_stable(s)
scores = [-1 -1 -1 -1 -inf +1] ;
labels = [-1 -1 -1 -1 +1 +1] ;
[rc1,pr1,info1] = vl_pr(labels, scores, 'includeInf', true, 'stable', true) ;
[rc2,pr2,info2] = vl_pr(labels, scores, 'includeInf', false, 'stable', true) ;
[rc1_,pr1_,info1_] = vl_pr(labels, scores, 'includeInf', true, 'stable', false) ;
[rc2_,pr2_,info2_] = vl_pr(labels, scores, 'includeInf', false, 'stable', false) ;
% stability does not change scores
vl_assert_almost_equal(info1,info1_) ;
vl_assert_almost_equal(info2,info2_) ;
% unstable with inf (first point (0,1) is conventional)
vl_assert_almost_equal(rc1_, [0 .5 .5 .5 .5 .5 1])
vl_assert_almost_equal(pr1_, [1 1 1/2 1/3 1/4 1/5 2/6])
% unstable without inf
vl_assert_almost_equal(rc2_, [0 .5 .5 .5 .5 .5])
vl_assert_almost_equal(pr2_, [1 1 1/2 1/3 1/4 1/5])
% stable with inf (no conventional point here)
vl_assert_almost_equal(rc1, [.5 .5 .5 .5 1 .5]) ;
vl_assert_almost_equal(pr1, [1/2 1/3 1/4 1/5 2/6 1]) ;
% stable without inf (no conventional point and -inf are NaN)
vl_assert_almost_equal(rc2, [.5 .5 .5 .5 NaN .5]) ;
vl_assert_almost_equal(pr2, [1/2 1/3 1/4 1/5 NaN 1]) ;
function test_normalised_pr(s)
scores = [+1 +2] ;
labels = [+1 -1] ;
[rc1,pr1,info1] = vl_pr(labels,scores) ;
[rc2,pr2,info2] = vl_pr(labels,scores,'normalizePrior',.5) ;
vl_assert_almost_equal(pr1, pr2) ;
vl_assert_almost_equal(rc1, rc2) ;
scores_ = [+1 +2 +2 +2] ;
labels_ = [+1 -1 -1 -1] ;
[rc3,pr3,info3] = vl_pr(labels_,scores_) ;
[rc4,pr4,info4] = vl_pr(labels,scores,'normalizePrior',1/4) ;
vl_assert_almost_equal(info3, info4) ;
function test_normalised_pr_corner_cases(s)
scores = 1:10 ;
labels = ones(1,10) ;
[rc1,pr1,info1] = vl_pr(labels,scores) ;
vl_assert_almost_equal(rc1, (0:10)/10) ;
vl_assert_almost_equal(pr1, ones(1,11)) ;
scores = 1:10 ;
labels = zeros(1,10) ;
[rc2,pr2,info2] = vl_pr(labels,scores) ;
vl_assert_almost_equal(rc2, zeros(1,11)) ;
vl_assert_almost_equal(pr2, ones(1,11)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_hog.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_hog.m
| 1,555 |
utf_8
|
eed7b2a116d142040587dc9c4eb7cd2e
|
function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic_call(s)
cellSize = 8 ;
hog = vl_hog(s.im, cellSize) ;
function test_bilinear_orientations(s)
cellSize = 8 ;
vl_hog(s.im, cellSize, 'bilinearOrientations') ;
function test_variants_and_flipping(s)
variants = {'uoctti', 'dalaltriggs'} ;
numOrientationsRange = 3:9 ;
cellSize = 8 ;
for cellSize = [4 8 16]
for i=1:numel(variants)
for j=1:numel(numOrientationsRange)
args = {'bilinearOrientations', ...
'variant', variants{i}, ...
'numOrientations', numOrientationsRange(j)} ;
hog = vl_hog(s.imSmall, cellSize, args{:}) ;
perm = vl_hog('permutation', args{:}) ;
hog1 = vl_hog(s.imSmallFlipped, cellSize, args{:}) ;
hog2 = hog(:,end:-1:1,perm) ;
%norm(hog1(:)-hog2(:))
vl_assert_almost_equal(hog1,hog2,1e-3) ;
end
end
end
function test_polar(s)
cellSize = 8 ;
im = s.round ;
for b = [0 1]
if b
args = {'bilinearOrientations'} ;
else
args = {} ;
end
hog1 = vl_hog(im, cellSize, args{:}) ;
[ix,iy] = vl_grad(im) ;
m = sqrt(ix.^2 + iy.^2) ;
a = atan2(iy,ix) ;
m(:,[1 end]) = 0 ;
m([1 end],:) = 0 ;
hog2 = vl_hog(cat(3,m,a), cellSize, 'DirectedPolarField', args{:}) ;
vl_assert_almost_equal(hog1,hog2,norm(hog1(:))/1000) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_argparse.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_argparse.m
| 795 |
utf_8
|
e72185b27206d0ee1dfdc19fe77a5be6
|
function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
opts = vl_argparse(opts, {'field1', 4, 'field1', 9}) ;
assert(isequal(opts, opts_)) ;
function test_error()
opts.field1 = 1 ;
try
opts = vl_argparse(opts, {'field2', 5}) ;
catch e
return ;
end
assert(false) ;
function test_leftovers()
opts1.field1 = 1 ;
opts2.field2 = 1 ;
opts1_.field1 = 2 ;
opts2_.field2 = 2 ;
[opts1,args] = vl_argparse(opts1, {'field1', 2, 'field2', 2}) ;
opts2 = vl_argparse(opts2, args) ;
assert(isequal(opts1,opts1_), isequal(opts2,opts2_)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_liop.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_liop.m
| 1,023 |
utf_8
|
a162be369073bed18e61210f44088cf3
|
function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity order pattern are equal. In
% particular, if the blob intensity decreases away from the center,
% then all local intensities sampled in a neighbourhood of 2 elements
% are already sorted (see LIOP details).
d = vl_liop(s.blob, ...
'IntensityThreshold', 0, ...
'NumNeighbours', 2, ...
'NumSpatialBins', 1) ;
assert(isequal(d, single([1;0]))) ;
function test_neighbours(s)
for n=2:5
for p=1:3
d = vl_liop(s.patch, 'NumNeighbours', n, 'NumSpatialBins', p) ;
assert(numel(d) == p * factorial(n)) ;
end
end
function test_multiple(s)
x = randn(31,31,3, 'single') ;
d = vl_liop(x) ;
for i=1:3
d_(:,i) = vl_liop(squeeze(x(:,:,i))) ;
end
assert(isequal(d,d_)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_test_binsearch.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/xtest/vl_test_binsearch.m
| 1,339 |
utf_8
|
85dc020adce3f228fe7dfb24cf3acc63
|
function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_assert_equal(vl_binsearch([-inf +inf], x), [1 1 1 1 2]) ;
function test_empty()
vl_assert_equal(vl_binsearch([], []), []) ;
function test_bnd()
vl_assert_equal(vl_binsearch([], [1]), [0]) ;
vl_assert_equal(vl_binsearch([], [-inf]), [0]) ;
vl_assert_equal(vl_binsearch([], [+inf]), [0]) ;
vl_assert_equal(vl_binsearch([1], [.9]), [0]) ;
vl_assert_equal(vl_binsearch([1], [1]), [1]) ;
vl_assert_equal(vl_binsearch([1], [-inf]), [0]) ;
vl_assert_equal(vl_binsearch([1], [+inf]), [1]) ;
function test_basic()
vl_assert_equal(vl_binsearch(-10:10, -10:10), 1:21) ;
vl_assert_equal(vl_binsearch(-10:10, -11:10), 0:21) ;
vl_assert_equal(vl_binsearch(-10:10, [-inf, -11:10, +inf]), [0 0:21 21]) ;
function test_frac()
vl_assert_equal(vl_binsearch(1:10, 1:.5:10), floor(1:.5:10))
vl_assert_equal(vl_binsearch(1:10, fliplr(1:.5:10)), ...
fliplr(floor(1:.5:10))) ;
function test_array()
a = reshape(1:100,10,10) ;
b = reshape(1:.5:100.5, 2, []) ;
c = floor(b) ;
vl_assert_equal(vl_binsearch(a,b), c) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_roc.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/plotop/vl_roc.m
| 10,113 |
utf_8
|
22fd8ff455ee62a96ffd94b9074eafeb
|
function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative one. SCORES is a row vector of
% corresponding sample scores, usually obtained from a
% classifier. The scores induce a ranking of the samples where
% larger scores should correspond to positive labels.
%
% Without output arguments, the function plots the ROC graph of the
% specified data in the current graphical axis.
%
% Otherwise, the function returns the true positive and true
% negative rates TPR and TNR. These are vectors of the same size of
% LABELS and SCORES and are computed as follows. Samples are ranked
% by decreasing scores, starting from rank 1. TPR(K) and TNR(K) are
% the true positive and true negative rates when samples of rank
% smaller or equal to K-1 are predicted to be positive. So for
% example TPR(3) is the true positive rate when the two samples with
% largest score are predicted to be positive. Similarly, TPR(1) is
% the true positive rate when no samples are predicted to be
% positive, i.e. the constant 0.
%
% Setting a label to zero ignores the corresponding sample in the
% calculations, as if the sample was removed from the data. Setting
% the score of a sample to -INF causes the function to assume that
% that sample was never retrieved. If there are samples with -INF
% score, the ROC curve is incomplete as the maximum recall is less
% than 1.
%
% [TPR,TNR,INFO] = VL_ROC(...) returns an additional structure INFO
% with the following fields:
%
% info.auc:: Area under the ROC curve (AUC).
% This is the area under the ROC plot, the parametric curve
% (FPR(S), TPR(S)). The PLOT option can be used to plot variants
% of this curve, which affects the calculation of a corresponding
% AUC.
%
% info.eer:: Equal error rate (EER).
% The equal error rate is the value of FPR (or FNR) when the ROC
% curves intersects the line connecting (0,0) to (1,1).
%
% info.eerThreshold:: EER threshold.
% The value of the score for which the EER is attained.
%
% VL_ROC() accepts the following options:
%
% Plot:: []
% Setting this option turns on plotting unconditionally. The
% following plot variants are supported:
%
% tntp:: Plot TPR against TNR (standard ROC plot).
% tptn:: Plot TNR against TPR (recall on the horizontal axis).
% fptp:: Plot TPR against FPR.
% fpfn:: Plot FNR against FPR (similar to a DET curve).
%
% Note that this option will affect the INFO.AUC value computation
% too.
%
% NumPositives:: []
% NumNegatives:: []
% If either of these parameters is set to a number, the function
% pretends that LABELS contains the specified number of
% positive/negative labels. NUMPOSITIVES/NUMNEGATIVES cannot be
% smaller than the actual number of positive/negative entries in
% LABELS. The additional positive/negative labels are appended to
% the end of the sequence as if they had -INF scores (as explained
% above, the function interprets such samples as `not
% retrieved'). This feature can be used to evaluate the
% performance of a large-scale retrieval experiment in which only
% a subset of highly-scoring results are recorded for efficiency
% reason.
%
% Stable:: false
% If set to true, TPR and TNR are returned in the same order
% of LABELS and SCORES rather than being sorted by decreasing
% score.
%
% About the ROC curve::
% Consider a classifier that predicts as positive all samples whose
% score is not smaller than a threshold S. The ROC curve represents
% the performance of such classifier as the threshold S is
% changed. Formally, define
%
% P = overall num. of positive samples,
% N = overall num. of negative samples,
%
% and for each threshold S
%
% TP(S) = num. of samples that are correctly classified as positive,
% TN(S) = num. of samples that are correctly classified as negative,
% FP(S) = num. of samples that are incorrectly classified as positive,
% FN(S) = num. of samples that are incorrectly classified as negative.
%
% Consider also the rates:
%
% TPR = TP(S) / P, FNR = FN(S) / P,
% TNR = TN(S) / N, FPR = FP(S) / N,
%
% and notice that, by definition,
%
% P = TP(S) + FN(S) , N = TN(S) + FP(S),
% 1 = TPR(S) + FNR(S), 1 = TNR(S) + FPR(S).
%
% The ROC curve is the parametric curve (FPR(S), TPR(S)) obtained
% as the classifier threshold S is varied in the reals. The TPR is
% the same as `recall' in a PR curve (see VL_PR()).
%
% The ROC curve is contained in the square with vertices (0,0) The
% (average) ROC curve of a random classifier is a line which
% connects (1,0) and (0,1).
%
% The ROC curve is independent of the prior probability of the
% labels (i.e. of P/(P+N) and N/(P+N)).
%
% REFERENCES:
% [1] http://en.wikipedia.org/wiki/Receiver_operating_characteristic
%
% See also: VL_PR(), VL_DET(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
[tp, fp, p, n, perm, varargin] = vl_tpfp(labels, scores, varargin{:}) ;
opts.plot = [] ;
opts.stable = false ;
opts = vl_argparse(opts,varargin) ;
% compute the rates
small = 1e-10 ;
tpr = tp / max(p, small) ;
fpr = fp / max(n, small) ;
fnr = 1 - tpr ;
tnr = 1 - fpr ;
do_plots = ~isempty(opts.plot) || nargout == 0 ;
if isempty(opts.plot), opts.plot = 'fptp' ; end
% --------------------------------------------------------------------
% Additional info
% --------------------------------------------------------------------
if nargout > 2 || do_plots
% Area under the curve. Since the curve is a staircase (in the
% sense that for each sample either tn is decremented by one
% or tp is incremented by one but the other remains fixed),
% the integral is particularly simple and exact.
switch opts.plot
case 'tntp', info.auc = -sum(tpr .* diff([0 tnr])) ;
case 'fptp', info.auc = +sum(tpr .* diff([0 fpr])) ;
case 'tptn', info.auc = +sum(tnr .* diff([0 tpr])) ;
case 'fpfn', info.auc = +sum(fnr .* diff([0 fpr])) ;
otherwise
error('''%s'' is not a valid PLOT type.', opts.plot);
end
% Equal error rate. One must find the index S in correspondence of
% which TNR(S) and TPR(s) cross. Note that TPR(S) is non-decreasing,
% TNR(S) is non-increasing, and from rank S to rank S+1 only one of
% the two quantities can change. Hence there are exactly two types
% of crossing points:
%
% 1) TNR(S) = TNR(S+1) = EER and TPR(S) <= EER, TPR(S+1) > EER,
% 2) TPR(S) = TPR(S+1) = EER and TNR(S) > EER, TNR(S+1) <= EER.
%
% Moreover, if the maximum TPR is smaller than 1, then it is
% possible that neither of the two cases realizes. In the latter
% case, we return EER=NaN.
s = max(find(tnr > tpr)) ;
if s == length(tpr)
info.eer = NaN ;
info.eerThreshold = 0 ;
else
if tpr(s) == tpr(s+1)
info.eer = 1 - tpr(s) ;
else
info.eer = 1 - tnr(s) ;
end
info.eerThreshold = scores(perm(s)) ;
end
end
% --------------------------------------------------------------------
% Plot
% --------------------------------------------------------------------
if do_plots
cla ; hold on ;
switch lower(opts.plot)
case 'tntp'
hroc = plot(tnr, tpr, 'b', 'linewidth', 2) ;
hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ;
spline([0 1], [0 1], 'k--', 'linewidth', 1) ;
plot(1-info.eer, 1-info.eer, 'k*', 'linewidth', 1) ;
xlabel('true negative rate') ;
ylabel('true positive rate (recall)') ;
loc = 'sw' ;
case 'fptp'
hroc = plot(fpr, tpr, 'b', 'linewidth', 2) ;
hrand = spline([0 1], [0 1], 'r--', 'linewidth', 2) ;
spline([1 0], [0 1], 'k--', 'linewidth', 1) ;
plot(info.eer, 1-info.eer, 'k*', 'linewidth', 1) ;
xlabel('false positive rate') ;
ylabel('true positive rate (recall)') ;
loc = 'se' ;
case 'tptn'
hroc = plot(tpr, tnr, 'b', 'linewidth', 2) ;
hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ;
spline([0 1], [0 1], 'k--', 'linewidth', 1) ;
plot(1-info.eer, 1-info.eer, 'k*', 'linewidth', 1) ;
xlabel('true positive rate (recall)') ;
ylabel('false positive rate') ;
loc = 'sw' ;
case 'fpfn'
hroc = plot(fpr, fnr, 'b', 'linewidth', 2) ;
hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ;
spline([0 1], [0 1], 'k--', 'linewidth', 1) ;
plot(info.eer, info.eer, 'k*', 'linewidth', 1) ;
xlabel('false positive (false alarm) rate') ;
ylabel('false negative (miss) rate') ;
loc = 'ne' ;
otherwise
error('''%s'' is not a valid PLOT type.', opts.plot);
end
grid on ;
xlim([0 1]) ;
ylim([0 1]) ;
axis square ;
title(sprintf('ROC (AUC: %.2f%%, EER: %.2f%%)', info.auc * 100, info.eer * 100), ...
'interpreter', 'none') ;
legend([hroc hrand], 'ROC', 'ROC rand.', 'location', loc) ;
end
% --------------------------------------------------------------------
% Stable output
% --------------------------------------------------------------------
if opts.stable
tpr(1) = [] ;
tnr(1) = [] ;
tpr_ = tpr ;
tnr_ = tnr ;
tpr = NaN(size(tpr)) ;
tnr = NaN(size(tnr)) ;
tpr(perm) = tpr_ ;
tnr(perm) = tnr_ ;
end
% --------------------------------------------------------------------
function h = spline(x,y,spec,varargin)
% --------------------------------------------------------------------
prop = vl_linespec2prop(spec) ;
h = line(x,y,prop{:},varargin{:}) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_click.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/plotop/vl_click.m
| 2,661 |
utf_8
|
6982e869cf80da57fdf68f5ebcd05a86
|
function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any key, in which case the
% empty matrix is returned.
%
% P=VL_CLICK(N) lets the user select N points in a row. The user can
% stop inserting points by pressing any key, in which case the
% partial list is returned.
%
% VL_CLICK() accepts the following options:
%
% PlotMarker:: [0]
% Plot a marker as points are selected. The markers are deleted on
% exiting the function.
%
% See also: VL_CLICKPOINT(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
plot_marker = 0 ;
for k=1:2:length(varargin)
switch lower(varargin{k})
case 'plotmarker'
plot_marker = varargin{k+1} ;
otherwise
error(['Uknown option ''', varargin{k}, '''.']) ;
end
end
if nargin < 1
N=1;
end
% --------------------------------------------------------------------
% Do job
% --------------------------------------------------------------------
fig = gcf ;
is_hold = ishold ;
hold on ;
bhandler = get(fig,'WindowButtonDownFcn') ;
khandler = get(fig,'KeyPressFcn') ;
pointer = get(fig,'Pointer') ;
set(fig,'WindowButtonDownFcn',@click_handler) ;
set(fig,'KeyPressFcn',@key_handler) ;
set(fig,'Pointer','crosshair') ;
P=[] ;
h=[] ;
data.exit=0;
guidata(fig,data) ;
while size(P,2) < N
uiwait(fig) ;
data = guidata(fig) ;
if(data.exit)
break ;
end
P = [P data.P] ;
if( plot_marker )
h=[h plot(data.P(1),data.P(2),'rx')] ;
end
end
if ~is_hold
hold off ;
end
if( plot_marker )
pause(.1);
delete(h) ;
end
set(fig,'WindowButtonDownFcn',bhandler) ;
set(fig,'KeyPressFcn',khandler) ;
set(fig,'Pointer',pointer) ;
% ====================================================================
function click_handler(obj,event)
% --------------------------------------------------------------------
data = guidata(gcbo) ;
P = get(gca, 'CurrentPoint') ;
P = [P(1,1); P(1,2)] ;
data.P = P ;
guidata(obj,data) ;
uiresume(gcbo) ;
% ====================================================================
function key_handler(obj,event)
% --------------------------------------------------------------------
data = guidata(gcbo) ;
data.exit = 1 ;
guidata(obj,data) ;
uiresume(gcbo) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_pr.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/plotop/vl_pr.m
| 9,138 |
utf_8
|
c7fe6832d2b6b9917896810c52a05479
|
function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are the scores of the samples obtained from
% a classifier, where lager scores should correspond to positive
% samples.
%
% Samples are ranked by decreasing scores, starting from rank 1.
% PRECISION(K) and RECALL(K) are the precison and recall when
% samples of rank smaller or equal to K-1 are predicted to be
% positive and the remaining to be negative. So for example
% PRECISION(3) is the percentage of positive samples among the two
% samples with largest score. PRECISION(1) is the precision when no
% samples are predicted to be positive and is conventionally set to
% the value 1.
%
% Set to zero the lables of samples that should be ignored in the
% evaluation. Set to -INF the scores of samples which are not
% retrieved. If there are samples with -INF score, then the PR curve
% may have maximum recall smaller than 1, unless the INCLUDEINF
% option is used (see below). The options NUMNEGATIVES and
% NUMPOSITIVES can be used to add additional surrogate samples with
% -INF score (see below).
%
% [RECALL, PRECISION, INFO] = VL_PR(...) returns an additional
% structure INFO with the following fields:
%
% info.auc::
% The area under the precision-recall curve. If the INTERPOLATE
% option is set to FALSE, then trapezoidal interpolation is used
% to integrate the PR curve. If the INTERPOLATE option is set to
% TRUE, then the curve is piecewise constant and no other
% approximation is introduced in the calculation of the area. In
% the latter case, INFO.AUC is the same as INFO.AP.
%
% info.ap::
% Average precision as defined by TREC. This is the average of the
% precision observed each time a new positive sample is
% recalled. In this calculation, any sample with -INF score
% (unless INCLUDEINF is used) and any additional positive induced
% by NUMPOSITIVES has precision equal to zero. If the INTERPOLATE
% option is set to true, the AP is computed from the interpolated
% precision and the result is the same as INFO.AUC. Note that AP
% as defined by TREC normally does not use interpolation [1].
%
% info.ap_interp_11::
% 11-points interpolated average precision as defined by TREC.
% This is the average of the maximum precision for recall levels
% greather than 0.0, 0.1, 0.2, ..., 1.0. This measure was used in
% the PASCAL VOC challenge up to the 2008 edition.
%
% info.auc_pa08::
% Deprecated. It is the same of INFO.AP_INTERP_11.
%
% VL_PR(...) with no output arguments plots the PR curve in the
% current axis.
%
% VL_PR() accepts the following options:
%
% Interpolate:: false
% If set to true, use interpolated precision. The interpolated
% precision is defined as the maximum precision for a given recall
% level and onwards. Here it is implemented as the culumative
% maximum from low to high scores of the precision.
%
% NumPositives:: []
% NumNegatives:: []
% If set to a number, pretend that LABELS contains this may
% positive/negative labels. NUMPOSITIVES/NUMNEGATIVES cannot be
% smaller than the actual number of positive/negative entrires in
% LABELS. The additional positive/negative labels are appended to
% the end of the sequence, as if they had -INF scores (not
% retrieved). This is useful to evaluate large retrieval systems
% for which one stores ony a handful of top results for efficiency
% reasons.
%
% IncludeInf:: false
% If set to true, data with -INF score SCORES is included in the
% evaluation and the maximum recall is 1 even if -INF scores are
% present. This option does not include any additional positive or
% negative data introduced by specifying NUMPOSITIVES and
% NUMNEGATIVES.
%
% Stable:: false
% If set to true, RECALL and PRECISION are returned in the same order
% of LABELS and SCORES rather than being sorted by decreasing
% score (increasing recall). Samples with -INF scores are assigned
% RECALL and PRECISION equal to NaN.
%
% NormalizePrior:: []
% If set to a scalar, reweights positive and negative labels so
% that the fraction of positive ones is equal to the specified
% value. This computes the normalised PR curves of [2]
%
% About the PR curve::
% This section uses the same symbols used in the documentation of
% the VL_ROC() function. In addition to those quantities, define:
%
% PRECISION(S) = TP(S) / (TP(S) + FP(S))
% RECALL(S) = TPR(S) = TP(S) / P
%
% The precision is the fraction of positivie predictions which are
% correct, and the recall is the fraction of positive labels that
% have been correctly classified (recalled). Notice that the recall
% is also equal to the true positive rate for the ROC curve (see
% VL_ROC()).
%
% REFERENCES:
% [1] C. D. Manning, P. Raghavan, and H. Schutze. An Introduction to
% Information Retrieval. Cambridge University Press, 2008.
% [2] D. Hoiem, Y. Chodpathumwan, and Q. Dai. Diagnosing error in
% object detectors. In Proc. ECCV, 2012.
%
% See also VL_ROC(), VL_HELP().
% Author: Andrea Vedaldi
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
% TP and FP are the vectors of true positie and false positve label
% counts for decreasing scores, P and N are the total number of
% positive and negative labels. Note that if certain options are used
% some labels may actually not be stored explicitly by LABELS, so P+N
% can be larger than the number of element of LABELS.
[tp, fp, p, n, perm, varargin] = vl_tpfp(labels, scores, varargin{:}) ;
opts.stable = false ;
opts.interpolate = false ;
opts.normalizePrior = [] ;
opts = vl_argparse(opts,varargin) ;
% compute precision and recall
small = 1e-10 ;
recall = tp / max(p, small) ;
if isempty(opts.normalizePrior)
precision = max(tp, small) ./ max(tp + fp, small) ;
else
a = opts.normalizePrior ;
precision = max(tp * a/max(p,small), small) ./ ...
max(tp * a/max(p,small) + fp * (1-a)/max(n,small), small) ;
end
% interpolate precision if needed
if opts.interpolate
precision = fliplr(vl_cummax(fliplr(precision))) ;
end
% --------------------------------------------------------------------
% Additional info
% --------------------------------------------------------------------
if nargout > 2 || nargout == 0
% area under the curve using trapezoid interpolation
if ~opts.interpolate
info.auc = 0.5 * sum((precision(1:end-1) + precision(2:end)) .* diff(recall)) ;
end
% average precision (for each recalled positive sample)
sel = find(diff(recall)) + 1 ;
info.ap = sum(precision(sel)) / p ;
if opts.interpolate
info.auc = info.ap ;
end
% TREC 11 points average interpolated precision
info.ap_interp_11 = 0.0 ;
for rc = linspace(0,1,11)
pr = max([0, precision(recall >= rc)]) ;
info.ap_interp_11 = info.ap_interp_11 + pr / 11 ;
end
% legacy definition
info.auc_pa08 = info.ap_interp_11 ;
end
% --------------------------------------------------------------------
% Plot
% --------------------------------------------------------------------
if nargout == 0
cla ; hold on ;
plot(recall,precision,'linewidth',2) ;
if isempty(opts.normalizePrior)
randomPrecision = p / (p + n) ;
else
randomPrecision = opts.normalizePrior ;
end
spline([0 1], [1 1] * randomPrecision, 'r--', 'linewidth', 2) ;
axis square ; grid on ;
xlim([0 1]) ; xlabel('recall') ;
ylim([0 1]) ; ylabel('precision') ;
title(sprintf('PR (AUC: %.2f%%, AP: %.2f%%, AP11: %.2f%%)', ...
info.auc * 100, ...
info.ap * 100, ...
info.ap_interp_11 * 100)) ;
if opts.interpolate
legend('PR interp.', 'PR rand.', 'Location', 'SouthEast') ;
else
legend('PR', 'PR rand.', 'Location', 'SouthEast') ;
end
clear recall precision info ;
end
% --------------------------------------------------------------------
% Stable output
% --------------------------------------------------------------------
if opts.stable
precision(1) = [] ;
recall(1) = [] ;
precision_ = precision ;
recall_ = recall ;
precision = NaN(size(precision)) ;
recall = NaN(size(recall)) ;
precision(perm) = precision_ ;
recall(perm) = recall_ ;
end
% --------------------------------------------------------------------
function h = spline(x,y,spec,varargin)
% --------------------------------------------------------------------
prop = vl_linespec2prop(spec) ;
h = line(x,y,prop{:},varargin{:}) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_ubcread.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/sift/vl_ubcread.m
| 3,015 |
utf_8
|
e8ddd3ecd87e76b6c738ba153fef050f
|
function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', 'OXFORD') assumes the format used by
% Oxford VGG implementations .
%
% See also: VL_SIFT(), VL_HELP().
% Authors: Andrea Vedaldi
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.verbosity = 0 ;
opts.format = 'ubc' ;
opts = vl_argparse(opts, varargin) ;
g = fopen(file, 'r');
if g == -1
error(['Could not open file ''', file, '''.']) ;
end
[header, count] = fscanf(g, '%d', [1 2]) ;
if count ~= 2
error('Invalid keypoint file header.');
end
switch opts.format
case 'ubc'
numKeypoints = header(1) ;
descrLen = header(2) ;
case 'oxford'
numKeypoints = header(2) ;
descrLen = header(1) ;
otherwise
error('Unknown format ''%s''.', opts.format) ;
end
if(opts.verbosity > 0)
fprintf('%d keypoints, %d descriptor length.\n', numKeypoints, descrLen) ;
end
%creates two output matrices
switch opts.format
case 'ubc'
P = zeros(4,numKeypoints) ;
case 'oxford'
P = zeros(5,numKeypoints) ;
end
L = zeros(descrLen, numKeypoints) ;
%parse tmp.key
for k = 1:numKeypoints
switch opts.format
case 'ubc'
% Record format: i,j,s,th
[record, count] = fscanf(g, '%f', [1 4]) ;
if count ~= 4
error(...
sprintf('Invalid keypoint file (parsing keypoint %d, frame part)',k) );
end
P(:,k) = record(:) ;
case 'oxford'
% Record format: x, y, a, b, c such that x' [a b ; b c] x = 1
[record, count] = fscanf(g, '%f', [1 5]) ;
if count ~= 5
error(...
sprintf('Invalid keypoint file (parsing keypoint %d, frame part)',k) );
end
P(:,k) = record(:) ;
end
% Record format: descriptor
[record, count] = fscanf(g, '%d', [1 descrLen]) ;
if count ~= descrLen
error(...
sprintf('Invalid keypoint file (parsing keypoint %d, descriptor part)',k) );
end
L(:,k) = record(:) ;
end
fclose(g) ;
switch opts.format
case 'ubc'
P(1:2,:) = flipud(P(1:2,:)) + 1 ; % i,j -> x,y
f=[ P(1:2,:) ; P(3,:) ; -P(4,:) ] ;
d=uint8(L) ;
p=[1 2 3 4 5 6 7 8] ;
q=[1 8 7 6 5 4 3 2] ;
for j=0:3
for i=0:3
d(8*(i+4*j)+p,:) = d(8*(i+4*j)+q,:) ;
end
end
case 'oxford'
P(1:2,:) = P(1:2,:) + 1 ; % matlab origin
f = P ;
f(3:5,:) = inv2x2(f(3:5,:)) ;
d = uint8(L) ;
end
% --------------------------------------------------------------------
function S = inv2x2(C)
% --------------------------------------------------------------------
den = C(1,:) .* C(3,:) - C(2,:) .* C(2,:) ;
S = [C(3,:) ; -C(2,:) ; C(1,:)] ./ den([1 1 1], :) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_frame2oell.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/sift/vl_frame2oell.m
| 2,806 |
utf_8
|
c93792632f630743485fa4c2cf12d647
|
function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an oriented disc, an ellipse,
% or an oriented ellipse. These are represented respectively by 2,
% 3, 4, 5 and 6 parameters each, as described in VL_PLOTFRAME(). An
% oriented ellipse is the most general geometric frame; hence, there
% is no loss of information in this conversion.
%
% If FRAME is an oriented disc or ellipse, then the conversion is
% immediate. If, however, FRAME is not oriented (it is either a
% point or an unoriented disc or ellipse), then an orientation must
% be assigned. The orientation is chosen in such a way that the
% affine transformation that maps the standard oriented frame into
% the output EFRAME does not rotate the Y axis. If frames represent
% detected visual features, this convention corresponds to assume
% that features are upright.
%
% If FRAME is a point, then the output is an ellipse with null area.
%
% See: <a href="matlab:vl_help('tut.frame')">feature frames</a>,
% VL_PLOTFRAME(), VL_HELP().
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
[D,K] = size(frames) ;
eframes = zeros(6,K) ;
switch D
case 2
eframes(1:2,:) = frames(1:2,:) ;
case 3
eframes(1:2,:) = frames(1:2,:) ;
eframes(3,:) = frames(3,:) ;
eframes(6,:) = frames(3,:) ;
case 4
r = frames(3,:) ;
c = r.*cos(frames(4,:)) ;
s = r.*sin(frames(4,:)) ;
eframes(1:2,:) = frames(1:2,:) ;
eframes(3:6,:) = [c ; s ; -s ; c] ;
case 5
eframes(1:2,:) = frames(1:2,:) ;
eframes(3:6,:) = mapFromS(frames(3:5,:)) ;
case 6
eframes = frames ;
otherwise
error('FRAMES format is unknown.') ;
end
% --------------------------------------------------------------------
function A = mapFromS(S)
% --------------------------------------------------------------------
% Returns the (stacking of the) 2x2 matrix A that maps the unit circle
% into the ellipses satisfying the equation x' inv(S) x = 1. Here S
% is a stacked covariance matrix, with elements S11, S12 and S22.
%
% The goal is to find A such that AA' = S. In order to let the Y
% direction unaffected (upright feature), the assumption is taht
% A = [a b ; 0 c]. Hence
%
% AA' = [a^2, ab ; ab, b^2+c^2] = S.
A = zeros(4,size(S,2)) ;
a = sqrt(S(1,:));
b = S(2,:) ./ max(a, 1e-18) ;
A(1,:) = a ;
A(2,:) = b ;
A(4,:) = sqrt(max(S(3,:) - b.*b, 0)) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
vl_plotsiftdescriptor.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/toolbox/sift/vl_plotsiftdescriptor.m
| 5,114 |
utf_8
|
a4e125a8916653f00143b61cceda2f23
|
function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT frames F, specified as columns of the matrix F. F has the
% same format used by VL_SIFT().
%
% H=VL_PLOTSIFTDESCRIPTOR(...) returns the handle H to the line
% drawing representing the descriptors.
%
% The function assumes that the SIFT descriptors use the standard
% configuration of 4x4 spatial bins and 8 orientations bins. The
% following parameters can be used to change this:
%
% NumSpatialBins:: 4
% Number of spatial bins in both spatial directions X and Y.
%
% NumOrientationBins:: 8
% Number of orientation bis.
%
% MagnificationFactor:: 3
% Magnification factor. The width of one bin is equal to the scale
% of the keypoint F multiplied by this factor.
%
% See also: VL_SIFT(), VL_PLOTFRAME(), VL_HELP().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.magnificationFactor = 3.0 ;
opts.numSpatialBins = 4 ;
opts.numOrientationBins = 8 ;
opts.maxValue = 0 ;
if nargin > 1
if ~ isnumeric(f)
error('F must be a numeric type (use [] to leave it unspecified)') ;
end
end
opts = vl_argparse(opts, varargin) ;
% --------------------------------------------------------------------
% Check the arguments
% --------------------------------------------------------------------
if(size(d,1) ~= opts.numSpatialBins^2 * opts.numOrientationBins)
error('The number of rows of D does not match the geometry of the descriptor') ;
end
if nargin > 1
if (~isempty(f) & (size(f,1) < 2 | size(f,1) > 6))
error('F must be either empty of have from 2 to six rows.');
end
if size(f,1) == 2
% translation only
f(3:6,:) = deal([10 0 0 10]') ;
%f = [f; 10 * ones(1, size(f,2)) ; 0 * zeros(1, size(f,2))] ;
end
if size(f,1) == 3
% translation and scale
f(3:6,:) = [1 0 0 1]' * f(3,:) ;
%f = [f; 0 * zeros(1, size(f,2))] ;
end
if size(f,1) == 4
c = cos(f(4,:)) ;
s = sin(f(4,:)) ;
f(3:6,:) = bsxfun(@times, f(3,:), [c ; s ; -s ; c]) ;
end
if size(f,1) == 5
assert(false) ;
c = cos(f(4,:)) ;
s = sin(f(4,:)) ;
f(3:6,:) = bsxfun(@times, f(3,:), [c ; s ; -s ; c]) ;
end
if(~isempty(f) & size(f,2) ~= size(d,2))
error('D and F have incompatible dimension') ;
end
end
% Descriptors are often non-double numeric arrays
d = double(d) ;
K = size(d,2) ;
if nargin < 2 | isempty(f)
f = repmat([0;0;1;0;0;1],1,K) ;
end
% --------------------------------------------------------------------
% Do the job
% --------------------------------------------------------------------
xall=[] ;
yall=[] ;
for k=1:K
[x,y] = render_descr(d(:,k), opts.numSpatialBins, opts.numOrientationBins, opts.maxValue) ;
xall = [xall opts.magnificationFactor*f(3,k)*x + opts.magnificationFactor*f(5,k)*y + f(1,k)] ;
yall = [yall opts.magnificationFactor*f(4,k)*x + opts.magnificationFactor*f(6,k)*y + f(2,k)] ;
end
h=line(xall,yall) ;
% --------------------------------------------------------------------
function [x,y] = render_descr(d, numSpatialBins, numOrientationBins, maxValue)
% --------------------------------------------------------------------
% Get the coordinates of the lines of the SIFT grid; each bin has side 1
[x,y] = meshgrid(-numSpatialBins/2:numSpatialBins/2,-numSpatialBins/2:numSpatialBins/2) ;
% Get the corresponding bin centers
xc = x(1:end-1,1:end-1) + 0.5 ;
yc = y(1:end-1,1:end-1) + 0.5 ;
% Rescale the descriptor range so that the biggest peak fits inside the bin diagram
if maxValue
d = 0.4 * d / maxValue ;
else
d = 0.4 * d / max(d(:)+eps) ;
end
% We scramble the the centers to have them in row major order
% (descriptor convention).
xc = xc' ;
yc = yc' ;
% Each spatial bin contains a star with numOrientationBins tips
xc = repmat(xc(:)',numOrientationBins,1) ;
yc = repmat(yc(:)',numOrientationBins,1) ;
% Do the stars
th=linspace(0,2*pi,numOrientationBins+1) ;
th=th(1:end-1) ;
xd = repmat(cos(th), 1, numSpatialBins*numSpatialBins) ;
yd = repmat(sin(th), 1, numSpatialBins*numSpatialBins) ;
xd = xd .* d(:)' ;
yd = yd .* d(:)' ;
% Re-arrange in sequential order the lines to draw
nans = NaN * ones(1,numSpatialBins^2*numOrientationBins) ;
x1 = xc(:)' ;
y1 = yc(:)' ;
x2 = x1 + xd ;
y2 = y1 + yd ;
xstars = [x1;x2;nans] ;
ystars = [y1;y2;nans] ;
% Horizontal lines of the grid
nans = NaN * ones(1,numSpatialBins+1);
xh = [x(:,1)' ; x(:,end)' ; nans] ;
yh = [y(:,1)' ; y(:,end)' ; nans] ;
% Verical lines of the grid
xv = [x(1,:) ; x(end,:) ; nans] ;
yv = [y(1,:) ; y(end,:) ; nans] ;
x=[xstars(:)' xh(:)' xv(:)'] ;
y=[ystars(:)' yh(:)' yv(:)'] ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
phow_caltech101.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/apps/phow_caltech101.m
| 11,594 |
utf_8
|
7f4890a2e6844ca56debbfe23cca64f3
|
function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT,
% kd-trees, and homogeneous kernel map. The program also
% demonstrates VLFeat PEGASOS SVM solver, although for this small
% dataset other solvers such as LIBLINEAR can be more efficient.
%
% By default 15 training images are used, which should result in
% about 64% performance (a good performance considering that only a
% single feature type is being used).
%
% Call PHOW_CALTECH101 to train and test a classifier on a small
% subset of the Caltech-101 data. Note that the program
% automatically downloads a copy of the Caltech-101 data from the
% Internet if it cannot find a local copy.
%
% Edit the PHOW_CALTECH101 file to change the program configuration.
%
% To run on the entire dataset change CONF.TINYPROBLEM to FALSE.
%
% The Caltech-101 data is saved into CONF.CALDIR, which defaults to
% 'data/caltech-101'. Change this path to the desired location, for
% instance to point to an existing copy of the Caltech-101 data.
%
% The program can also be used to train a model on custom data by
% pointing CONF.CALDIR to it. Just create a subdirectory for each
% class and put the training images there. Make sure to adjust
% CONF.NUMTRAIN accordingly.
%
% Intermediate files are stored in the directory CONF.DATADIR. All
% such files begin with the prefix CONF.PREFIX, which can be changed
% to test different parameter settings without overriding previous
% results.
%
% The program saves the trained model in
% <CONF.DATADIR>/<CONF.PREFIX>-model.mat. This model can be used to
% test novel images independently of the Caltech data.
%
% load('data/baseline-model.mat') ; # change to the model path
% label = model.classify(model, im) ;
%
% Author: Andrea Vedaldi
% Copyright (C) 2011-2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
conf.calDir = 'data/caltech-101' ;
conf.dataDir = 'data/' ;
conf.autoDownloadData = true ;
conf.numTrain = 15 ;
conf.numTest = 15 ;
conf.numClasses = 102 ;
conf.numWords = 600 ;
conf.numSpatialX = [2 4] ;
conf.numSpatialY = [2 4] ;
conf.quantizer = 'kdtree' ;
conf.svm.C = 10 ;
conf.svm.solver = 'sdca' ;
%conf.svm.solver = 'sgd' ;
%conf.svm.solver = 'liblinear' ;
conf.svm.biasMultiplier = 1 ;
conf.phowOpts = {'Step', 3} ;
conf.clobber = false ;
conf.tinyProblem = true ;
conf.prefix = 'baseline' ;
conf.randSeed = 1 ;
if conf.tinyProblem
conf.prefix = 'tiny' ;
conf.numClasses = 5 ;
conf.numSpatialX = 2 ;
conf.numSpatialY = 2 ;
conf.numWords = 300 ;
conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ;
end
conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ;
conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ;
conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ;
conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ;
randn('state',conf.randSeed) ;
rand('state',conf.randSeed) ;
vl_twister('state',conf.randSeed) ;
% --------------------------------------------------------------------
% Download Caltech-101 data
% --------------------------------------------------------------------
if ~exist(conf.calDir, 'dir') || ...
(~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ...
~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes')))
if ~conf.autoDownloadData
error(...
['Caltech-101 data not found. ' ...
'Set conf.autoDownloadData=true to download the required data.']) ;
end
vl_xmkdir(conf.calDir) ;
calUrl = ['http://www.vision.caltech.edu/Image_Datasets/' ...
'Caltech101/101_ObjectCategories.tar.gz'] ;
fprintf('Downloading Caltech-101 data to ''%s''. This will take a while.', conf.calDir) ;
untar(calUrl, conf.calDir) ;
end
if ~exist(fullfile(conf.calDir, 'airplanes'),'dir')
conf.calDir = fullfile(conf.calDir, '101_ObjectCategories') ;
end
% --------------------------------------------------------------------
% Setup data
% --------------------------------------------------------------------
classes = dir(conf.calDir) ;
classes = classes([classes.isdir]) ;
classes = {classes(3:conf.numClasses+2).name} ;
images = {} ;
imageClass = {} ;
for ci = 1:length(classes)
ims = dir(fullfile(conf.calDir, classes{ci}, '*.jpg'))' ;
ims = vl_colsubset(ims, conf.numTrain + conf.numTest) ;
ims = cellfun(@(x)fullfile(classes{ci},x),{ims.name},'UniformOutput',false) ;
images = {images{:}, ims{:}} ;
imageClass{end+1} = ci * ones(1,length(ims)) ;
end
selTrain = find(mod(0:length(images)-1, conf.numTrain+conf.numTest) < conf.numTrain) ;
selTest = setdiff(1:length(images), selTrain) ;
imageClass = cat(2, imageClass{:}) ;
model.classes = classes ;
model.phowOpts = conf.phowOpts ;
model.numSpatialX = conf.numSpatialX ;
model.numSpatialY = conf.numSpatialY ;
model.quantizer = conf.quantizer ;
model.vocab = [] ;
model.w = [] ;
model.b = [] ;
model.classify = @classify ;
% --------------------------------------------------------------------
% Train vocabulary
% --------------------------------------------------------------------
if ~exist(conf.vocabPath) || conf.clobber
% Get some PHOW descriptors to train the dictionary
selTrainFeats = vl_colsubset(selTrain, 30) ;
descrs = {} ;
%for ii = 1:length(selTrainFeats)
parfor ii = 1:length(selTrainFeats)
im = imread(fullfile(conf.calDir, images{selTrainFeats(ii)})) ;
im = standarizeImage(im) ;
[drop, descrs{ii}] = vl_phow(im, model.phowOpts{:}) ;
end
descrs = vl_colsubset(cat(2, descrs{:}), 10e4) ;
descrs = single(descrs) ;
% Quantize the descriptors to get the visual words
vocab = vl_kmeans(descrs, conf.numWords, 'verbose', 'algorithm', 'elkan', 'MaxNumIterations', 50) ;
save(conf.vocabPath, 'vocab') ;
else
load(conf.vocabPath) ;
end
model.vocab = vocab ;
if strcmp(model.quantizer, 'kdtree')
model.kdtree = vl_kdtreebuild(vocab) ;
end
% --------------------------------------------------------------------
% Compute spatial histograms
% --------------------------------------------------------------------
if ~exist(conf.histPath) || conf.clobber
hists = {} ;
parfor ii = 1:length(images)
% for ii = 1:length(images)
fprintf('Processing %s (%.2f %%)\n', images{ii}, 100 * ii / length(images)) ;
im = imread(fullfile(conf.calDir, images{ii})) ;
hists{ii} = getImageDescriptor(model, im);
end
hists = cat(2, hists{:}) ;
save(conf.histPath, 'hists') ;
else
load(conf.histPath) ;
end
% --------------------------------------------------------------------
% Compute feature map
% --------------------------------------------------------------------
psix = vl_homkermap(hists, 1, 'kchi2', 'gamma', .5) ;
% --------------------------------------------------------------------
% Train SVM
% --------------------------------------------------------------------
if ~exist(conf.modelPath) || conf.clobber
switch conf.svm.solver
case {'sgd', 'sdca'}
lambda = 1 / (conf.svm.C * length(selTrain)) ;
w = [] ;
parfor ci = 1:length(classes)
perm = randperm(length(selTrain)) ;
fprintf('Training model for class %s\n', classes{ci}) ;
y = 2 * (imageClass(selTrain) == ci) - 1 ;
[w(:,ci) b(ci) info] = vl_svmtrain(psix(:, selTrain(perm)), y(perm), lambda, ...
'Solver', conf.svm.solver, ...
'MaxNumIterations', 50/lambda, ...
'BiasMultiplier', conf.svm.biasMultiplier, ...
'Epsilon', 1e-3);
end
case 'liblinear'
svm = train(imageClass(selTrain)', ...
sparse(double(psix(:,selTrain))), ...
sprintf(' -s 3 -B %f -c %f', ...
conf.svm.biasMultiplier, conf.svm.C), ...
'col') ;
w = svm.w(:,1:end-1)' ;
b = svm.w(:,end)' ;
end
model.b = conf.svm.biasMultiplier * b ;
model.w = w ;
save(conf.modelPath, 'model') ;
else
load(conf.modelPath) ;
end
% --------------------------------------------------------------------
% Test SVM and evaluate
% --------------------------------------------------------------------
% Estimate the class of the test images
scores = model.w' * psix + model.b' * ones(1,size(psix,2)) ;
[drop, imageEstClass] = max(scores, [], 1) ;
% Compute the confusion matrix
idx = sub2ind([length(classes), length(classes)], ...
imageClass(selTest), imageEstClass(selTest)) ;
confus = zeros(length(classes)) ;
confus = vl_binsum(confus, ones(size(idx)), idx) ;
% Plots
figure(1) ; clf;
subplot(1,2,1) ;
imagesc(scores(:,[selTrain selTest])) ; title('Scores') ;
set(gca, 'ytick', 1:length(classes), 'yticklabel', classes) ;
subplot(1,2,2) ;
imagesc(confus) ;
title(sprintf('Confusion matrix (%.2f %% accuracy)', ...
100 * mean(diag(confus)/conf.numTest) )) ;
print('-depsc2', [conf.resultPath '.ps']) ;
save([conf.resultPath '.mat'], 'confus', 'conf') ;
% -------------------------------------------------------------------------
function im = standarizeImage(im)
% -------------------------------------------------------------------------
im = im2single(im) ;
if size(im,1) > 480, im = imresize(im, [480 NaN]) ; end
% -------------------------------------------------------------------------
function hist = getImageDescriptor(model, im)
% -------------------------------------------------------------------------
im = standarizeImage(im) ;
width = size(im,2) ;
height = size(im,1) ;
numWords = size(model.vocab, 2) ;
% get PHOW features
[frames, descrs] = vl_phow(im, model.phowOpts{:}) ;
% quantize local descriptors into visual words
switch model.quantizer
case 'vq'
[drop, binsa] = min(vl_alldist(model.vocab, single(descrs)), [], 1) ;
case 'kdtree'
binsa = double(vl_kdtreequery(model.kdtree, model.vocab, ...
single(descrs), ...
'MaxComparisons', 50)) ;
end
for i = 1:length(model.numSpatialX)
binsx = vl_binsearch(linspace(1,width,model.numSpatialX(i)+1), frames(1,:)) ;
binsy = vl_binsearch(linspace(1,height,model.numSpatialY(i)+1), frames(2,:)) ;
% combined quantization
bins = sub2ind([model.numSpatialY(i), model.numSpatialX(i), numWords], ...
binsy,binsx,binsa) ;
hist = zeros(model.numSpatialY(i) * model.numSpatialX(i) * numWords, 1) ;
hist = vl_binsum(hist, ones(size(bins)), bins) ;
hists{i} = single(hist / sum(hist)) ;
end
hist = cat(1,hists{:}) ;
hist = hist / sum(hist) ;
% -------------------------------------------------------------------------
function [className, score] = classify(model, im)
% -------------------------------------------------------------------------
hist = getImageDescriptor(model, im) ;
psix = vl_homkermap(hist, 1, 'kchi2', 'gamma', .5) ;
scores = model.w' * psix + model.b' ;
[score, best] = max(scores) ;
className = model.classes{best} ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
sift_mosaic.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/apps/sift_mosaic.m
| 4,621 |
utf_8
|
8fa3ad91b401b8f2400fb65944c79712
|
function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAIC(IM1,IM2) to compute the mosaic of two
% custom images IM1 and IM2.
% AUTORIGHTS
if nargin == 0
im1 = imread(fullfile(vl_root, 'data', 'river1.jpg')) ;
im2 = imread(fullfile(vl_root, 'data', 'river2.jpg')) ;
end
% make single
im1 = im2single(im1) ;
im2 = im2single(im2) ;
% make grayscale
if size(im1,3) > 1, im1g = rgb2gray(im1) ; else im1g = im1 ; end
if size(im2,3) > 1, im2g = rgb2gray(im2) ; else im2g = im2 ; end
% --------------------------------------------------------------------
% SIFT matches
% --------------------------------------------------------------------
[f1,d1] = vl_sift(im1g) ;
[f2,d2] = vl_sift(im2g) ;
[matches, scores] = vl_ubcmatch(d1,d2) ;
numMatches = size(matches,2) ;
X1 = f1(1:2,matches(1,:)) ; X1(3,:) = 1 ;
X2 = f2(1:2,matches(2,:)) ; X2(3,:) = 1 ;
% --------------------------------------------------------------------
% RANSAC with homography model
% --------------------------------------------------------------------
clear H score ok ;
for t = 1:100
% estimate homograpyh
subset = vl_colsubset(1:numMatches, 4) ;
A = [] ;
for i = subset
A = cat(1, A, kron(X1(:,i)', vl_hat(X2(:,i)))) ;
end
[U,S,V] = svd(A) ;
H{t} = reshape(V(:,9),3,3) ;
% score homography
X2_ = H{t} * X1 ;
du = X2_(1,:)./X2_(3,:) - X2(1,:)./X2(3,:) ;
dv = X2_(2,:)./X2_(3,:) - X2(2,:)./X2(3,:) ;
ok{t} = (du.*du + dv.*dv) < 6*6 ;
score(t) = sum(ok{t}) ;
end
[score, best] = max(score) ;
H = H{best} ;
ok = ok{best} ;
% --------------------------------------------------------------------
% Optional refinement
% --------------------------------------------------------------------
function err = residual(H)
u = H(1) * X1(1,ok) + H(4) * X1(2,ok) + H(7) ;
v = H(2) * X1(1,ok) + H(5) * X1(2,ok) + H(8) ;
d = H(3) * X1(1,ok) + H(6) * X1(2,ok) + 1 ;
du = X2(1,ok) - u ./ d ;
dv = X2(2,ok) - v ./ d ;
err = sum(du.*du + dv.*dv) ;
end
if exist('fminsearch') == 2
H = H / H(3,3) ;
opts = optimset('Display', 'none', 'TolFun', 1e-8, 'TolX', 1e-8) ;
H(1:8) = fminsearch(@residual, H(1:8)', opts) ;
else
warning('Refinement disabled as fminsearch was not found.') ;
end
% --------------------------------------------------------------------
% Show matches
% --------------------------------------------------------------------
dh1 = max(size(im2,1)-size(im1,1),0) ;
dh2 = max(size(im1,1)-size(im2,1),0) ;
figure(1) ; clf ;
subplot(2,1,1) ;
imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ;
o = size(im1,2) ;
line([f1(1,matches(1,:));f2(1,matches(2,:))+o], ...
[f1(2,matches(1,:));f2(2,matches(2,:))]) ;
title(sprintf('%d tentative matches', numMatches)) ;
axis image off ;
subplot(2,1,2) ;
imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ;
o = size(im1,2) ;
line([f1(1,matches(1,ok));f2(1,matches(2,ok))+o], ...
[f1(2,matches(1,ok));f2(2,matches(2,ok))]) ;
title(sprintf('%d (%.2f%%) inliner matches out of %d', ...
sum(ok), ...
100*sum(ok)/numMatches, ...
numMatches)) ;
axis image off ;
drawnow ;
% --------------------------------------------------------------------
% Mosaic
% --------------------------------------------------------------------
box2 = [1 size(im2,2) size(im2,2) 1 ;
1 1 size(im2,1) size(im2,1) ;
1 1 1 1 ] ;
box2_ = inv(H) * box2 ;
box2_(1,:) = box2_(1,:) ./ box2_(3,:) ;
box2_(2,:) = box2_(2,:) ./ box2_(3,:) ;
ur = min([1 box2_(1,:)]):max([size(im1,2) box2_(1,:)]) ;
vr = min([1 box2_(2,:)]):max([size(im1,1) box2_(2,:)]) ;
[u,v] = meshgrid(ur,vr) ;
im1_ = vl_imwbackward(im2double(im1),u,v) ;
z_ = H(3,1) * u + H(3,2) * v + H(3,3) ;
u_ = (H(1,1) * u + H(1,2) * v + H(1,3)) ./ z_ ;
v_ = (H(2,1) * u + H(2,2) * v + H(2,3)) ./ z_ ;
im2_ = vl_imwbackward(im2double(im2),u_,v_) ;
mass = ~isnan(im1_) + ~isnan(im2_) ;
im1_(isnan(im1_)) = 0 ;
im2_(isnan(im2_)) = 0 ;
mosaic = (im1_ + im2_) ./ mass ;
figure(2) ; clf ;
imagesc(mosaic) ; axis image off ;
title('Mosaic') ;
if nargout == 0, clear mosaic ; end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
encodeImage.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/apps/recognition/encodeImage.m
| 5,278 |
utf_8
|
5d9dc6161995b8e10366b5649bf4fda4
|
function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple images.
%
% ENCODEIMAGE(ENCODER, IM, CACHE) utilizes the specified CACHE
% directory to store encodings for the given images. The cache
% is used only if the images are specified as file names.
%
% See also: TRAINENCODER().
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.cacheDir = [] ;
opts.cacheChunkSize = 512 ;
opts = vl_argparse(opts,varargin) ;
if ~iscell(im), im = {im} ; end
% break the computation into cached chunks
startTime = tic ;
descrs = cell(1, numel(im)) ;
numChunks = ceil(numel(im) / opts.cacheChunkSize) ;
for c = 1:numChunks
n = min(opts.cacheChunkSize, numel(im) - (c-1)*opts.cacheChunkSize) ;
chunkPath = fullfile(opts.cacheDir, sprintf('chunk-%03d.mat',c)) ;
if ~isempty(opts.cacheDir) && exist(chunkPath)
fprintf('%s: loading descriptors from %s\n', mfilename, chunkPath) ;
load(chunkPath, 'data') ;
else
range = (c-1)*opts.cacheChunkSize + (1:n) ;
fprintf('%s: processing a chunk of %d images (%3d of %3d, %5.1fs to go)\n', ...
mfilename, numel(range), ...
c, numChunks, toc(startTime) / (c - 1) * (numChunks - c + 1)) ;
data = processChunk(encoder, im(range)) ;
if ~isempty(opts.cacheDir)
save(chunkPath, 'data') ;
end
end
descrs{c} = data ;
clear data ;
end
descrs = cat(2,descrs{:}) ;
% --------------------------------------------------------------------
function psi = processChunk(encoder, im)
% --------------------------------------------------------------------
psi = cell(1,numel(im)) ;
if numel(im) > 1 & matlabpool('size') > 1
parfor i = 1:numel(im)
psi{i} = encodeOne(encoder, im{i}) ;
end
else
% avoiding parfor makes debugging easier
for i = 1:numel(im)
psi{i} = encodeOne(encoder, im{i}) ;
end
end
psi = cat(2, psi{:}) ;
% --------------------------------------------------------------------
function psi = encodeOne(encoder, im)
% --------------------------------------------------------------------
im = encoder.readImageFn(im) ;
features = encoder.extractorFn(im) ;
imageSize = size(im) ;
psi = {} ;
for i = 1:size(encoder.subdivisions,2)
minx = encoder.subdivisions(1,i) * imageSize(2) ;
miny = encoder.subdivisions(2,i) * imageSize(1) ;
maxx = encoder.subdivisions(3,i) * imageSize(2) ;
maxy = encoder.subdivisions(4,i) * imageSize(1) ;
ok = ...
minx <= features.frame(1,:) & features.frame(1,:) < maxx & ...
miny <= features.frame(2,:) & features.frame(2,:) < maxy ;
descrs = encoder.projection * bsxfun(@minus, ...
features.descr(:,ok), ...
encoder.projectionCenter) ;
if encoder.renormalize
descrs = bsxfun(@times, descrs, 1./max(1e-12, sqrt(sum(descrs.^2)))) ;
end
w = size(im,2) ;
h = size(im,1) ;
frames = features.frame(1:2,:) ;
frames = bsxfun(@times, bsxfun(@minus, frames, [w;h]/2), 1./[w;h]) ;
descrs = extendDescriptorsWithGeometry(encoder.geometricExtension, frames, descrs) ;
switch encoder.type
case 'bovw'
[words,distances] = vl_kdtreequery(encoder.kdtree, encoder.words, ...
descrs, ...
'MaxComparisons', 100) ;
z = vl_binsum(zeros(encoder.numWords,1), 1, double(words)) ;
z = sqrt(z) ;
case 'fv'
z = vl_fisher(descrs, ...
encoder.means, ...
encoder.covariances, ...
encoder.priors, ...
'Improved') ;
case 'vlad'
[words,distances] = vl_kdtreequery(encoder.kdtree, encoder.words, ...
descrs, ...
'MaxComparisons', 15) ;
assign = zeros(encoder.numWords, numel(words), 'single') ;
assign(sub2ind(size(assign), double(words), 1:numel(words))) = 1 ;
z = vl_vlad(descrs, ...
encoder.words, ...
assign, ...
'SquareRoot', ...
'NormalizeComponents') ;
end
z = z / max(sqrt(sum(z.^2)), 1e-12) ;
psi{i} = z(:) ;
end
psi = cat(1, psi{:}) ;
% --------------------------------------------------------------------
function psi = getFromCache(name, cache)
% --------------------------------------------------------------------
[drop, name] = fileparts(name) ;
cachePath = fullfile(cache, [name '.mat']) ;
if exist(cachePath, 'file')
data = load(cachePath) ;
psi = data.psi ;
else
psi = [] ;
end
% --------------------------------------------------------------------
function storeToCache(name, cache, psi)
% --------------------------------------------------------------------
[drop, name] = fileparts(name) ;
cachePath = fullfile(cache, [name '.mat']) ;
vl_xmkdir(cache) ;
data.psi = psi ;
save(cachePath, '-STRUCT', 'data') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
experiments.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/apps/recognition/experiments.m
| 6,905 |
utf_8
|
1e4a4911eed4a451b9488b9e6cc9b39c
|
function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all of them on tiny subsets of the actual data.
% This is used only for testing; to run the actual experiments,
% set the lite variable to false.
%
% Running all the experiments is a slow process. Using parallel
% MATLAB and several cores/machiens is suggested.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
lite = true ;
clear ex ;
ex(1).prefix = 'fv-aug' ;
ex(1).trainOpts = {'C', 10} ;
ex(1).datasets = {'fmd', 'scene67'} ;
ex(1).seed = 1 ;
ex(1).opts = {...
'type', 'fv', ...
'numWords', 256, ...
'layouts', {'1x1'}, ...
'geometricExtension', 'xy', ...
'numPcaDimensions', 80, ...
'extractorFn', @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(1:-.5:-3))};
ex(2) = ex(1) ;
ex(2).datasets = {'caltech101'} ;
ex(2).opts{end} = @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(0:-.5:-3)) ;
ex(3) = ex(1) ;
ex(3).datasets = {'voc07'} ;
ex(3).C = 1 ;
ex(4) = ex(1) ;
ex(4).prefix = 'vlad-aug' ;
ex(4).opts = {...
'type', 'vlad', ...
'numWords', 256, ...
'layouts', {'1x1'}, ...
'geometricExtension', 'xy', ...
'numPcaDimensions', 100, ...
'whitening', true, ...
'whiteningRegul', 0.01, ...
'renormalize', true, ...
'extractorFn', @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(1:-.5:-3))};
ex(5) = ex(4) ;
ex(5).datasets = {'caltech101'} ;
ex(5).opts{end} = ex(2).opts{end} ;
ex(6) = ex(4) ;
ex(6).datasets = {'voc07'} ;
ex(6).C = 1 ;
ex(7) = ex(1) ;
ex(7).prefix = 'bovw-aug' ;
ex(7).opts = {...
'type', 'bovw', ...
'numWords', 4096, ...
'layouts', {'1x1'}, ...
'geometricExtension', 'xy', ...
'numPcaDimensions', 100, ...
'whitening', true, ...
'whiteningRegul', 0.01, ...
'renormalize', true, ...
'extractorFn', @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(1:-.5:-3))};
ex(8) = ex(7) ;
ex(8).datasets = {'caltech101'} ;
ex(8).opts{end} = ex(2).opts{end} ;
ex(9) = ex(7) ;
ex(9).datasets = {'voc07'} ;
ex(9).C = 1 ;
ex(10).prefix = 'fv' ;
ex(10).trainOpts = {'C', 10} ;
ex(10).datasets = {'fmd', 'scene67'} ;
ex(10).seed = 1 ;
ex(10).opts = {...
'type', 'fv', ...
'numWords', 256, ...
'layouts', {'1x1'}, ...
'geometricExtension', 'none', ...
'numPcaDimensions', 80, ...
'extractorFn', @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(1:-.5:-3))};
ex(11) = ex(10) ;
ex(11).datasets = {'caltech101'} ;
ex(11).opts{end} = @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(0:-.5:-3)) ;
ex(12) = ex(10) ;
ex(12).datasets = {'voc07'} ;
ex(12).C = 1 ;
ex(13).prefix = 'fv-sp' ;
ex(13).trainOpts = {'C', 10} ;
ex(13).datasets = {'fmd', 'scene67'} ;
ex(13).seed = 1 ;
ex(13).opts = {...
'type', 'fv', ...
'numWords', 256, ...
'layouts', {'1x1', '3x1'}, ...
'geometricExtension', 'none', ...
'numPcaDimensions', 80, ...
'extractorFn', @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(1:-.5:-3))};
ex(14) = ex(13) ;
ex(14).datasets = {'caltech101'} ;
ex(14).opts{6} = {'1x1', '2x2'} ;
ex(14).opts{end} = @(x) getDenseSIFT(x, ...
'step', 4, ...
'scales', 2.^(0:-.5:-3)) ;
ex(15) = ex(13) ;
ex(15).datasets = {'voc07'} ;
ex(15).C = 1 ;
if lite, tag = 'lite' ;
else, tag = 'ex' ; end
for i=1:numel(ex)
for j=1:numel(ex(i).datasets)
dataset = ex(i).datasets{j} ;
if ~isfield(ex(i), 'trainOpts') || ~iscell(ex(i).trainOpts)
ex(i).trainOpts = {} ;
end
traintest(...
'prefix', [tag '-' dataset '-' ex(i).prefix], ...
'seed', ex(i).seed, ...
'dataset', char(dataset), ...
'datasetDir', fullfile('data', dataset), ...
'lite', lite, ...
ex(i).trainOpts{:}, ...
'encoderParams', ex(i).opts) ;
end
end
% print HTML table
pf('<table>\n') ;
ph('method', 'VOC07', 'Caltech 101', 'Scene 67', 'FMD') ;
pr('FV', ...
ge([tag '-voc07-fv'],'ap11'), ...
ge([tag '-caltech101-fv']), ...
ge([tag '-scene67-fv']), ...
ge([tag '-fmd-fv'])) ;
pr('FV + aug.', ...
ge([tag '-voc07-fv-aug'],'ap11'), ...
ge([tag '-caltech101-fv-aug']), ...
ge([tag '-scene67-fv-aug']), ...
ge([tag '-fmd-fv-aug'])) ;
pr('FV + s.p.', ...
ge([tag '-voc07-fv-sp'],'ap11'), ...
ge([tag '-caltech101-fv-sp']), ...
ge([tag '-scene67-fv-sp']), ...
ge([tag '-fmd-fv-sp'])) ;
%pr('VLAD', ...
% ge([tag '-voc07-vlad'],'ap11'), ...
% ge([tag '-caltech101-vlad']), ...
% ge([tag '-scene67-vlad']), ...
% ge([tag '-fmd-vlad'])) ;
pr('VLAD + aug.', ...
ge([tag '-voc07-vlad-aug'],'ap11'), ...
ge([tag '-caltech101-vlad-aug']), ...
ge([tag '-scene67-vlad-aug']), ...
ge([tag '-fmd-vlad-aug'])) ;
%pr('VLAD+sp', ...
% ge([tag '-voc07-vlad-sp'],'ap11'), ...
% ge([tag '-caltech101-vlad-sp']), ...
% ge([tag '-scene67-vlad-sp']), ...
% ge([tag '-fmd-vlad-sp'])) ;
%pr('BOVW', ...
% ge([tag '-voc07-bovw'],'ap11'), ...
% ge([tag '-caltech101-bovw']), ...
% ge([tag '-scene67-bovw']), ...
% ge([tag '-fmd-bovw'])) ;
pr('BOVW + aug.', ...
ge([tag '-voc07-bovw-aug'],'ap11'), ...
ge([tag '-caltech101-bovw-aug']), ...
ge([tag '-scene67-bovw-aug']), ...
ge([tag '-fmd-bovw-aug'])) ;
%pr('BOVW+sp', ...
% ge([tag '-voc07-bovw-sp'],'ap11'), ...
% ge([tag '-caltech101-bovw-sp']), ...
% ge([tag '-scene67-bovw-sp']), ...
% ge([tag '-fmd-bovw-sp'])) ;
pf('</table>\n');
function pf(str)
fprintf(str) ;
function str = ge(name, format)
if nargin == 1, format = 'acc'; end
data = load(fullfile('data', name, 'result.mat')) ;
switch format
case 'acc'
str = sprintf('%.2f%% <span style="font-size:8px;">Acc</span>', mean(diag(data.confusion)) * 100) ;
case 'ap11'
str = sprintf('%.2f%% <span style="font-size:8px;">mAP</span>', mean(data.ap11) * 100) ;
end
function pr(varargin)
fprintf('<tr>') ;
for i=1:numel(varargin), fprintf('<td>%s</td>',varargin{i}) ; end
fprintf('</tr>\n') ;
function ph(varargin)
fprintf('<tr>') ;
for i=1:numel(varargin), fprintf('<th>%s</th>',varargin{i}) ; end
fprintf('</tr>\n') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
getDenseSIFT.m
|
.m
|
CNN-without-BN-master/dependencies/vlfeat/apps/recognition/getDenseSIFT.m
| 1,679 |
utf_8
|
2059c0a2a4e762226d89121408c6e51c
|
function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.scales = logspace(log10(1), log10(.25), 5) ;
opts.contrastthreshold = 0 ;
opts.step = 3 ;
opts.rootSift = false ;
opts.normalizeSift = true ;
opts.binSize = 8 ;
opts.geometry = [4 4 8] ;
opts.sigma = 0 ;
opts = vl_argparse(opts, varargin) ;
dsiftOpts = {'norm', 'fast', 'floatdescriptors', ...
'step', opts.step, ...
'size', opts.binSize, ...
'geometry', opts.geometry} ;
if size(im,3)>1, im = rgb2gray(im) ; end
im = im2single(im) ;
im = vl_imsmooth(im, opts.sigma) ;
for si = 1:numel(opts.scales)
im_ = imresize(im, opts.scales(si)) ;
[frames{si}, descrs{si}] = vl_dsift(im_, dsiftOpts{:}) ;
% root SIFT
if opts.rootSift
descrs{si} = sqrt(descrs{si}) ;
end
if opts.normalizeSift
descrs{si} = snorm(descrs{si}) ;
end
% zero low contrast descriptors
info.contrast{si} = frames{si}(3,:) ;
kill = info.contrast{si} < opts.contrastthreshold ;
descrs{si}(:,kill) = 0 ;
% store frames
frames{si}(1:2,:) = (frames{si}(1:2,:)-1) / opts.scales(si) + 1 ;
frames{si}(3,:) = opts.binSize / opts.scales(si) / 3 ;
end
features.frame = cat(2, frames{:}) ;
features.descr = cat(2, descrs{:}) ;
features.contrast = cat(2, info.contrast{:}) ;
function x = snorm(x)
x = bsxfun(@times, x, 1./max(1e-5,sqrt(sum(x.^2,1)))) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
test_examples.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/utils/test_examples.m
| 1,591 |
utf_8
|
16831be7382a9343beff5cc3fe301e51
|
function test_examples()
%TEST_EXAMPLES Test some of the examples in the `examples/` directory
addpath examples/mnist ;
addpath examples/cifar ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
num = 1 ;
exps = {} ;
for networkType = {'dagnn', 'simplenn'}
for index = 1:4
clear ex ;
ex.trainOpts = trainOpts ;
ex.networkType = char(networkType) ;
ex.index = index ;
exps{end+1} = ex ;
end
end
if num > 1
if isempty(gcp('nocreate')),
parpool('local',num) ;
end
parfor e = 1:numel(exps)
test_one(exps{e}) ;
end
else
for e = 1:numel(exps)
test_one(exps{e}) ;
end
end
% ------------------------------------------------------------------------
function test_one(ex)
% -------------------------------------------------------------------------
suffix = ['-' ex.networkType] ;
switch ex.index
case 1
cnn_mnist(...
'expDir', ['data/test-mnist' suffix], ...
'batchNormalization', false, ...
'networkType', ex.networkType, ...
'train', ex.trainOpts) ;
case 2
cnn_mnist(...
'expDir', ['data/test-mnist-bnorm' suffix], ...
'batchNormalization', true, ...
'networkType', ex.networkType, ...
'train', ex.trainOpts) ;
case 3
cnn_cifar(...
'expDir', ['data/test-cifar-lenet' suffix], ...
'modelType', 'lenet', ...
'networkType', ex.networkType, ...
'train', ex.trainOpts) ;
case 4
cnn_cifar(...
'expDir', ['data/test-cifar-nin' suffix], ...
'modelType', 'nin', ...
'networkType', ex.networkType, ...
'train', ex.trainOpts) ;
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
simplenn_caffe_compare.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/utils/simplenn_caffe_compare.m
| 5,638 |
utf_8
|
8e9862ffbf247836e6ff7579d1e6dc85
|
function diffStats = simplenn_caffe_compare( net, caffeModelBaseName, testData, varargin)
% SIMPLENN_CAFFE_COMPARE compare the simplenn network and caffe models
% SIMPLENN_CAFFE_COMPARE(NET, CAFFE_BASE_MODELNAME) Evaluates a forward
% pass of a simplenn network NET and caffe models stored in
% CAFFE_BASE_MODELNAME and numerically compares the network outputs using
% a random input data.
%
% SIMPLENN_CAFFE_COMPARE(NET, CAFFE_BASE_MODELNAME, TEST_DATA) Evaluates
% the simplenn network and Caffe model on a given data. If TEST_DATA is
% an empty array, uses a random input.
%
% RES = SIMPLENN_CAFFE_COMPARE(...) returns a structure with the
% statistics of the differences where each field of a structure RES is
% named after a blob and contains basic statistics:
% `[MIN_DIFF, MEAN_DIFF, MAX_DIFF]`
%
% This script attempts to match the NET layer names and caffe blob names
% and shows the MIN, MEAN and MAX difference between the outputs. For
% caffe model, the mean image stored with the caffe model is used (see
% `simplenn_caffe_deploy` for details). Furthermore the script compares
% the execution time of both networks.
%
% Compiled MatCaffe (usually located in `<caffe_dir>/matlab`, built
% with the `matcaffe` target) must be in path.
%
% SIMPLENN_CAFFE_COMPARE(..., 'OPT', VAL, ...) takes the following
% options:
%
% `numRepetitions`:: `1`
% Evaluate the network multiple times. Useful to compare the execution
% time.
%
% `device`:: `cpu`
% Evaluate the network on the specified device (CPU or GPU). For GPU
% evaluation, the current GPU is used for both Caffe and simplenn.
%
% `silent`:: `false`
% When true, supress all outputs to stdin.
%
% See Also: simplenn_caffe_deploy
% Copyright (C) 2016 Karel Lenc, Zohar Bar-Yehuda
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.numRepetitions = 1;
opts.randScale = 100;
opts.device = 'cpu';
opts.silent = false;
opts = vl_argparse(opts, varargin);
info = @(varargin) fprintf(1, varargin{:});
if opts.silent, info = @(varargin) []; end;
if ~exist('caffe.Net', 'class'), error('MatCaffe not in path.'); end
prototxtFilename = [caffeModelBaseName '.prototxt'];
if ~exist(prototxtFilename, 'file')
error('Caffe net definition `%s` not found', prototxtFilename);
end;
modelFilename = [caffeModelBaseName '.caffemodel'];
if ~exist(prototxtFilename, 'file')
error('Caffe net model `%s` not found', modelFilename);
end;
meanFilename = [caffeModelBaseName, '_mean_image.binaryproto'];
net = vl_simplenn_tidy(net);
net = vl_simplenn_move(net, opts.device);
netBlobNames = [{'data'}, cellfun(@(l) l.name, net.layers, ...
'UniformOutput', false)];
% Load the Caffe model
caffeNet = caffe.Net(prototxtFilename, modelFilename, 'test');
switch opts.device
case 'cpu'
caffe.set_mode_cpu();
case 'gpu'
caffe.set_mode_gpu();
gpuDev = gpuDevice();
caffe.set_device(gpuDev.Index - 1);
end
caffeBlobNames = caffeNet.blob_names';
[caffeLayerFound, caffe2netres] = ismember(caffeBlobNames, netBlobNames);
info('Found %d matches between simplenn layers and caffe blob names.\n',...
sum(caffeLayerFound));
% If testData not supplied, use random input
imSize = net.meta.normalization.imageSize;
if ~exist('testData', 'var') || isempty(testData)
testData = rand(imSize, 'single') * opts.randScale;
end
if ischar(testData), testData = imread(testData); end
testDataSize = [size(testData), 1, 1];
assert(all(testDataSize(1:3) == imSize(1:3)), 'Invalid test data size.');
testData = single(testData);
dataCaffe = matlab_img_to_caffe(testData);
if isfield(net.meta.normalization, 'averageImage') && ...
~isempty(net.meta.normalization.averageImage)
avImage = net.meta.normalization.averageImage;
if numel(avImage) == imSize(3)
avImage = reshape(avImage, 1, 1, imSize(3));
end
testData = bsxfun(@minus, testData, avImage);
end
% Test MatConvNet model
stime = tic;
for rep = 1:opts.numRepetitions
res = vl_simplenn(net, testData, [], [], 'ConserveMemory', false);
end
info('MatConvNet %s time: %.1f ms.\n', opts.device, ...
toc(stime)/opts.numRepetitions*1000);
if ~isempty(meanFilename) && exist(meanFilename, 'file')
mean_img_caffe = caffe.io.read_mean(meanFilename);
dataCaffe = bsxfun(@minus, dataCaffe, mean_img_caffe);
end
% Test Caffe model
stime = tic;
for rep = 1:opts.numRepetitions
caffeNet.forward({dataCaffe});
end
info('Caffe %s time: %.1f ms.\n', opts.device, ...
toc(stime)/opts.numRepetitions*1000);
diffStats = struct();
for li = 1:numel(caffeBlobNames)
blob = caffeNet.blobs(caffeBlobNames{li});
caffeData = permute(blob.get_data(), [2, 1, 3, 4]);
if li == 1 && size(caffeData, 3) == 3
caffeData = caffeData(:, :, [3, 2, 1]);
end
mcnData = gather(res(caffe2netres(li)).x);
diff = abs(caffeData(:) - mcnData(:));
diffStats.(caffeBlobNames{li}) = [min(diff), mean(diff), max(diff)]';
end
if ~opts.silent
pp = '% 10s % 10s % 10s % 10s\n';
precp = '% 10.2e';
fprintf(pp, 'Layer name', 'Min', 'Mean', 'Max');
for li = 1:numel(caffeBlobNames)
lstats = diffStats.(caffeBlobNames{li});
fprintf(pp, caffeBlobNames{li}, sprintf(precp, lstats(1)), ...
sprintf(precp, lstats(2)), sprintf(precp, lstats(3)));
end
fprintf('\n');
end
end
function img = matlab_img_to_caffe(img)
img = single(img);
% Convert from HxWxCxN to WxHxCxN per Caffe's convention
img = permute(img, [2 1 3 4]);
if size(img,3) == 3
% Convert from RGB to BGR channel order per Caffe's convention
img = img(:,:, [3 2 1], :);
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_train_dag.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/cnn_train_dag.m
| 13,639 |
utf_8
|
d07b353a98ae0de8b5ee7be804801741
|
function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.expDir = fullfile('data','exp') ;
opts.continue = true ;
opts.batchSize = 256 ;
opts.numSubBatches = 1 ;
opts.train = [] ;
opts.val = [] ;
opts.gpus = [] ;
opts.prefetch = false ;
opts.numEpochs = 300 ;
opts.learningRate = 0.001 ;
opts.weightDecay = 0.0005 ;
opts.momentum = 0.9 ;
opts.saveMomentum = true ;
opts.nesterovUpdate = false ;
opts.randomSeed = 0 ;
opts.profile = false ;
opts.parameterServer.method = 'mmap' ;
opts.parameterServer.prefix = 'mcn' ;
opts.derOutputs = {'objective', 1} ;
opts.extractStatsFn = @extractStats ;
opts.plotStatistics = true;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
if isnan(opts.val), opts.val = [] ; end
% -------------------------------------------------------------------------
% Initialization
% -------------------------------------------------------------------------
evaluateMode = isempty(opts.train) ;
if ~evaluateMode
if isempty(opts.derOutputs)
error('DEROUTPUTS must be specified when training.\n') ;
end
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
start = opts.continue * findLastCheckpoint(opts.expDir) ;
if start >= 1
fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ;
[net, state, stats] = loadState(modelPath(start)) ;
else
state = [] ;
end
for epoch=start+1:opts.numEpochs
% Set the random seed based on the epoch and opts.randomSeed.
% This is important for reproducibility, including when training
% is restarted from a checkpoint.
rng(epoch + opts.randomSeed) ;
prepareGPUs(opts, epoch == start+1) ;
% Train for one epoch.
params = opts ;
params.epoch = epoch ;
params.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ;
params.train = opts.train(randperm(numel(opts.train))) ; % shuffle
params.val = opts.val(randperm(numel(opts.val))) ;
params.imdb = imdb ;
params.getBatch = getBatch ;
if numel(opts.gpus) <= 1
[net, state] = processEpoch(net, state, params, 'train') ;
[net, state] = processEpoch(net, state, params, 'val') ;
if ~evaluateMode
saveState(modelPath(epoch), net, state) ;
end
lastStats = state.stats ;
else
spmd
[net, state] = processEpoch(net, state, params, 'train') ;
[net, state] = processEpoch(net, state, params, 'val') ;
if labindex == 1 && ~evaluateMode
saveState(modelPath(epoch), net, state) ;
end
lastStats = state.stats ;
end
lastStats = accumulateStats(lastStats) ;
end
stats.train(epoch) = lastStats.train ;
stats.val(epoch) = lastStats.val ;
clear lastStats ;
saveStats(modelPath(epoch), stats) ;
if opts.plotStatistics
switchFigure(1) ; clf ;
plots = setdiff(...
cat(2,...
fieldnames(stats.train)', ...
fieldnames(stats.val)'), {'num', 'time'}) ;
for p = plots
p = char(p) ;
values = zeros(0, epoch) ;
leg = {} ;
for f = {'train', 'val'}
f = char(f) ;
if isfield(stats.(f), p)
tmp = [stats.(f).(p)] ;
values(end+1,:) = tmp(1,:)' ;
leg{end+1} = f ;
end
end
subplot(1,numel(plots),find(strcmp(p,plots))) ;
plot(1:epoch, values','o-') ;
xlabel('epoch') ;
title(p) ;
legend(leg{:}) ;
grid on ;
end
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% With multiple GPUs, return one copy
if isa(net, 'Composite'), net = net{1} ; end
% -------------------------------------------------------------------------
function [net, state] = processEpoch(net, state, params, mode)
% -------------------------------------------------------------------------
% Note that net is not strictly needed as an output argument as net
% is a handle class. However, this fixes some aliasing issue in the
% spmd caller.
% initialize with momentum 0
if isempty(state) || isempty(state.momentum)
state.momentum = num2cell(zeros(1, numel(net.params))) ;
end
% move CNN to GPU as needed
numGpus = numel(params.gpus) ;
if numGpus >= 1
net.move('gpu') ;
state.momentum = cellfun(@gpuArray, state.momentum, 'uniformoutput', false) ;
end
if numGpus > 1
parserv = ParameterServer(params.parameterServer) ;
net.setParameterServer(parserv) ;
else
parserv = [] ;
end
% profile
if params.profile
if numGpus <= 1
profile clear ;
profile on ;
else
mpiprofile reset ;
mpiprofile on ;
end
end
num = 0 ;
epoch = params.epoch ;
subset = params.(mode) ;
adjustTime = 0 ;
stats.num = 0 ; % return something even if subset = []
stats.time = 0 ;
start = tic ;
for t=1:params.batchSize:numel(subset)
fprintf('%s: epoch %02d: %3d/%3d:', mode, epoch, ...
fix((t-1)/params.batchSize)+1, ceil(numel(subset)/params.batchSize)) ;
batchSize = min(params.batchSize, numel(subset) - t + 1) ;
for s=1:params.numSubBatches
% get this image batch and prefetch the next
batchStart = t + (labindex-1) + (s-1) * numlabs ;
batchEnd = min(t+params.batchSize-1, numel(subset)) ;
batch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ;
num = num + numel(batch) ;
if numel(batch) == 0, continue ; end
inputs = params.getBatch(params.imdb, batch) ;
if params.prefetch
if s == params.numSubBatches
batchStart = t + (labindex-1) + params.batchSize ;
batchEnd = min(t+2*params.batchSize-1, numel(subset)) ;
else
batchStart = batchStart + numlabs ;
end
nextBatch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ;
params.getBatch(params.imdb, nextBatch) ;
end
if strcmp(mode, 'train')
net.mode = 'normal' ;
net.accumulateParamDers = (s ~= 1) ;
net.eval(inputs, params.derOutputs, 'holdOn', s < params.numSubBatches) ;
else
net.mode = 'test' ;
net.eval(inputs) ;
end
end
% Accumulate gradient.
if strcmp(mode, 'train')
if ~isempty(parserv), parserv.sync() ; end
state = accumulateGradients(net, state, params, batchSize, parserv) ;
end
% Get statistics.
time = toc(start) + adjustTime ;
batchTime = time - stats.time ;
stats.num = num ;
stats.time = time ;
stats = params.extractStatsFn(stats,net) ;
currentSpeed = batchSize / batchTime ;
averageSpeed = (t + batchSize - 1) / time ;
if t == 3*params.batchSize + 1
% compensate for the first three iterations, which are outliers
adjustTime = 4*batchTime - time ;
stats.time = time + adjustTime ;
end
fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ;
for f = setdiff(fieldnames(stats)', {'num', 'time'})
f = char(f) ;
fprintf(' %s: %.3f', f, stats.(f)) ;
end
fprintf('\n') ;
end
% Save back to state.
state.stats.(mode) = stats ;
if params.profile
if numGpus <= 1
state.prof.(mode) = profile('info') ;
profile off ;
else
state.prof.(mode) = mpiprofile('info');
mpiprofile off ;
end
end
if ~params.saveMomentum
state.momentum = [] ;
else
state.momentum = cellfun(@gather, state.momentum, 'uniformoutput', false) ;
end
net.reset() ;
net.move('cpu') ;
% -------------------------------------------------------------------------
function state = accumulateGradients(net, state, params, batchSize, parserv)
% -------------------------------------------------------------------------
numGpus = numel(params.gpus) ;
otherGpus = setdiff(1:numGpus, labindex) ;
for p=1:numel(net.params)
if ~isempty(parserv)
parDer = parserv.pullWithIndex(p) ;
else
parDer = net.params(p).der ;
end
switch net.params(p).trainMethod
case 'average' % mainly for batch normalization
thisLR = net.params(p).learningRate ;
net.params(p).value = vl_taccum(...
1 - thisLR, net.params(p).value, ...
(thisLR/batchSize/net.params(p).fanout), parDer) ;
case 'gradient'
thisDecay = params.weightDecay * net.params(p).weightDecay ;
thisLR = params.learningRate * net.params(p).learningRate ;
if thisLR>0 || thisDecay>0
% Normalize gradient and incorporate weight decay.
parDer = vl_taccum(1/batchSize, parDer, ...
thisDecay, net.params(p).value) ;
% Update momentum.
state.momentum{p} = vl_taccum(...
params.momentum, state.momentum{p}, ...
-1, parDer) ;
% Nesterov update (aka one step ahead).
if params.nesterovUpdate
delta = vl_taccum(...
params.momentum, state.momentum{p}, ...
-1, parDer) ;
else
delta = state.momentum{p} ;
end
% Update parameters.
net.params(p).value = vl_taccum(...
1, net.params(p).value, thisLR, delta) ;
end
otherwise
error('Unknown training method ''%s'' for parameter ''%s''.', ...
net.params(p).trainMethod, ...
net.params(p).name) ;
end
end
% -------------------------------------------------------------------------
function stats = accumulateStats(stats_)
% -------------------------------------------------------------------------
for s = {'train', 'val'}
s = char(s) ;
total = 0 ;
% initialize stats stucture with same fields and same order as
% stats_{1}
stats__ = stats_{1} ;
names = fieldnames(stats__.(s))' ;
values = zeros(1, numel(names)) ;
fields = cat(1, names, num2cell(values)) ;
stats.(s) = struct(fields{:}) ;
for g = 1:numel(stats_)
stats__ = stats_{g} ;
num__ = stats__.(s).num ;
total = total + num__ ;
for f = setdiff(fieldnames(stats__.(s))', 'num')
f = char(f) ;
stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;
if g == numel(stats_)
stats.(s).(f) = stats.(s).(f) / total ;
end
end
end
stats.(s).num = total ;
end
% -------------------------------------------------------------------------
function stats = extractStats(stats, net)
% -------------------------------------------------------------------------
sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ;
for i = 1:numel(sel)
stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ;
end
% -------------------------------------------------------------------------
function saveState(fileName, net_, state)
% -------------------------------------------------------------------------
net = net_.saveobj() ;
save(fileName, 'net', 'state') ;
% -------------------------------------------------------------------------
function saveStats(fileName, stats)
% -------------------------------------------------------------------------
if exist(fileName)
save(fileName, 'stats', '-append') ;
else
save(fileName, 'stats') ;
end
% -------------------------------------------------------------------------
function [net, state, stats] = loadState(fileName)
% -------------------------------------------------------------------------
load(fileName, 'net', 'state', 'stats') ;
net = dagnn.DagNN.loadobj(net) ;
if isempty(whos('stats'))
error('Epoch ''%s'' was only partially saved. Delete this file and try again.', ...
fileName) ;
end
% -------------------------------------------------------------------------
function epoch = findLastCheckpoint(modelDir)
% -------------------------------------------------------------------------
list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ;
epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
epoch = max([epoch 0]) ;
% -------------------------------------------------------------------------
function switchFigure(n)
% -------------------------------------------------------------------------
if get(0,'CurrentFigure') ~= n
try
set(0,'CurrentFigure',n) ;
catch
figure(n) ;
end
end
% -------------------------------------------------------------------------
function clearMex()
% -------------------------------------------------------------------------
clear vl_tmove vl_imreadjpeg ;
% -------------------------------------------------------------------------
function prepareGPUs(opts, cold)
% -------------------------------------------------------------------------
numGpus = numel(opts.gpus) ;
if numGpus > 1
% check parallel pool integrity as it could have timed out
pool = gcp('nocreate') ;
if ~isempty(pool) && pool.NumWorkers ~= numGpus
delete(pool) ;
end
pool = gcp('nocreate') ;
if isempty(pool)
parpool('local', numGpus) ;
cold = true ;
end
end
if numGpus >= 1 && cold
fprintf('%s: resetting GPU\n', mfilename)
clearMex() ;
if numGpus == 1
gpuDevice(opts.gpus)
else
spmd
clearMex() ;
gpuDevice(opts.gpus(labindex))
end
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_train.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/cnn_train.m
| 19,153 |
utf_8
|
c6c0c0c8532f9c3653af4410497f80c3
|
function [net, stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable
% getBatch function.
%
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.expDir = fullfile('data','exp') ;
opts.continue = true ;
opts.batchSize = 256 ;
opts.numSubBatches = 1 ;
opts.train = [] ;
opts.val = [] ;
opts.gpus = [] ;
opts.prefetch = false ;
opts.numEpochs = 300 ;
opts.learningRate = 0.001 ;
opts.weightDecay = 0.0005 ;
opts.momentum = 0.9 ;
opts.saveMomentum = true ;
opts.nesterovUpdate = false ;
opts.randomSeed = 0 ;
opts.memoryMapFile = fullfile(tempdir, 'matconvnet.bin') ;
opts.profile = false ;
opts.parameterServer.method = 'mmap' ;
opts.parameterServer.prefix = 'mcn' ;
opts.conserveMemory = true ;
opts.backPropDepth = +inf ;
opts.sync = false ;
opts.cudnn = true ;
opts.errorFunction = 'multiclass' ;
opts.errorLabels = {} ;
opts.plotDiagnostics = false ;
opts.plotStatistics = true;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
if isnan(opts.val), opts.val = [] ; end
% -------------------------------------------------------------------------
% Initialization
% -------------------------------------------------------------------------
net = vl_simplenn_tidy(net); % fill in some eventually missing values
net.layers{end-1}.precious = 1; % do not remove predictions, used for error
vl_simplenn_display(net, 'batchSize', opts.batchSize) ;
evaluateMode = isempty(opts.train) ;
if ~evaluateMode
for i=1:numel(net.layers)
J = numel(net.layers{i}.weights) ;
if ~isfield(net.layers{i}, 'learningRate')
net.layers{i}.learningRate = ones(1, J) ;
end
if ~isfield(net.layers{i}, 'weightDecay')
net.layers{i}.weightDecay = ones(1, J) ;
end
end
end
% setup error calculation function
hasError = true ;
if isstr(opts.errorFunction)
switch opts.errorFunction
case 'none'
opts.errorFunction = @error_none ;
hasError = false ;
case 'multiclass'
opts.errorFunction = @error_multiclass ;
if isempty(opts.errorLabels), opts.errorLabels = {'top1err', 'top5err'} ; end
case 'binary'
opts.errorFunction = @error_binary ;
if isempty(opts.errorLabels), opts.errorLabels = {'binerr'} ; end
otherwise
error('Unknown error function ''%s''.', opts.errorFunction) ;
end
end
state.getBatch = getBatch ;
stats = [] ;
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
start = opts.continue * findLastCheckpoint(opts.expDir) ;
if start >= 1
fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ;
[net, state, stats] = loadState(modelPath(start)) ;
else
state = [] ;
end
for epoch=start+1:opts.numEpochs
% Set the random seed based on the epoch and opts.randomSeed.
% This is important for reproducibility, including when training
% is restarted from a checkpoint.
rng(epoch + opts.randomSeed) ;
prepareGPUs(opts, epoch == start+1) ;
% Train for one epoch.
params = opts ;
params.epoch = epoch ;
params.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ;
params.train = opts.train(randperm(numel(opts.train))) ; % shuffle
params.val = opts.val(randperm(numel(opts.val))) ;
params.imdb = imdb ;
params.getBatch = getBatch ;
if numel(params.gpus) <= 1
[net, state] = processEpoch(net, state, params, 'train') ;
[net, state] = processEpoch(net, state, params, 'val') ;
if ~evaluateMode
saveState(modelPath(epoch), net, state) ;
end
lastStats = state.stats ;
else
spmd
[net, state] = processEpoch(net, state, params, 'train') ;
[net, state] = processEpoch(net, state, params, 'val') ;
if labindex == 1 && ~evaluateMode
saveState(modelPath(epoch), net, state) ;
end
lastStats = state.stats ;
end
lastStats = accumulateStats(lastStats) ;
end
stats.train(epoch) = lastStats.train ;
stats.val(epoch) = lastStats.val ;
clear lastStats ;
saveStats(modelPath(epoch), stats) ;
if params.plotStatistics
switchFigure(1) ; clf ;
plots = setdiff(...
cat(2,...
fieldnames(stats.train)', ...
fieldnames(stats.val)'), {'num', 'time'}) ;
for p = plots
p = char(p) ;
values = zeros(0, epoch) ;
leg = {} ;
for f = {'train', 'val'}
f = char(f) ;
if isfield(stats.(f), p)
tmp = [stats.(f).(p)] ;
values(end+1,:) = tmp(1,:)' ;
leg{end+1} = f ;
end
end
subplot(1,numel(plots),find(strcmp(p,plots))) ;
plot(1:epoch, values','o-') ;
xlabel('epoch') ;
title(p) ;
legend(leg{:}) ;
grid on ;
end
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% With multiple GPUs, return one copy
if isa(net, 'Composite'), net = net{1} ; end
% -------------------------------------------------------------------------
function err = error_multiclass(params, labels, res)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
[~,predictions] = sort(predictions, 3, 'descend') ;
% be resilient to badly formatted labels
if numel(labels) == size(predictions, 4)
labels = reshape(labels,1,1,1,[]) ;
end
% skip null labels
mass = single(labels(:,:,1,:) > 0) ;
if size(labels,3) == 2
% if there is a second channel in labels, used it as weights
mass = mass .* labels(:,:,2,:) ;
labels(:,:,2,:) = [] ;
end
m = min(5, size(predictions,3)) ;
error = ~bsxfun(@eq, predictions, labels) ;
err(1,1) = sum(sum(sum(mass .* error(:,:,1,:)))) ;
err(2,1) = sum(sum(sum(mass .* min(error(:,:,1:m,:),[],3)))) ;
% -------------------------------------------------------------------------
function err = error_binary(params, labels, res)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
error = bsxfun(@times, predictions, labels) < 0 ;
err = sum(error(:)) ;
% -------------------------------------------------------------------------
function err = error_none(params, labels, res)
% -------------------------------------------------------------------------
err = zeros(0,1) ;
% -------------------------------------------------------------------------
function [net, state] = processEpoch(net, state, params, mode)
% -------------------------------------------------------------------------
% Note that net is not strictly needed as an output argument as net
% is a handle class. However, this fixes some aliasing issue in the
% spmd caller.
% initialize with momentum 0
if isempty(state) || isempty(state.momentum)
for i = 1:numel(net.layers)
for j = 1:numel(net.layers{i}.weights)
state.momentum{i}{j} = 0 ;
end
end
end
% move CNN to GPU as needed
numGpus = numel(params.gpus) ;
if numGpus >= 1
net = vl_simplenn_move(net, 'gpu') ;
for i = 1:numel(state.momentum)
for j = 1:numel(state.momentum{i})
state.momentum{i}{j} = gpuArray(state.momentum{i}{j}) ;
end
end
end
if numGpus > 1
parserv = ParameterServer(params.parameterServer) ;
vl_simplenn_start_parserv(net, parserv) ;
else
parserv = [] ;
end
% profile
if params.profile
if numGpus <= 1
profile clear ;
profile on ;
else
mpiprofile reset ;
mpiprofile on ;
end
end
subset = params.(mode) ;
num = 0 ;
stats.num = 0 ; % return something even if subset = []
stats.time = 0 ;
adjustTime = 0 ;
res = [] ;
error = [] ;
start = tic ;
for t=1:params.batchSize:numel(subset)
fprintf('%s: epoch %02d: %3d/%3d:', mode, params.epoch, ...
fix((t-1)/params.batchSize)+1, ceil(numel(subset)/params.batchSize)) ;
batchSize = min(params.batchSize, numel(subset) - t + 1) ;
for s=1:params.numSubBatches
% get this image batch and prefetch the next
batchStart = t + (labindex-1) + (s-1) * numlabs ;
batchEnd = min(t+params.batchSize-1, numel(subset)) ;
batch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ;
num = num + numel(batch) ;
if numel(batch) == 0, continue ; end
[im, labels] = params.getBatch(params.imdb, batch) ;
if params.prefetch
if s == params.numSubBatches
batchStart = t + (labindex-1) + params.batchSize ;
batchEnd = min(t+2*params.batchSize-1, numel(subset)) ;
else
batchStart = batchStart + numlabs ;
end
nextBatch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ;
params.getBatch(params.imdb, nextBatch) ;
end
if numGpus >= 1
im = gpuArray(im) ;
end
if strcmp(mode, 'train')
dzdy = 1 ;
evalMode = 'normal' ;
else
dzdy = [] ;
evalMode = 'test' ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, dzdy, res, ...
'accumulate', s ~= 1, ...
'mode', evalMode, ...
'conserveMemory', params.conserveMemory, ...
'backPropDepth', params.backPropDepth, ...
'sync', params.sync, ...
'cudnn', params.cudnn, ...
'parameterServer', parserv, ...
'holdOn', s < params.numSubBatches) ;
% accumulate errors
error = sum([error, [...
sum(double(gather(res(end).x))) ;
reshape(params.errorFunction(params, labels, res),[],1) ; ]],2) ;
end
% accumulate gradient
if strcmp(mode, 'train')
if ~isempty(parserv), parserv.sync() ; end
[net, res, state] = accumulateGradients(net, res, state, params, batchSize, parserv) ;
end
% get statistics
time = toc(start) + adjustTime ;
batchTime = time - stats.time ;
stats = extractStats(net, params, error / num) ;
stats.num = num ;
stats.time = time ;
currentSpeed = batchSize / batchTime ;
averageSpeed = (t + batchSize - 1) / time ;
if t == 3*params.batchSize + 1
% compensate for the first three iterations, which are outliers
adjustTime = 4*batchTime - time ;
stats.time = time + adjustTime ;
end
fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ;
for f = setdiff(fieldnames(stats)', {'num', 'time'})
f = char(f) ;
fprintf(' %s: %.3f', f, stats.(f)) ;
end
fprintf('\n') ;
% collect diagnostic statistics
if strcmp(mode, 'train') && params.plotDiagnostics
switchFigure(2) ; clf ;
diagn = [res.stats] ;
diagnvar = horzcat(diagn.variation) ;
diagnpow = horzcat(diagn.power) ;
subplot(2,2,1) ; barh(diagnvar) ;
set(gca,'TickLabelInterpreter', 'none', ...
'YTick', 1:numel(diagnvar), ...
'YTickLabel',horzcat(diagn.label), ...
'YDir', 'reverse', ...
'XScale', 'log', ...
'XLim', [1e-5 1], ...
'XTick', 10.^(-5:1)) ;
grid on ;
subplot(2,2,2) ; barh(sqrt(diagnpow)) ;
set(gca,'TickLabelInterpreter', 'none', ...
'YTick', 1:numel(diagnpow), ...
'YTickLabel',{diagn.powerLabel}, ...
'YDir', 'reverse', ...
'XScale', 'log', ...
'XLim', [1e-5 1e5], ...
'XTick', 10.^(-5:5)) ;
grid on ;
subplot(2,2,3); plot(squeeze(res(end-1).x)) ;
drawnow ;
end
end
% Save back to state.
state.stats.(mode) = stats ;
if params.profile
if numGpus <= 1
state.prof.(mode) = profile('info') ;
profile off ;
else
state.prof.(mode) = mpiprofile('info');
mpiprofile off ;
end
end
if ~params.saveMomentum
state.momentum = [] ;
else
for i = 1:numel(state.momentum)
for j = 1:numel(state.momentum{i})
state.momentum{i}{j} = gather(state.momentum{i}{j}) ;
end
end
end
net = vl_simplenn_move(net, 'cpu') ;
% -------------------------------------------------------------------------
function [net, res, state] = accumulateGradients(net, res, state, params, batchSize, parserv)
% -------------------------------------------------------------------------
numGpus = numel(params.gpus) ;
otherGpus = setdiff(1:numGpus, labindex) ;
for l=numel(net.layers):-1:1
for j=numel(res(l).dzdw):-1:1
if ~isempty(parserv)
tag = sprintf('l%d_%d',l,j) ;
parDer = parserv.pull(tag) ;
else
parDer = res(l).dzdw{j} ;
end
if j == 3 && strcmp(net.layers{l}.type, 'bnorm')
% special case for learning bnorm moments
thisLR = net.layers{l}.learningRate(j) ;
net.layers{l}.weights{j} = vl_taccum(...
1 - thisLR, ...
net.layers{l}.weights{j}, ...
thisLR / batchSize, ...
parDer) ;
else
% Standard gradient training.
thisDecay = params.weightDecay * net.layers{l}.weightDecay(j) ;
thisLR = params.learningRate * net.layers{l}.learningRate(j) ;
if thisLR>0 || thisDecay>0
% Normalize gradient and incorporate weight decay.
parDer = vl_taccum(1/batchSize, parDer, ...
thisDecay, net.layers{l}.weights{j}) ;
% Update momentum.
state.momentum{l}{j} = vl_taccum(...
params.momentum, state.momentum{l}{j}, ...
-1, parDer) ;
% Nesterov update (aka one step ahead).
if params.nesterovUpdate
delta = vl_taccum(...
params.momentum, state.momentum{l}{j}, ...
-1, parDer) ;
else
delta = state.momentum{l}{j} ;
end
% Update parameters.
net.layers{l}.weights{j} = vl_taccum(...
1, net.layers{l}.weights{j}, ...
thisLR, delta) ;
end
end
% if requested, collect some useful stats for debugging
if params.plotDiagnostics
variation = [] ;
label = '' ;
switch net.layers{l}.type
case {'conv','convt'}
variation = thisLR * mean(abs(state.momentum{l}{j}(:))) ;
power = mean(res(l+1).x(:).^2) ;
if j == 1 % fiters
base = mean(net.layers{l}.weights{j}(:).^2) ;
label = 'filters' ;
else % biases
base = sqrt(power) ;%mean(abs(res(l+1).x(:))) ;
label = 'biases' ;
end
variation = variation / base ;
label = sprintf('%s_%s', net.layers{l}.name, label) ;
end
res(l).stats.variation(j) = variation ;
res(l).stats.power = power ;
res(l).stats.powerLabel = net.layers{l}.name ;
res(l).stats.label{j} = label ;
end
end
end
% -------------------------------------------------------------------------
function stats = accumulateStats(stats_)
% -------------------------------------------------------------------------
for s = {'train', 'val'}
s = char(s) ;
total = 0 ;
% initialize stats stucture with same fields and same order as
% stats_{1}
stats__ = stats_{1} ;
names = fieldnames(stats__.(s))' ;
values = zeros(1, numel(names)) ;
fields = cat(1, names, num2cell(values)) ;
stats.(s) = struct(fields{:}) ;
for g = 1:numel(stats_)
stats__ = stats_{g} ;
num__ = stats__.(s).num ;
total = total + num__ ;
for f = setdiff(fieldnames(stats__.(s))', 'num')
f = char(f) ;
stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;
if g == numel(stats_)
stats.(s).(f) = stats.(s).(f) / total ;
end
end
end
stats.(s).num = total ;
end
% -------------------------------------------------------------------------
function stats = extractStats(net, params, errors)
% -------------------------------------------------------------------------
stats.objective = errors(1) ;
for i = 1:numel(params.errorLabels)
stats.(params.errorLabels{i}) = errors(i+1) ;
end
% -------------------------------------------------------------------------
function saveState(fileName, net, state)
% -------------------------------------------------------------------------
save(fileName, 'net', 'state') ;
% -------------------------------------------------------------------------
function saveStats(fileName, stats)
% -------------------------------------------------------------------------
if exist(fileName)
save(fileName, 'stats', '-append') ;
else
save(fileName, 'stats') ;
end
% -------------------------------------------------------------------------
function [net, state, stats] = loadState(fileName)
% -------------------------------------------------------------------------
load(fileName, 'net', 'state', 'stats') ;
net = vl_simplenn_tidy(net) ;
if isempty(whos('stats'))
error('Epoch ''%s'' was only partially saved. Delete this file and try again.', ...
fileName) ;
end
% -------------------------------------------------------------------------
function epoch = findLastCheckpoint(modelDir)
% -------------------------------------------------------------------------
list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ;
epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
epoch = max([epoch 0]) ;
% -------------------------------------------------------------------------
function switchFigure(n)
% -------------------------------------------------------------------------
if get(0,'CurrentFigure') ~= n
try
set(0,'CurrentFigure',n) ;
catch
figure(n) ;
end
end
% -------------------------------------------------------------------------
function clearMex()
% -------------------------------------------------------------------------
%clear vl_tmove vl_imreadjpeg ;
disp('Clearing mex files') ;
clear mex ;
clear vl_tmove vl_imreadjpeg ;
% -------------------------------------------------------------------------
function prepareGPUs(params, cold)
% -------------------------------------------------------------------------
numGpus = numel(params.gpus) ;
if numGpus > 1
% check parallel pool integrity as it could have timed out
pool = gcp('nocreate') ;
if ~isempty(pool) && pool.NumWorkers ~= numGpus
delete(pool) ;
end
pool = gcp('nocreate') ;
if isempty(pool)
parpool('local', numGpus) ;
cold = true ;
end
end
if numGpus >= 1 && cold
fprintf('%s: resetting GPU\n', mfilename) ;
clearMex() ;
if numGpus == 1
disp(gpuDevice(params.gpus)) ;
else
spmd
clearMex() ;
disp(gpuDevice(params.gpus(labindex))) ;
end
end
end
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_stn_cluttered_mnist.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/spatial_transformer/cnn_stn_cluttered_mnist.m
| 3,872 |
utf_8
|
3235801f70028cc27d54d15ec2964808
|
function [net, info] = cnn_stn_cluttered_mnist(varargin)
%CNN_STN_CLUTTERED_MNIST Demonstrates training a spatial transformer
% The spatial transformer network (STN) is trained on the
% cluttered MNIST dataset.
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile(vl_rootnn, 'data') ;
opts.useSpatialTransformer = true ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataPath = fullfile(opts.dataDir,'cluttered-mnist.mat') ;
if opts.useSpatialTransformer
opts.expDir = fullfile(vl_rootnn, 'data', 'cluttered-mnist-stn') ;
else
opts.expDir = fullfile(vl_rootnn, 'data', 'cluttered-mnist-no-stn') ;
end
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.dataURL = 'http://www.vlfeat.org/matconvnet/download/data/cluttered-mnist.mat' ;
opts.train = struct() ;
opts = vl_argparse(opts, varargin) ;
if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
if exist(opts.imdbPath, 'file')
imdb = load(opts.imdbPath) ;
else
imdb = getImdDB(opts) ;
mkdir(opts.expDir) ;
save(opts.imdbPath, '-struct', 'imdb') ;
end
net = cnn_stn_cluttered_mnist_init([60 60], true) ; % initialize the network
net.meta.classes.name = arrayfun(@(x)sprintf('%d',x),1:10,'UniformOutput',false) ;
% --------------------------------------------------------------------
% Train
% --------------------------------------------------------------------
fbatch = @(i,b) getBatch(opts.train,i,b);
[net, info] = cnn_train_dag(net, imdb, fbatch, ...
'expDir', opts.expDir, ...
net.meta.trainOpts, ...
opts.train, ...
'val', find(imdb.images.set == 2)) ;
% --------------------------------------------------------------------
% Show transformer
% --------------------------------------------------------------------
figure(100) ; clf ;
v = net.getVarIndex('xST') ;
net.vars(v).precious = true ;
net.eval({'input',imdb.images.data(:,:,:,1:6)}) ;
for t = 1:6
subplot(2,6,t) ; imagesc(imdb.images.data(:,:,:,t)) ; axis image off ;
subplot(2,6,6+t) ; imagesc(net.vars(v).value(:,:,:,t)) ; axis image off ;
colormap gray ;
end
% --------------------------------------------------------------------
function inputs = getBatch(opts, imdb, batch)
% --------------------------------------------------------------------
if ~isa(imdb.images.data, 'gpuArray') && numel(opts.gpus) > 0
imdb.images.data = gpuArray(imdb.images.data);
imdb.images.labels = gpuArray(imdb.images.labels);
end
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
inputs = {'input', images, 'label', labels} ;
% --------------------------------------------------------------------
function imdb = getImdDB(opts)
% --------------------------------------------------------------------
% Prepare the IMDB structure:
if ~exist(opts.dataDir, 'dir')
mkdir(opts.dataDir) ;
end
if ~exist(opts.dataPath)
fprintf('Downloading %s to %s.\n', opts.dataURL, opts.dataPath) ;
urlwrite(opts.dataURL, opts.dataPath) ;
end
dat = load(opts.dataPath);
set = [ones(1,numel(dat.y_tr)) 2*ones(1,numel(dat.y_vl)) 3*ones(1,numel(dat.y_ts))];
data = single(cat(4,dat.x_tr,dat.x_vl,dat.x_ts));
imdb.images.data = data ;
imdb.images.labels = single(cat(2, dat.y_tr,dat.y_vl,dat.y_ts)) ;
imdb.images.set = set ;
imdb.meta.sets = {'train', 'val', 'test'} ;
imdb.meta.classes = arrayfun(@(x)sprintf('%d',x),0:9,'uniformoutput',false) ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
fast_rcnn_train.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/fast_rcnn/fast_rcnn_train.m
| 6,399 |
utf_8
|
54b0bc7fa26d672ed6673d3f1832944e
|
function [net, info] = fast_rcnn_train(varargin)
%FAST_RCNN_TRAIN Demonstrates training a Fast-RCNN detector
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
addpath(fullfile(vl_rootnn,'examples','fast_rcnn','bbox_functions'));
addpath(fullfile(vl_rootnn,'examples','fast_rcnn','datasets'));
opts.dataDir = fullfile(vl_rootnn, 'data') ;
opts.sswDir = fullfile(vl_rootnn, 'data', 'SSW');
opts.expDir = fullfile(vl_rootnn, 'data', 'fast-rcnn-vgg16-pascal07') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.modelPath = fullfile(opts.dataDir, 'models', ...
'imagenet-vgg-verydeep-16.mat') ;
opts.piecewise = true; % piecewise training (+bbox regression)
opts.train.gpus = [] ;
opts.train.batchSize = 2 ;
opts.train.numSubBatches = 1 ;
opts.train.continue = true ;
opts.train.prefetch = false ; % does not help for two images in a batch
opts.train.learningRate = 1e-3 / 64 * [ones(1,6) 0.1*ones(1,6)];
opts.train.weightDecay = 0.0005 ;
opts.train.numEpochs = 12 ;
opts.train.derOutputs = {'losscls', 1, 'lossbbox', 1} ;
opts.lite = false ;
opts.numFetchThreads = 2 ;
opts = vl_argparse(opts, varargin) ;
display(opts);
opts.train.expDir = opts.expDir ;
opts.train.numEpochs = numel(opts.train.learningRate) ;
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
net = fast_rcnn_init(...
'piecewise',opts.piecewise,...
'modelPath',opts.modelPath);
% -------------------------------------------------------------------------
% Database initialization
% -------------------------------------------------------------------------
if exist(opts.imdbPath,'file') == 2
fprintf('Loading imdb...');
imdb = load(opts.imdbPath) ;
else
if ~exist(opts.expDir,'dir')
mkdir(opts.expDir);
end
fprintf('Setting VOC2007 up, this may take a few minutes\n');
imdb = cnn_setup_data_voc07_ssw(...
'dataDir', opts.dataDir, ...
'sswDir', opts.sswDir, ...
'addFlipped', true, ...
'useDifficult', true) ;
save(opts.imdbPath,'-struct', 'imdb','-v7.3');
fprintf('\n');
end
fprintf('done\n');
% --------------------------------------------------------------------
% Train
% --------------------------------------------------------------------
% use train + val split to train
imdb.images.set(imdb.images.set == 2) = 1;
% minibatch options
bopts = net.meta.normalization;
bopts.useGpu = numel(opts.train.gpus) > 0 ;
bopts.numFgRoisPerImg = 16;
bopts.numRoisPerImg = 64;
bopts.maxScale = 1000;
bopts.scale = 600;
bopts.bgLabel = numel(imdb.classes.name)+1;
bopts.visualize = 0;
bopts.interpolation = net.meta.normalization.interpolation;
bopts.numThreads = opts.numFetchThreads;
bopts.prefetch = opts.train.prefetch;
[net,info] = cnn_train_dag(net, imdb, @(i,b) ...
getBatch(bopts,i,b), ...
opts.train) ;
% --------------------------------------------------------------------
% Deploy
% --------------------------------------------------------------------
modelPath = fullfile(opts.expDir, 'net-deployed.mat');
if ~exist(modelPath,'file')
net = deployFRCNN(net,imdb);
net_ = net.saveobj() ;
save(modelPath, '-struct', 'net_') ;
clear net_ ;
end
% --------------------------------------------------------------------
function inputs = getBatch(opts, imdb, batch)
% --------------------------------------------------------------------
opts.visualize = 0;
if isempty(batch)
return;
end
images = strcat([imdb.imageDir filesep], imdb.images.name(batch)) ;
opts.prefetch = (nargout == 0);
[im,rois,labels,btargets] = fast_rcnn_train_get_batch(images,imdb,...
batch, opts);
if opts.prefetch, return; end
nb = numel(labels);
nc = numel(imdb.classes.name) + 1;
% regression error only for positives
instance_weights = zeros(1,1,4*nc,nb,'single');
targets = zeros(1,1,4*nc,nb,'single');
for b=1:nb
if labels(b)>0 && labels(b)~=opts.bgLabel
targets(1,1,4*(labels(b)-1)+1:4*labels(b),b) = btargets(b,:)';
instance_weights(1,1,4*(labels(b)-1)+1:4*labels(b),b) = 1;
end
end
rois = single(rois);
if opts.useGpu > 0
im = gpuArray(im) ;
rois = gpuArray(rois) ;
targets = gpuArray(targets) ;
instance_weights = gpuArray(instance_weights) ;
end
inputs = {'input', im, 'label', labels, 'rois', rois, 'targets', targets, ...
'instance_weights', instance_weights} ;
% --------------------------------------------------------------------
function net = deployFRCNN(net,imdb)
% --------------------------------------------------------------------
% function net = deployFRCNN(net)
for l = numel(net.layers):-1:1
if isa(net.layers(l).block, 'dagnn.Loss') || ...
isa(net.layers(l).block, 'dagnn.DropOut')
layer = net.layers(l);
net.removeLayer(layer.name);
net.renameVar(layer.outputs{1}, layer.inputs{1}, 'quiet', true) ;
end
end
net.rebuild();
pfc8 = net.getLayerIndex('predcls') ;
net.addLayer('probcls',dagnn.SoftMax(),net.layers(pfc8).outputs{1},...
'probcls',{});
net.vars(net.getVarIndex('probcls')).precious = true ;
idxBox = net.getLayerIndex('predbbox') ;
if ~isnan(idxBox)
net.vars(net.layers(idxBox).outputIndexes(1)).precious = true ;
% incorporate mean and std to bbox regression parameters
blayer = net.layers(idxBox) ;
filters = net.params(net.getParamIndex(blayer.params{1})).value ;
biases = net.params(net.getParamIndex(blayer.params{2})).value ;
boxMeans = single(imdb.boxes.bboxMeanStd{1}');
boxStds = single(imdb.boxes.bboxMeanStd{2}');
net.params(net.getParamIndex(blayer.params{1})).value = ...
bsxfun(@times,filters,...
reshape([boxStds(:)' zeros(1,4,'single')]',...
[1 1 1 4*numel(net.meta.classes.name)]));
biases = biases .* [boxStds(:)' zeros(1,4,'single')];
net.params(net.getParamIndex(blayer.params{2})).value = ...
bsxfun(@plus,biases, [boxMeans(:)' zeros(1,4,'single')]);
end
net.mode = 'test' ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
fast_rcnn_evaluate.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/fast_rcnn/fast_rcnn_evaluate.m
| 6,941 |
utf_8
|
a54a3f8c3c8e5a8ff7ebe4e2b12ede30
|
function [aps, speed] = fast_rcnn_evaluate(varargin)
%FAST_RCNN_EVALUATE Evaluate a trained Fast-RCNN model on PASCAL VOC 2007
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
addpath(fullfile(vl_rootnn, 'data', 'VOCdevkit', 'VOCcode'));
addpath(genpath(fullfile(vl_rootnn, 'examples', 'fast_rcnn')));
opts.dataDir = fullfile(vl_rootnn, 'data') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.sswDir = fullfile(opts.dataDir, 'SSW');
opts.expDir = fullfile(opts.dataDir, 'fast-rcnn-vgg16-pascal07') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.modelPath = fullfile(opts.expDir, 'net-deployed.mat') ;
opts.gpu = [] ;
opts.numFetchThreads = 1 ;
opts.nmsThresh = 0.3 ;
opts.maxPerImage = 100 ;
opts = vl_argparse(opts, varargin) ;
display(opts) ;
if ~exist(opts.expDir,'dir')
mkdir(opts.expDir) ;
end
if ~isempty(opts.gpu)
gpuDevice(opts.gpu)
end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
net = dagnn.DagNN.loadobj(load(opts.modelPath)) ;
net.mode = 'test' ;
if ~isempty(opts.gpu)
net.move('gpu') ;
end
% -------------------------------------------------------------------------
% Database initialization
% -------------------------------------------------------------------------
if exist(opts.imdbPath,'file')
fprintf('Loading precomputed imdb...\n');
imdb = load(opts.imdbPath) ;
else
fprintf('Obtaining dataset and imdb...\n');
imdb = cnn_setup_data_voc07_ssw(...
'dataDir',opts.dataDir,...
'sswDir',opts.sswDir);
save(opts.imdbPath,'-struct', 'imdb','-v7.3');
end
fprintf('done\n');
bopts.averageImage = net.meta.normalization.averageImage;
bopts.useGpu = numel(opts.gpu) > 0 ;
bopts.maxScale = 1000;
bopts.bgLabel = 21;
bopts.visualize = 0;
bopts.scale = 600;
bopts.interpolation = net.meta.normalization.interpolation;
bopts.numThreads = opts.numFetchThreads;
% -------------------------------------------------------------------------
% Evaluate
% -------------------------------------------------------------------------
VOCinit;
VOCopts.testset='test';
testIdx = find(imdb.images.set == 3) ;
cls_probs = cell(1,numel(testIdx)) ;
box_deltas = cell(1,numel(testIdx)) ;
boxscores_nms = cell(numel(VOCopts.classes),numel(testIdx)) ;
ids = cell(numel(VOCopts.classes),numel(testIdx)) ;
dataVar = 'input' ;
probVarI = net.getVarIndex('probcls') ;
boxVarI = net.getVarIndex('predbbox') ;
if isnan(probVarI)
dataVar = 'data' ;
probVarI = net.getVarIndex('cls_prob') ;
boxVarI = net.getVarIndex('bbox_pred') ;
end
net.vars(probVarI).precious = true ;
net.vars(boxVarI).precious = true ;
start = tic ;
for t=1:numel(testIdx)
speed = t/toc(start) ;
fprintf('Image %d of %d (%.f HZ)\n', t, numel(testIdx), speed) ;
batch = testIdx(t);
inputs = getBatch(bopts, imdb, batch);
inputs{1} = dataVar ;
net.eval(inputs) ;
cls_probs{t} = squeeze(gather(net.vars(probVarI).value)) ;
box_deltas{t} = squeeze(gather(net.vars(boxVarI).value)) ;
end
% heuristic: keep an average of 40 detections per class per images prior
% to NMS
max_per_set = 40 * numel(testIdx);
% detection thresold for each class (this is adaptively set based on the
% max_per_set constraint)
cls_thresholds = zeros(1,numel(VOCopts.classes));
cls_probs_concat = horzcat(cls_probs{:});
for c = 1:numel(VOCopts.classes)
q = find(strcmp(VOCopts.classes{c}, net.meta.classes.name)) ;
so = sort(cls_probs_concat(q,:),'descend');
cls_thresholds(q) = so(min(max_per_set,numel(so)));
fprintf('Applying NMS for %s\n',VOCopts.classes{c});
for t=1:numel(testIdx)
si = find(cls_probs{t}(q,:) >= cls_thresholds(q)) ;
if isempty(si), continue; end
cls_prob = cls_probs{t}(q,si)';
pbox = imdb.boxes.pbox{testIdx(t)}(si,:);
% back-transform bounding box corrections
delta = box_deltas{t}(4*(q-1)+1:4*q,si)';
pred_box = bbox_transform_inv(pbox, delta);
im_size = imdb.images.size(testIdx(t),[2 1]);
pred_box = bbox_clip(round(pred_box), im_size);
% Threshold. Heuristic: keep at most 100 detection per class per image
% prior to NMS.
boxscore = [pred_box cls_prob];
[~,si] = sort(boxscore(:,5),'descend');
boxscore = boxscore(si,:);
boxscore = boxscore(1:min(size(boxscore,1),opts.maxPerImage),:);
% NMS
pick = bbox_nms(double(boxscore),opts.nmsThresh);
boxscores_nms{c,t} = boxscore(pick,:) ;
ids{c,t} = repmat({imdb.images.name{testIdx(t)}(1:end-4)},numel(pick),1) ;
if 0
figure(1) ; clf ;
idx = boxscores_nms{c,t}(:,5)>0.5;
if sum(idx)==0, continue; end
bbox_draw(imread(fullfile(imdb.imageDir,imdb.images.name{testIdx(t)})), ...
boxscores_nms{c,t}(idx,:)) ;
title(net.meta.classes.name{q}) ;
drawnow ;
pause;
%keyboard
end
end
end
%% PASCAL VOC evaluation
VOCdevkitPath = fullfile(vl_rootnn,'data','VOCdevkit');
aps = zeros(numel(VOCopts.classes),1);
% fix voc folders
VOCopts.imgsetpath = fullfile(VOCdevkitPath,'VOC2007','ImageSets','Main','%s.txt');
VOCopts.annopath = fullfile(VOCdevkitPath,'VOC2007','Annotations','%s.xml');
VOCopts.localdir = fullfile(VOCdevkitPath,'local','VOC2007');
VOCopts.detrespath = fullfile(VOCdevkitPath, 'results', 'VOC2007', 'Main', ['%s_det_', VOCopts.testset, '_%s.txt']);
% write det results to txt files
for c=1:numel(VOCopts.classes)
fid = fopen(sprintf(VOCopts.detrespath,'comp3',VOCopts.classes{c}),'w');
for i=1:numel(testIdx)
if isempty(boxscores_nms{c,i}), continue; end
dets = boxscores_nms{c,i};
for j=1:size(dets,1)
fprintf(fid,'%s %.6f %d %d %d %d\n', ...
imdb.images.name{testIdx(i)}(1:end-4), ...
dets(j,5),dets(j,1:4)) ;
end
end
fclose(fid);
[rec,prec,ap] = VOCevaldet(VOCopts,'comp3',VOCopts.classes{c},0);
fprintf('%s ap %.1f\n',VOCopts.classes{c},100*ap);
aps(c) = ap;
end
fprintf('mean ap %.1f\n',100*mean(aps));
% --------------------------------------------------------------------
function inputs = getBatch(opts, imdb, batch)
% --------------------------------------------------------------------
if isempty(batch)
return;
end
images = strcat([imdb.imageDir filesep], imdb.images.name(batch)) ;
opts.prefetch = (nargout == 0);
[im,rois] = fast_rcnn_eval_get_batch(images, imdb, batch, opts);
rois = single(rois);
if opts.useGpu > 0
im = gpuArray(im) ;
rois = gpuArray(rois) ;
end
inputs = {'input', im, 'rois', rois} ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_cifar.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/cifar/cnn_cifar.m
| 5,334 |
utf_8
|
eb9aa887d804ee635c4295a7a397206f
|
function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.modelType = 'lenet' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.expDir = fullfile(vl_rootnn, 'data', ...
sprintf('cifar-%s', opts.modelType)) ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataDir = fullfile(vl_rootnn, 'data','cifar') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.whitenData = true ;
opts.contrastNormalization = true ;
opts.networkType = 'simplenn' ;
opts.train = struct() ;
opts = vl_argparse(opts, varargin) ;
if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end;
% -------------------------------------------------------------------------
% Prepare model and data
% -------------------------------------------------------------------------
switch opts.modelType
case 'lenet'
net = cnn_cifar_init('networkType', opts.networkType) ;
case 'nin'
net = cnn_cifar_init_nin('networkType', opts.networkType) ;
otherwise
error('Unknown model type ''%s''.', opts.modelType) ;
end
if exist(opts.imdbPath, 'file')
imdb = load(opts.imdbPath) ;
else
imdb = getCifarImdb(opts) ;
mkdir(opts.expDir) ;
save(opts.imdbPath, '-struct', 'imdb') ;
end
net.meta.classes.name = imdb.meta.classes(:)' ;
% -------------------------------------------------------------------------
% Train
% -------------------------------------------------------------------------
switch opts.networkType
case 'simplenn', trainfn = @cnn_train ;
case 'dagnn', trainfn = @cnn_train_dag ;
end
[net, info] = trainfn(net, imdb, getBatch(opts), ...
'expDir', opts.expDir, ...
net.meta.trainOpts, ...
opts.train, ...
'val', find(imdb.images.set == 3)) ;
% -------------------------------------------------------------------------
function fn = getBatch(opts)
% -------------------------------------------------------------------------
switch lower(opts.networkType)
case 'simplenn'
fn = @(x,y) getSimpleNNBatch(x,y) ;
case 'dagnn'
bopts = struct('numGpus', numel(opts.train.gpus)) ;
fn = @(x,y) getDagNNBatch(bopts,x,y) ;
end
% -------------------------------------------------------------------------
function [images, labels] = getSimpleNNBatch(imdb, batch)
% -------------------------------------------------------------------------
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
if rand > 0.5, images=fliplr(images) ; end
% -------------------------------------------------------------------------
function inputs = getDagNNBatch(opts, imdb, batch)
% -------------------------------------------------------------------------
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
if rand > 0.5, images=fliplr(images) ; end
if opts.numGpus > 0
images = gpuArray(images) ;
end
inputs = {'input', images, 'label', labels} ;
% -------------------------------------------------------------------------
function imdb = getCifarImdb(opts)
% -------------------------------------------------------------------------
% Preapre the imdb structure, returns image data with mean image subtracted
unpackPath = fullfile(opts.dataDir, 'cifar-10-batches-mat');
files = [arrayfun(@(n) sprintf('data_batch_%d.mat', n), 1:5, 'UniformOutput', false) ...
{'test_batch.mat'}];
files = cellfun(@(fn) fullfile(unpackPath, fn), files, 'UniformOutput', false);
file_set = uint8([ones(1, 5), 3]);
if any(cellfun(@(fn) ~exist(fn, 'file'), files))
url = 'http://www.cs.toronto.edu/~kriz/cifar-10-matlab.tar.gz' ;
fprintf('downloading %s\n', url) ;
untar(url, opts.dataDir) ;
end
data = cell(1, numel(files));
labels = cell(1, numel(files));
sets = cell(1, numel(files));
for fi = 1:numel(files)
fd = load(files{fi}) ;
data{fi} = permute(reshape(fd.data',32,32,3,[]),[2 1 3 4]) ;
labels{fi} = fd.labels' + 1; % Index from 1
sets{fi} = repmat(file_set(fi), size(labels{fi}));
end
set = cat(2, sets{:});
data = single(cat(4, data{:}));
% remove mean in any case
dataMean = mean(data(:,:,:,set == 1), 4);
data = bsxfun(@minus, data, dataMean);
% normalize by image mean and std as suggested in `An Analysis of
% Single-Layer Networks in Unsupervised Feature Learning` Adam
% Coates, Honglak Lee, Andrew Y. Ng
if opts.contrastNormalization
z = reshape(data,[],60000) ;
z = bsxfun(@minus, z, mean(z,1)) ;
n = std(z,0,1) ;
z = bsxfun(@times, z, mean(n) ./ max(n, 40)) ;
data = reshape(z, 32, 32, 3, []) ;
end
if opts.whitenData
z = reshape(data,[],60000) ;
W = z(:,set == 1)*z(:,set == 1)'/60000 ;
[V,D] = eig(W) ;
% the scale is selected to approximately preserve the norm of W
d2 = diag(D) ;
en = sqrt(mean(d2)) ;
z = V*diag(en./max(sqrt(d2), 10))*V'*z ;
data = reshape(z, 32, 32, 3, []) ;
end
clNames = load(fullfile(unpackPath, 'batches.meta.mat'));
imdb.images.data = data ;
imdb.images.labels = single(cat(2, labels{:})) ;
imdb.images.set = set;
imdb.meta.sets = {'train', 'val', 'test'} ;
imdb.meta.classes = clNames.label_names;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_cifar_init_nin.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/cifar/cnn_cifar_init_nin.m
| 5,561 |
utf_8
|
aca711e04a8cd82821f658922218368c
|
function net = cnn_cifar_init_nin(varargin)
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
% CIFAR-10 model from
% M. Lin, Q. Chen, and S. Yan. Network in network. CoRR,
% abs/1312.4400, 2013.
%
% It reproduces the NIN + Dropout result of Table 1 (<= 10.41% top1 error).
net.layers = {} ;
lr = [1 10] ;
% Block 1
net.layers{end+1} = struct('type', 'conv', ...
'name', 'conv1', ...
'weights', {init_weights(5,3,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 2) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu1') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp1', ...
'weights', {init_weights(1,192,160)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp1') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp2', ...
'weights', {init_weights(1,160,96)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp2') ;
net.layers{end+1} = struct('name', 'pool1', ...
'type', 'pool', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'dropout', 'name', 'dropout1', 'rate', 0.5) ;
% Block 2
net.layers{end+1} = struct('type', 'conv', ...
'name', 'conv2', ...
'weights', {init_weights(5,96,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 2) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu2') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp3', ...
'weights', {init_weights(1,192,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp3') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp4', ...
'weights', {init_weights(1,192,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp4') ;
net.layers{end+1} = struct('name', 'pool2', ...
'type', 'pool', ...
'method', 'avg', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'dropout', 'name', 'dropout2', 'rate', 0.5) ;
% Block 3
net.layers{end+1} = struct('type', 'conv', ...
'name', 'conv3', ...
'weights', {init_weights(3,192,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 1) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu3') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp5', ...
'weights', {init_weights(1,192,192)}, ...
'learningRate', lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp5') ;
net.layers{end+1} = struct('type', 'conv', ...
'name', 'cccp6', ...
'weights', {init_weights(1,192,10)}, ...
'learningRate', 0.001*lr, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end}.weights{1} = 0.1 * net.layers{end}.weights{1} ;
%net.layers{end+1} = struct('type', 'relu', 'name', 'relu_cccp6') ;
net.layers{end+1} = struct('type', 'pool', ...
'name', 'pool3', ...
'method', 'avg', ...
'pool', [7 7], ...
'stride', 1, ...
'pad', 0) ;
% Loss layer
net.layers{end+1} = struct('type', 'softmaxloss') ;
% Meta parameters
net.meta.inputSize = [32 32 3] ;
net.meta.trainOpts.learningRate = [0.002, 0.01, 0.02, 0.04 * ones(1,80), 0.004 * ones(1,10), 0.0004 * ones(1,10)] ;
net.meta.trainOpts.weightDecay = 0.0005 ;
net.meta.trainOpts.batchSize = 100 ;
net.meta.trainOpts.numEpochs = numel(net.meta.trainOpts.learningRate) ;
% Fill in default values
net = vl_simplenn_tidy(net) ;
% Switch to DagNN if requested
switch lower(opts.networkType)
case 'simplenn'
% done
case 'dagnn'
net = dagnn.DagNN.fromSimpleNN(net, 'canonicalNames', true) ;
net.addLayer('error', dagnn.Loss('loss', 'classerror'), ...
{'prediction','label'}, 'error') ;
otherwise
assert(false) ;
end
function weights = init_weights(k,m,n)
weights{1} = randn(k,k,m,n,'single') * sqrt(2/(k*k*m)) ;
weights{2} = zeros(n,1,'single') ;
|
github
|
Steganalysis-CNN/CNN-without-BN-master
|
cnn_imagenet_init_resnet.m
|
.m
|
CNN-without-BN-master/dependencies/matconvnet/examples/imagenet/cnn_imagenet_init_resnet.m
| 6,776 |
utf_8
|
2bdef2921f7e7b0733d1a58f724cccdf
|
function net = cnn_imagenet_init_resnet(varargin)
%CNN_IMAGENET_INIT_RESNET Initialize the ResNet-50 model for ImageNet classification
opts.classNames = {} ;
opts.classDescriptions = {} ;
opts.averageImage = zeros(3,1) ;
opts.colorDeviation = zeros(3) ;
opts.cudnnWorkspaceLimit = 1024*1024*1204 ; % 1GB
opts = vl_argparse(opts, varargin) ;
net = dagnn.DagNN() ;
lastAdded.var = 'input' ;
lastAdded.depth = 3 ;
function Conv(name, ksize, depth, varargin)
% Helper function to add a Convolutional + BatchNorm + ReLU
% sequence to the network.
args.relu = true ;
args.downsample = false ;
args.bias = false ;
args = vl_argparse(args, varargin) ;
if args.downsample, stride = 2 ; else stride = 1 ; end
if args.bias, pars = {[name '_f'], [name '_b']} ; else pars = {[name '_f']} ; end
net.addLayer([name '_conv'], ...
dagnn.Conv('size', [ksize ksize lastAdded.depth depth], ...
'stride', stride, ....
'pad', (ksize - 1) / 2, ...
'hasBias', args.bias, ...
'opts', {'cudnnworkspacelimit', opts.cudnnWorkspaceLimit}), ...
lastAdded.var, ...
[name '_conv'], ...
pars) ;
net.addLayer([name '_bn'], ...
dagnn.BatchNorm('numChannels', depth, 'epsilon', 1e-5), ...
[name '_conv'], ...
[name '_bn'], ...
{[name '_bn_w'], [name '_bn_b'], [name '_bn_m']}) ;
lastAdded.depth = depth ;
lastAdded.var = [name '_bn'] ;
if args.relu
net.addLayer([name '_relu'] , ...
dagnn.ReLU(), ...
lastAdded.var, ...
[name '_relu']) ;
lastAdded.var = [name '_relu'] ;
end
end
% -------------------------------------------------------------------------
% Add input section
% -------------------------------------------------------------------------
Conv('conv1', 7, 64, ...
'relu', true, ...
'bias', false, ...
'downsample', true) ;
net.addLayer(...
'conv1_pool' , ...
dagnn.Pooling('poolSize', [3 3], ...
'stride', 2, ...
'pad', 1, ...
'method', 'max'), ...
lastAdded.var, ...
'conv1') ;
lastAdded.var = 'conv1' ;
% -------------------------------------------------------------------------
% Add intermediate sections
% -------------------------------------------------------------------------
for s = 2:5
switch s
case 2, sectionLen = 3 ;
case 3, sectionLen = 4 ; % 8 ;
case 4, sectionLen = 6 ; % 23 ; % 36 ;
case 5, sectionLen = 3 ;
end
% -----------------------------------------------------------------------
% Add intermediate segments for each section
for l = 1:sectionLen
depth = 2^(s+4) ;
sectionInput = lastAdded ;
name = sprintf('conv%d_%d', s, l) ;
% Optional adapter layer
if l == 1
Conv([name '_adapt_conv'], 1, 2^(s+6), 'downsample', s >= 3, 'relu', false) ;
end
sumInput = lastAdded ;
% ABC: 1x1, 3x3, 1x1; downsample if first segment in section from
% section 2 onwards.
lastAdded = sectionInput ;
%Conv([name 'a'], 1, 2^(s+4), 'downsample', (s >= 3) & l == 1) ;
%Conv([name 'b'], 3, 2^(s+4)) ;
Conv([name 'a'], 1, 2^(s+4)) ;
Conv([name 'b'], 3, 2^(s+4), 'downsample', (s >= 3) & l == 1) ;
Conv([name 'c'], 1, 2^(s+6), 'relu', false) ;
% Sum layer
net.addLayer([name '_sum'] , ...
dagnn.Sum(), ...
{sumInput.var, lastAdded.var}, ...
[name '_sum']) ;
net.addLayer([name '_relu'] , ...
dagnn.ReLU(), ...
[name '_sum'], ...
name) ;
lastAdded.var = name ;
end
end
net.addLayer('prediction_avg' , ...
dagnn.Pooling('poolSize', [7 7], 'method', 'avg'), ...
lastAdded.var, ...
'prediction_avg') ;
net.addLayer('prediction' , ...
dagnn.Conv('size', [1 1 2048 1000]), ...
'prediction_avg', ...
'prediction', ...
{'prediction_f', 'prediction_b'}) ;
net.addLayer('loss', ...
dagnn.Loss('loss', 'softmaxlog') ,...
{'prediction', 'label'}, ...
'objective') ;
net.addLayer('top1error', ...
dagnn.Loss('loss', 'classerror'), ...
{'prediction', 'label'}, ...
'top1error') ;
net.addLayer('top5error', ...
dagnn.Loss('loss', 'topkerror', 'opts', {'topK', 5}), ...
{'prediction', 'label'}, ...
'top5error') ;
% -------------------------------------------------------------------------
% Meta parameters
% -------------------------------------------------------------------------
net.meta.normalization.imageSize = [224 224 3] ;
net.meta.inputSize = [net.meta.normalization.imageSize, 32] ;
net.meta.normalization.cropSize = net.meta.normalization.imageSize(1) / 256 ;
net.meta.normalization.averageImage = opts.averageImage ;
net.meta.classes.name = opts.classNames ;
net.meta.classes.description = opts.classDescriptions ;
net.meta.augmentation.jitterLocation = true ;
net.meta.augmentation.jitterFlip = true ;
net.meta.augmentation.jitterBrightness = double(0.1 * opts.colorDeviation) ;
net.meta.augmentation.jitterAspect = [3/4, 4/3] ;
net.meta.augmentation.jitterScale = [0.4, 1.1] ;
%net.meta.augmentation.jitterSaturation = 0.4 ;
%net.meta.augmentation.jitterContrast = 0.4 ;
net.meta.inputSize = {'input', [net.meta.normalization.imageSize 32]} ;
%lr = logspace(-1, -3, 60) ;
%lr = [0.1 * ones(1,30), 0.01*ones(1,30), 0.001*ones(1,30)] ;
lr = [0.1 * ones(1,1), 0.01*ones(1,1), 0.001*ones(1,1)] ;
net.meta.trainOpts.learningRate = lr ;
net.meta.trainOpts.numEpochs = numel(lr) ;
net.meta.trainOpts.momentum = 0.9 ;
net.meta.trainOpts.batchSize = 256 ;
net.meta.trainOpts.numSubBatches = 4 ;
net.meta.trainOpts.weightDecay = 0.0001 ;
% Init parameters randomly
net.initParams() ;
% For uniformity with the other ImageNet networks, t
% the input data is *not* normalized to have unit standard deviation,
% whereas this is enforced by batch normalization deeper down.
% The ImageNet standard deviation (for each of R, G, and B) is about 60, so
% we adjust the weights and learing rate accordingly in the first layer.
%
% This simple change improves performance almost +1% top 1 error.
p = net.getParamIndex('conv1_f') ;
net.params(p).value = net.params(p).value / 100 ;
net.params(p).learningRate = net.params(p).learningRate / 100^2 ;
for l = 1:numel(net.layers)
if isa(net.layers(l).block, 'dagnn.BatchNorm')
k = net.getParamIndex(net.layers(l).params{3}) ;
net.params(k).learningRate = 0.3 ;
end
end
end
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