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github | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
'Parameters (C, sigma) for Dataset 3', ...
}, ...
{ ...
'3', ...
{ 'processEmail.m' }, ...
'Email Preprocessing', ...
}, ...
{ ...
'4', ...
{ 'emailFeatures.m' }, ...
'Email Feature Extraction', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
x1 = sin(1:10)';
x2 = cos(1:10)';
ec = 'the quick brown fox jumped over the lazy dog';
wi = 1 + abs(round(x1 * 1863));
wi = [wi ; wi];
if partId == '1'
sim = gaussianKernel(x1, x2, 2);
out = sprintf('%0.5f ', sim);
elseif partId == '2'
load('ex6data3.mat');
[C, sigma] = dataset3Params(X, y, Xval, yval);
out = sprintf('%0.5f ', C);
out = [out sprintf('%0.5f ', sigma)];
elseif partId == '3'
word_indices = processEmail(ec);
out = sprintf('%d ', word_indices);
elseif partId == '4'
x = emailFeatures(wi);
out = sprintf('%d ', x);
end
end
|
github | jellis18/ML-Course-Solutions-master | porterStemmer.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.txt
% The main part of the stemming algorithm starts here. b is an array of
% characters, holding the word to be stemmed. The letters are in b[k0],
% b[k0+1] ending at b[k]. In fact k0 = 1 in this demo program (since
% matlab begins indexing by 1 instead of 0). k is readjusted downwards as
% the stemming progresses. Zero termination is not in fact used in the
% algorithm.
% To call this function, use the string to be stemmed as the input
% argument. This function returns the stemmed word as a string.
% Lower-case string
inString = lower(inString);
global j;
b = inString;
k = length(b);
k0 = 1;
j = k;
% With this if statement, strings of length 1 or 2 don't go through the
% stemming process. Remove this conditional to match the published
% algorithm.
stem = b;
if k > 2
% Output displays per step are commented out.
%disp(sprintf('Word to stem: %s', b));
x = step1ab(b, k, k0);
%disp(sprintf('Steps 1A and B yield: %s', x{1}));
x = step1c(x{1}, x{2}, k0);
%disp(sprintf('Step 1C yields: %s', x{1}));
x = step2(x{1}, x{2}, k0);
%disp(sprintf('Step 2 yields: %s', x{1}));
x = step3(x{1}, x{2}, k0);
%disp(sprintf('Step 3 yields: %s', x{1}));
x = step4(x{1}, x{2}, k0);
%disp(sprintf('Step 4 yields: %s', x{1}));
x = step5(x{1}, x{2}, k0);
%disp(sprintf('Step 5 yields: %s', x{1}));
stem = x{1};
end
% cons(j) is TRUE <=> b[j] is a consonant.
function c = cons(i, b, k0)
c = true;
switch(b(i))
case {'a', 'e', 'i', 'o', 'u'}
c = false;
case 'y'
if i == k0
c = true;
else
c = ~cons(i - 1, b, k0);
end
end
% mseq() measures the number of consonant sequences between k0 and j. If
% c is a consonant sequence and v a vowel sequence, and <..> indicates
% arbitrary presence,
% <c><v> gives 0
% <c>vc<v> gives 1
% <c>vcvc<v> gives 2
% <c>vcvcvc<v> gives 3
% ....
function n = measure(b, k0)
global j;
n = 0;
i = k0;
while true
if i > j
return
end
if ~cons(i, b, k0)
break;
end
i = i + 1;
end
i = i + 1;
while true
while true
if i > j
return
end
if cons(i, b, k0)
break;
end
i = i + 1;
end
i = i + 1;
n = n + 1;
while true
if i > j
return
end
if ~cons(i, b, k0)
break;
end
i = i + 1;
end
i = i + 1;
end
% vowelinstem() is TRUE <=> k0,...j contains a vowel
function vis = vowelinstem(b, k0)
global j;
for i = k0:j,
if ~cons(i, b, k0)
vis = true;
return
end
end
vis = false;
%doublec(i) is TRUE <=> i,(i-1) contain a double consonant.
function dc = doublec(i, b, k0)
if i < k0+1
dc = false;
return
end
if b(i) ~= b(i-1)
dc = false;
return
end
dc = cons(i, b, k0);
% cvc(j) is TRUE <=> j-2,j-1,j has the form consonant - vowel - consonant
% and also if the second c is not w,x or y. this is used when trying to
% restore an e at the end of a short word. e.g.
%
% cav(e), lov(e), hop(e), crim(e), but
% snow, box, tray.
function c1 = cvc(i, b, k0)
if ((i < (k0+2)) || ~cons(i, b, k0) || cons(i-1, b, k0) || ~cons(i-2, b, k0))
c1 = false;
else
if (b(i) == 'w' || b(i) == 'x' || b(i) == 'y')
c1 = false;
return
end
c1 = true;
end
% ends(s) is TRUE <=> k0,...k ends with the string s.
function s = ends(str, b, k)
global j;
if (str(length(str)) ~= b(k))
s = false;
return
end % tiny speed-up
if (length(str) > k)
s = false;
return
end
if strcmp(b(k-length(str)+1:k), str)
s = true;
j = k - length(str);
return
else
s = false;
end
% setto(s) sets (j+1),...k to the characters in the string s, readjusting
% k accordingly.
function so = setto(s, b, k)
global j;
for i = j+1:(j+length(s))
b(i) = s(i-j);
end
if k > j+length(s)
b((j+length(s)+1):k) = '';
end
k = length(b);
so = {b, k};
% rs(s) is used further down.
% [Note: possible null/value for r if rs is called]
function r = rs(str, b, k, k0)
r = {b, k};
if measure(b, k0) > 0
r = setto(str, b, k);
end
% step1ab() gets rid of plurals and -ed or -ing. e.g.
% caresses -> caress
% ponies -> poni
% ties -> ti
% caress -> caress
% cats -> cat
% feed -> feed
% agreed -> agree
% disabled -> disable
% matting -> mat
% mating -> mate
% meeting -> meet
% milling -> mill
% messing -> mess
% meetings -> meet
function s1ab = step1ab(b, k, k0)
global j;
if b(k) == 's'
if ends('sses', b, k)
k = k-2;
elseif ends('ies', b, k)
retVal = setto('i', b, k);
b = retVal{1};
k = retVal{2};
elseif (b(k-1) ~= 's')
k = k-1;
end
end
if ends('eed', b, k)
if measure(b, k0) > 0;
k = k-1;
end
elseif (ends('ed', b, k) || ends('ing', b, k)) && vowelinstem(b, k0)
k = j;
retVal = {b, k};
if ends('at', b, k)
retVal = setto('ate', b(k0:k), k);
elseif ends('bl', b, k)
retVal = setto('ble', b(k0:k), k);
elseif ends('iz', b, k)
retVal = setto('ize', b(k0:k), k);
elseif doublec(k, b, k0)
retVal = {b, k-1};
if b(retVal{2}) == 'l' || b(retVal{2}) == 's' || ...
b(retVal{2}) == 'z'
retVal = {retVal{1}, retVal{2}+1};
end
elseif measure(b, k0) == 1 && cvc(k, b, k0)
retVal = setto('e', b(k0:k), k);
end
k = retVal{2};
b = retVal{1}(k0:k);
end
j = k;
s1ab = {b(k0:k), k};
% step1c() turns terminal y to i when there is another vowel in the stem.
function s1c = step1c(b, k, k0)
global j;
if ends('y', b, k) && vowelinstem(b, k0)
b(k) = 'i';
end
j = k;
s1c = {b, k};
% step2() maps double suffices to single ones. so -ization ( = -ize plus
% -ation) maps to -ize etc. note that the string before the suffix must give
% m() > 0.
function s2 = step2(b, k, k0)
global j;
s2 = {b, k};
switch b(k-1)
case {'a'}
if ends('ational', b, k) s2 = rs('ate', b, k, k0);
elseif ends('tional', b, k) s2 = rs('tion', b, k, k0); end;
case {'c'}
if ends('enci', b, k) s2 = rs('ence', b, k, k0);
elseif ends('anci', b, k) s2 = rs('ance', b, k, k0); end;
case {'e'}
if ends('izer', b, k) s2 = rs('ize', b, k, k0); end;
case {'l'}
if ends('bli', b, k) s2 = rs('ble', b, k, k0);
elseif ends('alli', b, k) s2 = rs('al', b, k, k0);
elseif ends('entli', b, k) s2 = rs('ent', b, k, k0);
elseif ends('eli', b, k) s2 = rs('e', b, k, k0);
elseif ends('ousli', b, k) s2 = rs('ous', b, k, k0); end;
case {'o'}
if ends('ization', b, k) s2 = rs('ize', b, k, k0);
elseif ends('ation', b, k) s2 = rs('ate', b, k, k0);
elseif ends('ator', b, k) s2 = rs('ate', b, k, k0); end;
case {'s'}
if ends('alism', b, k) s2 = rs('al', b, k, k0);
elseif ends('iveness', b, k) s2 = rs('ive', b, k, k0);
elseif ends('fulness', b, k) s2 = rs('ful', b, k, k0);
elseif ends('ousness', b, k) s2 = rs('ous', b, k, k0); end;
case {'t'}
if ends('aliti', b, k) s2 = rs('al', b, k, k0);
elseif ends('iviti', b, k) s2 = rs('ive', b, k, k0);
elseif ends('biliti', b, k) s2 = rs('ble', b, k, k0); end;
case {'g'}
if ends('logi', b, k) s2 = rs('log', b, k, k0); end;
end
j = s2{2};
% step3() deals with -ic-, -full, -ness etc. similar strategy to step2.
function s3 = step3(b, k, k0)
global j;
s3 = {b, k};
switch b(k)
case {'e'}
if ends('icate', b, k) s3 = rs('ic', b, k, k0);
elseif ends('ative', b, k) s3 = rs('', b, k, k0);
elseif ends('alize', b, k) s3 = rs('al', b, k, k0); end;
case {'i'}
if ends('iciti', b, k) s3 = rs('ic', b, k, k0); end;
case {'l'}
if ends('ical', b, k) s3 = rs('ic', b, k, k0);
elseif ends('ful', b, k) s3 = rs('', b, k, k0); end;
case {'s'}
if ends('ness', b, k) s3 = rs('', b, k, k0); end;
end
j = s3{2};
% step4() takes off -ant, -ence etc., in context <c>vcvc<v>.
function s4 = step4(b, k, k0)
global j;
switch b(k-1)
case {'a'}
if ends('al', b, k) end;
case {'c'}
if ends('ance', b, k)
elseif ends('ence', b, k) end;
case {'e'}
if ends('er', b, k) end;
case {'i'}
if ends('ic', b, k) end;
case {'l'}
if ends('able', b, k)
elseif ends('ible', b, k) end;
case {'n'}
if ends('ant', b, k)
elseif ends('ement', b, k)
elseif ends('ment', b, k)
elseif ends('ent', b, k) end;
case {'o'}
if ends('ion', b, k)
if j == 0
elseif ~(strcmp(b(j),'s') || strcmp(b(j),'t'))
j = k;
end
elseif ends('ou', b, k) end;
case {'s'}
if ends('ism', b, k) end;
case {'t'}
if ends('ate', b, k)
elseif ends('iti', b, k) end;
case {'u'}
if ends('ous', b, k) end;
case {'v'}
if ends('ive', b, k) end;
case {'z'}
if ends('ize', b, k) end;
end
if measure(b, k0) > 1
s4 = {b(k0:j), j};
else
s4 = {b(k0:k), k};
end
% step5() removes a final -e if m() > 1, and changes -ll to -l if m() > 1.
function s5 = step5(b, k, k0)
global j;
j = k;
if b(k) == 'e'
a = measure(b, k0);
if (a > 1) || ((a == 1) && ~cvc(k-1, b, k0))
k = k-1;
end
end
if (b(k) == 'l') && doublec(k, b, k0) && (measure(b, k0) > 1)
k = k-1;
end
s5 = {b(k0:k), k};
|
github | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex6/ex6_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
{ 'computeCentroids.m' }, ...
'Compute Centroid Means (k-Means)', ...
}, ...
{ ...
'3', ...
{ 'pca.m' }, ...
'PCA', ...
}, ...
{ ...
'4', ...
{ 'projectData.m' }, ...
'Project Data (PCA)', ...
}, ...
{ ...
'5', ...
{ 'recoverData.m' }, ...
'Recover Data (PCA)', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
X = reshape(sin(1:165), 15, 11);
Z = reshape(cos(1:121), 11, 11);
C = Z(1:5, :);
idx = (1 + mod(1:15, 3))';
if partId == '1'
idx = findClosestCentroids(X, C);
out = sprintf('%0.5f ', idx(:));
elseif partId == '2'
centroids = computeCentroids(X, idx, 3);
out = sprintf('%0.5f ', centroids(:));
elseif partId == '3'
[U, S] = pca(X);
out = sprintf('%0.5f ', abs([U(:); S(:)]));
elseif partId == '4'
X_proj = projectData(X, Z, 5);
out = sprintf('%0.5f ', X_proj(:));
elseif partId == '5'
X_rec = recoverData(X(:,1:5), Z, 5);
out = sprintf('%0.5f ', X_rec(:));
end
end
|
github | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex7/ex7_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic Regression Cost', ...
}, ...
{ ...
'3', ...
{ 'costFunction.m' }, ...
'Logistic Regression Gradient', ...
}, ...
{ ...
'4', ...
{ 'predict.m' }, ...
'Predict', ...
}, ...
{ ...
'5', ...
{ 'costFunctionReg.m' }, ...
'Regularized Logistic Regression Cost', ...
}, ...
{ ...
'6', ...
{ 'costFunctionReg.m' }, ...
'Regularized Logistic Regression Gradient', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% 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;
if partId == '1'
out = sprintf('%0.5f ', sigmoid(X));
elseif partId == '2'
out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));
elseif partId == '3'
[cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);
out = sprintf('%0.5f ', grad);
elseif partId == '4'
out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));
elseif partId == '5'
out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));
elseif partId == '6'
[cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);
out = sprintf('%0.5f ', grad);
end
end
|
github | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex2/ex2_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunction.m' }, ...
'Regularized Cost Function', ...
}, ...
{ ...
'3', ...
{ 'sigmoidGradient.m' }, ...
'Sigmoid Gradient', ...
}, ...
{ ...
'4', ...
{ 'nnCostFunction.m' }, ...
'Neural Network Gradient (Backpropagation)', ...
}, ...
{ ...
'5', ...
{ 'nnCostFunction.m' }, ...
'Regularized Gradient', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
X = reshape(3 * sin(1:1:30), 3, 10);
Xm = reshape(sin(1:32), 16, 2) / 5;
ym = 1 + mod(1:16,4)';
t1 = sin(reshape(1:2:24, 4, 3));
t2 = cos(reshape(1:2:40, 4, 5));
t = [t1(:) ; t2(:)];
if partId == '1'
[J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
out = sprintf('%0.5f ', J);
elseif partId == '2'
[J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
out = sprintf('%0.5f ', J);
elseif partId == '3'
out = sprintf('%0.5f ', sigmoidGradient(X));
elseif partId == '4'
[J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
out = sprintf('%0.5f ', J);
out = [out sprintf('%0.5f ', grad)];
elseif partId == '5'
[J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
out = sprintf('%0.5f ', J);
out = [out sprintf('%0.5f ', grad)];
end
end
|
github | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex4/ex4_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/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 | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex3/ex3_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/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 | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex1/ex1_octave/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 | jellis18/ML-Course-Solutions-master | submit.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Function', ...
}, ...
{ ...
'2', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Gradient', ...
}, ...
{ ...
'3', ...
{ 'learningCurve.m' }, ...
'Learning Curve', ...
}, ...
{ ...
'4', ...
{ 'polyFeatures.m' }, ...
'Polynomial Feature Mapping', ...
}, ...
{ ...
'5', ...
{ 'validationCurve.m' }, ...
'Validation Curve', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
X = [ones(10,1) sin(1:1.5:15)' cos(1:1.5:15)'];
y = sin(1:3:30)';
Xval = [ones(10,1) sin(0:1.5:14)' cos(0:1.5:14)'];
yval = sin(1:10)';
if partId == '1'
[J] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);
out = sprintf('%0.5f ', J);
elseif partId == '2'
[J, grad] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);
out = sprintf('%0.5f ', grad);
elseif partId == '3'
[error_train, error_val] = ...
learningCurve(X, y, Xval, yval, 1);
out = sprintf('%0.5f ', [error_train(:); error_val(:)]);
elseif partId == '4'
[X_poly] = polyFeatures(X(2,:)', 8);
out = sprintf('%0.5f ', X_poly);
elseif partId == '5'
[lambda_vec, error_train, error_val] = ...
validationCurve(X, y, Xval, yval);
out = sprintf('%0.5f ', ...
[lambda_vec(:); error_train(:); error_val(:)]);
end
end
|
github | jellis18/ML-Course-Solutions-master | submitWithConfiguration.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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( ...
'!! Submission failed: unexpected error: %s\n', ...
e.message);
fprintf('!! Please try again later.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
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();
params = {'jsonBody', body};
responseBody = urlread(submissionUrl, 'post', params);
response = loadjson(responseBody);
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end
|
github | jellis18/ML-Course-Solutions-master | savejson.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/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 | jellis18/ML-Course-Solutions-master | loadjson.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/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 | jellis18/ML-Course-Solutions-master | loadubjson.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/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 | jellis18/ML-Course-Solutions-master | saveubjson.m | .m | ML-Course-Solutions-master/ex5/ex5_octave/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 | cheeyi/matlab-viola-jones-master | getCorners.m | .m | matlab-viola-jones-master/trainHaar/getCorners.m | 476 | utf_8 | 7f937ea5258eed38ff1175b9a50cbdda | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% getCorners.m - takes in an integral image and computes the sum of intensities
% in the area bounded by the four coordinates
function intensity = getCorners(img,startX,startY,endX,endY)
a = img(startY,startX);
b = img(startY,endX);
c = img(endY,startX);
d = img(endY,endX);
intensity = d-(b+c)+a; % by property of the integral image
end |
github | cheeyi/matlab-viola-jones-master | adaboost.m | .m | matlab-viola-jones-master/trainHaar/adaboost.m | 2,009 | utf_8 | 20c681c7f800a1767191118007cddcf3 | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% adaboost.m - boosts classifiers adaptively by updating their weights
% alpha values, and for individual images by updating image weights
function [newWeights,alpha] = adaboost(classifier, images, imgWeights)
imgsSize = 2429+4547; % total number of images in MIT database
faceSize = 2429; % number of face images
captures = zeros(imgsSize,1);
error = 0;
for i = 1:imgsSize
img = images{i};
% obtains classifier metadata from fields in the row vector
haar = classifier(1);
pixelX = classifier(2);
pixelY = classifier(3);
haarX = classifier(4);
haarY = classifier(5);
% calculates intensity difference between black-white region of the
% Haar feature and checks against the precalculated range
haarVal = calcHaarVal(img,haar,pixelX,pixelY,haarX,haarY);
if haarVal >= classifier(9) && haarVal <= classifier(10) % if falls between correct value
if i <= faceSize % if its a face
captures(i) = 1; % correct capture
else
captures(i) = 0; % error
error = error + imgWeights(i); % increase weighted error count
end
else % if falls outside the expected range
if i <= faceSize % if is a face
captures(i) = 0;
error = error + imgWeights(i); % error
else
captures(i) = 1;
end
end
end
alpha = 0.5*log((1-error)/error); % updates classifier weight (alpha)
% modifies images' weights by whether it is a successful capture or not
% correct captures result in lower weights; false captures result in higher
% weight to put more emphasis on them
for i = 1:imgsSize
if captures(i) == 0
imgWeights(i) = imgWeights(i).*exp(alpha);
else
imgWeights(i) = imgWeights(i).*exp(-alpha);
end
end
imgWeights = imgWeights./sum(imgWeights); % normalize image weights
newWeights = imgWeights; % pass as function output
end
|
github | cheeyi/matlab-viola-jones-master | integralImg.m | .m | matlab-viola-jones-master/trainHaar/integralImg.m | 408 | utf_8 | b43aef069cc743777107f0d98e0c5049 | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% integralImg.m - computes integral images for face detection using Viola-Jones algorithm
function outimg = integralImg (inimg)
% cumulative sum for each pixel of all rows and columns to the left and
% above the corresponding pixel
outimg = cumsum(cumsum(double(inimg),2));
end
|
github | cheeyi/matlab-viola-jones-master | calcHaarVal.m | .m | matlab-viola-jones-master/trainHaar/calcHaarVal.m | 2,211 | utf_8 | 90754d086b3f1a2d9cca31b688737a8a | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% calcHaarVal.m - computes intensity differences between white/black region of Haar features
function val = calcHaarVal(img,haar,pixelX,pixelY,haarX,haarY)
% img: integral image of an input image
% haar: which Haar feature (1-5)
% pixelX/Y: start point in (X,Y)
% haarX/Y: Haar feature size in X and Y directions
% getCorners() finds the total of the pixel intensity values in a white/black "box"
moveX = haarX-1;
moveY = haarY-1;
if haar == 1 % top/down white-black
white = getCorners(img,pixelX,pixelY,pixelX+moveX,pixelY+floor(moveY/2));
black = getCorners(img,pixelX,pixelY+ceil(moveY/2),pixelX+moveX,pixelY+moveY);
val = white-black;
elseif haar == 2 % left/right white-black
white = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/2),pixelY+moveY);
black = getCorners(img,pixelX+ceil(moveX/2),pixelY,pixelX+moveX,pixelY+moveY);
val = white-black;
elseif haar == 3 % top/mid/bottom white-black-white
white1 = getCorners(img,pixelX,pixelY,pixelX+moveX,pixelY+floor(moveY/3));
black = getCorners(img,pixelX,pixelY+ceil(moveY/3),pixelX+moveX,pixelY+floor((moveY)*(2/3)));
white2 = getCorners(img,pixelX,pixelY+ceil((moveY)*(2/3)),pixelX+moveX,pixelY+moveY);
val = white1 + white2 - black;
elseif haar == 4 % left/mid/right white-black-white
white1 = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/3),pixelY+moveY);
black = getCorners(img,pixelX+ceil(moveX/3),pixelY,pixelX+floor((moveX)*(2/3)),pixelY+moveY);
white2 = getCorners(img,pixelX+ceil((moveX)*(2/3)),pixelY,pixelX+moveX,pixelY+moveY);
val = white1 + white2 - black;
elseif haar == 5 % checkerboard-style white-black-white-black
white1 = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/2),pixelY+floor(moveY/2));
black1 = getCorners(img,pixelX+ceil(moveX/2),pixelY,pixelX+moveX,pixelY+floor(moveY/2));
black2 = getCorners(img,pixelX,pixelY+ceil(moveY/2),pixelX+floor(moveX/2),pixelY+moveY);
white2 = getCorners(img,pixelX+ceil(moveX/2),pixelY+ceil(moveY/2),pixelX+moveX,pixelY+moveY);
val = white1+white2-(black1+black2);
end |
github | cheeyi/matlab-viola-jones-master | detectFaces.m | .m | matlab-viola-jones-master/detectFaces/detectFaces.m | 5,100 | utf_8 | 6beba44eabfe8673425995a9d26f7c57 | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% detectFaces.m - detects face using trained classifiers
function [faces,faceBound] = detectFaces(img)
% preprocessing by Gaussian filtering
img2 = img; % keep a copy of the original color 3D image
img = imread(img);
img = rgb2gray(img);
img = conv2(img,fspecial('gaussian',3,3),'same');
% get image parameters
[m,n] = size(img);
% other variables
scanItr = 8; % can be modified depending on how big face is relative to image
% scanItr of 8 works well for images with size about 300x400
faces = []; % empty by default
% compute integral image
intImg = integralImg(img);
% load finalClassifiers
load '../trainHaar/trainedClassifiers.mat' % 286 classifiers
%%%%% Cascaded Detector Structure: 7 levels, 200 classifiers %%%%%
class1 = selectedClassifiers(1:2,:);
class2 = selectedClassifiers(3:12,:);
class3 = selectedClassifiers(13:20,:);
class4 = selectedClassifiers(21:40,:);
class5 = selectedClassifiers(41:70,:);
class6 = selectedClassifiers(71:150,:);
class7 = selectedClassifiers(151:200,:);
% iterate through each window size/pyramid level
for itr = 1:scanItr
printout = strcat('Iteration #',int2str(itr),'\n');
fprintf(printout);
for i = 1:2:m-19
if i + 19 > m
break; % boundary case check
end
for j = 1:2:n-19
if j + 19 > n
break; % boundary case check
end
window = intImg(i:i+18,j:j+18); % 19x19 window as per training
check1 = cascade(class1,window,1);
if check1 == 1
check2 = cascade(class2,window,.5);
if check2 == 1
check3 = cascade(class3,window,.5);
if check3 == 1
check4 = cascade(class4,window,.5);
if check4 == 1
check5 = cascade(class5,window,.6);
if check5 == 1
check6 = cascade(class6,window,.6);
if check6 == 1
fprintf('Passed level 6 cascade.\n');
check7 = cascade(class7,window,.5);
if check7 == 1
% save rectangular corner coordinates
bounds = [j,i,j+18,i+18,itr];
fprintf('Face detected!\n');
faces = [faces;bounds];
end
end
end
end
end
end
end
end
end
% create next image pyramid level
tempImg = imresize(img,.8);
img = tempImg;
[m,n] = size(img);
intImg = integralImg(img);
end
if size(faces,1) == 0 % no faces detected
error('No face detected! Try again with a larger value of scanItr.');
end
%%%%% Get Best Bounding Box %%%%%
% upscale rectangular bound coordinates back to base level of pyramid
faceBound = zeros(size(faces,1),4);
maxItr = max(faces(:,5)); % higher iterations have larger bounding boxes
for i = 1:size(faces,1)
if faces(i,5) ~= maxItr
continue; % only interested in large bounding boxes
end
faceBound(i,:) = floor(faces(i,1:4)*1.25^(faces(i,5)-1));
end
% filter out overlapping rectangular bounding boxes
startRow = 1;
for i = 1:size(faceBound,1)
if faceBound(i,1) == 0
startRow = startRow+1; % start with next row
end
end
faceBound = faceBound(startRow:end,:); % trim faceBound to get rid of 0-filled rows
% get the union of the areas of overlapping boxes
faceBound = [min(faceBound(:,1)),min(faceBound(:,2)),max(faceBound(:,3)),max(faceBound(:,4))];
% Show the detected face(s) with original image
figure,imshow(img2), hold on;
if(~isempty(faceBound));
for n=1:size(faceBound,1)
toleranceX = floor(0.1*(faceBound(n,3)-faceBound(n,1)));
toleranceY = floor(0.1*(faceBound(n,4)-faceBound(n,2)));
% original bounds
x1=faceBound(n,1); y1=faceBound(n,2);
x2=faceBound(n,3); y2=faceBound(n,4);
% adjusted bounds to get wider face capture
x1t=faceBound(n,1)-toleranceX; y1t=faceBound(n,2)-toleranceY;
x2t=faceBound(n,3)+toleranceX; y2t=faceBound(n,4)+toleranceY;
imSize = size(imread(img2));
% if adjusted bounds will lead to out-of-bounds plotting, use original bounds
if x1t < 1 || y1t < 1 || x2t > imSize(2) || y2t > imSize(1)
fprintf('Out of bounds adjustments. Plotting original values...\n');
plot([x1 x1 x2 x2 x1],[y1 y2 y2 y1 y1],'LineWidth',2);
else
plot([x1t x1t x2t x2t x1t],[y1t y2t y2t y1t y1t],'LineWidth',2);
end
end
end
title('Detected face in image');
hold off;
end
|
github | cheeyi/matlab-viola-jones-master | getCorners.m | .m | matlab-viola-jones-master/detectFaces/getCorners.m | 476 | utf_8 | 7f937ea5258eed38ff1175b9a50cbdda | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% getCorners.m - takes in an integral image and computes the sum of intensities
% in the area bounded by the four coordinates
function intensity = getCorners(img,startX,startY,endX,endY)
a = img(startY,startX);
b = img(startY,endX);
c = img(endY,startX);
d = img(endY,endX);
intensity = d-(b+c)+a; % by property of the integral image
end |
github | cheeyi/matlab-viola-jones-master | integralImg.m | .m | matlab-viola-jones-master/detectFaces/integralImg.m | 408 | utf_8 | b43aef069cc743777107f0d98e0c5049 | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% integralImg.m - computes integral images for face detection using Viola-Jones algorithm
function outimg = integralImg (inimg)
% cumulative sum for each pixel of all rows and columns to the left and
% above the corresponding pixel
outimg = cumsum(cumsum(double(inimg),2));
end
|
github | cheeyi/matlab-viola-jones-master | cascade.m | .m | matlab-viola-jones-master/detectFaces/cascade.m | 1,100 | utf_8 | 980b8eab5cff6a4b3b313c8997ebfc7c | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% cascade.m - takes in a set of classifiers and an image subwindow and
% classifies it as either a face or non-face
function output = cascade(classifiers,img,thresh)
result = 0;
px = size(classifiers,1);
weightSum = sum(classifiers(:,12));
% iterate through each classifier
for i = 1:px
classifier = classifiers(i,:);
haar = classifier(1);
pixelX = classifier(2);
pixelY = classifier(3);
haarX = classifier(4);
haarY = classifier(5);
% calculate the feature value for the subwindow using the current
% classifier
haarVal = calcHaarVal(img,haar,pixelX,pixelY,haarX,haarY);
if haarVal >= classifier(9) && haarVal <= classifier(10)
% increase score by the weight of the corresponding classifier
score = classifier(12);
else
score = 0;
end
result = result + score;
end
% compare resulting weighted success rate to the threshold value
if result >= weightSum*thresh
output = 1; % hit
else
output = 0; % miss
end
end |
github | cheeyi/matlab-viola-jones-master | calcHaarVal.m | .m | matlab-viola-jones-master/detectFaces/calcHaarVal.m | 2,211 | utf_8 | 90754d086b3f1a2d9cca31b688737a8a | % CSCi 5561 Spring 2015 - Semester Project
% Authors: Stephen Peyton, Chee Yi Ong
% Team: Who Is This (WIT)
% calcHaarVal.m - computes intensity differences between white/black region of Haar features
function val = calcHaarVal(img,haar,pixelX,pixelY,haarX,haarY)
% img: integral image of an input image
% haar: which Haar feature (1-5)
% pixelX/Y: start point in (X,Y)
% haarX/Y: Haar feature size in X and Y directions
% getCorners() finds the total of the pixel intensity values in a white/black "box"
moveX = haarX-1;
moveY = haarY-1;
if haar == 1 % top/down white-black
white = getCorners(img,pixelX,pixelY,pixelX+moveX,pixelY+floor(moveY/2));
black = getCorners(img,pixelX,pixelY+ceil(moveY/2),pixelX+moveX,pixelY+moveY);
val = white-black;
elseif haar == 2 % left/right white-black
white = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/2),pixelY+moveY);
black = getCorners(img,pixelX+ceil(moveX/2),pixelY,pixelX+moveX,pixelY+moveY);
val = white-black;
elseif haar == 3 % top/mid/bottom white-black-white
white1 = getCorners(img,pixelX,pixelY,pixelX+moveX,pixelY+floor(moveY/3));
black = getCorners(img,pixelX,pixelY+ceil(moveY/3),pixelX+moveX,pixelY+floor((moveY)*(2/3)));
white2 = getCorners(img,pixelX,pixelY+ceil((moveY)*(2/3)),pixelX+moveX,pixelY+moveY);
val = white1 + white2 - black;
elseif haar == 4 % left/mid/right white-black-white
white1 = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/3),pixelY+moveY);
black = getCorners(img,pixelX+ceil(moveX/3),pixelY,pixelX+floor((moveX)*(2/3)),pixelY+moveY);
white2 = getCorners(img,pixelX+ceil((moveX)*(2/3)),pixelY,pixelX+moveX,pixelY+moveY);
val = white1 + white2 - black;
elseif haar == 5 % checkerboard-style white-black-white-black
white1 = getCorners(img,pixelX,pixelY,pixelX+floor(moveX/2),pixelY+floor(moveY/2));
black1 = getCorners(img,pixelX+ceil(moveX/2),pixelY,pixelX+moveX,pixelY+floor(moveY/2));
black2 = getCorners(img,pixelX,pixelY+ceil(moveY/2),pixelX+floor(moveX/2),pixelY+moveY);
white2 = getCorners(img,pixelX+ceil(moveX/2),pixelY+ceil(moveY/2),pixelX+moveX,pixelY+moveY);
val = white1+white2-(black1+black2);
end |
github | ddtm/OpenFace-master | ParseSEMAINEAnnotations.m | .m | OpenFace-master/matlab_version/AU_training/data extraction/ParseSEMAINEAnnotations.m | 6,528 | utf_8 | 0795976652454c15fa1dcb350c725f30 |
% Function ParseSEMAINEAnnotations is intended to demonstrate example usage
% of SEMAINE Action Unit annotations made with ELAN annotation toolbox.
% This function loads the XML structure from an ELAN annotation file with
% ".eaf" extension, parses it and returns a numerical matrix called
% "activations" of size NUMBER OF FRAMES X NUMBER OF ACTION UNITS. The
% matrix holds binary activation status for each frame / AU combination.
% The matrix also has a row header showing which AU corresponds to which
% row as well as a column header displaying original frame indexes.
% The function takes 1 compulsory and 2 optional arguments:
% - "filepath" (compulsory) - complete path to an annotation file to parse.
% For example, "/matlab/annotation.eaf" or "C:\matlab\annotation.eaf" on
% Windows.
% - "startFrame" (optional) - ignore all annotations before "startFrame".
% Default is 1.
% - "endFrame" (optional) - ignore all annotations after "endFrame".
% Default is the last frame of a video.
% The function requires XML IO Toolbox
% (http://www.mathworks.com/matlabcentral/fileexchange/12907-xml-io-tools)
% to run properly (supplied).
function activations = ParseSEMAINEAnnotations (filepath, startFrame, endFrame)
activations = [];
% Framerate value used to convert ELAN millisecond time slots to more
% usual frames. 50 is a valid framerate for all SEMAINE videos.
framerate = 50;
% A fixed set of 6 Action Units selected for the challenge from the
% SEMAINE annotations
aus = [2 12 17 25 28 45];
% Total number of AUs.
naus = length(aus);
% Load XML structure from the file, return in case of a problem.
[success, XML] = OpenXML(filepath);
if ~success
return
end
% Parse annotation time slots
tslots = ParseTimeSlots(XML);
% Init start and end frames with default values
if nargin < 2
startFrame = 1;
end
if nargin < 3
% Get total number of time slots
ntslots = length(tslots);
% Get last slot ID
lastID = strcat('ts', num2str(ntslots));
% Get last time slot value in ms
lastValue = tslots(lastID);
% Convert last time slot value in ms to frames
endFrame = floor((lastValue / 1000) * framerate);
end
% Get total number of tiers. There are 65 of them, 1 for speech, 32 for
% activations (1 per AU) and 32 for intensities. We are going to ignore
% intensity tiers.
ntiers = length(XML.TIER);
% Compose vector of frame indexes to extract annotations from
frames = (startFrame:endFrame);
% Preallocate activations matrix
activations = zeros(length(frames), naus);
indx = 1;
% Go through all tiers skipping the first one (speech) as well as every
% intensity tier. A single activation tier is processed at every
% iteration.
for k = 2:2:ntiers
tier = XML.TIER(k);
% Only extract annotations of selected AUs, skip the rest
au = strcat('AU', num2str(aus(indx)));
if strcmp(au, tier.ATTRIBUTE.TIER_ID)
% Read all activation periods from the current tier
activationTier = ParseActivationTier(tier, tslots);
% Convert of all activation periods into frame level numerical
% representation
activations(:, indx) = ParseOccurrences(activationTier, frames, framerate);
indx = indx + 1;
end
if indx > naus
break
end
end
activations = [frames' activations];
activations = [[0 aus]; activations];
end
function occurrences = ParseOccurrences (activations, frames, framerate)
% Preallocate activations vector
occurrences = zeros(length(frames), 1);
% Go through all activation periods, convert ms into frames and init
% corresponding values of activations vector with 1 leaving the rest be 0
for i = 1:length(activations)
% Convert ms into frames
sframe = floor((activations(i).start / 1000) * framerate);
eframe = floor((activations(i).end / 1000) * framerate);
% Determine indexes of frames vector corresponding to the above
% time frame
sindx = find(frames == sframe);
eindx = find(frames == eframe);
% Mark active set of frames with 1
occurrences(sindx:eindx) = 1;
end
end
function activationTier = ParseActivationTier (tier, tslots)
% Get total number of activation periods
nactivations = length(tier.ANNOTATION);
% Preallocate activation tier structure holding start and end time
% stamps of all activation periods for the given AU
activationTier = repmat(struct('start', 0, 'end', 0), nactivations, 1);
% Go through all activation periods and init activation tier
% structure array
for i = 1:nactivations
% Read start time slot ID of the current activation period
t = tier.ANNOTATION(i).ALIGNABLE_ANNOTATION.ATTRIBUTE.TIME_SLOT_REF1;
% Read time in ms corresponding to the time slot ID
activationTier(i).start = tslots(t);
% Read end time slot ID of the current activation period
t = tier.ANNOTATION(i).ALIGNABLE_ANNOTATION.ATTRIBUTE.TIME_SLOT_REF2;
% Read time in ms corresponding to the time slot ID
activationTier(i).end = tslots(t);
end
end
function tslots = ParseTimeSlots (xmlObject)
% Get total number of time slots
nslots = length(xmlObject.TIME_ORDER.TIME_SLOT);
% Preallocate cell arrays of time slot IDs and values
tids = cell(nslots, 1);
tvalues = zeros(nslots, 1);
% Read all time slot IDs and numerical values (in ms)
for i = 1:nslots
tids{i} = xmlObject.TIME_ORDER.TIME_SLOT(i).ATTRIBUTE.TIME_SLOT_ID;
tvalues(i) = xmlObject.TIME_ORDER.TIME_SLOT(i).ATTRIBUTE.TIME_VALUE;
end
% Map time slot IDs and values together so that values are accessible
% by their IDs
tslots = containers.Map(tids, tvalues);
end
function [success, xmlObject] = OpenXML (xmlPath)
fprintf(' *** Attempting to load \"%s\" ... ', xmlPath);
xmlObject = [];
success = false;
% Check if the specified file exists and return error otherwise
if exist(xmlPath, 'file')
% Load XML structure
xmlObject = xml_read(xmlPath);
% Check if XML object loaded correctly, return error otherwise
if isempty(xmlObject)
fprintf(' ERROR - unable to read xml tree *** \n');
return
else
success = true;
end
else
fprintf(' ERROR - specified path does not exist *** \n');
return
end
fprintf(' Done *** \n');
end |
github | ddtm/OpenFace-master | xml_write.m | .m | OpenFace-master/matlab_version/AU_training/data extraction/xml_io_tools_2010_11_05/xml_write.m | 18,325 | utf_8 | 24bd3dc683e5a0a0ad4080deaa6a93a5 | function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'filename' using Matlab's xmlwrite
% function. Optionally one can also use alternative version of xmlwrite
% function which directly calls JAVA functions for XML writing without
% MATLAB middleware. This function is provided as a patch to existing
% bugs in xmlwrite (in R2006b).
%
% xml_write(filename, tree, RootName, Pref) allows you to specify
% additional preferences about file format
%
% DOMnode = xml_write([], tree) same as above except that DOM node is
% not saved to the file but returned.
%
% INPUT
% filename file name
% tree Matlab structure tree to store in xml file.
% RootName String with XML tag name used for root (top level) node
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% Pref Other preferences:
% Pref.ItemName - default 'item' - name of a special tag used to
% itemize cell or struct arrays
% Pref.XmlEngine - let you choose the XML engine. Currently default is
% 'Xerces', which is using directly the apache xerces java file.
% Other option is 'Matlab' which uses MATLAB's xmlwrite and its
% XMLUtils java file. Both options create identical results except in
% case of CDATA sections where xmlwrite fails.
% Pref.CellItem - default 'true' - allow cell arrays to use 'item'
% notation. See below.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.StructItem - default 'true' - allow arrays of structs to use
% 'item' notation. For example "Pref.StructItem = true" gives:
% <a>
% <b>
% <item> ... <\item>
% <item> ... <\item>
% <\b>
% <\a>
% while "Pref.StructItem = false" gives:
% <a>
% <b> ... <\b>
% <b> ... <\b>
% <\a>
%
%
% Several special xml node types can be created if special tags are used
% for field names of 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields
% (usually ATTRIBUTE are present. Usually data section is stored
% directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - create comment child node from the string. For global
% comments see "RootName" input variable.
% - node.PROCESSING_INSTRUCTIONS - create "processing instruction" child
% node from the string. For global "processing instructions" see
% "RootName" input variable.
% - node.CDATA_SECTION - stores node's CDATA section (string). Only works
% if Pref.XmlEngine='Xerces'. For more info, see comments of F_xmlwrite.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes and notation nodes are not being handled by
% 'xml_write' at the moment.
%
% OUTPUT
% DOMnode Document Object Model (DOM) node tree in the format
% required as input to xmlwrite. (optional)
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% type('test.xml')
% %See also xml_tutorial.m
%
% See also
% xml_read, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
%% Check Matlab Version
v = ver('MATLAB');
v = str2double(regexp(v.Version, '\d.\d','match','once'));
if (v<7)
error('Your MATLAB version is too old. You need version 7.0 or newer.');
end
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.StructItem = true; % allow arrays of structs to use 'item' notation
DPref.CellItem = true; % allow cell arrays to use 'item' notation
DPref.StructTable= 'Html';
DPref.CellTable = 'Html';
DPref.XmlEngine = 'Matlab'; % use matlab provided XMLUtils
%DPref.XmlEngine = 'Xerces'; % use Xerces xml generator directly
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % Input is root node only
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
%% read user preferences
if (nargin>3)
if (isfield(Pref, 'TableName' )), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'StructItem')), DPref.StructItem = Pref.StructItem; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'CellTable')), DPref.CellTable = Pref.CellTable; end
if (isfield(Pref, 'StructTable')), DPref.StructTable= Pref.StructTable; end
if (isfield(Pref, 'XmlEngine' )), DPref.XmlEngine = Pref.XmlEngine; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if (nargin<3 || isempty(RootName)), RootName=inputname(2); end
if (isempty(RootName)), RootName='ROOT'; end
if (iscell(RootName)) % RootName also stores global text node data
rName = RootName;
RootName = char(rName{1});
if (length(rName)>1), GlobalProcInst = char(rName{2}); end
if (length(rName)>2), GlobalComment = char(rName{3}); end
if (length(rName)>3), GlobalDocType = char(rName{4}); end
end
if(~RootOnly && isstruct(tree)) % if struct than deal with each field separatly
fields = fieldnames(tree);
for i=1:length(fields)
field = fields{i};
x = tree(1).(field);
if (strcmp(field, 'COMMENT'))
GlobalComment = x;
elseif (strcmp(field, 'PROCESSING_INSTRUCTION'))
GlobalProcInst = x;
elseif (strcmp(field, 'DOCUMENT_TYPE'))
GlobalDocType = x;
else
RootName = field;
t = x;
end
end
tree = t;
end
%% Initialize jave object that will store xml data structure
RootName = varName2str(RootName);
if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = com.mathworks.xml.XMLUtils.createDocumentType(GlobalDocType);
% end
% DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName, dtype);
warning('xml_io_tools:write:docType', ...
'DOCUMENT_TYPE node was encountered which is not supported yet. Ignoring.');
end
DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName);
%% Use recursive function to convert matlab data structure to XML
root = DOMnode.getDocumentElement;
struct2DOMnode(DOMnode, root, tree, DPref.ItemName, DPref);
%% Remove the only child of the root node
root = DOMnode.getDocumentElement;
Child = root.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
if (nChild==1)
node = root.removeChild(root.getFirstChild);
while(node.hasChildNodes)
root.appendChild(node.removeChild(node.getFirstChild));
end
while(node.hasAttributes) % copy all attributes
root.setAttributeNode(node.removeAttributeNode(node.getAttributes.item(0)));
end
end
%% Save exotic Global nodes
if (~isempty(GlobalComment))
DOMnode.insertBefore(DOMnode.createComment(GlobalComment), DOMnode.getFirstChild());
end
if (~isempty(GlobalProcInst))
n = strfind(GlobalProcInst, ' ');
if (~isempty(n))
proc = DOMnode.createProcessingInstruction(GlobalProcInst(1:(n(1)-1)),...
GlobalProcInst((n(1)+1):end));
DOMnode.insertBefore(proc, DOMnode.getFirstChild());
end
end
% Not supported yet as the code below does not work
% if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = DOMnode.createDocumentType(GlobalDocType);
% DOMnode.insertBefore(dtype, DOMnode.getFirstChild());
% end
% end
%% save java DOM tree to XML file
if (~isempty(filename))
if (strcmpi(DPref.XmlEngine, 'Xerces'))
xmlwrite_xerces(filename, DOMnode);
else
xmlwrite(filename, DOMnode);
end
end
%% =======================================================================
% === struct2DOMnode Function ===========================================
% =======================================================================
function [] = struct2DOMnode(xml, parent, s, TagName, Pref)
% struct2DOMnode is a recursive function that converts matlab's structs to
% DOM nodes.
% INPUTS:
% xml - jave object that will store xml data structure
% parent - parent DOM Element
% s - Matlab data structure to save
% TagName - name to be used in xml tags describing 's'
% Pref - preferenced
% OUTPUT:
% parent - modified 'parent'
% perform some conversions
if (ischar(s) && min(size(s))>1) % if 2D array of characters
s=cellstr(s); % than convert to cell array
end
% if (strcmp(TagName, 'CONTENT'))
% while (iscell(s) && length(s)==1), s = s{1}; end % unwrap cell arrays of length 1
% end
TagName = varName2str(TagName);
%% == node is a 2D cell array ==
% convert to some other format prior to further processing
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
if (iscell(s) && nDim==2 && strcmpi(Pref.CellTable, 'Matlab'))
s = var2str(s, Pref.PreserveSpace);
end
if (nDim==2 && (iscell (s) && strcmpi(Pref.CellTable, 'Vector')) || ...
(isstruct(s) && strcmpi(Pref.StructTable, 'Vector')))
s = s(:);
end
if (nDim>2), s = s(:); end % can not handle this case well
nItem = numel(s);
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
%% == node is a cell ==
if (iscell(s)) % if this is a cell or cell array
if ((nDim==2 && strcmpi(Pref.CellTable,'Html')) || (nDim< 2 && Pref.CellItem))
% if 2D array of cells than can use HTML-like notation or if 1D array
% than can use item notation
if (strcmp(TagName, 'CONTENT')) % CONTENT nodes already have <TagName> ... </TagName>
array2DOMnode(xml, parent, s, Pref.ItemName, Pref ); % recursive call
else
node = xml.createElement(TagName); % <TagName> ... </TagName>
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
end
else % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
end
%% == node is a struct ==
elseif (isstruct(s)) % if struct than deal with each field separatly
if ((nDim==2 && strcmpi(Pref.StructTable,'Html')) || (nItem>1 && Pref.StructItem))
% if 2D array of structs than can use HTML-like notation or
% if 1D array of structs than can use 'items' notation
node = xml.createElement(TagName);
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
elseif (nItem>1) % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
else % otherwise save each struct separatelly
fields = fieldnames(s);
node = xml.createElement(TagName);
for i=1:length(fields) % add field by field to the node
field = fields{i};
x = s.(field);
switch field
case {'COMMENT', 'CDATA_SECTION', 'PROCESSING_INSTRUCTION'}
if iscellstr(x) % cell array of strings -> add them one by one
array2DOMnode(xml, node, x(:), field, Pref ); % recursive call will modify 'node'
elseif ischar(x) % single string -> add it
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
else % not a string - Ignore
warning('xml_io_tools:write:badSpecialNode', ...
['Struct field named ',field,' encountered which was not a string. Ignoring.']);
end
case 'ATTRIBUTE' % set attributes of the node
if (isempty(x)), continue; end
if (isstruct(x))
attName = fieldnames(x); % get names of all the attributes
for k=1:length(attName) % attach them to the node
att = xml.createAttribute(varName2str(attName(k)));
att.setValue(var2str(x.(attName{k}),Pref.PreserveSpace));
node.setAttributeNode(att);
end
else
warning('xml_io_tools:write:badAttribute', ...
'Struct field named ATTRIBUTE encountered which was not a struct. Ignoring.');
end
otherwise % set children of the node
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
end
end % end for i=1:nFields
parent.appendChild(node);
end
%% == node is a leaf node ==
else % if not a struct and not a cell than it is a leaf node
switch TagName % different processing depending on desired type of the node
case 'COMMENT' % create comment node
com = xml.createComment(s);
parent.appendChild(com);
case 'CDATA_SECTION' % create CDATA Section
cdt = xml.createCDATASection(s);
parent.appendChild(cdt);
case 'PROCESSING_INSTRUCTION' % set attributes of the node
OK = false;
if (ischar(s))
n = strfind(s, ' ');
if (~isempty(n))
proc = xml.createProcessingInstruction(s(1:(n(1)-1)),s((n(1)+1):end));
parent.insertBefore(proc, parent.getFirstChild());
OK = true;
end
end
if (~OK)
warning('xml_io_tools:write:badProcInst', ...
['Struct field named PROCESSING_INSTRUCTION need to be',...
' a string, for example: xml-stylesheet type="text/css" ', ...
'href="myStyleSheet.css". Ignoring.']);
end
case 'CONTENT' % this is text part of already existing node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace)); % convert to text
parent.appendChild(txt);
otherwise % I guess it is a regular text leaf node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace));
node = xml.createElement(TagName);
node.appendChild(txt);
parent.appendChild(node);
end
end % of struct2DOMnode function
%% =======================================================================
% === array2DOMnode Function ============================================
% =======================================================================
function [] = array2DOMnode(xml, parent, s, TagName, Pref)
% Deal with 1D and 2D arrays of cell or struct. Will modify 'parent'.
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
switch nDim
case 2 % 2D array
for r=1:size(s,1)
subnode = xml.createElement(Pref.TableName{1});
for c=1:size(s,2)
v = s(r,c);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, subnode, v, Pref.TableName{2}, Pref ); % recursive call
end
parent.appendChild(subnode);
end
case 1 %1D array
for iItem=1:numel(s)
v = s(iItem);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, parent, v, TagName, Pref ); % recursive call
end
case 0 % scalar -> this case should never be called
if ~isempty(s)
if iscell(s), s = s{1}; end
struct2DOMnode(xml, parent, s, TagName, Pref );
end
end
%% =======================================================================
% === var2str Function ==================================================
% =======================================================================
function str = var2str(object, PreserveSpace)
% convert matlab variables to a string
switch (1)
case isempty(object)
str = '';
case (isnumeric(object) || islogical(object))
if ndims(object)>2, object=object(:); end % can't handle arrays with dimention > 2
str=mat2str(object); % convert matrix to a string
% mark logical scalars with [] (logical arrays already have them) so the xml_read
% recognizes them as MATLAB objects instead of strings. Same with sparse
% matrices
if ((islogical(object) && isscalar(object)) || issparse(object)),
str = ['[' str ']'];
end
if (isinteger(object)),
str = ['[', class(object), '(', str ')]'];
end
case iscell(object)
if ndims(object)>2, object=object(:); end % can't handle cell arrays with dimention > 2
[nr nc] = size(object);
obj2 = object;
for i=1:length(object(:))
str = var2str(object{i}, PreserveSpace);
if (ischar(object{i})), object{i} = ['''' object{i} '''']; else object{i}=str; end
obj2{i} = [object{i} ','];
end
for r = 1:nr, obj2{r,nc} = [object{r,nc} ';']; end
obj2 = obj2.';
str = ['{' obj2{:} '}'];
case isstruct(object)
str='';
warning('xml_io_tools:write:var2str', ...
'Struct was encountered where string was expected. Ignoring.');
case isa(object, 'function_handle')
str = ['[@' char(object) ']'];
case ischar(object)
str = object;
otherwise
str = char(object);
end
%% string clean-up
str=str(:); str=str.'; % make sure this is a row vector of char's
if (~isempty(str))
str(str<32|str==127)=' '; % convert no-printable characters to spaces
if (~PreserveSpace)
str = strtrim(str); % remove spaces from begining and the end
str = regexprep(str,'\s+',' '); % remove multiple spaces
end
end
%% =======================================================================
% === var2Namestr Function ==============================================
% =======================================================================
function str = varName2str(str)
% convert matlab variable names to a sting
str = char(str);
p = strfind(str,'0x');
if (~isempty(p))
for i=1:length(p)
before = str( p(i)+(0:3) ); % string to replace
after = char(hex2dec(before(3:4))); % string to replace with
str = regexprep(str,before,after, 'once', 'ignorecase');
p=p-3; % since 4 characters were replaced with one - compensate
end
end
str = regexprep(str,'_COLON_',':', 'once', 'ignorecase');
str = regexprep(str,'_DASH_' ,'-', 'once', 'ignorecase');
|
github | ddtm/OpenFace-master | xml_read.m | .m | OpenFace-master/matlab_version/AU_training/data extraction/xml_io_tools_2010_11_05/xml_read.m | 23,858 | utf_8 | d68b7e27ad197bc94b445c3a833b9f23 | function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your preferences
%
% [tree, RootName, DOMnode] = xml_read(xmlfile) get additional information
% about XML file
%
% INPUT:
% xmlfile URL or filename of xml file to read
% Pref Preferences:
% Pref.ItemName - default 'item' - name of a special tag used to itemize
% cell arrays
% Pref.ReadAttr - default true - allow reading attributes
% Pref.ReadSpec - default true - allow reading special nodes
% Pref.Str2Num - default 'smart' - convert strings that look like numbers
% to numbers. Options: "always", "never", and "smart"
% Pref.KeepNS - default true - keep or strip namespace info
% Pref.NoCells - default true - force output to have no cell arrays
% Pref.Debug - default false - show mode specific error messages
% Pref.NumLevels- default infinity - how many recursive levels are
% allowed. Can be used to speed up the function by prunning the tree.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.CellItem - default 'true' - leave 'item' nodes in cell notation.
% OUTPUT:
% tree tree of structs and/or cell arrays corresponding to xml file
% RootName XML tag name used for root (top level) node.
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% DOMnode output of xmlread
%
% DETAILS:
% Function xml_read first calls MATLAB's xmlread function and than
% converts its output ('Document Object Model' tree of Java objects)
% to tree of MATLAB struct's. The output is in format of nested structs
% and cells. In the output data structure field names are based on
% XML tags, except in cases when tags produce illegal variable names.
%
% Several special xml node types result in special tags for fields of
% 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields are
% present. Usually data section is stored directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - stores node's comment section (string). For global
% comments see "RootName" output variable.
% - node.CDATA_SECTION - stores node's CDATA section (string).
% - node.PROCESSING_INSTRUCTIONS - stores "processing instruction" child
% node. For global "processing instructions" see "RootName" output variable.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes, notation nodes and processing instruction nodes
% will be treated like regular nodes
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% [tree treeName] = xml_read ('test.xml');
% disp(treeName)
% gen_object_display()
% % See also xml_examples.m
%
% See also:
% xml_write, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
% References:
% - Function inspired by Example 3 found in xmlread function.
% - Output data structures inspired by xml_toolbox structures.
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.CellItem = false; % leave 'item' nodes in cell notation
DPref.ReadAttr = true; % allow reading attributes
DPref.ReadSpec = true; % allow reading special nodes: comments, CData, etc.
DPref.KeepNS = true; % Keep or strip namespace info
DPref.Str2Num = 'smart';% convert strings that look like numbers to numbers
DPref.NoCells = true; % force output to have no cell arrays
DPref.NumLevels = 1e10; % number of recurence levels
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % return root node with no top level special nodes
Debug = false; % show specific errors (true) or general (false)?
tree = [];
RootName = [];
%% Check Matlab Version
v = ver('MATLAB');
version = str2double(regexp(v.Version, '\d.\d','match','once'));
if (version<7.1)
error('Your MATLAB version is too old. You need version 7.1 or newer.');
end
%% read user preferences
if (nargin>1)
if (isfield(Pref, 'TableName')), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'Str2Num' )), DPref.Str2Num = Pref.Str2Num ; end
if (isfield(Pref, 'NoCells' )), DPref.NoCells = Pref.NoCells ; end
if (isfield(Pref, 'NumLevels')), DPref.NumLevels = Pref.NumLevels; end
if (isfield(Pref, 'ReadAttr' )), DPref.ReadAttr = Pref.ReadAttr; end
if (isfield(Pref, 'ReadSpec' )), DPref.ReadSpec = Pref.ReadSpec; end
if (isfield(Pref, 'KeepNS' )), DPref.KeepNS = Pref.KeepNS; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'Debug' )), Debug = Pref.Debug ; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if ischar(DPref.Str2Num), % convert from character description to numbers
DPref.Str2Num = find(strcmpi(DPref.Str2Num, {'never', 'smart', 'always'}))-1;
if isempty(DPref.Str2Num), DPref.Str2Num=1; end % 1-smart by default
end
%% read xml file using Matlab function
if isa(xmlfile, 'org.apache.xerces.dom.DeferredDocumentImpl');
% if xmlfile is a DOMnode than skip the call to xmlread
try
try
DOMnode = xmlfile;
catch ME
error('Invalid DOM node: \n%s.', getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Invalid DOM node. \n');
end
else % we assume xmlfile is a filename
if (Debug) % in debuging mode crashes are allowed
DOMnode = xmlread(xmlfile);
else % in normal mode crashes are not allowed
try
try
DOMnode = xmlread(xmlfile);
catch ME
error('Failed to read XML file %s: \n%s',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Failed to read XML file %s\n',xmlfile);
end
end
end
Node = DOMnode.getFirstChild;
%% Find the Root node. Also store data from Global Comment and Processing
% Instruction nodes, if any.
GlobalTextNodes = cell(1,3);
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
while (~isempty(Node))
if (Node.getNodeType==Node.ELEMENT_NODE)
RootNode=Node;
elseif (Node.getNodeType==Node.PROCESSING_INSTRUCTION_NODE)
data = strtrim(char(Node.getData));
target = strtrim(char(Node.getTarget));
GlobalProcInst = [target, ' ', data];
GlobalTextNodes{2} = GlobalProcInst;
elseif (Node.getNodeType==Node.COMMENT_NODE)
GlobalComment = strtrim(char(Node.getData));
GlobalTextNodes{3} = GlobalComment;
% elseif (Node.getNodeType==Node.DOCUMENT_TYPE_NODE)
% GlobalTextNodes{4} = GlobalDocType;
end
Node = Node.getNextSibling;
end
%% parse xml file through calls to recursive DOMnode2struct function
if (Debug) % in debuging mode crashes are allowed
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
else % in normal mode crashes are not allowed
try
try
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
catch ME
error('Unable to parse XML file %s: \n %s.',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Unable to parse XML file %s.',xmlfile);
end
end
%% If there were any Global Text nodes than return them
if (~RootOnly)
if (~isempty(GlobalProcInst) && DPref.ReadSpec)
t.PROCESSING_INSTRUCTION = GlobalProcInst;
end
if (~isempty(GlobalComment) && DPref.ReadSpec)
t.COMMENT = GlobalComment;
end
if (~isempty(GlobalDocType) && DPref.ReadSpec)
t.DOCUMENT_TYPE = GlobalDocType;
end
t.(RootName) = tree;
tree=t;
end
if (~isempty(GlobalTextNodes))
GlobalTextNodes{1} = RootName;
RootName = GlobalTextNodes;
end
%% =======================================================================
% === DOMnode2struct Function ===========================================
% =======================================================================
function [s TagName LeafNode] = DOMnode2struct(node, Pref, level)
%% === Step 1: Get node name and check if it is a leaf node ==============
[TagName LeafNode] = NodeName(node, Pref.KeepNS);
s = []; % initialize output structure
%% === Step 2: Process Leaf Nodes (nodes with no children) ===============
if (LeafNode)
if (LeafNode>1 && ~Pref.ReadSpec), LeafNode=-1; end % tags only so ignore special nodes
if (LeafNode>0) % supported leaf node types
try
try % use try-catch: errors here are often due to VERY large fields (like images) that overflow java memory
s = char(node.getData);
if (isempty(s)), s = ' '; end % make it a string
% for some reason current xmlread 'creates' a lot of empty text
% fields with first chatacter=10 - those will be deleted.
if (~Pref.PreserveSpace || s(1)==10)
if (isspace(s(1)) || isspace(s(end))), s = strtrim(s); end % trim speces is any
end
if (LeafNode==1), s=str2var(s, Pref.Str2Num, 0); end % convert to number(s) if needed
catch ME % catch for mablab versions 7.5 and higher
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
getReport(ME)
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
end
end
if (LeafNode==3) % ProcessingInstructions need special treatment
target = strtrim(char(node.getTarget));
s = [target, ' ', s];
end
return % We are done the rest of the function deals with nodes with children
end
if (level>Pref.NumLevels+1), return; end % if Pref.NumLevels is reached than we are done
%% === Step 3: Process nodes with children ===============================
if (node.hasChildNodes) % children present
Child = node.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
% --- pass 1: how many children with each name -----------------------
f = [];
for iChild = 1:nChild % read in each child
[cname cLeaf] = NodeName(Child.item(iChild-1), Pref.KeepNS);
if (cLeaf<0), continue; end % unsupported leaf node types
if (~isfield(f,cname)),
f.(cname)=0; % initialize first time I see this name
end
f.(cname) = f.(cname)+1; % add to the counter
end % end for iChild
% text_nodes become CONTENT & for some reason current xmlread 'creates' a
% lot of empty text fields so f.CONTENT value should not be trusted
if (isfield(f,'CONTENT') && f.CONTENT>2), f.CONTENT=2; end
% --- pass 2: store all the children as struct of cell arrays ----------
for iChild = 1:nChild % read in each child
[c cname cLeaf] = DOMnode2struct(Child.item(iChild-1), Pref, level+1);
if (cLeaf && isempty(c)) % if empty leaf node than skip
continue; % usually empty text node or one of unhandled node types
elseif (nChild==1 && cLeaf==1)
s=c; % shortcut for a common case
else % if normal node
if (level>Pref.NumLevels), continue; end
n = f.(cname); % how many of them in the array so far?
if (~isfield(s,cname)) % encountered this name for the first time
if (n==1) % if there will be only one of them ...
s.(cname) = c; % than save it in format it came in
else % if there will be many of them ...
s.(cname) = cell(1,n);
s.(cname){1} = c; % than save as cell array
end
f.(cname) = 1; % initialize the counter
else % already have seen this name
s.(cname){n+1} = c; % add to the array
f.(cname) = n+1; % add to the array counter
end
end
end % for iChild
end % end if (node.hasChildNodes)
%% === Step 4: Post-process struct's created for nodes with children =====
if (isstruct(s))
fields = fieldnames(s);
nField = length(fields);
% Detect structure that looks like Html table and store it in cell Matrix
if (nField==1 && strcmpi(fields{1},Pref.TableName{1}))
tr = s.(Pref.TableName{1});
fields2 = fieldnames(tr{1});
if (length(fields2)==1 && strcmpi(fields2{1},Pref.TableName{2}))
% This seems to be a special structure such that for
% Pref.TableName = {'tr','td'} 's' corresponds to
% <tr> <td>M11</td> <td>M12</td> </tr>
% <tr> <td>M12</td> <td>M22</td> </tr>
% Recognize it as encoding for 2D struct
nr = length(tr);
for r = 1:nr
row = tr{r}.(Pref.TableName{2});
Table(r,1:length(row)) = row; %#ok<AGROW>
end
s = Table;
end
end
% --- Post-processing: convert 'struct of cell-arrays' to 'array of structs'
% Example: let say s has 3 fields s.a, s.b & s.c and each field is an
% cell-array with more than one cell-element and all 3 have the same length.
% Then change it to array of structs, each with single cell.
% This way element s.a{1} will be now accessed through s(1).a
vec = zeros(size(fields));
for i=1:nField, vec(i) = f.(fields{i}); end
if (numel(vec)>1 && vec(1)>1 && var(vec)==0) % convert from struct of
s = cell2struct(struct2cell(s), fields, 1); % arrays to array of struct
end % if anyone knows better way to do above conversion please let me know.
end
%% === Step 5: Process nodes with attributes =============================
if (node.hasAttributes && Pref.ReadAttr)
if (~isstruct(s)), % make into struct if is not already
ss.CONTENT=s;
s=ss;
end
Attr = node.getAttributes; % list of all attributes
for iAttr = 1:Attr.getLength % for each attribute
name = char(Attr.item(iAttr-1).getName); % attribute name
name = str2varName(name, Pref.KeepNS); % fix name if needed
value = char(Attr.item(iAttr-1).getValue); % attribute value
value = str2var(value, Pref.Str2Num, 1); % convert to number if possible
s.ATTRIBUTE.(name) = value; % save again
end % end iAttr loop
end % done with attributes
if (~isstruct(s)), return; end %The rest of the code deals with struct's
%% === Post-processing: fields of "s"
% convert 'cell-array of structs' to 'arrays of structs'
fields = fieldnames(s); % get field names
nField = length(fields);
for iItem=1:length(s) % for each struct in the array - usually one
for iField=1:length(fields)
field = fields{iField}; % get field name
% if this is an 'item' field and user want to leave those as cells
% than skip this one
if (strcmpi(field, Pref.ItemName) && Pref.CellItem), continue; end
x = s(iItem).(field);
if (iscell(x) && all(cellfun(@isstruct,x(:))) && numel(x)>1) % it's cell-array of structs
% numel(x)>1 check is to keep 1 cell-arrays created when Pref.CellItem=1
try % this operation fails sometimes
% example: change s(1).a{1}.b='jack'; s(1).a{2}.b='john'; to
% more convinient s(1).a(1).b='jack'; s(1).a(2).b='john';
s(iItem).(field) = [x{:}]'; %#ok<AGROW> % converted to arrays of structs
catch %#ok<CTCH>
% above operation will fail if s(1).a{1} and s(1).a{2} have
% different fields. If desired, function forceCell2Struct can force
% them to the same field structure by adding empty fields.
if (Pref.NoCells)
s(iItem).(field) = forceCell2Struct(x); %#ok<AGROW>
end
end % end catch
end
end
end
%% === Step 4: Post-process struct's created for nodes with children =====
% --- Post-processing: remove special 'item' tags ---------------------
% many xml writes (including xml_write) use a special keyword to mark
% arrays of nodes (see xml_write for examples). The code below converts
% s.item to s.CONTENT
ItemContent = false;
if (isfield(s,Pref.ItemName))
s.CONTENT = s.(Pref.ItemName);
s = rmfield(s,Pref.ItemName);
ItemContent = Pref.CellItem; % if CellItem than keep s.CONTENT as cells
end
% --- Post-processing: clean up CONTENT tags ---------------------
% if s.CONTENT is a cell-array with empty elements at the end than trim
% the length of this cell-array. Also if s.CONTENT is the only field than
% remove .CONTENT part and store it as s.
if (isfield(s,'CONTENT'))
if (iscell(s.CONTENT) && isvector(s.CONTENT))
x = s.CONTENT;
for i=numel(x):-1:1, if ~isempty(x{i}), break; end; end
if (i==1 && ~ItemContent)
s.CONTENT = x{1}; % delete cell structure
else
s.CONTENT = x(1:i); % delete empty cells
end
end
if (nField==1)
if (ItemContent)
ss = s.CONTENT; % only child: remove a level but ensure output is a cell-array
s=[]; s{1}=ss;
else
s = s.CONTENT; % only child: remove a level
end
end
end
%% =======================================================================
% === forceCell2Struct Function =========================================
% =======================================================================
function s = forceCell2Struct(x)
% Convert cell-array of structs, where not all of structs have the same
% fields, to a single array of structs
%% Convert 1D cell array of structs to 2D cell array, where each row
% represents item in original array and each column corresponds to a unique
% field name. Array "AllFields" store fieldnames for each column
AllFields = fieldnames(x{1}); % get field names of the first struct
CellMat = cell(length(x), length(AllFields));
for iItem=1:length(x)
fields = fieldnames(x{iItem}); % get field names of the next struct
for iField=1:length(fields) % inspect all fieldnames and find those
field = fields{iField}; % get field name
col = find(strcmp(field,AllFields),1);
if isempty(col) % no column for such fieldname yet
AllFields = [AllFields; field]; %#ok<AGROW>
col = length(AllFields); % create a new column for it
end
CellMat{iItem,col} = x{iItem}.(field); % store rearanged data
end
end
%% Convert 2D cell array to array of structs
s = cell2struct(CellMat, AllFields, 2);
%% =======================================================================
% === str2var Function ==================================================
% =======================================================================
function val=str2var(str, option, attribute)
% Can this string 'str' be converted to a number? if so than do it.
val = str;
len = numel(str);
if (len==0 || option==0), return; end % Str2Num="never" of empty string -> do not do enything
if (len>10000 && option==1), return; end % Str2Num="smart" and string is very long -> probably base64 encoded binary
digits = '(Inf)|(NaN)|(pi)|[\t\n\d\+\-\*\.ei EI\[\]\;\,]';
s = regexprep(str, digits, ''); % remove all the digits and other allowed characters
if (~all(~isempty(s))) % if nothing left than this is probably a number
if (~isempty(strfind(str, ' '))), option=2; end %if str has white-spaces assume by default that it is not a date string
if (~isempty(strfind(str, '['))), option=2; end % same with brackets
str(strfind(str, '\n')) = ';';% parse data tables into 2D arrays, if any
if (option==1) % the 'smart' option
try % try to convert to a date, like 2007-12-05
datenum(str); % if successful than leave it as string
catch %#ok<CTCH> % if this is not a date than ...
option=2; % ... try converting to a number
end
end
if (option==2)
if (attribute)
num = str2double(str); % try converting to a single number using sscanf function
if isnan(num), return; end % So, it wasn't really a number after all
else
num = str2num(str); %#ok<ST2NM> % try converting to a single number or array using eval function
end
if(isnumeric(num) && numel(num)>0), val=num; end % if convertion to a single was succesful than save
end
elseif ((str(1)=='[' && str(end)==']') || (str(1)=='{' && str(end)=='}')) % this looks like a (cell) array encoded as a string
try
val = eval(str);
catch %#ok<CTCH>
val = str;
end
elseif (~attribute) % see if it is a boolean array with no [] brackets
str1 = lower(str);
str1 = strrep(str1, 'false', '0');
str1 = strrep(str1, 'true' , '1');
s = regexprep(str1, '[01 \;\,]', ''); % remove all 0/1, spaces, commas and semicolons
if (~all(~isempty(s))) % if nothing left than this is probably a boolean array
num = str2num(str1); %#ok<ST2NM>
if(isnumeric(num) && numel(num)>0), val = (num>0); end % if convertion was succesful than save as logical
end
end
%% =======================================================================
% === str2varName Function ==============================================
% =======================================================================
function str = str2varName(str, KeepNS)
% convert a sting to a valid matlab variable name
if(KeepNS)
str = regexprep(str,':','_COLON_', 'once', 'ignorecase');
else
k = strfind(str,':');
if (~isempty(k))
str = str(k+1:end);
end
end
str = regexprep(str,'-','_DASH_' ,'once', 'ignorecase');
if (~isvarname(str)) && (~iskeyword(str))
str = genvarname(str);
end
%% =======================================================================
% === NodeName Function =================================================
% =======================================================================
function [Name LeafNode] = NodeName(node, KeepNS)
% get node name and make sure it is a valid variable name in Matlab.
% also get node type:
% LeafNode=0 - normal element node,
% LeafNode=1 - text node
% LeafNode=2 - supported non-text leaf node,
% LeafNode=3 - supported processing instructions leaf node,
% LeafNode=-1 - unsupported non-text leaf node
switch (node.getNodeType)
case node.ELEMENT_NODE
Name = char(node.getNodeName);% capture name of the node
Name = str2varName(Name, KeepNS); % if Name is not a good variable name - fix it
LeafNode = 0;
case node.TEXT_NODE
Name = 'CONTENT';
LeafNode = 1;
case node.COMMENT_NODE
Name = 'COMMENT';
LeafNode = 2;
case node.CDATA_SECTION_NODE
Name = 'CDATA_SECTION';
LeafNode = 2;
case node.DOCUMENT_TYPE_NODE
Name = 'DOCUMENT_TYPE';
LeafNode = 2;
case node.PROCESSING_INSTRUCTION_NODE
Name = 'PROCESSING_INSTRUCTION';
LeafNode = 3;
otherwise
NodeType = {'ELEMENT','ATTRIBUTE','TEXT','CDATA_SECTION', ...
'ENTITY_REFERENCE', 'ENTITY', 'PROCESSING_INSTRUCTION', 'COMMENT',...
'DOCUMENT', 'DOCUMENT_TYPE', 'DOCUMENT_FRAGMENT', 'NOTATION'};
Name = char(node.getNodeName);% capture name of the node
warning('xml_io_tools:read:unkNode', ...
'Unknown node type encountered: %s_NODE (%s)', NodeType{node.getNodeType}, Name);
LeafNode = -1;
end
|
github | ddtm/OpenFace-master | writeMatrixBin.m | .m | OpenFace-master/matlab_version/AU_training/experiments/utilities/writeMatrixBin.m | 911 | utf_8 | 636b1a9c9f27421bfde056250858f51e | % for easier readibility write them row by row
function writeMatrixBin(fileID, M, type)
% 4 bytes each for the description
fwrite(fileID, size(M,1), 'uint');
fwrite(fileID, size(M,2), 'uint');
fwrite(fileID, type, 'uint');
% Convert the matrix to OpenCV format (row minor as opposed to column
% minor)
M = M';
% type 0 - uint8, 1 - int8, 2 - uint16, 3 - int16, 4 - int, 5 -
% float32, 6 - float64
% Write out the matrix itself
switch type
case 0
type = 'uint8';
case 1
type = 'int8';
case 2
type = 'uint16';
case 3
type = 'int16';
case 4
type = 'int';
case 5
type = 'float32';
case 6
type = 'float64';
otherwise
type = 'float32';
end
fwrite(fileID, M, type);
end |
github | ddtm/OpenFace-master | demo.m | .m | OpenFace-master/matlab_version/face_detection/face_detection_yu/demo.m | 2,293 | utf_8 | a03fbb6302d44b6640ec7a0045c77dea | % Function:
% demo
%
% Usage:
% This function demonstrates how to call the functions we provided to
% detect facial landmarks and get pose information. In the demo version,
% we only choose the largest face ROI detected to further localize its
% landmraks. And the current version is only suitable for windows
% platform. The matlab version is recommended above R2009a and the C++
% compiler above MS. Visual C++ 9.0. Those versions before are not tested.
%
% Params:
% None
%
% Return: None
%
% Author:
% Xiang Yu, [email protected]
%
% Citation:
% X. Yu, J. Huang, S. Zhang, W. Yan and D.N. Metaxas, Pose-free Facial
% Landmark Fitting via Optimized Part Mixures and Cascaded Deformable
% Shape Model. In ICCV, 2013.
%
% Creation Date: 10/12/2013
%
function demo()
clear all;
close all;
clc;
addpath('.\model');
S = load('model_param.mat');
Model = S.Model;
pc_version = computer();
if(strcmp(pc_version,'PCWIN')) % currently the code just supports windows OS
addpath('.\face_detect_32');
addpath('.\mex_32');
elseif(strcmp(pc_version, 'PCWIN64'))
addpath('.\face_detect_64');
addpath('.\mex_64');
end
Model.frontalL = @(X) Select(X, Model.frontal_landmark);
Model.leftL = @(X) Select(X, Model.left_landmark);
Model.rightL = @(X) Select(X, Model.right_landmark);
% change your image folder and image name here
img_fold = '.\test\';
img_name = '4.jpg';
img = imread([img_fold,img_name]);
%------------------------------------------------------------------
% 3 types of face alignment input
% (1) no initial landmarks
% [shape, pglobal, visible] = faceAlign(img, Model, []);
%
% (2) initial landmarks
% l_shape = [xxx...x yyy...yy] input should be set by user
% [shape, pglobal, visible] = faceAlign(img, Model, l_shape);
load('test.mat');
% img = image;
[shape, pglobal, visible] = faceAlign(img, Model, []);
figure,
% imshow(imread([img_fold,img_name]));
imshow(img);
hold on;
if(~isempty(shape))
% input: shape, visible, line_color, marker color, marker size, line width, style
drawLine(reshape(shape,Model.nPts,2), visible, 'b', 'g', 5, 2, '.');
hold off;
end
function Y = Select(X, rows) % x,y,z,x,y,z,...
Y1 = X(3*(rows-1)+1, :);
Y2 = X(3*(rows-1)+2, :);
Y3 = X(3*(rows-1)+3, :);
Y = [Y1; Y2; Y3]; |
github | ddtm/OpenFace-master | detect.m | .m | OpenFace-master/matlab_version/face_detection/face_detection_yu/face_detect_64/detect.m | 5,497 | utf_8 | 8056df5cc27320fd2ab86a86bf4c3868 | function boxes = detect(input, model, thresh)
% Keep track of detected boxes and features
BOXCACHESIZE = 1000;
cnt = 0;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
% Compute the feature pyramid and prepare filters
pyra = featpyramid(input,model);
[components,filters,resp] = modelcomponents(model,pyra);
% matlabpool open 4
% tic;
% parfor ss = (model.interval+1):length(pyra.feat)
% resp{ss} = fconv(pyra.feat{ss}, filters, 1, length(filters));
% end
% toc;
% ress_ = fconv2(pyra.feat,filters,model.interval+1, length(pyra.feat), 1, length(filters));
% matlabpool close
for c = randperm(length(components)),
minlevel = model.interval+1;
levels = minlevel:length(pyra.feat);
for rlevel = levels(randperm(length(levels))),
parts = components{c};
numparts = length(parts);
% Local part scores
for k = 1:numparts,
f = parts(k).filterid;
level = rlevel-parts(k).scale*model.interval;
if isempty(resp{level}),
resp{level} = fconvMT(pyra.feat{level},filters,1,length(filters)); %1,length(filters));
end
parts(k).score = resp{level}{f};
% parts(k).score = ress_{rlevel-model.interval,f};
parts(k).level = level;
end
% Walk from leaves to root of tree, passing message to parent
% Given a 2D array of filter scores 'child', shiftdt() does the following:
% (1) Apply distance transform
% (2) Shift by anchor position (child.startxy) of part wrt parent
% (3) Downsample by child.step
for k = numparts:-1:2,
child = parts(k);
par = child.parent;
[Ny,Nx,foo] = size(parts(par).score);
[msg,parts(k).Ix,parts(k).Iy] = shiftdt(child.score, child.w(1),child.w(2),child.w(3),child.w(4), ...
child.startx, child.starty, Nx, Ny, child.step);
parts(par).score = parts(par).score + msg;
end
% Add bias to root score
rscore = parts(1).score + parts(1).w;
[Y,X] = find(rscore >= thresh);
if ~isempty(X)
XY = backtrack( X, Y, parts, pyra);
end
% Walk back down tree following pointers
for i = 1:length(X)
x = X(i);
y = Y(i);
if cnt == BOXCACHESIZE
b0 = nms_face(boxes,0.3);
clear boxes;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
cnt = length(b0);
boxes(1:cnt) = b0;
end
cnt = cnt + 1;
boxes(cnt).c = c;
boxes(cnt).s = rscore(y,x);
boxes(cnt).level = rlevel;
boxes(cnt).xy = XY(:,:,i);
end
end
end
boxes = boxes(1:cnt);
% Backtrack through dynamic programming messages to estimate part locations
% and the associated feature vector
function box = backtrack(x,y,parts,pyra)
numparts = length(parts);
ptr = zeros(numparts,2,length(x));
box = zeros(numparts,4,length(x));
k = 1;
p = parts(k);
ptr(k,1,:) = x;
ptr(k,2,:) = y;
% image coordinates of root
scale = pyra.scale(p.level);
padx = pyra.padx;
pady = pyra.pady;
box(k,1,:) = (x-1-padx)*scale + 1;
box(k,2,:) = (y-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
for k = 2:numparts,
p = parts(k);
par = p.parent;
x = ptr(par,1,:);
y = ptr(par,2,:);
inds = sub2ind(size(p.Ix), y, x);
ptr(k,1,:) = p.Ix(inds);
ptr(k,2,:) = p.Iy(inds);
% image coordinates of part k
scale = pyra.scale(p.level);
box(k,1,:) = (ptr(k,1,:)-1-padx)*scale + 1;
box(k,2,:) = (ptr(k,2,:)-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
end
% Cache various statistics from the model data structure for later use
function [components,filters,resp] = modelcomponents(model,pyra)
components = cell(length(model.components),1);
for c = 1:length(model.components),
for k = 1:length(model.components{c}),
p = model.components{c}(k);
x = model.filters(p.filterid);
[p.sizy p.sizx foo] = size(x.w);
p.filterI = x.i;
x = model.defs(p.defid);
p.defI = x.i;
p.w = x.w;
% store the scale of each part relative to the component root
par = p.parent;
assert(par < k);
ax = x.anchor(1);
ay = x.anchor(2);
ds = x.anchor(3);
if par > 0,
p.scale = ds + components{c}(par).scale;
else
assert(k == 1);
p.scale = 0;
end
% amount of (virtual) padding to hallucinate
step = 2^ds;
virtpady = (step-1)*pyra.pady;
virtpadx = (step-1)*pyra.padx;
% starting points (simulates additional padding at finer scales)
p.starty = ay-virtpady;
p.startx = ax-virtpadx;
p.step = step;
p.level = 0;
p.score = 0;
p.Ix = 0;
p.Iy = 0;
components{c}(k) = p;
end
end
resp = cell(length(pyra.feat),1);
filters = cell(length(model.filters),1);
for i = 1:length(filters),
filters{i} = model.filters(i).w;
end
|
github | ddtm/OpenFace-master | detect.m | .m | OpenFace-master/matlab_version/face_detection/face_detection_yu/face_detect_32/detect.m | 5,497 | utf_8 | 8056df5cc27320fd2ab86a86bf4c3868 | function boxes = detect(input, model, thresh)
% Keep track of detected boxes and features
BOXCACHESIZE = 1000;
cnt = 0;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
% Compute the feature pyramid and prepare filters
pyra = featpyramid(input,model);
[components,filters,resp] = modelcomponents(model,pyra);
% matlabpool open 4
% tic;
% parfor ss = (model.interval+1):length(pyra.feat)
% resp{ss} = fconv(pyra.feat{ss}, filters, 1, length(filters));
% end
% toc;
% ress_ = fconv2(pyra.feat,filters,model.interval+1, length(pyra.feat), 1, length(filters));
% matlabpool close
for c = randperm(length(components)),
minlevel = model.interval+1;
levels = minlevel:length(pyra.feat);
for rlevel = levels(randperm(length(levels))),
parts = components{c};
numparts = length(parts);
% Local part scores
for k = 1:numparts,
f = parts(k).filterid;
level = rlevel-parts(k).scale*model.interval;
if isempty(resp{level}),
resp{level} = fconvMT(pyra.feat{level},filters,1,length(filters)); %1,length(filters));
end
parts(k).score = resp{level}{f};
% parts(k).score = ress_{rlevel-model.interval,f};
parts(k).level = level;
end
% Walk from leaves to root of tree, passing message to parent
% Given a 2D array of filter scores 'child', shiftdt() does the following:
% (1) Apply distance transform
% (2) Shift by anchor position (child.startxy) of part wrt parent
% (3) Downsample by child.step
for k = numparts:-1:2,
child = parts(k);
par = child.parent;
[Ny,Nx,foo] = size(parts(par).score);
[msg,parts(k).Ix,parts(k).Iy] = shiftdt(child.score, child.w(1),child.w(2),child.w(3),child.w(4), ...
child.startx, child.starty, Nx, Ny, child.step);
parts(par).score = parts(par).score + msg;
end
% Add bias to root score
rscore = parts(1).score + parts(1).w;
[Y,X] = find(rscore >= thresh);
if ~isempty(X)
XY = backtrack( X, Y, parts, pyra);
end
% Walk back down tree following pointers
for i = 1:length(X)
x = X(i);
y = Y(i);
if cnt == BOXCACHESIZE
b0 = nms_face(boxes,0.3);
clear boxes;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
cnt = length(b0);
boxes(1:cnt) = b0;
end
cnt = cnt + 1;
boxes(cnt).c = c;
boxes(cnt).s = rscore(y,x);
boxes(cnt).level = rlevel;
boxes(cnt).xy = XY(:,:,i);
end
end
end
boxes = boxes(1:cnt);
% Backtrack through dynamic programming messages to estimate part locations
% and the associated feature vector
function box = backtrack(x,y,parts,pyra)
numparts = length(parts);
ptr = zeros(numparts,2,length(x));
box = zeros(numparts,4,length(x));
k = 1;
p = parts(k);
ptr(k,1,:) = x;
ptr(k,2,:) = y;
% image coordinates of root
scale = pyra.scale(p.level);
padx = pyra.padx;
pady = pyra.pady;
box(k,1,:) = (x-1-padx)*scale + 1;
box(k,2,:) = (y-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
for k = 2:numparts,
p = parts(k);
par = p.parent;
x = ptr(par,1,:);
y = ptr(par,2,:);
inds = sub2ind(size(p.Ix), y, x);
ptr(k,1,:) = p.Ix(inds);
ptr(k,2,:) = p.Iy(inds);
% image coordinates of part k
scale = pyra.scale(p.level);
box(k,1,:) = (ptr(k,1,:)-1-padx)*scale + 1;
box(k,2,:) = (ptr(k,2,:)-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
end
% Cache various statistics from the model data structure for later use
function [components,filters,resp] = modelcomponents(model,pyra)
components = cell(length(model.components),1);
for c = 1:length(model.components),
for k = 1:length(model.components{c}),
p = model.components{c}(k);
x = model.filters(p.filterid);
[p.sizy p.sizx foo] = size(x.w);
p.filterI = x.i;
x = model.defs(p.defid);
p.defI = x.i;
p.w = x.w;
% store the scale of each part relative to the component root
par = p.parent;
assert(par < k);
ax = x.anchor(1);
ay = x.anchor(2);
ds = x.anchor(3);
if par > 0,
p.scale = ds + components{c}(par).scale;
else
assert(k == 1);
p.scale = 0;
end
% amount of (virtual) padding to hallucinate
step = 2^ds;
virtpady = (step-1)*pyra.pady;
virtpadx = (step-1)*pyra.padx;
% starting points (simulates additional padding at finer scales)
p.starty = ay-virtpady;
p.startx = ax-virtpadx;
p.step = step;
p.level = 0;
p.score = 0;
p.Ix = 0;
p.Iy = 0;
components{c}(k) = p;
end
end
resp = cell(length(pyra.feat),1);
filters = cell(length(model.filters),1);
for i = 1:length(filters),
filters{i} = model.filters(i).w;
end
|
github | ddtm/OpenFace-master | detect.m | .m | OpenFace-master/matlab_version/face_detection/face_detection_zhu/face-release1.0-basic/detect.m | 5,142 | utf_8 | cd759876abb45da1a5be34a1237acd0a | function boxes = detect(input, model, thresh)
% Keep track of detected boxes and features
BOXCACHESIZE = 100000;
cnt = 0;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
% Compute the feature pyramid and prepare filters
pyra = featpyramid(input,model);
[components,filters,resp] = modelcomponents(model,pyra);
for c = randperm(length(components)),
minlevel = model.interval+1;
levels = minlevel:length(pyra.feat);
for rlevel = levels(randperm(length(levels))),
parts = components{c};
numparts = length(parts);
% Local part scores
for k = 1:numparts,
f = parts(k).filterid;
level = rlevel-parts(k).scale*model.interval;
if isempty(resp{level}),
resp{level} = fconv(pyra.feat{level},filters,1,length(filters));
end
parts(k).score = resp{level}{f};
parts(k).level = level;
end
% Walk from leaves to root of tree, passing message to parent
% Given a 2D array of filter scores 'child', shiftdt() does the following:
% (1) Apply distance transform
% (2) Shift by anchor position (child.startxy) of part wrt parent
% (3) Downsample by child.step
for k = numparts:-1:2,
child = parts(k);
par = child.parent;
[Ny,Nx,foo] = size(parts(par).score);
[msg,parts(k).Ix,parts(k).Iy] = shiftdt(child.score, child.w(1),child.w(2),child.w(3),child.w(4), ...
child.startx, child.starty, Nx, Ny, child.step);
parts(par).score = parts(par).score + msg;
end
% Add bias to root score
rscore = parts(1).score + parts(1).w;
[Y,X] = find(rscore >= thresh);
if ~isempty(X)
XY = backtrack( X, Y, parts, pyra);
end
% Walk back down tree following pointers
for i = 1:length(X)
x = X(i);
y = Y(i);
if cnt == BOXCACHESIZE
b0 = nms_face(boxes,0.3);
clear boxes;
boxes.s = 0;
boxes.c = 0;
boxes.xy = 0;
boxes.level = 0;
boxes(BOXCACHESIZE) = boxes;
cnt = length(b0);
boxes(1:cnt) = b0;
end
cnt = cnt + 1;
boxes(cnt).c = c;
boxes(cnt).s = rscore(y,x);
boxes(cnt).level = rlevel;
boxes(cnt).xy = XY(:,:,i);
end
end
end
boxes = boxes(1:cnt);
% Backtrack through dynamic programming messages to estimate part locations
% and the associated feature vector
function box = backtrack(x,y,parts,pyra)
numparts = length(parts);
ptr = zeros(numparts,2,length(x));
box = zeros(numparts,4,length(x));
k = 1;
p = parts(k);
ptr(k,1,:) = x;
ptr(k,2,:) = y;
% image coordinates of root
scale = pyra.scale(p.level);
padx = pyra.padx;
pady = pyra.pady;
box(k,1,:) = (x-1-padx)*scale + 1;
box(k,2,:) = (y-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
for k = 2:numparts,
p = parts(k);
par = p.parent;
x = ptr(par,1,:);
y = ptr(par,2,:);
inds = sub2ind(size(p.Ix), y, x);
ptr(k,1,:) = p.Ix(inds);
ptr(k,2,:) = p.Iy(inds);
% image coordinates of part k
scale = pyra.scale(p.level);
box(k,1,:) = (ptr(k,1,:)-1-padx)*scale + 1;
box(k,2,:) = (ptr(k,2,:)-1-pady)*scale + 1;
box(k,3,:) = box(k,1,:) + p.sizx*scale - 1;
box(k,4,:) = box(k,2,:) + p.sizy*scale - 1;
end
% Cache various statistics from the model data structure for later use
function [components,filters,resp] = modelcomponents(model,pyra)
components = cell(length(model.components),1);
for c = 1:length(model.components),
for k = 1:length(model.components{c}),
p = model.components{c}(k);
x = model.filters(p.filterid);
[p.sizy p.sizx foo] = size(x.w);
p.filterI = x.i;
x = model.defs(p.defid);
p.defI = x.i;
p.w = x.w;
% store the scale of each part relative to the component root
par = p.parent;
assert(par < k);
ax = x.anchor(1);
ay = x.anchor(2);
ds = x.anchor(3);
if par > 0,
p.scale = ds + components{c}(par).scale;
else
assert(k == 1);
p.scale = 0;
end
% amount of (virtual) padding to hallucinate
step = 2^ds;
virtpady = (step-1)*pyra.pady;
virtpadx = (step-1)*pyra.padx;
% starting points (simulates additional padding at finer scales)
p.starty = ay-virtpady;
p.startx = ax-virtpadx;
p.step = step;
p.level = 0;
p.score = 0;
p.Ix = 0;
p.Iy = 0;
components{c}(k) = p;
end
end
resp = cell(length(pyra.feat),1);
filters = cell(length(model.filters),1);
for i = 1:length(filters),
filters{i} = model.filters(i).w;
end
|
github | ddtm/OpenFace-master | visualizemodel.m | .m | OpenFace-master/matlab_version/face_detection/face_detection_zhu/face-release1.0-basic/visualizemodel.m | 3,577 | utf_8 | 251d5578a4c745e11ce795c8a8a4e1ed | function visualizemodel(model,compid)
if nargin<2
compid = 1:length(model.components);
end
pad = 2;
bs = 20;
for i = compid
c = model.components{i};
numparts = length(c);
Nmix = zeros(1,numparts);
for k = 1:numparts
Nmix(k) = length(c(k).filterid);
end
for k = 2:numparts
part = c(k);
anchor = zeros(Nmix(k),2);
for j = 1:Nmix(k)
def = model.defs(part.defid(j));
anchor(j,:) = [def.anchor(1) def.anchor(2)];
end
end
part = c(1);
% part filter
w = model.filters(part.filterid(1)).w;
w = foldHOG(w);
scale = max(abs(w(:)));
p = HOGpicture(w, bs);
p = padarray(p, [pad pad], 0);
p = uint8(p*(255/scale));
% border
p(:,1:2*pad) = 128;
p(:,end-2*pad+1:end) = 128;
p(1:2*pad,:) = 128;
p(end-2*pad+1:end,:) = 128;
im = p;
startpoint = zeros(numparts,2);
startpoint(1,:) = [0 0];
partsize = zeros(numparts,1);
partsize(1) = size(p,1);
for k = 2:numparts
part = c(k);
parent = c(k).parent;
% part filter
w = model.filters(part.filterid(1)).w;
w = foldHOG(w);
scale = max(abs(w(:)));
p = HOGpicture(w, bs);
p = padarray(p, [pad pad], 0);
p = uint8(p*(255/scale));
% border
p(:,1:2*pad) = 128;
p(:,end-2*pad+1:end) = 128;
p(1:2*pad,:) = 128;
p(end-2*pad+1:end,:) = 128;
% paste into root
def = model.defs(part.defid(1));
x1 = (def.anchor(1)-1)*bs+1 + startpoint(parent,1);
y1 = (def.anchor(2)-1)*bs+1 + startpoint(parent,2);
[H W] = size(im);
imnew = zeros(H + max(0,1-y1), W + max(0,1-x1));
imnew(1+max(0,1-y1):H+max(0,1-y1),1+max(0,1-x1):W+max(0,1-x1)) = im;
im = imnew;
startpoint = startpoint + repmat([max(0,1-x1) max(0,1-y1)],[numparts,1]);
x1 = max(1,x1);
y1 = max(1,y1);
x2 = x1 + size(p,2)-1;
y2 = y1 + size(p,1)-1;
startpoint(k,1) = x1 - 1;
startpoint(k,2) = y1 - 1;
im(y1:y2, x1:x2) = p;
partsize(k) = size(p,1);
end
% plot parts
figure,imagesc(im);
colormap gray;
axis equal; axis off;
drawnow;
title(sprintf('Component %d',i));
hold on;
% % plot connections
for k = 2:numparts
parent = c(k).parent;
sx = startpoint(k,1)+partsize(k)/2;
endx = startpoint(parent,1)+partsize(parent)/2;
sy = startpoint(k,2)+partsize(k)/2;
endy = startpoint(parent,2)+partsize(parent)/2;
plot([sx endx],[sy endy],'r','linewidth',2);
end
end
function f = foldHOG(w)
% f = foldHOG(w)
% Condense HOG features into one orientation histogram.
% Used for displaying a feature.
f=max(w(:,:,1:9),0)+max(w(:,:,10:18),0)+max(w(:,:,19:27),0);
function im = HOGpicture(w, bs)
% HOGpicture(w, bs)
% Make picture of positive HOG weights.
% construct a "glyph" for each orientaion
bim1 = zeros(bs, bs);
bim1(:,round(bs/2):round(bs/2)+1) = 1;
bim = zeros([size(bim1) 9]);
bim(:,:,1) = bim1;
for i = 2:9,
bim(:,:,i) = imrotate(bim1, -(i-1)*20, 'crop');
end
% make pictures of positive weights bs adding up weighted glyphs
s = size(w);
w(w < 0) = 0;
im = zeros(bs*s(1), bs*s(2));
for i = 1:s(1),
iis = (i-1)*bs+1:i*bs;
for j = 1:s(2),
jjs = (j-1)*bs+1:j*bs;
for k = 1:9,
im(iis,jjs) = im(iis,jjs) + bim(:,:,k) * w(i,j,k);
end
end
end
|
github | ddtm/OpenFace-master | Fitting_from_bb.m | .m | OpenFace-master/matlab_version/fitting/Fitting_from_bb.m | 10,736 | utf_8 | 45746313845759df5516fb5d4b9934b7 | function [ shape2D, global_params, local_params, final_lhood, landmark_lhoods, view_used ] = Fitting_from_bb( Image, DepthImage, bounding_box, PDM, patchExperts, clmParams, varargin)
%FITTING Summary of this function goes here
% Detailed explanation goes here
% the bounding box format is [minX, minY, maxX, maxY];
% the mean model shape
M = PDM.M;
num_points = numel(M) / 3;
if(any(strcmp(varargin,'orientation')))
orientation = varargin{find(strcmp(varargin, 'orientation'))+1};
rot = Euler2Rot(orientation);
else
rot = eye(3);
orientation = [0;0;0];
end
rot_m = rot * reshape(M, num_points, 3)';
width_model = max(rot_m(1,:)) - min(rot_m(1,:));
height_model = max(rot_m(2,:)) - min(rot_m(2,:));
a = (((bounding_box(3) - bounding_box(1)) / width_model) + ((bounding_box(4) - bounding_box(2))/ height_model)) / 2;
tx = (bounding_box(3) + bounding_box(1))/2;
ty = (bounding_box(4) + bounding_box(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(rot_m(1,:)) + max(rot_m(1,:)))/2;
ty = ty - a*(min(rot_m(2,:)) + max(rot_m(2,:)))/2;
% visualisation of the initial state
%hold off;imshow(Image);hold on;plot(a*rot_m(1,:)+tx, a*rot_m(2,:)+ty,'.r');hold on;rectangle('Position', [bounding_box(1), bounding_box(2), bounding_box(3)-bounding_box(1), bounding_box(4)-bounding_box(2)]);
global_params = [a, 0, 0, 0, tx, ty]';
global_params(2:4) = orientation;
local_params = zeros(numel(PDM.E), 1);
if(any(strcmp(varargin,'gparam')))
global_params = varargin{find(strcmp(varargin, 'gparam'))+1};
end
if(any(strcmp(varargin,'lparam')))
local_params = varargin{find(strcmp(varargin, 'lparam'))+1};
end
scale = clmParams.startScale;
if(size(Image, 3) == 1)
GrayImage = Image;
else
GrayImage = rgb2gray(Image);
end
[heightImg, widthImg] = size(GrayImage);
% Some predefinitions for faster patch extraction
[xi, yi] = meshgrid(0:widthImg-1,0:heightImg-1);
xi = double(xi);
yi = double(yi);
GrayImageDb = double(GrayImage);
clmParams_old = clmParams;
% multi iteration refinement using NU-RLMS in each one
for i=1:clmParams.numPatchIters
current_patch_scaling = patchExperts(scale).trainingScale;
visibilities = patchExperts(scale).visibilities;
view = GetView(patchExperts(scale).centers, global_params(2:4));
% The shape fitting is performed in the reference frame of the
% patch training scale
refGlobal = [current_patch_scaling, 0, 0, 0, 0, 0]';
% the reference shape
refShape = GetShapeOrtho(M, PDM.V, local_params, refGlobal);
% shape around which the patch experts will be evaluated in the original image
[shape2D] = GetShapeOrtho(M, PDM.V, local_params, global_params);
shape2D_img = shape2D(:,1:2);
% Create transform using a slightly modified version of Kabsch that
% takes scaling into account as well, in essence we get a
% similarity transform from current estimate to reference shape
[A_img2ref, T_img2ref, ~, ~] = AlignShapesWithScale(shape2D_img(:,1:2),refShape(:,1:2));
% Create a transform, from shape in image to reference shape
T = maketform('affine', [A_img2ref;T_img2ref]);
shape_2D_ref = tformfwd(T, shape2D_img);
% transform the current shape to the reference one, so we can
% interpolate
shape2D_in_ref = (A_img2ref * shape2D_img')';
sideSizeX = (clmParams.window_size(i,1) - 1)/2;
sideSizeY = (clmParams.window_size(i,2) - 1)/2;
patches = zeros(size(shape2D_in_ref,1), clmParams.window_size(i,1) * clmParams.window_size(i,2));
Ainv = inv(A_img2ref);
% extract patches on which patch experts will be evaluted
for l=1:size(shape2D_in_ref,1)
if(visibilities(view,l))
xs = (shape2D_in_ref(l,1)-sideSizeX):(shape2D_in_ref(l,1)+sideSizeX);
ys = (shape2D_in_ref(l,2)-sideSizeY):(shape2D_in_ref(l,2)+sideSizeY);
[xs, ys] = meshgrid(xs, ys);
pairs = [xs(:), ys(:)];
actualLocs = (Ainv * pairs')';
actualLocs(actualLocs(:,1) < 0,1) = 0;
actualLocs(actualLocs(:,2) < 0,2) = 0;
actualLocs(actualLocs(:,1) > widthImg - 1,1) = widthImg - 1;
actualLocs(actualLocs(:,2) > heightImg - 1,2) = heightImg - 1;
[t_patch] = interp2_mine(xi, yi, GrayImageDb, actualLocs(:,1), actualLocs(:,2), 'bilinear');
t_patch = reshape(t_patch, size(xs));
patches(l,:) = t_patch(:);
end
end
% Calculate patch responses, either SVR or CCNF
if(strcmp(patchExperts(scale).type, 'SVR'))
responses = PatchResponseSVM_multi_modal( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale).normalisationOptionsCol, clmParams, clmParams.window_size(i,:));
elseif(strcmp(patchExperts(scale).type, 'CCNF'))
responses = PatchResponseCCNF( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale), clmParams.window_size(i,:));
end
% If a depth image is provided compute patch experts around it as
% well (unless it's the final iteration)
if(~isempty(DepthImage) && (i ~= clmParams.numPatchIters))
% Extracting the depth patches here
patches_depth = zeros(size(shape2D_in_ref,1), clmParams.window_size(i,1) * clmParams.window_size(i,2));
% extract patches on which patch experts will be evaluted
for l=1:size(shape2D_in_ref,1)
if(visibilities(view,l))
xs = (shape2D_in_ref(l,1)-sideSizeX):(shape2D_in_ref(l,1)+sideSizeX);
ys = (shape2D_in_ref(l,2)-sideSizeY):(shape2D_in_ref(l,2)+sideSizeY);
[xs, ys] = meshgrid(xs, ys);
pairs = [xs(:), ys(:)];
actualLocs = (Ainv * pairs')';
actualLocs(actualLocs(:,1) < 1,1) = 1;
actualLocs(actualLocs(:,2) < 1,2) = 1;
actualLocs(actualLocs(:,1) > widthImg,1) = widthImg;
actualLocs(actualLocs(:,2) > heightImg,2) = heightImg;
% use nearest neighbour interpolation as bilinear would
% produce artefacts in depth image (when missing data
% is there)
[t_patch] = interp2_mine(xi, yi, DepthImage, actualLocs(:,1), actualLocs(:,2), 'nearest');
t_patch = reshape(t_patch, size(xs));
patches_depth(l,:) = t_patch(:);
end
end
old_mm = clmParams.use_multi_modal;
clmParams.use_multi_modal = 0;
responses_depth = PatchResponseSVM_multi_modal( patches_depth, patchExperts(scale).patch_experts_depth(view,:), visibilities(view,:), patchExperts(scale).normalisationOptionsDepth, clmParams, clmParams.window_size(i,:));
clmParams.use_multi_modal = old_mm;
% Combining the patch responses from different channels here
for l=1:size(shape2D_in_ref,1)
responses{l} = responses{l} + responses_depth{l};
end
end
% the better the correlation in training the more reliable the feature
% the reliabilities are independent for every modality in SVR, so
% combine them (also correlation is inverse to variance)
if(strcmp(patchExperts(scale).type, 'SVR'))
if(clmParams.use_multi_modal)
reliabilities = patchExperts(scale).patch_experts{1,1}(end).correlations{1};
else
reliabilities = patchExperts(scale).patch_experts{1,1}(1).correlations{1};
end
else
% for CCNF the modalities work together
reliabilities = patchExperts(scale).correlations;
end
reliabilities = reliabilities(view,:);
% deal with the fact that params might be different for different
% scales
if(numel(clmParams_old.regFactor) > 1)
clmParams.regFactor = clmParams_old.regFactor(i);
end
if(numel(clmParams_old.sigmaMeanShift) > 1)
clmParams.sigmaMeanShift = clmParams_old.sigmaMeanShift(i);
end
if(numel(clmParams_old.tikhonov_factor) > 1)
clmParams.tikhonov_factor = clmParams_old.tikhonov_factor(i);
end
% The actual NU-RLMS step
% first the rigid transform
[global_params, local_params] = NU_RLMS(global_params, local_params, PDM, responses, visibilities, view, reliabilities, shape2D_img, T, true, clmParams, []);
% second the combined transform
[global_params, local_params, final_lhood, landmark_lhoods] = ...
NU_RLMS(global_params, local_params, PDM, responses, visibilities, view, reliabilities, shape2D_img, T, false, clmParams, []);
% Clamp orientation and make sure it doesn't get out of hand
orientation = global_params(2:4);
orientation(orientation < -pi/2) = -pi/2;
orientation(orientation > pi/2) = pi/2;
global_params(2:4) = orientation;
% move up a scale if possible
if(clmParams.useMultiScale && scale ~= numel(patchExperts))
% only go up a scale if we don't need to upsample
if(0.9 * patchExperts(scale+1).trainingScale < global_params(1))
scale = scale + 1;
else
break;
end
end
end
% the view in last iteration
view_used = view;
% See how good the tracking was in the end
[shape2D] = GetShapeOrtho(M, PDM.V, local_params, global_params);
% Moving to matlab format
shape2D = shape2D(:,1:2) + 1;
end
function [id] = GetView(centers, rotation)
[~,id] = min(sum((centers * pi/180 - repmat(rotation', size(centers,1), 1)).^2,2));
end |
github | ddtm/OpenFace-master | interp2_mine.m | .m | OpenFace-master/matlab_version/fitting/interp2_mine.m | 20,801 | utf_8 | 220aa792ce5b22e812b4bad3b375b329 | function zi = interp2_mine(varargin)
%INTERP2 2-D interpolation (table lookup).
% ZI = INTERP2(X,Y,Z,XI,YI) interpolates to find ZI, the values of the
% underlying 2-D function Z at the points in matrices XI and YI.
% Matrices X and Y specify the points at which the data Z is given.
%
% XI can be a row vector, in which case it specifies a matrix with
% constant columns. Similarly, YI can be a column vector and it
% specifies a matrix with constant rows.
%
% ZI = INTERP2(Z,XI,YI) assumes X=1:N and Y=1:M where [M,N]=SIZE(Z).
% ZI = INTERP2(Z,NTIMES) expands Z by interleaving interpolates between
% every element, working recursively for NTIMES. INTERP2(Z) is the
% same as INTERP2(Z,1).
%
% ZI = INTERP2(...,METHOD) specifies alternate methods. The default
% is linear interpolation. Available methods are:
%
% 'nearest' - nearest neighbor interpolation
% 'linear' - bilinear interpolation
% 'spline' - spline interpolation
% 'cubic' - bicubic interpolation as long as the data is
% uniformly spaced, otherwise the same as 'spline'
%
% For faster interpolation when X and Y are equally spaced and monotonic,
% use the syntax ZI = INTERP2(...,*METHOD).
%
% ZI = INTERP2(...,METHOD,EXTRAPVAL) specificies a method and a scalar
% value for ZI outside of the domain created by X and Y. Thus, ZI will
% equal EXTRAPVAL for any value of YI or XI which is not spanned by Y
% or X respectively. A method must be specified for EXTRAPVAL to be used,
% the default method is 'linear'.
%
% All the interpolation methods require that X and Y be monotonic and
% plaid (as if they were created using MESHGRID). If you provide two
% monotonic vectors, interp2 changes them to a plaid internally.
% X and Y can be non-uniformly spaced.
%
% For example, to generate a coarse approximation of PEAKS and
% interpolate over a finer mesh:
% [x,y,z] = peaks(10); [xi,yi] = meshgrid(-3:.1:3,-3:.1:3);
% zi = interp2(x,y,z,xi,yi); mesh(xi,yi,zi)
%
% Class support for inputs X, Y, Z, XI, YI:
% float: double, single
%
% See also INTERP1, INTERP3, INTERPN, MESHGRID, TriScatteredInterp.
% Copyright 1984-2011 The MathWorks, Inc.
% $Revision: 5.33.4.24 $ $Date: 2011/05/17 02:32:27 $
error(nargchk(1,7,nargin,'struct')); % allowing for an ExtrapVal
bypass = false;
uniform = true;
if (nargin > 1)
if nargin == 7 && ~isnumeric(varargin{end})
error(message('MATLAB:interp2:extrapvalNotNumeric'));
end
if ischar(varargin{end})
narg = nargin-1;
method = [varargin{end} ' ']; % Protect against short string.
if strncmpi(method,'s',1) || strncmpi(method, '*s', 2)
ExtrapVal = 'extrap'; % Splines can extrapolate
else
ExtrapVal = nan; % setting default ExtrapVal as NAN
end
index = 1; %subtract off the elements not in method
elseif ischar(varargin{end-1}) && isnumeric(varargin{end})
narg = nargin-2;
method = [ varargin{end-1} ' '];
ExtrapVal = varargin{end}; % user specified ExtrapVal
index = 2; % subtract off the elements not in method and ExtrapVal
else
narg = nargin;
method = 'linear';
ExtrapVal = nan; % protecting default
index = 0;
end
if strncmpi(method,'*',1) % Direct call bypass.
if (narg ==5 || narg ==3)
xitemp = varargin{end-index - 1};
yitemp = varargin{end-index};
if isrow(xitemp) && iscolumn(yitemp)
varargin{end-index - 1} = repmat(xitemp, [size(yitemp,1), 1]);
varargin{end-index} = repmat(yitemp, [1, size(xitemp,2)]);
elseif iscolumn(xitemp) && isrow(yitemp)
varargin{end-index - 1} = repmat(xitemp', [size(yitemp, 2), 1]);
varargin{end-index} = repmat(yitemp', [1, size(xitemp,1)]);
end
end
if strcmpi(method(2),'l') || strcmpi(method(2:4),'bil')
% bilinear interpolation.
zi = linear(ExtrapVal, varargin{1:end-index});
return
elseif strcmpi(method(2),'c') || strcmpi(method(2:4),'bic')
% bicubic interpolation
zi = cubic(ExtrapVal, varargin{1:end-index});
return
elseif strcmpi(method(2),'n')
% Nearest neighbor interpolation
zi = nearest(ExtrapVal, varargin{1:end-index});
return
elseif strcmpi(method(2),'s')
% spline interpolation
method = 'spline'; bypass = true;
else
error(message('MATLAB:interp2:InvalidMethod', deblank( method )));
end
elseif strncmpi(method,'s',1), % Spline interpolation
method = 'spline'; bypass = true;
end
else
narg = nargin;
method = 'linear';
ExtrapVal = nan; % default ExtrapVal is NaN
end
% if narg==1, % interp2(z), % Expand Z
% [nrows,ncols] = size(varargin{1});
% xi = 1:.5:ncols; yi = (1:.5:nrows)';
% x = 1:ncols; y = 1:nrows;
% [msg,x,y,z,xi,yi] = xyzchk(x,y,varargin{1},xi,yi);
%
% elseif narg==2. % interp2(z,n), Expand Z n times
% [nrows,ncols] = size(varargin{1});
% ntimes = floor(varargin{2}(1));
% xi = 1:1/(2^ntimes):ncols; yi = (1:1/(2^ntimes):nrows)';
% x = 1:ncols; y = 1:nrows;
% [msg,x,y,z,xi,yi] = xyzchk(x,y,varargin{1},xi,yi);
%
% elseif narg==3, % interp2(z,xi,yi)
% [nrows,ncols] = size(varargin{1});
% x = 1:ncols; y = 1:nrows;
% [msg,x,y,z,xi,yi] = xyzchk(x,y,varargin{1:3});
%
% elseif narg==4,
% error(message('MATLAB:interp2:nargin'));
% elseif narg==5, % linear(x,y,z,xi,yi)
% [msg,x,y,z,xi,yi] = xyzchk(varargin{1:5});
%
% end
x = varargin{1};
y = varargin{2};
z = varargin{3};
xi = varargin{4};
yi = varargin{5};
% if ~isempty(msg)
% error(message(msg.identifier));
% end
%
% Check for plaid data.
%
xx = x(1,:); yy = y(:,1);
% if (size(x,2)>1 && ~isequal(repmat(xx,size(x,1),1),x)) || ...
% (size(y,1)>1 && ~isequal(repmat(yy,1,size(y,2)),y)),
% error(message('MATLAB:interp2:meshgrid'));
% end
%
% Check for non-equally spaced data. If so, map (x,y) and
% (xi,yi) to matrix (row,col) coordinate system.
%
if ~bypass,
xx = xx.'; % Make sure it's a column.
dx = diff(xx); dy = diff(yy);
xdiff = max(abs(diff(dx))); if isempty(xdiff), xdiff = 0; end
ydiff = max(abs(diff(dy))); if isempty(ydiff), ydiff = 0; end
if (xdiff > eps(class(xx))*max(abs(xx))) || (ydiff > eps(class(yy))*max(abs(yy)))
if any(dx < 0), % Flip orientation of data so x is increasing.
x = fliplr(x); y = fliplr(y); z = fliplr(z);
xx = flipud(xx); dx = -flipud(dx);
end
if any(dy < 0), % Flip orientation of data so y is increasing.
x = flipud(x); y = flipud(y); z = flipud(z);
yy = flipud(yy); dy = -flipud(dy);
end
if any(dx<=0) || any(dy<=0),
error(message('MATLAB:interp2:XorYNotMonotonic'));
end
% Bypass mapping code for cubic
if ~strncmp(method(1),'c',1)
% Determine the nearest location of xi in x
[xxi,j] = sort(xi(:));
[~,i] = sort([xx;xxi]);
ui(i) = 1:length(i);
ui = (ui(length(xx)+1:end)-(1:length(xxi)))';
ui(j) = ui;
% Map values in xi to index offset (ui) via linear interpolation
ui(ui<1) = 1;
ui(ui>length(xx)-1) = length(xx)-1;
ui = ui + (xi(:)-xx(ui))./(xx(ui+1)-xx(ui));
% Determine the nearest location of yi in y
[yyi,j] = sort(yi(:));
[~,i] = sort([yy;yyi(:)]);
vi(i) = 1:length(i);
vi = (vi(length(yy)+1:end)-(1:length(yyi)))';
vi(j) = vi;
% Map values in yi to index offset (vi) via linear interpolation
vi(vi<1) = 1;
vi(vi>length(yy)-1) = length(yy)-1;
vi = vi + (yi(:)-yy(vi))./(yy(vi+1)-yy(vi));
[x,y] = meshgrid(ones(class(x)):size(x,2),ones(class(y)):size(y,1));
xi(:) = ui; yi(:) = vi;
else
uniform = false;
end
end
end
% Now do the interpolation based on method.
if strncmpi(method,'l',1) || strncmpi(method,'bil',3) % bilinear interpolation.
zi = linear(ExtrapVal,x,y,z,xi,yi);
elseif strncmpi(method,'c',1) || strncmpi(method,'bic',3) % bicubic interpolation
if uniform
zi = cubic(ExtrapVal,x,y,z,xi,yi);
else
zi = spline2(x,y,z,xi,yi,ExtrapVal);
end
elseif strncmpi(method,'n',1) % Nearest neighbor interpolation
zi = nearest(ExtrapVal,x,y,z,xi,yi);
elseif strncmpi(method,'s',1) % Spline interpolation
% A column is removed from z if it contains a NaN.
% Orient to preserve as much data as possible.
[inan, jnan] = find(isnan(z));
ncolnan = length(unique(jnan));
nrownan = length(unique(inan));
if ncolnan > nrownan
zi = spline2(y',x',z',yi,xi,ExtrapVal);
else
zi = spline2(x,y,z,xi,yi,ExtrapVal);
end
else
error(message('MATLAB:interp2:InvalidMethod', deblank( method )));
end
%------------------------------------------------------
function F = linear(ExtrapVal,arg1,arg2,arg3,arg4,arg5)
%LINEAR 2-D bilinear data interpolation.
% ZI = LINEAR(EXTRAPVAL,X,Y,Z,XI,YI) uses bilinear interpolation to
% find ZI, the values of the underlying 2-D function in Z at the points
% in matrices XI and YI. Matrices X and Y specify the points at which
% the data Z is given. X and Y can also be vectors specifying the
% abscissae for the matrix Z as for MESHGRID. In both cases, X
% and Y must be equally spaced and monotonic.
%
% Values of EXTRAPVAL are returned in ZI for values of XI and YI that are
% outside of the range of X and Y.
%
% If XI and YI are vectors, LINEAR returns vector ZI containing
% the interpolated values at the corresponding points (XI,YI).
%
% ZI = LINEAR(EXTRAPVAL,Z,XI,YI) assumes X = 1:N and Y = 1:M, where
% [M,N] = SIZE(Z).
%
% ZI = LINEAR(EXTRAPVAL,Z,NTIMES) returns the matrix Z expanded by
% interleaving bilinear interpolates between every element, working
% recursively for NTIMES. LINEAR(EXTRAPVAL,Z) is the same as
% LINEAR(EXTRAPVAL,Z,1).
%
% See also INTERP2, CUBIC.
if nargin==2 % linear(extrapval,z), Expand Z
[nrows,ncols] = size(arg1);
s = 1:.5:ncols; lengths = length(s);
t = (1:.5:nrows)'; lengtht = length(t);
s = repmat(s,lengtht,1);
t = repmat(t,1,lengths);
elseif nargin==3 % linear(extrapval,z,n), Expand Z n times
[nrows,ncols] = size(arg1);
ntimes = floor(arg2);
s = 1:1/(2^ntimes):ncols; lengths = length(s);
t = (1:1/(2^ntimes):nrows)'; lengtht = length(t);
s = repmat(s,lengtht,1);
t = repmat(t,1,lengths);
elseif nargin==4 % linear(extrapval,z,s,t), No X or Y specified.
[nrows,ncols] = size(arg1);
s = arg2; t = arg3;
elseif nargin==5
error(message('MATLAB:interp2:linear:nargin'));
elseif nargin==6 % linear(extrapval,x,y,z,s,t), X and Y specified.
[nrows,ncols] = size(arg3);
mx = numel(arg1); my = numel(arg2);
if (mx ~= ncols || my ~= nrows) && ~isequal(size(arg1),size(arg2),size(arg3))
error(message('MATLAB:interp2:linear:XYZLengthMismatch'));
end
if nrows < 2 || ncols < 2
error(message('MATLAB:interp2:linear:sizeZ'));
end
s = 1 + (arg4-arg1(1))/(arg1(end)-arg1(1))*(ncols-1);
t = 1 + (arg5-arg2(1))/(arg2(end)-arg2(1))*(nrows-1);
end
if nrows < 2 || ncols < 2
error(message('MATLAB:interp2:linear:sizeZsq'));
end
if ~isequal(size(s),size(t))
error(message('MATLAB:interp2:linear:XIandYISizeMismatch'));
end
% Check for out of range values of s and set to 1
sout = find((s<1)|(s>ncols));
if ~isempty(sout), s(sout) = 1; end
% Check for out of range values of t and set to 1
tout = find((t<1)|(t>nrows));
if ~isempty(tout), t(tout) = 1; end
% Matrix element indexing
ndx = floor(t)+floor(s-1)*nrows;
% Compute intepolation parameters, check for boundary value.
if isempty(s), d = s; else d = find(s==ncols); end
s(:) = (s - floor(s));
if ~isempty(d), s(d) = s(d)+1; ndx(d) = ndx(d)-nrows; end
% Compute intepolation parameters, check for boundary value.
if isempty(t), d = t; else d = find(t==nrows); end
t(:) = (t - floor(t));
if ~isempty(d), t(d) = t(d)+1; ndx(d) = ndx(d)-1; end
% Now interpolate.
onemt = 1-t;
if nargin==6,
F = ( arg3(ndx).*(onemt) + arg3(ndx+1).*t ).*(1-s) + ...
( arg3(ndx+nrows).*(onemt) + arg3(ndx+(nrows+1)).*t ).*s;
else
F = ( arg1(ndx).*(onemt) + arg1(ndx+1).*t ).*(1-s) + ...
( arg1(ndx+nrows).*(onemt) + arg1(ndx+(nrows+1)).*t ).*s;
end
% Now set out of range values to ExtrapVal.
if ~isempty(sout), F(sout) = ExtrapVal; end
if ~isempty(tout), F(tout) = ExtrapVal; end
%------------------------------------------------------
function F = cubic(ExtrapVal,arg1,arg2,arg3,arg4,arg5)
%CUBIC 2-D bicubic data interpolation.
% CUBIC(...) is the same as LINEAR(....) except that it uses
% bicubic interpolation.
%
% This function needs about 7-8 times SIZE(XI) memory to be available.
%
% See also LINEAR.
% Based on "Cubic Convolution Interpolation for Digital Image
% Processing", Robert G. Keys, IEEE Trans. on Acoustics, Speech, and
% Signal Processing, Vol. 29, No. 6, Dec. 1981, pp. 1153-1160.
if nargin==2, % cubic(extrapval,z), Expand Z
[nrows,ncols] = size(arg1);
s = 1:.5:ncols; lengths = length(s);
t = (1:.5:nrows)'; lengtht = length(t);
s = repmat(s,lengtht,1);
t = repmat(t,1,lengths);
elseif nargin==3, % cubic(extrapval,z,n), Expand Z n times
[nrows,ncols] = size(arg1);
ntimes = floor(arg2);
s = 1:1/(2^ntimes):ncols; lengths = length(s);
t = (1:1/(2^ntimes):nrows)'; lengtht = length(t);
s = repmat(s,lengtht,1);
t = repmat(t,1,lengths);
elseif nargin==4, % cubic(extrapval,z,s,t), No X or Y specified.
[nrows,ncols] = size(arg1);
s = arg2; t = arg3;
elseif nargin==5,
error(message('MATLAB:interp2:cubic:nargin'));
elseif nargin==6, % cubic(extrapval,x,y,z,s,t), X and Y specified.
[nrows,ncols] = size(arg3);
mx = numel(arg1); my = numel(arg2);
if (mx ~= ncols || my ~= nrows) && ~isequal(size(arg1),size(arg2),size(arg3))
error(message('MATLAB:interp2:cubic:XYZLengthMismatch'));
end
if nrows < 3 || ncols < 3
error(message('MATLAB:interp2:cubic:sizeZ'));
end
s = 1 + (arg4-arg1(1))/(arg1(end)-arg1(1))*(ncols-1);
t = 1 + (arg5-arg2(1))/(arg2(end)-arg2(1))*(nrows-1);
end
if nrows < 3 || ncols < 3
error(message('MATLAB:interp2:cubic:sizeZsq'));
end
if ~isequal(size(s),size(t)),
error(message('MATLAB:interp2:cubic:XIandYISizeMismatch'));
end
% Check for out of range values of s and set to 1
sout = find((s<1)|(s>ncols));
if ~isempty(sout), s(sout) = 1; end
% Check for out of range values of t and set to 1
tout = find((t<1)|(t>nrows));
if ~isempty(tout), t(tout) = 1; end
% Matrix element indexing
ndx = floor(t)+floor(s-1)*(nrows+2);
% Compute intepolation parameters, check for boundary value.
if isempty(s), d = s; else d = find(s==ncols); end
s(:) = (s - floor(s));
if ~isempty(d), s(d) = s(d)+1; ndx(d) = ndx(d)-nrows-2; end
% Compute intepolation parameters, check for boundary value.
if isempty(t), d = t; else d = find(t==nrows); end
t(:) = (t - floor(t));
if ~isempty(d), t(d) = t(d)+1; ndx(d) = ndx(d)-1; end
if nargin==6,
% Expand z so interpolation is valid at the boundaries.
zz = zeros(size(arg3)+2);
zz(1,2:ncols+1) = 3*arg3(1,:)-3*arg3(2,:)+arg3(3,:);
zz(2:nrows+1,2:ncols+1) = arg3;
zz(nrows+2,2:ncols+1) = 3*arg3(nrows,:)-3*arg3(nrows-1,:)+arg3(nrows-2,:);
zz(:,1) = 3*zz(:,2)-3*zz(:,3)+zz(:,4);
zz(:,ncols+2) = 3*zz(:,ncols+1)-3*zz(:,ncols)+zz(:,ncols-1);
nrows = nrows+2; %also ncols = ncols+2;
else
% Expand z so interpolation is valid at the boundaries.
zz = zeros(size(arg1)+2);
zz(1,2:ncols+1) = 3*arg1(1,:)-3*arg1(2,:)+arg1(3,:);
zz(2:nrows+1,2:ncols+1) = arg1;
zz(nrows+2,2:ncols+1) = 3*arg1(nrows,:)-3*arg1(nrows-1,:)+arg1(nrows-2,:);
zz(:,1) = 3*zz(:,2)-3*zz(:,3)+zz(:,4);
zz(:,ncols+2) = 3*zz(:,ncols+1)-3*zz(:,ncols)+zz(:,ncols-1);
nrows = nrows+2; %also ncols = ncols+2;
end
% Now interpolate using computationally efficient algorithm.
t0 = ((2-t).*t-1).*t;
t1 = (3*t-5).*t.*t+2;
t2 = ((4-3*t).*t+1).*t;
t(:) = (t-1).*t.*t;
F = ( zz(ndx).*t0 + zz(ndx+1).*t1 + zz(ndx+2).*t2 + zz(ndx+3).*t ) ...
.* (((2-s).*s-1).*s);
ndx(:) = ndx + nrows;
F(:) = F + ( zz(ndx).*t0 + zz(ndx+1).*t1 + zz(ndx+2).*t2 + zz(ndx+3).*t ) ...
.* ((3*s-5).*s.*s+2);
ndx(:) = ndx + nrows;
F(:) = F + ( zz(ndx).*t0 + zz(ndx+1).*t1 + zz(ndx+2).*t2 + zz(ndx+3).*t ) ...
.* (((4-3*s).*s+1).*s);
ndx(:) = ndx + nrows;
F(:) = F + ( zz(ndx).*t0 + zz(ndx+1).*t1 + zz(ndx+2).*t2 + zz(ndx+3).*t ) ...
.* ((s-1).*s.*s);
F(:) = F/4;
% Now set out of range values to ExtrapVal.
if ~isempty(sout), F(sout) = ExtrapVal; end
if ~isempty(tout), F(tout) = ExtrapVal; end
%------------------------------------------------------
function F = nearest(ExtrapVal,arg1,arg2,arg3,arg4,arg5)
%NEAREST 2-D Nearest neighbor interpolation.
% ZI = NEAREST(EXTRAPVAL,X,Y,Z,XI,YI) uses nearest neighbor interpolation
% to find ZI, the values of the underlying 2-D function in Z at the points
% in matrices XI and YI. Matrices X and Y specify the points at which
% the data Z is given. X and Y can also be vectors specifying the
% abscissae for the matrix Z as for MESHGRID. In both cases, X
% and Y must be equally spaced and monotonic.
%
% Values of EXTRAPVAL are returned in ZI for values of XI and YI that are
% outside of the range of X and Y.
%
% If XI and YI are vectors, NEAREST returns vector ZI containing
% the interpolated values at the corresponding points (XI,YI).
%
% ZI = NEAREST(EXTRAPVAL,Z,XI,YI) assumes X = 1:N and Y = 1:M, where
% [M,N] = SIZE(Z).
%
% F = NEAREST(EXTRAPVAL,Z,NTIMES) returns the matrix Z expanded by
% interleaving interpolates between every element. NEAREST(EXTRAPVAL,Z)
% is the same as NEAREST(EXTRAPVAL,Z,1).
%
% See also INTERP2, LINEAR, CUBIC.
if nargin==2, % nearest(z), Expand Z
[nrows,ncols] = size(arg1);
u = 1:.5:ncols; lengthu = length(u);
v = (1:.5:nrows)'; lengthv = length(v);
u = repmat(u,lengthv,1);
v = repmat(v,1,lengthu);
elseif nargin==3, % nearest(z,n), Expand Z n times
[nrows,ncols] = size(arg1);
ntimes = floor(arg2);
u = 1:1/(2^ntimes):ncols; lengthu = length(u);
v = (1:1/(2^ntimes):nrows)'; lengthv = length(v);
u = repmat(u,lengthv,1);
v = repmat(v,1,lengthu);
elseif nargin==4, % nearest(z,u,v)
[nrows,ncols] = size(arg1);
u = arg2; v = arg3;
elseif nargin==5,
error(message('MATLAB:interp2:nearest:nargin'));
elseif nargin==6, % nearest(x,y,z,u,v), X and Y specified.
[nrows,ncols] = size(arg3);
mx = numel(arg1); my = numel(arg2);
if (mx ~= ncols || my ~= nrows) && ...
~isequal(size(arg1),size(arg2),size(arg3))
error(message('MATLAB:interp2:nearest:XYZLengthMismatch'));
end
if nrows > 1 && ncols > 1
u = 1 + (arg4-arg1(1))/(arg1(mx)-arg1(1))*(ncols-1);
v = 1 + (arg5-arg2(1))/(arg2(my)-arg2(1))*(nrows-1);
else
u = 1 + (arg4-arg1(1));
v = 1 + (arg5-arg2(1));
end
end
if ~isequal(size(u),size(v))
error(message('MATLAB:interp2:nearest:XIandYISizeMismatch'));
end
% Check for out of range values of u and set to 1
uout = (u<.5)|(u>=ncols+.5);
anyuout = any(uout(:));
if anyuout, u(uout) = 1; end
% Check for out of range values of v and set to 1
vout = (v<.5)|(v>=nrows+.5);
anyvout = any(vout(:));
if anyvout, v(vout) = 1; end
% Interpolation parameters
u = round(u); v = round(v);
% Now interpolate
ndx = v+(u-1)*nrows;
if nargin==6,
F = arg3(ndx);
else
F = arg1(ndx);
end
% Now set out of range values to ExtrapVal.
if anyuout, F(uout) = ExtrapVal; end
if anyvout, F(vout) = ExtrapVal; end
%----------------------------------------------------------
function F = spline2(varargin)
%2-D spline interpolation
% Determine abscissa vectors
varargin{1} = varargin{1}(1,:);
varargin{2} = varargin{2}(:,1).';
%
% Check for plaid data.
%
xi = varargin{4}; yi = varargin{5};
xxi = xi(1,:); yyi = yi(:,1);
if ~isequal(repmat(xxi,size(xi,1),1),xi) || ...
~isequal(repmat(yyi,1,size(yi,2)),yi)
F = splncore(varargin(2:-1:1),varargin{3},varargin(5:-1:4));
else
F = splncore(varargin(2:-1:1),varargin{3},{yyi(:).' xxi},'gridded');
end
ExtrapVal = varargin{6};
% Set out-of-range values to ExtrapVal
if isnumeric(ExtrapVal)
d = xi < min(varargin{1}) | xi > max(varargin{1}) | ...
yi < min(varargin{2}) | yi > max(varargin{2});
F(d) = ExtrapVal;
end
|
github | ddtm/OpenFace-master | CalcJacobian.m | .m | OpenFace-master/matlab_version/fitting/CalcJacobian.m | 1,333 | utf_8 | 1b62f9a4104c629ff80049c1bfea9567 | % This calculates the combined rigid with non-rigid Jacobian (non-rigid can
% eiher be expression or identity one)
function J = CalcJacobian(M, V, p_local, p_global)
n = size(M, 1)/3;
non_rigid_modes = size(V,2);
J = zeros(n*2, 6 + non_rigid_modes);
% now the layour is
% ---------- Rigid part -------------------|----Non rigid part--------|
% dx_1/ds, dx_1/dr1, ... dx_1/dtx, dx_1/dty dx_1/dp_1 ... dx_1/dp_m
% dx_2/ds, dx_2/dr1, ... dx_2/dtx, dx_2/dty dx_2/dp_1 ... dx_2/dp_m
% ...
% dx_n/ds, dx_n/dr1, ... dx_n/dtx, dx_n/dty dx_n/dp_1 ... dx_n/dp_m
% dy_1/ds, dy_1/dr1, ... dy_1/dtx, dy_1/dty dy_1/dp_1 ... dy_1/dp_m
% ...
% dy_n/ds, dy_n/dr1, ... dy_n/dtx, dy_n/dty dy_n/dp_1 ... dy_n/dp_m
% getting the rigid part
J(:,1:6) = CalcRigidJacobian(M, V, p_local, p_global);
% constructing the non-rigid part
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% 'rotate' and 'scale' the principal components
% First reshape to 3D
V_X = V(1:n,:);
V_Y = V(n+1:2*n,:);
V_Z = V(2*n+1:end,:);
J_x_non_rigid = s*(R(1,1)*V_X + R(1,2)*V_Y + R(1,3)*V_Z);
J_y_non_rigid = s*(R(2,1)*V_X + R(2,2)*V_Y + R(2,3)*V_Z);
J(1:n, 7:end) = J_x_non_rigid;
J(n+1:end, 7:end) = J_y_non_rigid;
end
|
github | ddtm/OpenFace-master | PatchResponseCCNF.m | .m | OpenFace-master/matlab_version/fitting/PatchResponseCCNF.m | 2,740 | utf_8 | 566d48e8656f756ae9af7d31d8b2ac55 | function [ responses ] = PatchResponseCCNF(patches, patch_experts_class, visibilities, patchExperts, window_size)
%PATCHRESPONSESVM Summary of this function goes here
% Detailed explanation goes here
normalisationOptions = patchExperts.normalisationOptionsCol;
patchSize = normalisationOptions.patchSize;
responses = cell(size(patches, 1), 1);
empty = zeros(window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
for i = 1:numel(patches(:,1))
responses{i} = empty;
if visibilities(i)
col_norm = normalisationOptions.useNormalisedCrossCorr == 1;
b = zeros(numel(empty), 1);
num_hl = size(patch_experts_class{i}.thetas,1);
smallRegionVec = patches(i,:);
smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
for hls = 1:num_hl
w = patch_experts_class{i}.w{hls};
% normalisation needed per each response
norm_w = patch_experts_class{i}.norm_w{hls};
response = -norm_w * SVMresponse(smallRegion, w, col_norm, patchSize) - patch_experts_class{i}.thetas(hls, 1);
% here we include the bias term as well, as it wasn't added
% during the response calculation
h1 = 1./(1 + exp(response(:)));
b = b + (2 * patch_experts_class{i}.alphas(hls) * h1);
end
% Sigma will be dependent on the size of the patch find the
% needed precomputed Sigma
rel_sigma = 1;
if(numel(patch_experts_class{i}.Sigma) > 1)
for sig=1:numel(patch_experts_class{i}.Sigma)
if(size(patch_experts_class{i}.Sigma{sig},2)==numel(b))
rel_sigma = sig;
break;
end
end
end
response = patch_experts_class{i}.Sigma{rel_sigma} * b;
% make sure we have no negative responses
response = response - min(response);
responses{i}(:) = response;
end
end
end
function response = SVMresponse(region, patchExpert, normalise_x_corr,patchSize)
if(normalise_x_corr)
% the fast mex convolution
[response] = normxcorr2_mex(patchExpert, region);
response = response(patchSize(1):end-patchSize(1)+1,patchSize(2):end-patchSize(2)+1);
else
% this assumes that the patch is already normed
template = rot90(patchExpert,2);
response = conv2(region, template, 'valid');
end
end
|
github | ddtm/OpenFace-master | NU_RLMS.m | .m | OpenFace-master/matlab_version/fitting/NU_RLMS.m | 9,547 | utf_8 | 313ae3c571133af4a520db68eb97435f | function [ final_global, final_local, final_lhood, landmark_lhoods ] = NU_RLMS( ...
init_global, init_local, PDM, patchResponses, visibilities,...
view, reliabilities, baseShape, OrigToRefTransform, rigid, ...
clmParams, gauss_resp)
%RLMS Summary of this function goes here
% Detailed explanation goes here
m = numel(PDM.E);
E = PDM.E;
V = PDM.V;
n = size(V, 1)/3;
current_local = init_local;
current_global = init_global;
pxWidth = size(patchResponses{1},1);
responseSize = size(patchResponses{1});
[iis,jjs] = meshgrid(1:pxWidth, 1:pxWidth);
% Grab all of the patch responses and convert them to single matrix
% representation for speed
patchResponsesFlat = reshape(cat(3,patchResponses{:}), responseSize(1)*responseSize(2), n)';
iisFlat = repmat(iis(:)', n, 1);
jjsFlat = repmat(jjs(:)', n, 1);
% An alternative formulation
reg_rigid = zeros(6,1);
if(rigid)
regularisations = reg_rigid;
regularisations = diag(regularisations);
else
regularisations = [reg_rigid; clmParams.regFactor ./ E]; % the above version, however, does not perform as well
regularisations = diag(regularisations);
end
% For generalised Tikhonov
if(clmParams.tikhonov_factor == 0)
P = eye(n*2);
else
% the worse the reliability the higher the variance of the
% prediction, so inverse variance correlate with reliability
P = clmParams.tikhonov_factor * diag(repmat(reliabilities',2,1));
end
for iter = 1:clmParams.num_RLMS_iter
% get the current estimates of x and y in image
currentShape = GetShapeOrtho(PDM.M, PDM.V, current_local, current_global);
currentShape = currentShape(:,1:2);
if(iter > 1)
% if the shape hasn't changed terminate
if(norm(currentShape - previousShape) < clmParams.fTol)
break;
end
end
previousShape = currentShape;
% calculate the appropriate Jacobians in 2D, even though the actual behaviour is in 3D, using small angle approximation and oriented shape
if(rigid)
J = CalcRigidJacobian(PDM.M, PDM.V, current_local, current_global);
else
J = CalcJacobian(PDM.M, PDM.V, current_local, current_global);
end
% as the mean shift is with reference to the point, we don't care about
% the translation
OrigToRefTransform.tdata.T(3,1:2) = 0;
OrigToRefTransform.tdata.Tinv(3,1:2) = 0;
% distance from center where the response was calculated around
% in reference frame of the patch
offsets = (currentShape - baseShape) * OrigToRefTransform.tdata.T(1:2,1:2)';
% perform the parallel version of the mean shift algorithm
dxs = offsets(:, 1) + (pxWidth-1)/2 + 1;
dys = offsets(:, 2) + (pxWidth-1)/2 + 1;
if(numel(gauss_resp) > 0)
meanShifts = meanShiftParallel_precalc(patchResponsesFlat, dxs, dys, iisFlat, jjsFlat, responseSize, gauss_resp.kd_precalc, gauss_resp.stepSize);
else
meanShifts = meanShiftParallel(patchResponsesFlat, clmParams.sigmaMeanShift, dxs, dys, iisFlat, jjsFlat, responseSize);
end
% invalidate illegal mean shifts
illegal_inds = find(~visibilities(view, :));
J(illegal_inds,:) = 0;
J(illegal_inds + n,:) = 0;
meanShifts(illegal_inds,:) = 0;
% Mean shift's here are calculate in the reference image frame,
% we want to move them back to actual image frame
meanShifts = meanShifts * OrigToRefTransform.tdata.Tinv(1:2,1:2)';
% put it into column format
meanShifts = meanShifts(:);
rigid_params = current_global - init_global;
rigid_params(2:4) = 0;
if(rigid)
params = rigid_params;
else
params = [rigid_params; current_local];
end
params_delta = (J'*P*J + regularisations) \ (J'*P*meanShifts - regularisations*params);
% update the reference
[current_local, current_global] = CalcReferenceUpdate(params_delta, current_local, current_global);
if(~rigid)
% clamp to the local parameters for valid expressions
current_local = ClampPDM(current_local, E);
end
end
if(nargout >= 4)
% get the current estimates of x and y in image
currentShape = GetShapeOrtho(PDM.M, PDM.V, current_local, current_global);
currentShape = currentShape(:,1:2);
% as the mean shift is with reference to the point, we don't care about
% the translation
OrigToRefTransform.tdata.T(3,1:2) = 0;
OrigToRefTransform.tdata.Tinv(3,1:2) = 0;
% distance from center where the response was calculated around
% in reference frame of the patch
offsets = (currentShape - baseShape) * OrigToRefTransform.tdata.T(1:2,1:2)';
% perform the parallel version of the mean shift algorithm
dxs = offsets(:, 1) + (pxWidth-1)/2 + 1;
dys = offsets(:, 2) + (pxWidth-1)/2 + 1;
landmark_lhoods = zeros(n,1);
prob = 0;
for i=1:n
if(visibilities(view, i))
dx = dxs(i);
dy = dys(i);
vxs = (-iis+dx).^2;
vys = (-jjs+dy).^2;
% Calculate the kde per patch
vs = patchResponses{i}.*exp(-0.5*(vxs + vys)/clmParams.sigmaMeanShift);
kde_est = sum(vs(:));
landmark_lhoods(i) = kde_est;
prob = prob + log(kde_est + 1e-8);
end
end
% Do not add the local parameter prior as it overpowers the
% log-likelihoods
final_lhood = prob / sum(visibilities(view, :));
% - LogPDMprior(current_local, E);
end
final_global = current_global;
final_local = current_local;
end
% This clamps the non-rigid parameters to stay within +- 3 standard
% deviations
function [non_rigid_params] = ClampPDM(non_rigid, E)
stds = sqrt(E);
non_rigid_params = non_rigid;
lower = non_rigid_params < -3 * stds;
non_rigid_params(lower) = -3*stds(lower);
higher = non_rigid_params > 3 * stds;
non_rigid_params(higher) = 3*stds(higher);
end
% This calculate the mean shift based on the kernel density response at dx,
% dy in the patch response, this can be used to find the mode
function [meanShifts] = meanShiftParallel(patchResponses, sigma, dxs, dys, iis, jjs, patchSize)
% Kernel density is
% K(x_i-x) = p(x_i)*exp(-0.5 * ||x_i-x||^2/sigma), so probability weighted
% distance from the center
% Mean shift is then m(x) = sum(K(x_i - dx)*x_i)/sum(K(x_i-dx))
% step_size = 0.1;
% gauss_resp_prec = precalc_kernel_densities(patchSize, sigma, step_size);
%
% %iis are row vectors of the locations of interest, for each patch
% nYs = numel(0:step_size:patchSize(2));
% % calculate the indices needed
%
% dxs2 = dxs - mod(dxs, step_size);
% dys2 = dys - mod(dys, step_size);
%
% xs = round(dxs2 * nYs * 1/step_size + (dys2 /step_size) +1);
%
% gauss_resp_c = gauss_resp_prec(xs,:);
% this part is doing (x_i - dx)^2
vxs = bsxfun(@plus, -iis, dxs);
vxs = vxs.^2;
% this part is doing (y_i - dy)^2
vys = bsxfun(@plus, -jjs, dys);
vys = vys.^2;
a = -0.5/(sigma.^2);
% this part is calculating K(x_i - x)
gauss_resp = exp(a*(vxs + vys));
vs = patchResponses.*gauss_resp;
% this part is calculating K(x_i - dx)*x_i
mxss = vs.*iis;
myss = vs.*jjs;
% this part is caluclating sum(K(x_i - dx)*x_i)
mxs = sum(mxss,2);
mys = sum(myss,2);
sumVs = sum(vs,2);
sumVs(sumVs == 0) = 1;
msx = mxs ./ sumVs - dxs;
msy = mys ./ sumVs - dys;
meanShifts = [msx, msy];
end
% This updates the parameters based on the updates from the RLMS
function [non_rigid, rigid] = CalcReferenceUpdate(params_delta, current_non_rigid, current_global)
rigid = zeros(6, 1);
% Same goes for scaling and translation parameters
rigid(1) = current_global(1) + params_delta(1);
rigid(5) = current_global(5) + params_delta(5);
rigid(6) = current_global(6) + params_delta(6);
% for rotation however, we want to make sure that the rotation matrix
% approximation we have
% R' = [1, -wz, wy
% wz, 1, -wx
% -wy, wx, 1]
% is a legal rotation matrix, and then we combine it with current
% rotation (through matrix multiplication) to acquire the new rotation
R = Euler2Rot(current_global(2:4));
wx = params_delta(2);
wy = params_delta(3);
wz = params_delta(4);
R_delta = [1, -wz, wy;
wz, 1, -wx;
-wy, wx, 1];
% Make sure R_delta is orthonormal
R_delta = OrthonormaliseRotation(R_delta);
% Combine rotations
R_final = R * R_delta;
% Extract euler angle
euler = Rot2Euler(R_final);
rigid(2:4) = euler;
if(length(params_delta) > 6)
% non-rigid parameters can just be added together
non_rigid = params_delta(7:end) + current_non_rigid;
else
non_rigid = current_non_rigid;
end
end
function R_ortho = OrthonormaliseRotation(R)
% U * V' is basically what we want, as it's guaranteed to be
% orthonormal
[U, ~, V] = svd(R);
% We also want to make sure no reflection happened
% get the orthogonal matrix from the initial rotation matrix
X = U*V';
% This makes sure that the handedness is preserved and no reflection happened
% by making sure the determinant is 1 and not -1
W = eye(3);
W(3,3) = det(X);
R_ortho = U*W*V';
end
function [prob] = LogPDMprior(params, E)
cov = diag(E);
prob = 0.5 * params'*inv(cov)*params;
end |
github | ddtm/OpenFace-master | PatchResponseSVM_multi_modal.m | .m | OpenFace-master/matlab_version/fitting/PatchResponseSVM_multi_modal.m | 5,129 | utf_8 | e736ddb434521d9608edc3f3fc7b188d | function [ responses ] = PatchResponseSVM_multi_modal( patches, patch_experts, visibilities, normalisationOptions, clmParameters, window_size)
%PATCHRESPONSESVM Summary of this function goes here
% Detailed explanation goes here
patchSize = normalisationOptions.patchSize;
responses = cell(size(patches, 1), 1);
empty = zeros(window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
% prepare the patches through either turning them to gradients or
if(clmParameters.use_multi_modal)
patches_to_use = cell(numel(clmParameters.multi_modal_types),1);
for t=1:numel(clmParameters.multi_modal_types)
if(strcmp(clmParameters.multi_modal_types{t}, 'reg'))
patches_reg = patches;
if(normalisationOptions.zscore)
meanCurr = mean(patches, 2);
stdCurr = std(double(patches), 0, 2);
stdCurr(stdCurr == 0) = 1;
patches_reg = bsxfun(@minus, patches, meanCurr);
patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
end
patches_to_use{t} = patches_reg;
elseif(strcmp(clmParameters.multi_modal_types{t}, 'grad'))
v = [1];
h = [-1 0 1];
grad_patches = zeros(size(patches));
for i = 1:numel(patches(:,1))
if visibilities(i)
currSample = reshape(patches(i,:), window_size(1), window_size(2));
edgeX = conv2(conv2(currSample, v, 'same'), h, 'same');
edgeY = conv2(conv2(currSample, v', 'same'), h', 'same');
grad = edgeX.^2 + edgeY.^2;
grad(1,:) = 0;
grad(:,1) = 0;
grad(end,:) = 0;
grad(:,end) = 0;
grad_patches(i,:) = reshape(grad, window_size(1) * window_size(1),1);
end
end
patches_to_use{t} = grad_patches;
end
end
else
patches_reg = patches;
if(normalisationOptions.zscore)
if(normalisationOptions.ignoreInvalidInMeanStd)
% invalid data represented with 0, ignore it when computing
% mean and standard deviation (useful for depth)
for i = 1:size(patches,1)
mask = patches(i,:) ~= 0;
meanCurr = mean(patches(i,mask));
stdCurr = std(patches(i,mask));
patches(i,mask) = patches(i, mask) - meanCurr;
if(stdCurr ~= 0)
patches(i, mask) = patches(i, mask) ./ meanCurr;
end
patches(i, ~mask) = normalisationOptions.setIllegalToPost;
end
patches_reg = patches;
else
meanCurr = mean(patches, 2);
stdCurr = std(double(patches), 0, 2);
stdCurr(stdCurr == 0) = 1;
patches_reg = bsxfun(@minus, patches, meanCurr);
patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
end
end
patches_to_use = {patches_reg};
end
for i = 1:numel(patches(:,1))
responses{i} = empty;
if visibilities(i)
% responses{i} = ones(size(empty));
colNorm = normalisationOptions.useNormalisedCrossCorr == 1;
for p=1:numel(patches_to_use)
smallRegionVec = patches_to_use{p}(i,:);
smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
% get the patch response
response = SVMresponse(smallRegion, patch_experts{i}(p).w, colNorm, patchSize);
response = (exp(-(patch_experts{i}(p).scaling*response+patch_experts{i}(p).bias))+1).^-1;
if(p==1)
responses{i} = response;
else
responses{i} = responses{i} .* response;
end
end
normOp = (sum(responses{i}(:)));
if(normOp ~= 0)
responses{i} = responses{i} ./ normOp;
end
end
end
end
function response = SVMresponse(region, patchExpert, normalise_x_corr,patchSize)
if(normalise_x_corr)
% the much faster mex version
[response] = normxcorr2_mex(patchExpert, region);
response = response(patchSize(1):end-patchSize(1)+1,patchSize(2):end-patchSize(2)+1);
else
% this assumes that the patch is already normed
template = rot90(patchExpert,2);
response = conv2(region, template, 'valid');
end
end
|
github | ddtm/OpenFace-master | Collect_wild_imgs.m | .m | OpenFace-master/matlab_version/experiments_iccv_300w/Collect_wild_imgs.m | 5,507 | utf_8 | 5b43676289f81ab146b99199ae89a6df | function [images, detections, labels] = Collect_wild_imgs(root_test_data)
use_afw = true;
use_lfpw = true;
use_helen = true;
use_ibug = true;
use_68 = true;
images = [];
labels = [];
detections = [];
if(use_afw)
[img, det, lbl] = Collect_AFW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_lfpw)
[img, det, lbl] = Collect_LFPW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_ibug)
[img, det, lbl] = Collect_ibug(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_helen)
[img, det, lbl] = Collect_helen(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
% convert to format expected by the Fitting method
detections(:,3) = detections(:,1) + detections(:,3);
detections(:,4) = detections(:,2) + detections(:,4);
end
function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
dataset_loc = [root_test_data, '/AFW/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
dataset_loc = [root_test_data, '/lfpw/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.png'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
dataset_loc = [root_test_data, '/ibug/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_helen(root_test_data, use_68)
dataset_loc = [root_test_data, '/helen/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
|
github | ddtm/OpenFace-master | writeMatrix.m | .m | OpenFace-master/matlab_version/PDM_helpers/writeMatrix.m | 428 | utf_8 | 3a2c87a966a8dc0f296d992d85f7d445 | % for easier readibility write them row by row
function writeMatrix(fileID, M, type)
fprintf(fileID, '%d\r\n', size(M,1));
fprintf(fileID, '%d\r\n', size(M,2));
fprintf(fileID, '%d\r\n', type);
for i=1:size(M,1)
if(type == 4 || type == 0)
fprintf(fileID, '%d ', M(i,:));
else
fprintf(fileID, '%.9f ', M(i,:));
end
fprintf(fileID, '\r\n');
end
end |
github | ddtm/OpenFace-master | fit_PDM_ortho_proj_to_2D_no_reg.m | .m | OpenFace-master/matlab_version/PDM_helpers/fit_PDM_ortho_proj_to_2D_no_reg.m | 9,726 | utf_8 | a7d2a08fb6a085786ca26efe54c2241b | function [ a, R, T, T3D, params, error, shapeOrtho ] = fit_PDM_ortho_proj_to_2D_no_reg( M, E, V, shape2D)
%FITPDMTO2DSHAPE Summary of this function goes here
% Detailed explanation goes here
hidden = false;
% if some of the points are unavailable modify M, V, and shape2D (can
% later infer the actual shape from this)
if(sum(shape2D(:)==0) > 0)
hidden = true;
% which indices to remove
inds_to_rem = shape2D(:,1) == 0 | shape2D(:,2) == 0;
shape2D = shape2D(~inds_to_rem,:);
inds_to_rem = repmat(inds_to_rem, 3, 1);
M_old = M;
V_old = V;
M = M(~inds_to_rem);
V = V(~inds_to_rem,:);
end
num_points = numel(M) / 3;
m = reshape(M, num_points, 3)';
width_model = max(m(1,:)) - min(m(1,:));
height_model = max(m(2,:)) - min(m(2,:));
bounding_box = [min(shape2D(:,1)), min(shape2D(:,2)),...
max(shape2D(:,1)), max(shape2D(:,2))];
a = (((bounding_box(3) - bounding_box(1)) / width_model) + ((bounding_box(4) - bounding_box(2))/ height_model)) / 2;
tx = (bounding_box(3) + bounding_box(1))/2;
ty = (bounding_box(4) + bounding_box(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(m(1,:)) + max(m(1,:)))/2;
ty = ty - a*(min(m(2,:)) + max(m(2,:)))/2;
R = eye(3);
T = [tx; ty];
params = zeros(size(E));
currShape = getShapeOrtho(M, V, params, R, T, a);
currError = getRMSerror(currShape, shape2D);
reg_rigid = zeros(6,1);
regFactor = 0.25;
regularisations = [reg_rigid; regFactor ./ E]; % the above version, however, does not perform as well
regularisations = diag(regularisations)*diag(regularisations);
red_in_a_row = 0;
for i=1:1000
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
% Now find the current residual error
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error_res = shape2D - currShape;
eul = Rot2Euler(R);
p_global = [a; eul'; T];
% get the Jacobians
J = CalcJacobian(M, V, params, p_global);
% RLMS style update
p_delta = (J'*J + regularisations) \ (J'*error_res(:) - regularisations*[p_global;params]);
% not to overshoot
p_delta = 0.5 * p_delta;
[params, p_global] = CalcReferenceUpdate(p_delta, params, p_global);
a = p_global(1);
R = Euler2Rot(p_global(2:4));
T = p_global(5:6);
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error = getRMSerror(currShape, shape2D);
if(0.999 * currError < error)
red_in_a_row = red_in_a_row + 1;
if(red_in_a_row == 5)
break;
end
end
currError = error;
end
if(hidden)
shapeOrtho = getShapeOrtho(M_old, V_old, params, R, T, a);
else
shapeOrtho = currShape;
end
T3D = [0;0;0];
end
function [shape2D] = getShapeOrtho(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
shape3D = getShape3D(M, V, p);
shape2D = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape2D] = getShapeOrthoFull(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
T = [T; 0];
shape3D = getShape3D(M, V, p);
shape2D = a * R*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape3D] = getShape3D(M, V, params)
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
end
function [error] = getRMSerror(shape2Dv1, shape2Dv2)
error = sqrt(mean(reshape(shape2Dv1 - shape2Dv2, numel(shape2Dv1), 1).^2));
end
% This calculates the combined rigid with non-rigid Jacobian
function J = CalcJacobian(M, V, p, p_global)
n = size(M, 1)/3;
non_rigid_modes = size(V,2);
J = zeros(n*2, 6 + non_rigid_modes);
% now the layour is
% ---------- Rigid part -------------------|----Non rigid part--------|
% dx_1/ds, dx_1/dr1, ... dx_1/dtx, dx_1/dty dx_1/dp_1 ... dx_1/dp_m
% dx_2/ds, dx_2/dr1, ... dx_2/dtx, dx_2/dty dx_2/dp_1 ... dx_2/dp_m
% ...
% dx_n/ds, dx_n/dr1, ... dx_n/dtx, dx_n/dty dx_n/dp_1 ... dx_n/dp_m
% dy_1/ds, dy_1/dr1, ... dy_1/dtx, dy_1/dty dy_1/dp_1 ... dy_1/dp_m
% ...
% dy_n/ds, dy_n/dr1, ... dy_n/dtx, dy_n/dty dy_n/dp_1 ... dy_n/dp_m
% getting the rigid part
J(:,1:6) = CalcRigidJacobian(M, V, p, p_global);
% constructing the non-rigid part
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% 'rotate' and 'scale' the principal components
% First reshape to 3D
V_X = V(1:n,:);
V_Y = V(n+1:2*n,:);
V_Z = V(2*n+1:end,:);
J_x_non_rigid = s*(R(1,1)*V_X + R(1,2)*V_Y + R(1,3)*V_Z);
J_y_non_rigid = s*(R(2,1)*V_X + R(2,2)*V_Y + R(2,3)*V_Z);
J(1:n, 7:end) = J_x_non_rigid;
J(n+1:end, 7:end) = J_y_non_rigid;
end
function J = CalcRigidJacobian(M, V, p, p_global)
n = size(M, 1)/3;
% Get the current 3D shape (not affected by global transform, as this
% is how the Jacobian was derived (for derivation please see
% ../derivations/orthoJacobian
shape3D = GetShape3D(M, V, p);
% Get the rotation matrix corresponding to current global orientation
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% Rigid Jacobian is laid out as follows
% dx_1/ds, dx_1/dr1, dx_1/dr2, dx_1/dr3, dx_1/dtx, dx_1/dty
% dx_2/ds, dx_2/dr1, dx_2/dr2, dx_2/dr3, dx_2/dtx, dx_2/dty
% ...
% dx_n/ds, dx_n/dr1, dx_n/dr2, dx_n/dr3, dx_n/dtx, dx_n/dty
% dy_1/ds, dy_1/dr1, dy_1/dr2, dy_1/dr3, dy_1/dtx, dy_1/dty
% ...
% dy_n/ds, dy_n/dr1, dy_n/dr2, dy_n/dr3, dy_n/dtx, dy_n/dty
J = zeros(n*2, 6);
% dx/ds = X * r11 + Y * r12 + Z * r13
% dx/dr1 = s*(r13 * Y - r12 * Z)
% dx/dr2 = -s*(r13 * X - r11 * Z)
% dx/dr3 = s*(r12 * X - r11 * Y)
% dx/dtx = 1
% dx/dty = 0
% dy/ds = X * r21 + Y * r22 + Z * r23
% dy/dr1 = s * (r23 * Y - r22 * Z)
% dy/dr2 = -s * (r23 * X - r21 * Z)
% dy/dr3 = s * (r22 * X - r21 * Y)
% dy/dtx = 0
% dy/dty = 1
% set the Jacobian for x's
% with respect to scaling factor
J(1:n,1) = shape3D * R(1,:)';
% with respect to angular rotation around x, y, and z axes
% Change of x with respect to change in axis angle rotation
dxdR = [ 0, R(1,3), -R(1,2);
-R(1,3), 0, R(1,1);
R(1,2), -R(1,1), 0];
J(1:n,2:4) = s*(dxdR * shape3D')';
% with respect to translation
J(1:n,5) = 1;
J(1:n,6) = 0;
% set the Jacobian for y's
% with respect to scaling factor
J(n+1:end,1) = shape3D * R(2,:)';
% with respect to angular rotation around x, y, and z axes
% Change of y with respect to change in axis angle rotation
dydR = [ 0, R(2,3), -R(2,2);
-R(2,3), 0, R(2,1);
R(2,2), -R(2,1), 0];
J(n+1:end,2:4) = s*(dydR * shape3D')';
% with respect to translation
J(n+1:end,5) = 0;
J(n+1:end,6) = 1;
end
% This updates the parameters based on the updates from the RLMS
function [non_rigid, rigid] = CalcReferenceUpdate(params_delta, current_non_rigid, current_global)
rigid = zeros(6, 1);
% Same goes for scaling and translation parameters
rigid(1) = current_global(1) + params_delta(1);
rigid(5) = current_global(5) + params_delta(5);
rigid(6) = current_global(6) + params_delta(6);
% for rotation however, we want to make sure that the rotation matrix
% approximation we have
% R' = [1, -wz, wy
% wz, 1, -wx
% -wy, wx, 1]
% is a legal rotation matrix, and then we combine it with current
% rotation (through matrix multiplication) to acquire the new rotation
R = Euler2Rot(current_global(2:4));
wx = params_delta(2);
wy = params_delta(3);
wz = params_delta(4);
R_delta = [1, -wz, wy;
wz, 1, -wx;
-wy, wx, 1];
% Make sure R_delta is orthonormal
R_delta = OrthonormaliseRotation(R_delta);
% Combine rotations
R_final = R * R_delta;
% Extract euler angle
euler = Rot2Euler(R_final);
rigid(2:4) = euler;
if(length(params_delta) > 6)
% non-rigid parameters can just be added together
non_rigid = params_delta(7:end) + current_non_rigid;
else
non_rigid = current_non_rigid;
end
end
function R_ortho = OrthonormaliseRotation(R)
% U * V' is basically what we want, as it's guaranteed to be
% orthonormal
[U, ~, V] = svd(R);
% We also want to make sure no reflection happened
% get the orthogonal matrix from the initial rotation matrix
X = U*V';
% This makes sure that the handedness is preserved and no reflection happened
% by making sure the determinant is 1 and not -1
W = eye(3);
W(3,3) = det(X);
R_ortho = U*W*V';
end |
github | ddtm/OpenFace-master | writeMatrixBin.m | .m | OpenFace-master/matlab_version/PDM_helpers/writeMatrixBin.m | 911 | utf_8 | 636b1a9c9f27421bfde056250858f51e | % for easier readibility write them row by row
function writeMatrixBin(fileID, M, type)
% 4 bytes each for the description
fwrite(fileID, size(M,1), 'uint');
fwrite(fileID, size(M,2), 'uint');
fwrite(fileID, type, 'uint');
% Convert the matrix to OpenCV format (row minor as opposed to column
% minor)
M = M';
% type 0 - uint8, 1 - int8, 2 - uint16, 3 - int16, 4 - int, 5 -
% float32, 6 - float64
% Write out the matrix itself
switch type
case 0
type = 'uint8';
case 1
type = 'int8';
case 2
type = 'uint16';
case 3
type = 'int16';
case 4
type = 'int';
case 5
type = 'float32';
case 6
type = 'float64';
otherwise
type = 'float32';
end
fwrite(fileID, M, type);
end |
github | ddtm/OpenFace-master | fit_PDM_ortho_proj_to_2D.m | .m | OpenFace-master/matlab_version/PDM_helpers/fit_PDM_ortho_proj_to_2D.m | 9,871 | utf_8 | 7accc2b0fee769eecc059448c7e69b26 | function [ a, R, T, T3D, params, error, shapeOrtho ] = fit_PDM_ortho_proj_to_2D( M, E, V, shape2D, f, cx, cy)
%FITPDMTO2DSHAPE Summary of this function goes here
% Detailed explanation goes here
params = zeros(size(E));
hidden = false;
% if some of the points are unavailable modify M, V, and shape2D (can
% later infer the actual shape from this)
if(sum(shape2D(:)==0) > 0)
hidden = true;
% which indices to remove
inds_to_rem = shape2D(:,1) == 0 | shape2D(:,2) == 0;
shape2D = shape2D(~inds_to_rem,:);
inds_to_rem = repmat(inds_to_rem, 3, 1);
M_old = M;
V_old = V;
M = M(~inds_to_rem);
V = V(~inds_to_rem,:);
end
num_points = numel(M) / 3;
m = reshape(M, num_points, 3)';
width_model = max(m(1,:)) - min(m(1,:));
height_model = max(m(2,:)) - min(m(2,:));
bounding_box = [min(shape2D(:,1)), min(shape2D(:,2)),...
max(shape2D(:,1)), max(shape2D(:,2))];
a = (((bounding_box(3) - bounding_box(1)) / width_model) + ((bounding_box(4) - bounding_box(2))/ height_model)) / 2;
tx = (bounding_box(3) + bounding_box(1))/2;
ty = (bounding_box(4) + bounding_box(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(m(1,:)) + max(m(1,:)))/2;
ty = ty - a*(min(m(2,:)) + max(m(2,:)))/2;
R = eye(3);
T = [tx; ty];
currShape = getShapeOrtho(M, V, params, R, T, a);
currError = getRMSerror(currShape, shape2D);
reg_rigid = zeros(6,1);
regFactor = 20;
regularisations = [reg_rigid; regFactor ./ E]; % the above version, however, does not perform as well
regularisations = diag(regularisations)*diag(regularisations);
red_in_a_row = 0;
for i=1:1000
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
% Now find the current residual error
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error_res = shape2D - currShape;
eul = Rot2Euler(R);
p_global = [a; eul'; T];
% get the Jacobians
J = CalcJacobian(M, V, params, p_global);
% RLMS style update
p_delta = (J'*J + regularisations) \ (J'*error_res(:) - regularisations*[p_global;params]);
[params, p_global] = CalcReferenceUpdate(p_delta, params, p_global);
a = p_global(1);
R = Euler2Rot(p_global(2:4));
T = p_global(5:6);
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error = getRMSerror(currShape, shape2D);
if(0.999 * currError < error)
red_in_a_row = red_in_a_row + 1;
if(red_in_a_row == 5)
break;
end
end
currError = error;
end
if(hidden)
shapeOrtho = getShapeOrtho(M_old, V_old, params, R, T, a);
else
shapeOrtho = currShape;
end
if(nargin == 7)
Zavg = f / a;
Xavg = (T(1) - cx) / a;
Yavg = (T(2) - cy) / a;
T3D = [Xavg;Yavg;Zavg];
else
T3D = [0;0;0];
end
end
function [shape2D] = getShapeOrtho(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
shape3D = getShape3D(M, V, p);
shape2D = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape2D] = getShapeOrthoFull(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
T = [T; 0];
shape3D = getShape3D(M, V, p);
shape2D = a * R*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape3D] = getShape3D(M, V, params)
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
end
function [error] = getRMSerror(shape2Dv1, shape2Dv2)
error = sqrt(mean(reshape(shape2Dv1 - shape2Dv2, numel(shape2Dv1), 1).^2));
end
% This calculates the combined rigid with non-rigid Jacobian
function J = CalcJacobian(M, V, p, p_global)
n = size(M, 1)/3;
non_rigid_modes = size(V,2);
J = zeros(n*2, 6 + non_rigid_modes);
% now the layour is
% ---------- Rigid part -------------------|----Non rigid part--------|
% dx_1/ds, dx_1/dr1, ... dx_1/dtx, dx_1/dty dx_1/dp_1 ... dx_1/dp_m
% dx_2/ds, dx_2/dr1, ... dx_2/dtx, dx_2/dty dx_2/dp_1 ... dx_2/dp_m
% ...
% dx_n/ds, dx_n/dr1, ... dx_n/dtx, dx_n/dty dx_n/dp_1 ... dx_n/dp_m
% dy_1/ds, dy_1/dr1, ... dy_1/dtx, dy_1/dty dy_1/dp_1 ... dy_1/dp_m
% ...
% dy_n/ds, dy_n/dr1, ... dy_n/dtx, dy_n/dty dy_n/dp_1 ... dy_n/dp_m
% getting the rigid part
J(:,1:6) = CalcRigidJacobian(M, V, p, p_global);
% constructing the non-rigid part
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% 'rotate' and 'scale' the principal components
% First reshape to 3D
V_X = V(1:n,:);
V_Y = V(n+1:2*n,:);
V_Z = V(2*n+1:end,:);
J_x_non_rigid = s*(R(1,1)*V_X + R(1,2)*V_Y + R(1,3)*V_Z);
J_y_non_rigid = s*(R(2,1)*V_X + R(2,2)*V_Y + R(2,3)*V_Z);
J(1:n, 7:end) = J_x_non_rigid;
J(n+1:end, 7:end) = J_y_non_rigid;
end
function J = CalcRigidJacobian(M, V, p, p_global)
n = size(M, 1)/3;
% Get the current 3D shape (not affected by global transform, as this
% is how the Jacobian was derived (for derivation please see
% ../derivations/orthoJacobian
shape3D = GetShape3D(M, V, p);
% Get the rotation matrix corresponding to current global orientation
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% Rigid Jacobian is laid out as follows
% dx_1/ds, dx_1/dr1, dx_1/dr2, dx_1/dr3, dx_1/dtx, dx_1/dty
% dx_2/ds, dx_2/dr1, dx_2/dr2, dx_2/dr3, dx_2/dtx, dx_2/dty
% ...
% dx_n/ds, dx_n/dr1, dx_n/dr2, dx_n/dr3, dx_n/dtx, dx_n/dty
% dy_1/ds, dy_1/dr1, dy_1/dr2, dy_1/dr3, dy_1/dtx, dy_1/dty
% ...
% dy_n/ds, dy_n/dr1, dy_n/dr2, dy_n/dr3, dy_n/dtx, dy_n/dty
J = zeros(n*2, 6);
% dx/ds = X * r11 + Y * r12 + Z * r13
% dx/dr1 = s*(r13 * Y - r12 * Z)
% dx/dr2 = -s*(r13 * X - r11 * Z)
% dx/dr3 = s*(r12 * X - r11 * Y)
% dx/dtx = 1
% dx/dty = 0
% dy/ds = X * r21 + Y * r22 + Z * r23
% dy/dr1 = s * (r23 * Y - r22 * Z)
% dy/dr2 = -s * (r23 * X - r21 * Z)
% dy/dr3 = s * (r22 * X - r21 * Y)
% dy/dtx = 0
% dy/dty = 1
% set the Jacobian for x's
% with respect to scaling factor
J(1:n,1) = shape3D * R(1,:)';
% with respect to angular rotation around x, y, and z axes
% Change of x with respect to change in axis angle rotation
dxdR = [ 0, R(1,3), -R(1,2);
-R(1,3), 0, R(1,1);
R(1,2), -R(1,1), 0];
J(1:n,2:4) = s*(dxdR * shape3D')';
% with respect to translation
J(1:n,5) = 1;
J(1:n,6) = 0;
% set the Jacobian for y's
% with respect to scaling factor
J(n+1:end,1) = shape3D * R(2,:)';
% with respect to angular rotation around x, y, and z axes
% Change of y with respect to change in axis angle rotation
dydR = [ 0, R(2,3), -R(2,2);
-R(2,3), 0, R(2,1);
R(2,2), -R(2,1), 0];
J(n+1:end,2:4) = s*(dydR * shape3D')';
% with respect to translation
J(n+1:end,5) = 0;
J(n+1:end,6) = 1;
end
% This updates the parameters based on the updates from the RLMS
function [non_rigid, rigid] = CalcReferenceUpdate(params_delta, current_non_rigid, current_global)
rigid = zeros(6, 1);
% Same goes for scaling and translation parameters
rigid(1) = current_global(1) + params_delta(1);
rigid(5) = current_global(5) + params_delta(5);
rigid(6) = current_global(6) + params_delta(6);
% for rotation however, we want to make sure that the rotation matrix
% approximation we have
% R' = [1, -wz, wy
% wz, 1, -wx
% -wy, wx, 1]
% is a legal rotation matrix, and then we combine it with current
% rotation (through matrix multiplication) to acquire the new rotation
R = Euler2Rot(current_global(2:4));
wx = params_delta(2);
wy = params_delta(3);
wz = params_delta(4);
R_delta = [1, -wz, wy;
wz, 1, -wx;
-wy, wx, 1];
% Make sure R_delta is orthonormal
R_delta = OrthonormaliseRotation(R_delta);
% Combine rotations
R_final = R * R_delta;
% Extract euler angle
euler = Rot2Euler(R_final);
rigid(2:4) = euler;
if(length(params_delta) > 6)
% non-rigid parameters can just be added together
non_rigid = params_delta(7:end) + current_non_rigid;
else
non_rigid = current_non_rigid;
end
end
function R_ortho = OrthonormaliseRotation(R)
% U * V' is basically what we want, as it's guaranteed to be
% orthonormal
[U, ~, V] = svd(R);
% We also want to make sure no reflection happened
% get the orthogonal matrix from the initial rotation matrix
X = U*V';
% This makes sure that the handedness is preserved and no reflection happened
% by making sure the determinant is 1 and not -1
W = eye(3);
W(3,3) = det(X);
R_ortho = U*W*V';
end |
github | ddtm/OpenFace-master | Collect_wild_imgs.m | .m | OpenFace-master/matlab_version/bounding_box_mapping/Collect_wild_imgs.m | 5,461 | utf_8 | 69afbb7f409efa978b4ecfdea73220ee | function [images, detections, labels] = Collect_wild_imgs(root_test_data, use_afw, use_lfpw, use_helen, use_ibug)
use_68 = true;
images = [];
labels = [];
detections = [];
if(use_afw)
[img, det, lbl] = Collect_AFW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_lfpw)
[img, det, lbl] = Collect_LFPW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_ibug)
[img, det, lbl] = Collect_ibug(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_helen)
[img, det, lbl] = Collect_helen(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
% convert to format expected by the Fitting method
detections(:,3) = detections(:,1) + detections(:,3);
detections(:,4) = detections(:,2) + detections(:,4);
end
function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
dataset_loc = [root_test_data, '/AFW/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
dataset_loc = [root_test_data, '/lfpw/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.png'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
dataset_loc = [root_test_data, '/ibug/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_helen(root_test_data, use_68)
dataset_loc = [root_test_data, '/helen/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
|
github | ddtm/OpenFace-master | Create_data_66.m | .m | OpenFace-master/matlab_version/face_validation/Create_data_66.m | 11,874 | utf_8 | 674e8b488296e88975ab8869f8db5d9b | function Create_data_66()
load '../models/pdm/pdm_66_multi_pie';
load '../models/tri_66.mat';
% This script uses the same format used for patch expert training, and
% expects the data to be there
dataset_loc = '../../../CCNF experiments/clnf/patch training/data_preparation/prepared_data/';
addpath('../PDM_helpers/');
scale = '0.5';
prefix= 'combined_';
% Find the available positive training data
data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
centres_all = [];
for i=1:numel(data_files)
% Load the orientation of the training data
load([dataset_loc, '/', data_files(i).name], 'centres');
centres_all = cat(1, centres_all, centres);
end
label_inds = [1:60,62:64,66:68];
% Construct mirror indices (which views need to be flipped to create other
% profile training data)
mirror_inds = zeros(size(centres_all,1), 1);
for i=1:numel(data_files)
% mirrored image has inverse yaw
mirrored_centre = centres_all(i,:);
mirrored_centre(2) = -mirrored_centre(2);
% if mirrored version has same orientation, do not need mirroring
if(~isequal(mirrored_centre, centres_all(i,:)))
centres_all = cat(1, centres_all, mirrored_centre);
mirror_inds = cat(1, mirror_inds, i);
end
end
outputLocation = './prep_data/';
num_more_neg = 10;
% Make sure same data generated all the time
rng(0);
neg_image_loc = './neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
max_img_used = 1500;
%% do it separately for centers due to memory limitations
for r=1:size(centres_all,1)
a_mod = 0.3;
mirror = false;
if(mirror_inds(r) ~= 0 )
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,63;66,64];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
% Convert to 66 point model
landmark_locations = landmark_locations(:,label_inds,:);
visiCurrent = logical(visiIndex);
% Flip the orientation and indices for mirror data
if(mirror)
centres = [centres(1), -centres(2), -centres(3)];
tmp1 = visiCurrent(label_mirror_inds(:,1));
tmp2 = visiCurrent(label_mirror_inds(:,2));
visiCurrent(label_mirror_inds(:,2)) = tmp1;
visiCurrent(label_mirror_inds(:,1)) = tmp2;
end
visibleVerts = 1:numel(visiCurrent);
visibleVerts = visibleVerts(visiCurrent)-1;
% Correct the triangulation to take into account the vertex
% visibilities
triangulation = [];
shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
shape = shape';
for i=1:size(T,1)
visib = 0;
for j=1:numel(visibleVerts)
if(T(i,1)==visibleVerts(j))
visib = visib+1;
end
if(T(i,2)==visibleVerts(j))
visib = visib+1;
end
if(T(i,3)==visibleVerts(j))
visib = visib+1;
end
end
% Only if all three of the vertices are visible
if(visib == 3)
% Also want to remove triangles facing the wrong way (self occluded)
v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
normal = cross((v2-v1), v3 - v2);
normal = normal / norm(normal);
direction = normal * [0,0,1]';
% And only if the triangle is facing the camera
if(direction > 0)
triangulation = cat(1, triangulation, T(i,:));
end
end
end
% Initialise the warp
[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
imgs_to_use = randperm(size(landmark_locations, 1));
if(size(landmark_locations, 1) > max_img_used)
imgs_to_use = imgs_to_use(1:max_img_used);
end
% Extracting relevant filenames
examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
unused_pos = 0;
curr_filled = 0;
for j=imgs_to_use
labels = squeeze(landmark_locations(j,:,:));
img = squeeze(all_images(j,:,:));
if(mirror)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
flippedLbls(label_mirror_inds(:,1),:) = tmp2;
labels = flippedLbls;
end
% If for some reason some of the labels are not visible in the
% current sample skip this label
non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
non_existent_inds = find(non_existent_labels)-1;
if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
unused_pos = unused_pos + 1;
continue;
end
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
examples(curr_filled,:) = features;
% Extract the correct PDM parameters for the model (we will perturb
% them for some negative examples)
[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
% a slightly perturbed example, too tight
% from 0.3 to 0.9
a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% a slightly perturbed example, too broad
% from 1.2 to 0.6
a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
for n=6:num_more_neg
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
if(size(neg_image,3) == 3)
neg_image = rgb2gray(neg_image);
end
[h_neg, w_neg] = size(neg_image);
% if the current labels fit just use them, if not, then resize
% to fit
max_x = max(labels(:,1));
max_y = max(labels(:,2));
if(max_x > w_neg || max_y > h_neg)
neg_image = imresize(neg_image, [max_y, max_x]);
end
[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Set high error to 3
errors(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
end
% add the pos example to the background
end
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_66_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
end
end
function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 1,1) = 1;
labels(labels(:,2) < 1,2) = 1;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;
crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
crop_img(isnan(crop_img)) = 0;
% vectorised version
features = reshape(crop_img(logical(mask)), 1, nPix);
% normalisations
features = (features - mean(features));
norms = std(features);
if(norms==0)
norms = 1;
end
features = features / norms;
end |
github | ddtm/OpenFace-master | Create_data_68.m | .m | OpenFace-master/matlab_version/face_validation/Create_data_68.m | 11,691 | utf_8 | 9569c6c8664f9edea1584a02a1349028 | function Create_data_68()
load '../models/pdm/pdm_68_multi_pie';
load '../models/tri_68.mat';
% This script uses the same format used for patch expert training, and
% expects the data to be there
dataset_loc = '../../../CCNF experiments/clnf/patch_training/data_preparation/prepared_data/';
addpath('../PDM_helpers/');
scale = '0.5';
prefix= 'combined_';
% Find the available positive training data
data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
centres_all = [];
for i=1:numel(data_files)
% Load the orientation of the training data
load([dataset_loc, '/', data_files(i).name], 'centres');
centres_all = cat(1, centres_all, centres);
end
% Construct mirror indices (which views need to be flipped to create other
% profile training data)
mirror_inds = zeros(size(centres_all,1), 1);
for i=1:numel(data_files)
% mirrored image has inverse yaw
mirrored_centre = centres_all(i,:);
mirrored_centre(2) = -mirrored_centre(2);
% if mirrored version has same orientation, do not need mirroring
if(~isequal(mirrored_centre, centres_all(i,:)))
centres_all = cat(1, centres_all, mirrored_centre);
mirror_inds = cat(1, mirror_inds, i);
end
end
outputLocation = './prep_data/';
num_more_neg = 10;
% Make sure same data generated all the time
rng(0);
neg_image_loc = './neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
max_img_used = 1500;
% do it separately for centers due to memory limitations
for r=1:size(centres_all,1)
a_mod = 0.3;
mirror = false;
if(mirror_inds(r) ~= 0 )
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,65;62,64;68,66];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
visiCurrent = logical(visiIndex);
if(mirror)
centres = [centres(1), -centres(2), -centres(3)];
tmp1 = visiCurrent(label_mirror_inds(:,1));
tmp2 = visiCurrent(label_mirror_inds(:,2));
visiCurrent(label_mirror_inds(:,2)) = tmp1;
visiCurrent(label_mirror_inds(:,1)) = tmp2;
end
visibleVerts = 1:numel(visiCurrent);
visibleVerts = visibleVerts(visiCurrent)-1;
% Correct the triangulation to take into account the vertex
% visibilities
triangulation = [];
shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
shape = shape';
for i=1:size(T,1)
visib = 0;
for j=1:numel(visibleVerts)
if(T(i,1)==visibleVerts(j))
visib = visib+1;
end
if(T(i,2)==visibleVerts(j))
visib = visib+1;
end
if(T(i,3)==visibleVerts(j))
visib = visib+1;
end
end
% Only if all three of the vertices are visible
if(visib == 3)
% Also want to remove triangles facing the wrong way (self occluded)
v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
normal = cross((v2-v1), v3 - v2);
normal = normal / norm(normal);
direction = normal * [0,0,1]';
% And only if the triangle is facing the camera
if(direction > 0)
triangulation = cat(1, triangulation, T(i,:));
end
end
end
% Initialise the warp
[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
mask = logical(mask);
imgs_to_use = randperm(size(landmark_locations, 1));
if(size(landmark_locations, 1) > max_img_used)
imgs_to_use = imgs_to_use(1:max_img_used);
end
% Extracting relevant filenames
examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
unused_pos = 0;
curr_filled = 0;
for j=imgs_to_use
labels = squeeze(landmark_locations(j,:,:));
img = squeeze(all_images(j,:,:));
if(mirror)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
flippedLbls(label_mirror_inds(:,1),:) = tmp2;
labels = flippedLbls;
end
% If for some reason some of the labels are not visible in the
% current sample skip this label
non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
non_existent_inds = find(non_existent_labels)-1;
if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
unused_pos = unused_pos + 1;
continue;
end
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
examples(curr_filled,:) = features;
% Extract the correct PDM parameters for the model (we will perturb
% them for some negative examples)
[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
% a slightly perturbed example, too tight
% from 0.3 to 0.9
a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% a slightly perturbed example, too broad
% from 1.2 to 0.6
a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
for n=6:num_more_neg
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
if(size(neg_image,3) == 3)
neg_image = rgb2gray(neg_image);
end
[h_neg, w_neg] = size(neg_image);
% if the current labels fit just use them, if not, then resize
% to fit
max_x = max(labels(:,1));
max_y = max(labels(:,2));
if(max_x > w_neg || max_y > h_neg)
neg_image = imresize(neg_image, [max_y, max_x]);
end
[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Set high error to 3
errors(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
end
% add the pos example to the background
end
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_68_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
end
end
function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 1,1) = 1;
labels(labels(:,2) < 1,2) = 1;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;
crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
crop_img(isnan(crop_img)) = 0;
% vectorised version
features = reshape(crop_img(logical(mask)), 1, nPix);
% normalisations
features = (features - mean(features));
norms = std(features);
if(norms==0)
norms = 1;
end
features = features / norms;
end |
github | ddtm/OpenFace-master | Create_data_68_large.m | .m | OpenFace-master/matlab_version/face_validation/Create_data_68_large.m | 12,397 | utf_8 | 53b5fd86000c1be10aa5f2ad2d32529e | function Create_data_68_large()
load '../models/pdm/pdm_68_aligned_wild';
load '../models/tri_68.mat';
% This script uses the same format used for patch expert training, and
% expects the data to be there (this can be found in
% https://github.com/TadasBaltrusaitis/CCNF)
% Replace with your location of training data
dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/';
addpath('../PDM_helpers/');
scale = '0.5';
prefix= 'combined_';
% Find the available positive training data
data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
centres_all = [];
for i=1:numel(data_files)
% Load the orientation of the training data
load([dataset_loc, '/', data_files(i).name], 'centres');
centres_all = cat(1, centres_all, centres);
end
% Do not use extreme pose
centres_all = centres_all(1:3,:);
% Construct mirror indices (which views need to be flipped to create other
% profile training data)
mirror_inds = zeros(size(centres_all,1), 1);
for i=1:numel(data_files)
% mirrored image has inverse yaw
mirrored_centre = centres_all(i,:);
mirrored_centre(2) = -mirrored_centre(2);
% if mirrored version has same orientation, do not need mirroring
if(~isequal(mirrored_centre, centres_all(i,:)))
centres_all = cat(1, centres_all, mirrored_centre);
mirror_inds = cat(1, mirror_inds, i);
end
end
% Replace with your location of training data
outputLocation = 'E:/datasets/detection_validation/prep_data/';
num_more_neg = 10;
% Make sure same data generated all the time
rng(0);
neg_image_loc = 'E:/datasets/detection_validation/neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
max_img_used = 4000;
% do it separately for centers due to memory limitations
for r=1:size(centres_all,1)
a_mod = 0.4;
mirror = false;
if(mirror_inds(r) ~= 0 )
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,65;62,64;68,66];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
visiCurrent = logical(visiIndex);
if(mirror)
centres = [centres(1), -centres(2), -centres(3)];
tmp1 = visiCurrent(label_mirror_inds(:,1));
tmp2 = visiCurrent(label_mirror_inds(:,2));
visiCurrent(label_mirror_inds(:,2)) = tmp1;
visiCurrent(label_mirror_inds(:,1)) = tmp2;
end
visibleVerts = 1:numel(visiCurrent);
visibleVerts = visibleVerts(visiCurrent)-1;
% Correct the triangulation to take into account the vertex
% visibilities
triangulation = [];
shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
shape = shape';
for i=1:size(T,1)
visib = 0;
for j=1:numel(visibleVerts)
if(T(i,1)==visibleVerts(j))
visib = visib+1;
end
if(T(i,2)==visibleVerts(j))
visib = visib+1;
end
if(T(i,3)==visibleVerts(j))
visib = visib+1;
end
end
% Only if all three of the vertices are visible
if(visib == 3)
% Also want to remove triangles facing the wrong way (self occluded)
v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
normal = cross((v2-v1), v3 - v2);
normal = normal / norm(normal);
direction = normal * [0,0,1]';
% And only if the triangle is facing the camera
if(direction > 0)
triangulation = cat(1, triangulation, T(i,:));
end
end
end
% Initialise the warp
[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
mask = logical(mask);
imgs_to_use = randperm(size(landmark_locations, 1));
if(size(landmark_locations, 1) > max_img_used)
imgs_to_use = imgs_to_use(1:max_img_used);
end
% Extracting relevant filenames
examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
unused_pos = 0;
curr_filled = 0;
for j=imgs_to_use
labels = squeeze(landmark_locations(j,:,:));
img = squeeze(all_images(j,:,:));
if(mirror)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1) + 1;
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
flippedLbls(label_mirror_inds(:,1),:) = tmp2;
labels = flippedLbls;
end
% If for some reason some of the labels are not visible in the
% current sample skip this label
non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
non_existent_inds = find(non_existent_labels)-1;
if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
unused_pos = unused_pos + 1;
continue;
end
% Centering the pixel so that 0,0 is center of the top left pixel
labels = labels - 1;
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
examples(curr_filled,:) = features;
errors(curr_filled,:) = 0;
% Extract the correct PDM parameters for the model (we will perturb
% them for some negative examples)
[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
% a slightly perturbed example, too tight
% from 0.3 to 0.9
a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% a slightly perturbed example, too broad
% from 1.2 to 0.6
a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 20;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.3;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
for n=6:num_more_neg
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
if(size(neg_image,3) == 3)
neg_image = rgb2gray(neg_image);
end
[h_neg, w_neg] = size(neg_image);
% if the current labels fit just use them, if not, then resize
% to fit
max_x = max(labels(:,1));
max_y = max(labels(:,2));
if(max_x > w_neg || max_y > h_neg)
neg_image = imresize(neg_image, [max_y, max_x]);
end
[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Set high error to 3
errors(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
end
% add the pos example to the background
end
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_large_68_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
end
end
function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 0,1) = 0;
labels(labels(:,2) < 0,2) = 0;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;
crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
crop_img(isnan(crop_img)) = 0;
% vectorised version
features = reshape(crop_img(logical(mask)), 1, nPix);
% normalisations
features = (features - mean(features));
norms = std(features);
if(norms==0)
norms = 1;
end
features = features / norms;
end |
github | ddtm/OpenFace-master | Collect_wild_imgs.m | .m | OpenFace-master/matlab_version/face_validation/Collect_wild_imgs.m | 5,454 | utf_8 | e0042374523fb6085a4a7afb9ec734cb | function [images, detections, labels] = Collect_wild_imgs(root_test_data)
use_afw = true;
use_lfpw = true;
use_helen = true;
use_ibug = true;
use_68 = true;
images = [];
labels = [];
detections = [];
if(use_afw)
[img, det, lbl] = Collect_AFW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_lfpw)
[img, det, lbl] = Collect_LFPW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_ibug)
[img, det, lbl] = Collect_ibug(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_helen)
[img, det, lbl] = Collect_helen(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
% convert to format expected by the Fitting method
detections(:,3) = detections(:,1) + detections(:,3);
detections(:,4) = detections(:,2) + detections(:,4);
end
function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
dataset_loc = [root_test_data, '/AFW/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
landmarks = landmarks.data;
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
dataset_loc = [root_test_data, '/lfpw/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
landmarks = landmarks.data;
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.png'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
dataset_loc = [root_test_data, '/ibug/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
landmarks = landmarks.data;
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_helen(root_test_data, use_68)
dataset_loc = [root_test_data, '/helen/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
landmarks = landmarks.data;
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
|
github | ddtm/OpenFace-master | InitialisePieceWiseAffine.m | .m | OpenFace-master/matlab_version/face_validation/InitialisePieceWiseAffine.m | 2,628 | utf_8 | b9968dd94a35da481cbae3dec1e73e48 | function [ alphas, betas, triX, mask, xmin, ymin, npix ] = InitialisePieceWiseAffine( triangulation, sourcePoints )
%INITIALISEPIECEWICEAFFINE Summary of this function goes here
% Detailed explanation goes here
triangulation = triangulation + 1;
numPoints = size(sourcePoints, 1);
numTris = size(triangulation, 1);
alphas = zeros(size(triangulation, 1), 3);
betas = zeros(size(triangulation, 1), 3);
xs = sourcePoints(:,1);
ys = sourcePoints(:,2);
for i = 1:numTris
j = triangulation(i, 1);
k = triangulation(i, 2);
l = triangulation(i, 3);
c1 = ys(l) - ys(j);
c2 = xs(l) - xs(j);
c4 = ys(k) - ys(j);
c3 = xs(k) - xs(j);
c5 = c3*c1 - c2*c4;
alphas(i, 1) = (ys(j) * c2 - xs(j) * c1) / c5;
alphas(i, 2) = c1/c5;
alphas(i, 3) = -c2/c5;
betas(i, 1) = (xs(j) * c4 - ys(j) * c3)/c5;
betas(i, 2) = -c4/c5;
betas(i, 3) = c3/c5;
end
xmin = min(xs);
ymin = min(ys);
xmax = max(xs);
ymax = max(ys);
w = int32(xmax - xmin + 1);
h = int32(ymax - ymin + 1);
mask = zeros(h, w);
triX = zeros(h, w);
shape = [xs, ys];
for i = 1:h
for j = 1:w
currTri = findTriangle(double([double(j)-1+xmin, double(i)-1+ymin])', triangulation, shape);
if(currTri ~= -1)
triX(i, j) = currTri - 1;
mask(i, j) = 1;
else
triX(i, j) = -1;
end
end
end
npix = sum(sum(mask));
end
function [tri] = findTriangle(point, tris, controlPoints)
numTris = size(tris, 1);
tri = -1;
for i=1:numTris
if(PointInTriangle(point, controlPoints(tris(i,1),:)', controlPoints(tris(i,2),:)', controlPoints(tris(i,3),:)'))
tri = i;
break;
end
end
end
function inTriangle = PointInTriangle(point, v1, v2, v3)
inTriangle = SameSide(point, v1,v2,v3) && SameSide(point, v2,v1,v3) && SameSide(point, v3,v1,v2);
end
function sameSide = SameSide(toTest,v1,v2,v3)
x0 = toTest(1);
y0 = toTest(2);
x1 = v1(1);
x2 = v2(1);
x3 = v3(1);
y1 = v1(2);
y2 = v2(2);
y3 = v3(2);
x = (x3-x2)*(y0-y2) - (x0-x2)*(y3-y2);
y = (x3-x2)*(y1-y2) - (x1-x2)*(y3-y2);
if(x*y >= 0)
sameSide = 1;
else
sameSide = 0;
end
% cross1 = cross( v3 - v2, toTest - v2);
% cross2 = cross( v3 - v2, v1 - v2);
%
% sameSide = (cross1 * cross2') >= 0;
end |
github | ddtm/OpenFace-master | Create_data_66_large.m | .m | OpenFace-master/matlab_version/face_validation/Create_data_66_large.m | 12,039 | utf_8 | 88cdd3874830743c782ad3eafd52867d | function Create_data_66_large()
load '../models/pdm/pdm_66_multi_pie';
load '../models/tri_66.mat';
% This script uses the same format used for patch expert training, and
% expects the data to be there (this can be found in
% https://github.com/TadasBaltrusaitis/CCNF)
% Replace with your location of training data
dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/';
addpath('../PDM_helpers/');
scale = '0.5';
prefix= 'combined_';
% Find the available positive training data
data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
centres_all = [];
for i=1:numel(data_files)
% Load the orientation of the training data
load([dataset_loc, '/', data_files(i).name], 'centres');
centres_all = cat(1, centres_all, centres);
end
label_inds = [1:60,62:64,66:68];
% Construct mirror indices (which views need to be flipped to create other
% profile training data)
mirror_inds = zeros(size(centres_all,1), 1);
for i=1:numel(data_files)
% mirrored image has inverse yaw
mirrored_centre = centres_all(i,:);
mirrored_centre(2) = -mirrored_centre(2);
% if mirrored version has same orientation, do not need mirroring
if(~isequal(mirrored_centre, centres_all(i,:)))
centres_all = cat(1, centres_all, mirrored_centre);
mirror_inds = cat(1, mirror_inds, i);
end
end
outputLocation = 'F:/datasets/detection_validation/prep_data/';
num_more_neg = 10;
% Make sure same data generated all the time
rng(0);
neg_image_loc = 'F:/datasets/detection_validation/neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
max_img_used = 2500;
% do it separately for centers due to memory limitations
for r=1:size(centres_all,1)
a_mod = 0.4;
mirror = false;
if(mirror_inds(r) ~= 0 )
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,63;66,64];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
% Convert to 66 point model
landmark_locations = landmark_locations(:,label_inds,:);
visiCurrent = logical(visiIndex);
if(mirror)
centres = [centres(1), -centres(2), -centres(3)];
tmp1 = visiCurrent(label_mirror_inds(:,1));
tmp2 = visiCurrent(label_mirror_inds(:,2));
visiCurrent(label_mirror_inds(:,2)) = tmp1;
visiCurrent(label_mirror_inds(:,1)) = tmp2;
end
visibleVerts = 1:numel(visiCurrent);
visibleVerts = visibleVerts(visiCurrent)-1;
% Correct the triangulation to take into account the vertex
% visibilities
triangulation = [];
shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
shape = shape';
for i=1:size(T,1)
visib = 0;
for j=1:numel(visibleVerts)
if(T(i,1)==visibleVerts(j))
visib = visib+1;
end
if(T(i,2)==visibleVerts(j))
visib = visib+1;
end
if(T(i,3)==visibleVerts(j))
visib = visib+1;
end
end
% Only if all three of the vertices are visible
if(visib == 3)
% Also want to remove triangles facing the wrong way (self occluded)
v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
normal = cross((v2-v1), v3 - v2);
normal = normal / norm(normal);
direction = normal * [0,0,1]';
% And only if the triangle is facing the camera
if(direction > 0)
triangulation = cat(1, triangulation, T(i,:));
end
end
end
% Initialise the warp
[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
mask = logical(mask);
imgs_to_use = randperm(size(landmark_locations, 1));
if(size(landmark_locations, 1) > max_img_used)
imgs_to_use = imgs_to_use(1:max_img_used);
end
% Extracting relevant filenames
examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
unused_pos = 0;
curr_filled = 0;
for j=imgs_to_use
labels = squeeze(landmark_locations(j,:,:));
img = squeeze(all_images(j,:,:));
if(mirror)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
flippedLbls(label_mirror_inds(:,1),:) = tmp2;
labels = flippedLbls;
end
% If for some reason some of the labels are not visible in the
% current sample skip this label
non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
non_existent_inds = find(non_existent_labels)-1;
if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
unused_pos = unused_pos + 1;
continue;
end
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
examples(curr_filled,:) = features;
errors(curr_filled,:) = 0;
% Extract the correct PDM parameters for the model (we will perturb
% them for some negative examples)
[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
% a slightly perturbed example, too tight
% from 0.3 to 0.9
a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% a slightly perturbed example, too broad
% from 1.2 to 0.6
a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
for n=6:num_more_neg
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
if(size(neg_image,3) == 3)
neg_image = rgb2gray(neg_image);
end
[h_neg, w_neg] = size(neg_image);
% if the current labels fit just use them, if not, then resize
% to fit
max_x = max(labels(:,1));
max_y = max(labels(:,2));
if(max_x > w_neg || max_y > h_neg)
neg_image = imresize(neg_image, [max_y, max_x]);
end
[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Set high error to 3
errors(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
end
% add the pos example to the background
end
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_large_66_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
end
end
function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 1,1) = 1;
labels(labels(:,2) < 1,2) = 1;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;
crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
crop_img(isnan(crop_img)) = 0;
% vectorised version
features = reshape(crop_img(logical(mask)), 1, nPix);
% normalisations
features = (features - mean(features));
norms = std(features);
if(norms==0)
norms = 1;
end
features = features / norms;
end |
github | ddtm/OpenFace-master | myOctaveVersion.m | .m | OpenFace-master/matlab_version/face_validation/DeepLearnToolbox/util/myOctaveVersion.m | 169 | utf_8 | d4603482a968c496b66a4ed4e7c72471 | % return OCTAVE_VERSION or 'undefined' as a string
function result = myOctaveVersion()
if isOctave()
result = OCTAVE_VERSION;
else
result = 'undefined';
end
|
github | ddtm/OpenFace-master | isOctave.m | .m | OpenFace-master/matlab_version/face_validation/DeepLearnToolbox/util/isOctave.m | 108 | utf_8 | 4695e8d7c4478e1e67733cca9903f9ef | %detects if we're running Octave
function result = isOctave()
result = exist('OCTAVE_VERSION') ~= 0;
end |
github | ddtm/OpenFace-master | makeLMfilters.m | .m | OpenFace-master/matlab_version/face_validation/DeepLearnToolbox/util/makeLMfilters.m | 1,895 | utf_8 | 21950924882d8a0c49ab03ef0681b618 | function F=makeLMfilters
% Returns the LML filter bank of size 49x49x48 in F. To convolve an
% image I with the filter bank you can either use the matlab function
% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the
% Fourier transform.
SUP=49; % Support of the largest filter (must be odd)
SCALEX=sqrt(2).^[1:3]; % Sigma_{x} for the oriented filters
NORIENT=6; % Number of orientations
NROTINV=12;
NBAR=length(SCALEX)*NORIENT;
NEDGE=length(SCALEX)*NORIENT;
NF=NBAR+NEDGE+NROTINV;
F=zeros(SUP,SUP,NF);
hsup=(SUP-1)/2;
[x,y]=meshgrid([-hsup:hsup],[hsup:-1:-hsup]);
orgpts=[x(:) y(:)]';
count=1;
for scale=1:length(SCALEX),
for orient=0:NORIENT-1,
angle=pi*orient/NORIENT; % Not 2pi as filters have symmetry
c=cos(angle);s=sin(angle);
rotpts=[c -s;s c]*orgpts;
F(:,:,count)=makefilter(SCALEX(scale),0,1,rotpts,SUP);
F(:,:,count+NEDGE)=makefilter(SCALEX(scale),0,2,rotpts,SUP);
count=count+1;
end;
end;
count=NBAR+NEDGE+1;
SCALES=sqrt(2).^[1:4];
for i=1:length(SCALES),
F(:,:,count)=normalise(fspecial('gaussian',SUP,SCALES(i)));
F(:,:,count+1)=normalise(fspecial('log',SUP,SCALES(i)));
F(:,:,count+2)=normalise(fspecial('log',SUP,3*SCALES(i)));
count=count+3;
end;
return
function f=makefilter(scale,phasex,phasey,pts,sup)
gx=gauss1d(3*scale,0,pts(1,:),phasex);
gy=gauss1d(scale,0,pts(2,:),phasey);
f=normalise(reshape(gx.*gy,sup,sup));
return
function g=gauss1d(sigma,mean,x,ord)
% Function to compute gaussian derivatives of order 0 <= ord < 3
% evaluated at x.
x=x-mean;num=x.*x;
variance=sigma^2;
denom=2*variance;
g=exp(-num/denom)/(pi*denom)^0.5;
switch ord,
case 1, g=-g.*(x/variance);
case 2, g=g.*((num-variance)/(variance^2));
end;
return
function f=normalise(f), f=f-mean(f(:)); f=f/sum(abs(f(:))); return |
github | ddtm/OpenFace-master | caenumgradcheck.m | .m | OpenFace-master/matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m | 3,618 | utf_8 | 6c481fc15ab7df32e0f476514100141a | function cae = caenumgradcheck(cae, x, y)
epsilon = 1e-4;
er = 1e-6;
disp('performing numerical gradient checking...')
for i = 1 : numel(cae.o)
p_cae = cae; p_cae.c{i} = p_cae.c{i} + epsilon;
m_cae = cae; m_cae.c{i} = m_cae.c{i} - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
d = (p_cae.L - m_cae.L) / (2 * epsilon);
e = abs(d - cae.dc{i});
if e > er
disp('OUTPUT BIAS numerical gradient checking failed');
disp(e);
disp(d / cae.dc{i});
keyboard
end
end
for a = 1 : numel(cae.a)
p_cae = cae; p_cae.b{a} = p_cae.b{a} + epsilon;
m_cae = cae; m_cae.b{a} = m_cae.b{a} - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
d = (p_cae.L - m_cae.L) / (2 * epsilon);
% cae.dok{i}{a}(u) = d;
e = abs(d - cae.db{a});
if e > er
disp('BIAS numerical gradient checking failed');
disp(e);
disp(d / cae.db{a});
keyboard
end
for i = 1 : numel(cae.o)
for u = 1 : numel(cae.ok{i}{a})
p_cae = cae; p_cae.ok{i}{a}(u) = p_cae.ok{i}{a}(u) + epsilon;
m_cae = cae; m_cae.ok{i}{a}(u) = m_cae.ok{i}{a}(u) - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
d = (p_cae.L - m_cae.L) / (2 * epsilon);
% cae.dok{i}{a}(u) = d;
e = abs(d - cae.dok{i}{a}(u));
if e > er
disp('OUTPUT KERNEL numerical gradient checking failed');
disp(e);
disp(d / cae.dok{i}{a}(u));
% keyboard
end
end
end
for i = 1 : numel(cae.i)
for u = 1 : numel(cae.ik{i}{a})
p_cae = cae;
m_cae = cae;
p_cae.ik{i}{a}(u) = p_cae.ik{i}{a}(u) + epsilon;
m_cae.ik{i}{a}(u) = m_cae.ik{i}{a}(u) - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
d = (p_cae.L - m_cae.L) / (2 * epsilon);
% cae.dik{i}{a}(u) = d;
e = abs(d - cae.dik{i}{a}(u));
if e > er
disp('INPUT KERNEL numerical gradient checking failed');
disp(e);
disp(d / cae.dik{i}{a}(u));
end
end
end
end
disp('done')
end
function [m_cae, p_cae] = caerun(m_cae, p_cae, x, y)
m_cae = caeup(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y);
p_cae = caeup(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y);
end
%function checknumgrad(cae,what,x,y)
% epsilon = 1e-4;
% er = 1e-9;
%
% for i = 1 : numel(eval(what))
% if iscell(eval(['cae.' what]))
% checknumgrad(cae,[what '{' num2str(i) '}'], x, y)
% else
% p_cae = cae;
% m_cae = cae;
% eval(['p_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) + epsilon;
% eval(['m_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) - epsilon;
%
% m_cae = caeff(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y);
% p_cae = caeff(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y);
%
% d = (p_cae.L - m_cae.L) / (2 * epsilon);
% e = abs(d - eval(['cae.d' what '(' num2str(i) ')']));
% if e > er
% error('numerical gradient checking failed');
% end
% end
% end
%
% end
|
github | ddtm/OpenFace-master | Collect_wild_imgs.m | .m | OpenFace-master/matlab_version/experiments_in_the_wild/Collect_wild_imgs.m | 5,507 | utf_8 | 5b43676289f81ab146b99199ae89a6df | function [images, detections, labels] = Collect_wild_imgs(root_test_data)
use_afw = true;
use_lfpw = true;
use_helen = true;
use_ibug = true;
use_68 = true;
images = [];
labels = [];
detections = [];
if(use_afw)
[img, det, lbl] = Collect_AFW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_lfpw)
[img, det, lbl] = Collect_LFPW(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_ibug)
[img, det, lbl] = Collect_ibug(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
if(use_helen)
[img, det, lbl] = Collect_helen(root_test_data, use_68);
images = cat(1, images, img');
detections = cat(1, detections, det);
labels = cat(1, labels, lbl);
end
% convert to format expected by the Fitting method
detections(:,3) = detections(:,1) + detections(:,3);
detections(:,4) = detections(:,2) + detections(:,4);
end
function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
dataset_loc = [root_test_data, '/AFW/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
dataset_loc = [root_test_data, '/lfpw/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.png'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
dataset_loc = [root_test_data, '/ibug/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
function [images, detections, labels] = Collect_helen(root_test_data, use_68)
dataset_loc = [root_test_data, '/helen/testset/'];
landmarkLabels = dir([dataset_loc '\*.pts']);
num_imgs = size(landmarkLabels,1);
images = struct;
if(use_68)
labels = zeros(num_imgs, 68, 2);
else
labels = zeros(num_imgs, 66, 2);
end
detections = zeros(num_imgs, 4);
load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
num_landmarks = 68;
for imgs = 1:num_imgs
[~,name,~] = fileparts(landmarkLabels(imgs).name);
landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
if(~use_68)
inds_frontal = [1:60,62:64,66:68];
landmarks = landmarks(inds_frontal,:);
end
images(imgs).img = [dataset_loc, name '.jpg'];
labels(imgs,:,:) = landmarks;
detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
end
detections(:,3) = detections(:,3) - detections(:,1);
detections(:,4) = detections(:,4) - detections(:,2);
end
|
github | ddtm/OpenFace-master | plotcov2.m | .m | OpenFace-master/matlab_version/experiments_in_the_wild/hierarch_checks/plotcov2.m | 5,217 | utf_8 | 6c070f75d902dd37b4ccc0311074d4c6 | % PLOTCOV2 - Plots a covariance ellipse with major and minor axes
% for a bivariate Gaussian distribution.
%
% Usage:
% h = plotcov2(mu, Sigma[, OPTIONS]);
%
% Inputs:
% mu - a 2 x 1 vector giving the mean of the distribution.
% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving
% the covariance of the distribution (or the zero matrix).
%
% Options:
% 'conf' - a scalar between 0 and 1 giving the confidence
% interval (i.e., the fraction of probability mass to
% be enclosed by the ellipse); default is 0.9.
% 'num-pts' - the number of points to be used to plot the
% ellipse; default is 100.
%
% This function also accepts options for PLOT.
%
% Outputs:
% h - a vector of figure handles to the ellipse boundary and
% its major and minor axes
%
% See also: PLOTCOV3
% Copyright (C) 2002 Mark A. Paskin
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h = plotcov2(mu, Sigma, varargin)
if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end
if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end
[p, ...
n, ...
plot_opts] = process_options(varargin, 'conf', 0.9, ...
'num-pts', 100);
h = [];
holding = ishold;
if (Sigma == zeros(2, 2))
z = mu;
else
% Compute the Mahalanobis radius of the ellipsoid that encloses
% the desired probability mass.
k = conf2mahal(p, 2);
% The major and minor axes of the covariance ellipse are given by
% the eigenvectors of the covariance matrix. Their lengths (for
% the ellipse with unit Mahalanobis radius) are given by the
% square roots of the corresponding eigenvalues.
if (issparse(Sigma))
[V, D] = eigs(Sigma);
else
[V, D] = eig(Sigma);
end
% Compute the points on the surface of the ellipse.
t = linspace(0, 2*pi, n);
u = [cos(t); sin(t)];
w = (k * V * sqrt(D)) * u;
z = repmat(mu, [1 n]) + w;
% Plot the major and minor axes.
L = k * sqrt(diag(D));
h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ...
[mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:});
hold on;
h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ...
[mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})];
end
h = [h; plot(z(1, :), z(2, :), plot_opts{:})];
if (~holding) hold off; end
end
function [varargout] = process_options(args, varargin)
% Check the number of input arguments
n = length(varargin);
if (mod(n, 2))
error('Each option must be a string/value pair.');
end
% Check the number of supplied output arguments
if (nargout < (n / 2))
error('Insufficient number of output arguments given');
elseif (nargout == (n / 2))
warn = 1;
nout = n / 2;
else
warn = 0;
nout = n / 2 + 1;
end
% Set outputs to be defaults
varargout = cell(1, nout);
for i=2:2:n
varargout{i/2} = varargin{i};
end
% Now process all arguments
nunused = 0;
for i=1:2:length(args)
found = 0;
for j=1:2:n
if strcmpi(args{i}, varargin{j})
varargout{(j + 1)/2} = args{i + 1};
found = 1;
break;
end
end
if (~found)
if (warn)
warning(sprintf('Option ''%s'' not used.', args{i}));
args{i}
else
nunused = nunused + 1;
unused{2 * nunused - 1} = args{i};
unused{2 * nunused} = args{i + 1};
end
end
end
% Assign the unused arguments
if (~warn)
if (nunused)
varargout{nout} = unused;
else
varargout{nout} = cell(0);
end
end
end
% CONF2MAHAL - Translates a confidence interval to a Mahalanobis
% distance. Consider a multivariate Gaussian
% distribution of the form
%
% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C)))
%
% where MD(x, m, P) is the Mahalanobis distance from x
% to m under P:
%
% MD(x, m, P) = (x - m) * P * (x - m)'
%
% A particular Mahalanobis distance k identifies an
% ellipsoid centered at the mean of the distribution.
% The confidence interval associated with this ellipsoid
% is the probability mass enclosed by it. Similarly,
% a particular confidence interval uniquely determines
% an ellipsoid with a fixed Mahalanobis distance.
%
% If X is an d dimensional Gaussian-distributed vector,
% then the Mahalanobis distance of X is distributed
% according to the Chi-squared distribution with d
% degrees of freedom. Thus, the Mahalanobis distance is
% determined by evaluating the inverse cumulative
% distribution function of the chi squared distribution
% up to the confidence value.
%
% Usage:
%
% m = conf2mahal(c, d);
%
% Inputs:
%
% c - the confidence interval
% d - the number of dimensions of the Gaussian distribution
%
% Outputs:
%
% m - the Mahalanobis radius of the ellipsoid enclosing the
% fraction c of the distribution's probability mass
%
% See also: MAHAL2CONF
% Copyright (C) 2002 Mark A. Paskin
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function m = conf2mahal(c, d)
m = chi2inv(c, d);
% pr = 0.95 ; c = (1 - pr)/2 ;
% m = norminv([c 1-c],0,1) ;
end |
github | ddtm/OpenFace-master | compute_error_point_to_line_right_eye.m | .m | OpenFace-master/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_right_eye.m | 2,807 | utf_8 | 8a3340813a8f382b8cbc66193d881e21 | function [ error_per_image ] = compute_error_point_to_line_right_eye( ground_truth_all, detected_points_all, occluded )
%compute_error
% compute the average point-to-point Euclidean error normalized by the
% inter-ocular distance (measured as the Euclidean distance between the
% outer corners of the eyes)
%
% Inputs:
% grounth_truth_all, size: num_of_points x 2 x num_of_images
% detected_points_all, size: num_of_points x 2 x num_of_images
% Output:
% error_per_image, size: num_of_images x 1
right_eye_inds_from_68 = [43,44,45,46,47,48,43];
right_eye_inds_from_28 = [9,11,13,15,17,19];
num_of_images = size(ground_truth_all,3);
num_points_gt = size(ground_truth_all,1);
num_points_det = size(detected_points_all,1);
error_per_image = zeros(num_of_images,1);
for i =1:num_of_images
if(num_points_det == 6)
detected_points = detected_points_all(:,:,i);
elseif(num_points_det == 68 || num_points_det == 66)
detected_points = detected_points_all(right_eye_inds_from_68,:,i);
elseif(num_points_det == 28)
detected_points = detected_points_all(right_eye_inds_from_28,:,i);
elseif(num_points_det == 49)
end
ground_truth_points = ground_truth_all(:,:,i);
if(num_points_gt == 66 || num_points_gt == 68)
interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
else
interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
end
sum=0;
for j=1:6
if(j== 1 || j == 6)
% eye corners should align perfectly
sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
else
% points between eye corners measured in distance to the two appropriate line
% segments
sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
end
end
error_per_image(i) = sum/(6*interocular_distance);
end
error_per_image = error_per_image(~occluded);
end
function seg_dist = point_to_segments(point, segments)
seg_dists = zeros(size(segments, 1)-1,1);
for i=1:size(segments, 1)-1
vec1 = point - segments(i,:);
vec2 = segments(i+1,:) - segments(i,:);
d = (vec1 * vec2') / (norm(vec2)^2);
if(d < 0)
seg_dists(i) = norm(vec1);
elseif(d > 1)
seg_dists(i) = norm(point - segments(i+1,:));
else
seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
end
end
seg_dist = min(seg_dists);
end |
github | ddtm/OpenFace-master | compute_error_point_to_line_left_eye.m | .m | OpenFace-master/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_left_eye.m | 2,806 | utf_8 | 7ee0c021e10837321563b4801cb8b499 | function [ error_per_image ] = compute_error_point_to_line_left_eye( ground_truth_all, detected_points_all, occluded )
%compute_error
% compute the average point-to-point Euclidean error normalized by the
% inter-ocular distance (measured as the Euclidean distance between the
% outer corners of the eyes)
%
% Inputs:
% grounth_truth_all, size: num_of_points x 2 x num_of_images
% detected_points_all, size: num_of_points x 2 x num_of_images
% Output:
% error_per_image, size: num_of_images x 1
right_eye_inds_from_68 = [37,38,39,40,41,42,37];
right_eye_inds_from_28 = [9,11,13,15,17,19];
num_of_images = size(ground_truth_all,3);
num_points_gt = size(ground_truth_all,1);
num_points_det = size(detected_points_all,1);
error_per_image = zeros(num_of_images,1);
for i =1:num_of_images
if(num_points_det == 6)
detected_points = detected_points_all(:,:,i);
elseif(num_points_det == 68 || num_points_det == 66)
detected_points = detected_points_all(right_eye_inds_from_68,:,i);
elseif(num_points_det == 28)
detected_points = detected_points_all(right_eye_inds_from_28,:,i);
elseif(num_points_det == 49)
end
ground_truth_points = ground_truth_all(:,:,i);
if(num_points_gt == 66 || num_points_gt == 68)
interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
else
interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
end
sum=0;
for j=1:6
if(j== 1 || j == 6)
% eye corners should align perfectly
sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
else
% points between eye corners measured in distance to the two appropriate line
% segments
sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
end
end
error_per_image(i) = sum/(6*interocular_distance);
end
error_per_image = error_per_image(~occluded);
end
function seg_dist = point_to_segments(point, segments)
seg_dists = zeros(size(segments, 1)-1,1);
for i=1:size(segments, 1)-1
vec1 = point - segments(i,:);
vec2 = segments(i+1,:) - segments(i,:);
d = (vec1 * vec2') / (norm(vec2)^2);
if(d < 0)
seg_dists(i) = norm(vec1);
elseif(d > 1)
seg_dists(i) = norm(point - segments(i+1,:));
else
seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
end
end
seg_dist = min(seg_dists);
end |
github | ddtm/OpenFace-master | compute_brow_error_to_line.m | .m | OpenFace-master/matlab_version/experiments_in_the_wild/hierarch_checks/compute_brow_error_to_line.m | 2,804 | utf_8 | 7039aa3de0164e4ea5bfa788c174dd85 | function [ error_per_image ] = compute_brow_error( ground_truth_all, detected_points_all, occluded )
%compute_error
% compute the average point-to-point Euclidean error of right eye normalized by the
% inter-ocular distance (measured as the Euclidean distance between the
% outer corners of the eyes)
%
% Inputs:
% grounth_truth_all, size: num_of_points x 2 x num_of_images
% detected_points_all, size: num_of_points x 2 x num_of_images
% Output:
% error_per_image, size: num_of_images x 1
brow_inds_from_68 = 18:27;
brow_inds_from_49 = 1:10;
brow_inds_from_66 = 18:27;
num_of_images = size(ground_truth_all,3);
num_points_gt = size(ground_truth_all,1);
num_points_det = size(detected_points_all,1);
error_per_image = zeros(num_of_images,1);
for i =1:num_of_images
if(num_points_det == 10)
detected_points = detected_points_all(:,:,i);
elseif(num_points_det == 68)
detected_points = detected_points_all(brow_inds_from_68,:,i);
elseif(num_points_det == 66)
detected_points = detected_points_all(brow_inds_from_66,:,i);
elseif(num_points_det == 49)
detected_points = detected_points_all(brow_inds_from_49,:,i);
end
ground_truth_points = ground_truth_all(:,:,i);
if(num_points_gt == 66 || num_points_gt == 68)
interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
ground_truth_points = ground_truth_points(brow_inds_from_68,:,:);
else
interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
ground_truth_points = ground_truth_points(brow_inds_from_68,:,:);
end
sum=0;
for j=1:size(detected_points,1)
if(j== 1 || j == 5 || j == 6 || j == 10)
% eye corners should align perfectly
sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
else
sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
end
end
error_per_image(i) = sum/(size(detected_points,1)*interocular_distance);
end
if(nargin > 2)
error_per_image = error_per_image(~occluded);
end
end
function seg_dist = point_to_segments(point, segments)
seg_dists = zeros(size(segments, 1)-1,1);
for i=1:size(segments, 1)-1
vec1 = point - segments(i,:);
vec2 = segments(i+1,:) - segments(i,:);
d = (vec1 * vec2') / (norm(vec2)^2);
if(d < 0)
seg_dists(i) = norm(vec1);
elseif(d > 1)
seg_dists(i) = norm(point - segments(i+1,:));
else
seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
end
end
seg_dist = min(seg_dists);
end |
github | ddtm/OpenFace-master | writeMatrix.m | .m | OpenFace-master/matlab_version/pdm_generation/PDM_helpers/writeMatrix.m | 428 | utf_8 | 3a2c87a966a8dc0f296d992d85f7d445 | % for easier readibility write them row by row
function writeMatrix(fileID, M, type)
fprintf(fileID, '%d\r\n', size(M,1));
fprintf(fileID, '%d\r\n', size(M,2));
fprintf(fileID, '%d\r\n', type);
for i=1:size(M,1)
if(type == 4 || type == 0)
fprintf(fileID, '%d ', M(i,:));
else
fprintf(fileID, '%.9f ', M(i,:));
end
fprintf(fileID, '\r\n');
end
end |
github | ddtm/OpenFace-master | fit_PDM_ortho_proj_to_2D.m | .m | OpenFace-master/matlab_version/pdm_generation/PDM_helpers/fit_PDM_ortho_proj_to_2D.m | 9,871 | utf_8 | 7accc2b0fee769eecc059448c7e69b26 | function [ a, R, T, T3D, params, error, shapeOrtho ] = fit_PDM_ortho_proj_to_2D( M, E, V, shape2D, f, cx, cy)
%FITPDMTO2DSHAPE Summary of this function goes here
% Detailed explanation goes here
params = zeros(size(E));
hidden = false;
% if some of the points are unavailable modify M, V, and shape2D (can
% later infer the actual shape from this)
if(sum(shape2D(:)==0) > 0)
hidden = true;
% which indices to remove
inds_to_rem = shape2D(:,1) == 0 | shape2D(:,2) == 0;
shape2D = shape2D(~inds_to_rem,:);
inds_to_rem = repmat(inds_to_rem, 3, 1);
M_old = M;
V_old = V;
M = M(~inds_to_rem);
V = V(~inds_to_rem,:);
end
num_points = numel(M) / 3;
m = reshape(M, num_points, 3)';
width_model = max(m(1,:)) - min(m(1,:));
height_model = max(m(2,:)) - min(m(2,:));
bounding_box = [min(shape2D(:,1)), min(shape2D(:,2)),...
max(shape2D(:,1)), max(shape2D(:,2))];
a = (((bounding_box(3) - bounding_box(1)) / width_model) + ((bounding_box(4) - bounding_box(2))/ height_model)) / 2;
tx = (bounding_box(3) + bounding_box(1))/2;
ty = (bounding_box(4) + bounding_box(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(m(1,:)) + max(m(1,:)))/2;
ty = ty - a*(min(m(2,:)) + max(m(2,:)))/2;
R = eye(3);
T = [tx; ty];
currShape = getShapeOrtho(M, V, params, R, T, a);
currError = getRMSerror(currShape, shape2D);
reg_rigid = zeros(6,1);
regFactor = 20;
regularisations = [reg_rigid; regFactor ./ E]; % the above version, however, does not perform as well
regularisations = diag(regularisations)*diag(regularisations);
red_in_a_row = 0;
for i=1:1000
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
% Now find the current residual error
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error_res = shape2D - currShape;
eul = Rot2Euler(R);
p_global = [a; eul'; T];
% get the Jacobians
J = CalcJacobian(M, V, params, p_global);
% RLMS style update
p_delta = (J'*J + regularisations) \ (J'*error_res(:) - regularisations*[p_global;params]);
[params, p_global] = CalcReferenceUpdate(p_delta, params, p_global);
a = p_global(1);
R = Euler2Rot(p_global(2:4));
T = p_global(5:6);
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
currShape = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
currShape = currShape';
error = getRMSerror(currShape, shape2D);
if(0.999 * currError < error)
red_in_a_row = red_in_a_row + 1;
if(red_in_a_row == 5)
break;
end
end
currError = error;
end
if(hidden)
shapeOrtho = getShapeOrtho(M_old, V_old, params, R, T, a);
else
shapeOrtho = currShape;
end
if(nargin == 7)
Zavg = f / a;
Xavg = (T(1) - cx) / a;
Yavg = (T(2) - cy) / a;
T3D = [Xavg;Yavg;Zavg];
else
T3D = [0;0;0];
end
end
function [shape2D] = getShapeOrtho(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
shape3D = getShape3D(M, V, p);
shape2D = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape2D] = getShapeOrthoFull(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
T = [T; 0];
shape3D = getShape3D(M, V, p);
shape2D = a * R*shape3D' + repmat(T, 1, numel(M)/3);
shape2D = shape2D';
end
function [shape3D] = getShape3D(M, V, params)
shape3D = M + V * params;
shape3D = reshape(shape3D, numel(shape3D) / 3, 3);
end
function [error] = getRMSerror(shape2Dv1, shape2Dv2)
error = sqrt(mean(reshape(shape2Dv1 - shape2Dv2, numel(shape2Dv1), 1).^2));
end
% This calculates the combined rigid with non-rigid Jacobian
function J = CalcJacobian(M, V, p, p_global)
n = size(M, 1)/3;
non_rigid_modes = size(V,2);
J = zeros(n*2, 6 + non_rigid_modes);
% now the layour is
% ---------- Rigid part -------------------|----Non rigid part--------|
% dx_1/ds, dx_1/dr1, ... dx_1/dtx, dx_1/dty dx_1/dp_1 ... dx_1/dp_m
% dx_2/ds, dx_2/dr1, ... dx_2/dtx, dx_2/dty dx_2/dp_1 ... dx_2/dp_m
% ...
% dx_n/ds, dx_n/dr1, ... dx_n/dtx, dx_n/dty dx_n/dp_1 ... dx_n/dp_m
% dy_1/ds, dy_1/dr1, ... dy_1/dtx, dy_1/dty dy_1/dp_1 ... dy_1/dp_m
% ...
% dy_n/ds, dy_n/dr1, ... dy_n/dtx, dy_n/dty dy_n/dp_1 ... dy_n/dp_m
% getting the rigid part
J(:,1:6) = CalcRigidJacobian(M, V, p, p_global);
% constructing the non-rigid part
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% 'rotate' and 'scale' the principal components
% First reshape to 3D
V_X = V(1:n,:);
V_Y = V(n+1:2*n,:);
V_Z = V(2*n+1:end,:);
J_x_non_rigid = s*(R(1,1)*V_X + R(1,2)*V_Y + R(1,3)*V_Z);
J_y_non_rigid = s*(R(2,1)*V_X + R(2,2)*V_Y + R(2,3)*V_Z);
J(1:n, 7:end) = J_x_non_rigid;
J(n+1:end, 7:end) = J_y_non_rigid;
end
function J = CalcRigidJacobian(M, V, p, p_global)
n = size(M, 1)/3;
% Get the current 3D shape (not affected by global transform, as this
% is how the Jacobian was derived (for derivation please see
% ../derivations/orthoJacobian
shape3D = GetShape3D(M, V, p);
% Get the rotation matrix corresponding to current global orientation
R = Euler2Rot(p_global(2:4));
s = p_global(1);
% Rigid Jacobian is laid out as follows
% dx_1/ds, dx_1/dr1, dx_1/dr2, dx_1/dr3, dx_1/dtx, dx_1/dty
% dx_2/ds, dx_2/dr1, dx_2/dr2, dx_2/dr3, dx_2/dtx, dx_2/dty
% ...
% dx_n/ds, dx_n/dr1, dx_n/dr2, dx_n/dr3, dx_n/dtx, dx_n/dty
% dy_1/ds, dy_1/dr1, dy_1/dr2, dy_1/dr3, dy_1/dtx, dy_1/dty
% ...
% dy_n/ds, dy_n/dr1, dy_n/dr2, dy_n/dr3, dy_n/dtx, dy_n/dty
J = zeros(n*2, 6);
% dx/ds = X * r11 + Y * r12 + Z * r13
% dx/dr1 = s*(r13 * Y - r12 * Z)
% dx/dr2 = -s*(r13 * X - r11 * Z)
% dx/dr3 = s*(r12 * X - r11 * Y)
% dx/dtx = 1
% dx/dty = 0
% dy/ds = X * r21 + Y * r22 + Z * r23
% dy/dr1 = s * (r23 * Y - r22 * Z)
% dy/dr2 = -s * (r23 * X - r21 * Z)
% dy/dr3 = s * (r22 * X - r21 * Y)
% dy/dtx = 0
% dy/dty = 1
% set the Jacobian for x's
% with respect to scaling factor
J(1:n,1) = shape3D * R(1,:)';
% with respect to angular rotation around x, y, and z axes
% Change of x with respect to change in axis angle rotation
dxdR = [ 0, R(1,3), -R(1,2);
-R(1,3), 0, R(1,1);
R(1,2), -R(1,1), 0];
J(1:n,2:4) = s*(dxdR * shape3D')';
% with respect to translation
J(1:n,5) = 1;
J(1:n,6) = 0;
% set the Jacobian for y's
% with respect to scaling factor
J(n+1:end,1) = shape3D * R(2,:)';
% with respect to angular rotation around x, y, and z axes
% Change of y with respect to change in axis angle rotation
dydR = [ 0, R(2,3), -R(2,2);
-R(2,3), 0, R(2,1);
R(2,2), -R(2,1), 0];
J(n+1:end,2:4) = s*(dydR * shape3D')';
% with respect to translation
J(n+1:end,5) = 0;
J(n+1:end,6) = 1;
end
% This updates the parameters based on the updates from the RLMS
function [non_rigid, rigid] = CalcReferenceUpdate(params_delta, current_non_rigid, current_global)
rigid = zeros(6, 1);
% Same goes for scaling and translation parameters
rigid(1) = current_global(1) + params_delta(1);
rigid(5) = current_global(5) + params_delta(5);
rigid(6) = current_global(6) + params_delta(6);
% for rotation however, we want to make sure that the rotation matrix
% approximation we have
% R' = [1, -wz, wy
% wz, 1, -wx
% -wy, wx, 1]
% is a legal rotation matrix, and then we combine it with current
% rotation (through matrix multiplication) to acquire the new rotation
R = Euler2Rot(current_global(2:4));
wx = params_delta(2);
wy = params_delta(3);
wz = params_delta(4);
R_delta = [1, -wz, wy;
wz, 1, -wx;
-wy, wx, 1];
% Make sure R_delta is orthonormal
R_delta = OrthonormaliseRotation(R_delta);
% Combine rotations
R_final = R * R_delta;
% Extract euler angle
euler = Rot2Euler(R_final);
rigid(2:4) = euler;
if(length(params_delta) > 6)
% non-rigid parameters can just be added together
non_rigid = params_delta(7:end) + current_non_rigid;
else
non_rigid = current_non_rigid;
end
end
function R_ortho = OrthonormaliseRotation(R)
% U * V' is basically what we want, as it's guaranteed to be
% orthonormal
[U, ~, V] = svd(R);
% We also want to make sure no reflection happened
% get the orthogonal matrix from the initial rotation matrix
X = U*V';
% This makes sure that the handedness is preserved and no reflection happened
% by making sure the determinant is 1 and not -1
W = eye(3);
W(3,3) = det(X);
R_ortho = U*W*V';
end |
github | ddtm/OpenFace-master | findG.m | .m | OpenFace-master/matlab_version/pdm_generation/nrsfm-em/findG.m | 1,188 | utf_8 | c81e58ce3f1a6f9066f2ecb4cb4dac67 | function G = findG(Rhat)
[F,D] = size(Rhat); F = F/2;
% Build matrix Q such that Q * v = [1,...,1,0,...,0] where v is a six
% element vector containg all six distinct elements of the Matrix C
%clear Q
for f = 1:F,
g = f + F;
h = g + F;
Q(f,:) = zt2(Rhat(f,:), Rhat(f,:));
Q(g,:) = zt2(Rhat(g,:), Rhat(g,:));
Q(h,:) = zt2(Rhat(f,:), Rhat(g,:));
end
% Solve for v
rhs = [ones(2*F,1); zeros(F,1)];
v = Q \ rhs;
% C is a symmetric 3x3 matrix such that C = G * transpose(G)
n = 1;
for x = 1:D,
for y = x:D,
C(x,y) = v(n); C(y,x) = v(n);
n = n+1;
end;
end;
e = eig(C);
%disp(e)
if (any(e<= 0)),
[uu,dd,vv] = svd(C);
G = uu*sqrt(dd);
cc = G*G';
err = sum((cc(:)-C(:)).^2);
%if err>0.03,
if 0 & err>30,
G = [];
dbstack; keyboard
end;
else
G = sqrtm(C);
end
%neg = 0;
%if e(1) <= 0, neg = 1; end
%if e(2) <= 0, neg = 1; end
%if e(3) <= 0, neg = 1; end
%if neg == 1, G = [];
%else G = sqrtm(C);
%end
%-------------------------------------------------
function M = zt2(i,j)
D = length(i);
M = [];
for x = 1:D,
for y = x:D,
if x==y, M = [M, i(x)*j(y)];
else, M = [M, i(x)*j(y)+i(y)*j(x)];
end;
end;
end;
|
github | ddtm/OpenFace-master | prune_observations.m | .m | OpenFace-master/matlab_version/pdm_generation/Wild_data_pdm/prune_observations.m | 1,145 | utf_8 | a9667d8c30c83debfac87a02012109cf | function [ observations ] = prune_observations( observations, percentage_to_keep )
%PRUNE_OBSERVATIONS Summary of this function goes here
% Detailed explanation goes here
distances = pdist(observations, @euclid_dist);
distances = squareform(distances);
m = max(distances(:));
distances(logical(eye(size(distances)))) = m;
to_rem = false(size(observations,1),1);
% need to get rid of the smallest distances
for i=size(observations,1):-1:round(percentage_to_keep * size(observations,1))
[~, ind] = min(distances(:));
[row, col] = ind2sub(size(distances),ind);
% always remove the row?
to_rem(row) = true;
distances(row,:) = m;
distances(:,row) = m;
end
observations = observations(~to_rem,:);
end
function [dist] = euclid_dist(XI, XJ)
x_dist = bsxfun(@plus, XJ(:, 1:end/3), -XI(1:end/3)).^2;
y_dist = bsxfun(@plus, XJ(:, end/3+1:2*end/3), -XI(end/3+1:2*end/3)).^2;
z_dist = bsxfun(@plus, XJ(:, 2*end/3+1:end), - XI(2*end/3+1:end)).^2;
dist = mean(sqrt(x_dist + y_dist + z_dist),2);
end |
github | ddtm/OpenFace-master | writePDM.m | .m | OpenFace-master/matlab_version/pdm_generation/Wild_data_pdm/writePDM.m | 1,235 | utf_8 | b0e7f7dff0c7231a80b75e35435d0828 | function writePDM( V, E, M, outputFile, Vmorph, Emorph )
%WRITEPDM Summary of this function goes here
% Detailed explanation goes here
fId = fopen(outputFile,'w');
% number of elements
% Comment
fprintf(fId, '# The mean values of the components (in mm)\n');
writeMatrix(fId, M, 6);
fprintf(fId, '# The principal components (eigenvectors) of identity or combined identity and expression model\n');
writeMatrix(fId, V, 6);
fprintf(fId, '# The variances of the components (eigenvalues) of identity or combined identity and expression model\n');
writeMatrix(fId, E', 6);
if(nargin > 4)
fprintf(fId, '# The principal components (eigenvectors) of expression\n');
writeMatrix(fId, Vmorph, 6);
fprintf(fId, '# The variances of the components (eigenvalues) of expression\n');
writeMatrix(fId, Emorph', 6);
end
fclose(fId);
end
% for easier readibility write them row by row
function writeMatrix(fileID, M, type)
fprintf(fileID, '%d\n', size(M,1));
fprintf(fileID, '%d\n', size(M,2));
fprintf(fileID, '%d\n', type);
for i=1:size(M,1)
fprintf(fileID, '%f ', M(i,:));
fprintf(fileID, '\n');
end
end |
github | Lumbrer/Racelogic-VBO-Converter-master | Launcher_VBO.m | .m | Racelogic-VBO-Converter-master/Launcher_VBO.m | 3,878 | utf_8 | d7ca0acb13f92f652d0a22f378b5dbcc | function varargout = Launcher_VBO(varargin)
% LAUNCHER_VBO MATLAB code for Launcher_VBO.fig
% LAUNCHER_VBO, by itself, creates a new LAUNCHER_VBO or raises the existing
% singleton*.
%
% H = LAUNCHER_VBO returns the handle to a new LAUNCHER_VBO or the handle to
% the existing singleton*.
%
% LAUNCHER_VBO('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in LAUNCHER_VBO.M with the given input arguments.
%
% LAUNCHER_VBO('Property','Value',...) creates a new LAUNCHER_VBO or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before Launcher_VBO_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to Launcher_VBO_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help Launcher_VBO
% Last Modified by GUIDE v2.5 22-Feb-2016 09:30:38
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Launcher_VBO_OpeningFcn, ...
'gui_OutputFcn', @Launcher_VBO_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
% --- Executes just before Launcher_VBO is made visible.
function Launcher_VBO_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to Launcher_VBO (see VARARGIN)
% Choose default command line output for Launcher_VBO
handles.output = hObject;
handles.p=gobjects(0);
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Launcher_VBO wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Launcher_VBO_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in startbutton.
function startbutton_Callback(hObject, eventdata, handles)
% hObject handle to startbutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
clc
if ishghandle(handles.p)
close(handles.p);
end
handles.p=figure('name','RaceLogic VBOX File Converter V1.0','units','normalized','NumberTitle','off','outerposition',[0.2 0.1 0.6 0.8],'ToolBar','none','Visible','off');
VBO_GUI_APP(handles.p)
if ishghandle(handles.p)
set(handles.p,'Visible','on')
end
guidata(hObject, handles);
% --- Executes during object deletion, before destroying properties.
function figure1_DeleteFcn(hObject, eventdata, handles)
if ishghandle(handles.p)
close(handles.p);
end
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
|
github | mindcont/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% ****************************************************************************
% For detailed documentation and usage on Caffe's Matlab interface, please
% refer to the Caffe Interface Tutorial at
% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
% ****************************************************************************
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
% maxlabel the label of the highest score
%
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
% and what versions are installed.
%
% Usage:
% im = imread('../../examples/images/cat.jpg');
% scores = classification_demo(im, 1);
% [score, class] = max(scores);
% Five things to be aware of:
% caffe uses row-major order
% matlab uses column-major order
% caffe uses BGR color channel order
% matlab uses RGB color channel order
% images need to have the data mean subtracted
% Data coming in from matlab needs to be in the order
% [width, height, channels, images]
% where width is the fastest dimension.
% Here is the rough matlab code for putting image data into the correct
% format in W x H x C with BGR channels:
% % permute channels from RGB to BGR
% im_data = im(:, :, [3, 2, 1]);
% % flip width and height to make width the fastest dimension
% im_data = permute(im_data, [2, 1, 3]);
% % convert from uint8 to single
% im_data = single(im_data);
% % reshape to a fixed size (e.g., 227x227).
% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% % subtract mean_data (already in W x H x C with BGR channels)
% im_data = im_data - mean_data;
% If you have multiple images, cat them with cat(4, ...)
% Add caffe/matlab to your Matlab search PATH in order to use matcaffe
if exist('../+caffe', 'dir')
addpath('..');
else
error('Please run this demo from caffe/matlab/demo');
end
% Set caffe mode
if exist('use_gpu', 'var') && use_gpu
caffe.set_mode_gpu();
gpu_id = 0; % we will use the first gpu in this demo
caffe.set_device(gpu_id);
else
caffe.set_mode_cpu();
end
% Initialize the network using BVLC CaffeNet for image classification
% Weights (parameter) file needs to be downloaded from Model Zoo.
model_dir = '../../models/bvlc_reference_caffenet/';
net_model = [model_dir 'deploy.prototxt'];
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
phase = 'test'; % run with phase test (so that dropout isn't applied)
if ~exist(net_weights, 'file')
error('Please download CaffeNet from Model Zoo before you run this demo');
end
% Initialize a network
net = caffe.Net(net_model, net_weights, phase);
if nargin < 1
% For demo purposes we will use the cat image
fprintf('using caffe/examples/images/cat.jpg as input image\n');
im = imread('../../examples/images/cat.jpg');
end
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};
toc;
% do forward pass to get scores
% scores are now Channels x Num, where Channels == 1000
tic;
% The net forward function. It takes in a cell array of N-D arrays
% (where N == 4 here) containing data of input blob(s) and outputs a cell
% array containing data from output blob(s)
scores = net.forward(input_data);
toc;
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
[~, maxlabel] = max(scores);
% call caffe.reset_all() to reset caffe
caffe.reset_all();
% ------------------------------------------------------------------------
function crops_data = prepare_image(im)
% ------------------------------------------------------------------------
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
% is already in W x H x C with BGR channels
d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% Convert an image returned by Matlab's imread to im_data in caffe's data
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data
im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR)
% oversample (4 corners, center, and their x-axis flips)
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = ...
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
|
github | bpalmintier/mepo-master | PlanDiff.m | .m | mepo-master/models/MATLAB/PlanDiff.m | 18,520 | utf_8 | 981012ed2b74865931a225de6e91b337 | function [ out, gen, excel_paste ] = PlanDiff(runs)
%PLANDIFF compute difference metrics between clustered an separate unit commit outputs
%
% [ out, gen, excel_paste ] = PlanDiff(runs)
% Allows the user to enter run information. RUNS can either be a struct
% array with dir and prefix fields for each run, or a cell array with
% dir information in the first column and output file name in the
% second. Blank entries in RUNS are used to divide comparision groups.
% The first run in each group is used as the baseline
%
% Note: only summary output file is required for comparison
%
% IMPORTANT: Scenarios within a group are assumed to have identical
% generator lists & for these lists to appear in the same order in the
% summary file
% HISTORY
% ver date time who changes made
% --- ---------- ----- ------------- ---------------------------------------
% 1 2012-08-26 23:30 BryanP Adapted from OpsDiff v10
% 2 2012-08-28 22:40 BryanP Prevent negative adjusted peaker capacity
% 3 2012-08-29 01:40 BryanP Clean selected non-numeric data
% 4 2012-08-29 23:10 BryanP Exclude non-expandable gens from new gen metrics
% 5 2012-09-03 12:15 BryanP Update excel_paste. Fix handling of missing/bad entries
% 6 2012-09-03 18:15 BryanP Added support for operations only runs by UnitCommit
% 7 2012-09-06 21:45 BryanP Also read in raw ops data
% 8 2012-09-17 18:15 BryanP Compute effective planning marg, Expand to Excel paste data
% 9 2012-10-06 19:25 BryanP Add par_threads to Excel data
% 10 2013-06-10 16:00 BryanP Ignore run codes starting with _
% 11 2013-12-31 14:50 BryanP Add missing summary fields for LP only runs
%-- Setup defaults
if nargin < 1 || isempty(runs)
runs = {'../capplan/out/' 'SCP_'
'../capplan/out2/' 'SCP_'
};
end
% If our run list is provided as a cell matrix, convert it to a structure
if iscell(runs)
runs = struct('dir', runs(:,1), 'run_code', runs(:,2));
end
%-- Read in scenario data
n_runs = length(runs);
r = 1;
baseline=1;
gen_data_is_read = false;
for r =1:n_runs
%Advance to the next valid run (non-blank code)
if isempty(runs(r).run_code)
if baseline~=r
%Compute statistics for last group
out(baseline:(r-1)) = PlanDiffHelper(in(baseline:(r-1)), gen);
end
baseline = r+1;
continue
end
if runs(r).run_code(1) == '_'
%Skip run codes beginning with _, typically those with blank real data
%but a non-blank modifier such as Y for ops, resulting in _ops
baseline = r+1;
continue
end
%Read output files. Note: true indicates summary only
raw_in = OpsRead(runs(r).dir, runs(r).run_code, true);
%Only store data if we read the input successfully. Skip NaN's to leave
%a blank entry
if isstruct(raw_in)
if isempty(strfind(runs(r).run_code,'_ops'))
fprintf(' Ops: ')
raw_in.ops = OpsRead(runs(r).dir, [runs(r).run_code '_ops'], true);
else
raw_in.ops = NaN;
end
in(r) = raw_in;
%Read in generator data on first successful data read
if not(gen_data_is_read)
gen = GenDataHelper(in(r).summary);
gen_data_is_read = true;
if not(isequal(sort(in(r).g_names), sort({gen.list.name}'))) %#ok<TRSRT> transpose needed to convert to column vector
error('advPwr:PlanDiff:gen_mismatch', ...
'Generator names from datafile don''t match those in summary')
end
%Reorder gen_data from data file to match that from summary
% This is required to handle GAMS reordering of wind
[~, gen_summary_order] = sort(in(r).g_names);
[~, gen_data_order] = sort({gen.list.name}'); %#ok<TRSRT> transpose needed to convert to column vector
gen.list(gen_summary_order) = gen.list(gen_data_order);
%-- And identify handy generator masks (using the new order)
%Identify renewables
gen.renew_mask = strcmpi('wind', horzcat({gen.list.fuel})) ...
| strcmpi('solar', horzcat({gen.list.fuel}));
gen.therm_mask = not(gen.renew_mask);
gen.expand_mask = [gen.list.cap_cur] < [gen.list.cap_max];
gen.n_expand = nnz(gen.expand_mask);
gen.g_expand_names = in(r).g_names(gen.expand_mask);
gen.renew_expand_mask = gen.renew_mask(gen.expand_mask);
gen.therm_expand_mask = gen.therm_mask(gen.expand_mask);
if not(gen.expand_mask(gen.peaker.idx))
error('AdvPwr:PlanDiff:PeakerNotExpand','The identified peaker is not elligable for expansion')
end
gen.peaker.expand_idx = nnz(gen.expand_mask(1:gen.peaker.idx));
end
else
if exist('in','var') && not(isempty(in))
in(r).run_code = runs(r).run_code;
end
end
end
% Avoid cryptic error messages when data not read. In this case "in" will
% only have the 'run_code' field, and hence length of 1.
if length(fields(in)) == 1
error('Unable to read any results. Check paths (includig trailing /) and try again... quitting')
end
%Compute difference statistics for final (or only) group
out(baseline:r) = PlanDiffHelper(in(baseline:r), gen);
%-- Create variable for easy Excel cut and paste
if nargout > 2
excel_paste = {};
fprintf('Creating Excel Paste data...')
%work backwards to init size with first assignment
for r = 1:n_runs
if not(isempty(out(r).summary))
%Hack to add in missing fields for LP only solves
if not(isfield(out(r).summary, 'run_mip_gap'))
out(r).summary.run_mip_gap = 0;
end
excel_paste(r,:) = ...
horzcat(...
{
out(r).summary.run_model_name
out(r).summary.RPS_target_fraction
out(r).summary.renew_fraction
out(r).summary.in_CO2e_cap_Kt
out(r).summary.CO2e_total_Mt
out(r).summary.in_CO2e_cost_usd_ton
out(r).summary.CO2e_price_usd_t
out(r).summary.run_mip_gap
out(r).summary.run_modstat
out(r).summary.run_solstat
out(r).summary.run_solver_time_sec
out(r).summary.valflag_par_threads
gen.n
gen.n_expand
out(r).summary.valflag_uc_ignore_unit_min
out(r).summary.valflag_uc_int_unit_min
out(r).summary.demand_max_GW
out(r).summary.flag_maint
out(r).summary.valflag_rsrv
out(r).summary.flag_ramp
out(r).summary.flag_unit_commit
out(r).summary.flag_startup
out(r).summary.flag_min_up_down
out(r).summary.flag_derate
out(r).summary.flag_derate_to_maint
out(r).summary.valflag_plan_margin
out(r).summary.valflag_rel_cheat
out(r).summary.valflag_mip_gap
out(r).summary.flag_adj_rsrv_for_nse
out(r).summary.cost_capital_Musd
out(r).summary.cost_ops_Musd
out(r).summary.cost_total_Musd
out(r).cost_tot_actual
out(r).norm_e_mix_rms
out(r).ops_norm_e_mix_rms
out(r).norm_new_cap_rms
}', ...
num2cell(out(r).new_cap), ...
{
out(r).eff_plan_marg
}', ...
num2cell(out(r).energy), ...
{
out(r).summary.energy_non_served_GWh
out(r).summary.shed_GWh_wind
out(r).summary.model_num_eq
out(r).summary.model_num_var
out(r).summary.model_num_discrete_var
out(r).summary.model_num_nonzero
out(r).summary.memo
}'); %#ok<AGROW>
else
if size(excel_paste,2)>1 || r==n_runs
%Make sure we have a results row for all runs
excel_paste{r,1} = 0; %#ok<AGROW>
end
end
end
end
fprintf('Done\n')
end
%% =========== Helper functions ==========
%----------------
% GenDataHelper
%----------------
function gen = GenDataHelper(first_data)
%Read in and process required generator data
%-- Read in generator parameters
%setup structure with filenames
gen.data_file = first_data.data_gens;
gen.add_data_file = first_data.data_gparams;
%Read data from file. true indicates verbose
gen = CpDpReadGenData(gen, first_data.data_dir, true);
%-- Find peaker
% compute per gen fixed costs
for g = 1:gen.n
%Fixed operating costs and payments on capital (M$/GW-yr)
gen.list(g).c_fix = ...
gen.list(g).c_fix_om ...
+ gen.list(g).c_cap ...
* CapitalRecoveryFactor(first_data.WACC, gen.list(g).life);
end
gen.peaker.c_fix = min(vertcat(gen.list.c_fix));
%If there is more than one with same lowest cost, pick last generator
%assuming it is the new
gen.peaker.idx = find(vertcat(gen.list.c_fix) == gen.peaker.c_fix, 1, 'last');
gen.peaker.name = gen.list(gen.peaker.idx).name;
end
%----------------
% PlanDiffHelper
%----------------
function data = PlanDiffHelper(data, gen)
%Compute the differences for one group of runs
n_runs = length(data);
baseline = 1;
if n_runs == 0
return
end
fprintf(' Computing difference statistics for %d runs...', n_runs)
%-- Compute values per scenario
for r = 1:n_runs
if isempty(data(r).summary)
if r == baseline
warning('AdvPwr:PlanDiff:BadBaseline','Bad data for baseline, using next run as baseline')
baseline=r+1;
end
continue
end
% Add any missing fields
if not(isfield(data(r).summary,'flag_derate_to_maint'))
data(r).summary.flag_derate_to_maint = 0;
end
if not(isfield(data(r).summary,'flag_adj_rsrv_for_nse'))
data(r).summary.flag_adj_rsrv_for_nse = 0;
end
if not(isfield(data(r).summary,'cost_capital_Musd'))
data(r).summary.cost_capital_Musd = '';
end
% Extract per generator quantities
% New capacity only for those eligiable for expansion
for g = gen.n_expand:-1:1
f_name = ['cap_new_GW_',gen.g_expand_names{g}];
if isfield(data(r).summary, f_name)
data(r).new_cap(g) = data(r).summary.(f_name);
else
data(r).new_cap(g) = NaN;
end
end
% Total capacity and energy for all gens
for g = gen.n:-1:1
data(r).tot_cap(g) = data(r).summary.(['cap_total_GW_',data(r).g_names{g}]);
data(r).energy(g) = data(r).summary.(['energy_TWh_',data(r).g_names{g}]);
end
data(r).nonserved = data(r).summary.energy_non_served_GWh/1000; %Convert to TWh
% % energy fraction
% data(r).e_fract = data(r).energy/sum(data(r).energy);
% new capacity fraction
data(r).new_cap_fract = data(r).new_cap/sum(data(r).new_cap);
% % variations on total capacity
% data(r).new_therm_cap = data(r).new_cap(not(gen.renew_expand_mask));
% data(r).new_renew_cap = data(r).new_cap(gen.renew_expand_mask);
% data(r).sum_new_therm_cap = sum(data(r).new_therm_cap);
% data(r).sum_new_renew_cap = sum(data(r).new_renew_cap);
data(r).tot_firm_cap = sum(data(r).tot_cap .* [gen.list.cap_credit]);
data(r).eff_plan_marg = (data(r).tot_firm_cap - data(r).summary.demand_max_GW)/data(r).summary.demand_max_GW;
if isstruct(data(r).ops) && not(isempty(data(r).ops.summary))...
&& data(r).ops.solved...
&& isempty(strfind(data(r).ops.summary.run_modstat, 'Infeasible'))
data(r).cost_tot_actual = ...
data(r).summary.cost_capital_Musd...
+ data(r).ops.summary.cost_ops_Musd;
for g = gen.n:-1:1
data(r).ops.energy(g) = data(r).ops.summary.(['energy_TWh_',data(r).g_names{g}]);
end
elseif not(isempty(strfind(data(r).run_code,'_full')))...
&& isempty(strfind(data(r).run_code,'_ops'))
% Use full results as their own ops results
data(r).cost_tot_actual = ...
data(r).summary.cost_total_Musd;
else
data(r).cost_tot_actual = NaN;
end
end
%-- compute relative metrics
for r = 1:n_runs
if isempty(data(r).summary)
continue
end
% Scalar quantities from summary
f_list = { 'cost_total_Musd'
'CO2e_total_Mt'
'CO2e_price_usd_t'
'energy_non_served_GWh'
'renew_fraction'
};
for f = 1:size(f_list)
f_name = f_list{f};
% Clean up non-numeric values
if not(isnumeric(data(r).summary.(f_name)))
data(r).summary.(f_name) = NaN;
end
%Comparisons with baseline
data(r).([f_name, '_diff']) = data(r).summary.(f_name) - data(baseline).summary.(f_name);
data(r).([f_name, '_predict_err']) = abs(data(r).([f_name, '_diff']))...
/ data(r).summary.(f_name);
data(r).([f_name, '_pdiff']) = abs(data(r).([f_name, '_diff']))...
/ data(baseline).summary.(f_name);
%Comparisons with our own operations run
if isstruct(data(r).ops) && not(isempty(data(r).ops.summary))
data(r).([f_name, '_ops_diff']) = data(r).summary.(f_name) - data(r).ops.summary.(f_name);
data(r).([f_name, '_ops_predict_err']) = abs(data(r).([f_name, '_ops_diff']))...
/ data(r).summary.(f_name);
data(r).([f_name, '_ops_pdiff']) = abs(data(r).([f_name, '_ops_diff']))...
/ data(baseline).summary.(f_name);
else
data(r).([f_name, '_ops_diff']) = NaN;
data(r).([f_name, '_ops_predict_err']) = NaN;
data(r).([f_name, '_ops_pdiff']) = NaN;
end
end
%Mean absolute difference metrics
% data(r).e_fract_madiff = mean(abs(data(r).e_fract - data(baseline).e_fract));
% data(r).new_cap_fract_madiff = mean(abs(data(r).new_cap_fract - data(baseline).new_cap_fract));
% data(r).new_cap_type_madiff = mean(abs(data(r).new_cap - data(baseline).new_cap));
%% ----- RMS Diff metrics -----
% Policy analyst: Energy vs baseline (first run) operations...
if isstruct(data(baseline).ops) ...
&& not(isempty(data(baseline).ops.summary)) ...
&& data(baseline).ops.solved
data(r).e_mix_rms = rms(data(r).energy - data(baseline).ops.energy);
data(r).norm_e_mix_rms = data(r).e_mix_rms/mean(data(baseline).ops.energy);
else
data(r).e_mix_rms = NaN;
data(r).norm_e_mix_rms = NaN;
end
% data(r).e_fract_rms = rms(data(r).e_fract - data(baseline).e_fract);
% data(r).new_cap_fract_rms = rms(data(r).new_cap_fract - data(baseline).new_cap_fract);
% Utility: Ops Energy vs plan energy...
if isstruct(data(r).ops) ...
&& not(isempty(data(r).ops.summary)) ...
&& data(r).ops.solved
data(r).ops_e_mix_rms = rms(data(r).energy - data(r).ops.energy);
data(r).ops_norm_e_mix_rms = data(r).ops_e_mix_rms/mean(data(r).energy);
else
data(r).ops_e_mix_rms = NaN;
data(r).ops_norm_e_mix_rms = NaN;
end
% Capacity vs baseline
data(r).new_cap_type_rms = rms(data(r).new_cap - data(baseline).new_cap);
data(r).norm_new_cap_rms = data(r).new_cap_type_rms/mean(data(baseline).new_cap);
%Additional metrics
% data(r).norm_new_cap_madiff = data(r).new_cap_type_madiff/sum(data(baseline).new_cap);
% data(r).new_therm_cap_pdiff = (data(r).new_therm_cap - data(baseline).new_therm_cap)...
% / data(baseline).new_therm_cap;
% data(r).new_renew_cap_pdiff = (data(r).new_renew_cap - data(baseline).new_renew_cap)...
% / data(baseline).new_renew_cap;
%% Planning margin capacity adjustment calculations (affects peaker capacity)
% Not used... run GAMS with adjusted planning margin instead!
% data(r).firm_diff = data(baseline).tot_firm_cap - data(r).tot_firm_cap;
% data(r).peaker_cap_adj = data(r).firm_diff / gen.list(gen.peaker.idx).cap_credit;
%
% data(r).adj_new_cap = data(r).new_cap;
% data(r).adj_new_cap(gen.peaker.expand_idx) = max(0, data(r).adj_new_cap(gen.peaker.expand_idx) ...
% + data(r).peaker_cap_adj);
% %Adjusted Metrics
% data(r).adj_cost_total_Musd_pdiff = data(r).cost_total_Musd_pdiff ...
% + data(r).peaker_cap_adj * gen.peaker.c_fix;
% data(r).adj_cap_mix_madiff = mean(abs(data(r).adj_new_cap - data(baseline).new_cap));
% data(r).adj_cap_mix_madiff_norm = data(r).adj_cap_mix_madiff / sum(data(baseline).new_cap);
%
% data(r).adj_cap_mix_rms = sqrt(mean((data(r).adj_new_cap - data(baseline).new_cap).^2));
% data(r).adj_cap_mix_rms_norm = data(r).adj_cap_mix_rms / sum(data(baseline).new_cap);
%
% data(r).adj_new_therm_cap = data(r).adj_new_cap(gen.therm_expand_mask);
% data(r).adj_new_therm_cap_pdiff = (sum(data(r).adj_new_therm_cap) - sum(data(baseline).adj_new_therm_cap))...
% / sum(data(baseline).adj_new_therm_cap);
% data(r).abs_adj_new_therm_cap_pdiff = abs(data(r).adj_new_therm_cap_pdiff);
end
fprintf('Done\n\n')
end
|
github | ajinkyakadu/ParametricLevelSet-master | QGNewton.m | .m | ParametricLevelSet-master/MATLAB/QGNewton.m | 5,373 | utf_8 | 44bb2a449a7a1de131d4d617a574ccd8 | function [x] = QGNewton(fh, x0, options)
%QGNewton A simple L-BFGS method with Wolfe linesearch for optimization.
%
% [xn, info] = QGNewton(fh, x0, options) minimizes an objective function
% using the L-BFGS method with a Wolfe linesearch strategy.
%
% INPUTS:
% fh - A function handle to the misfit function. The misfit function must
% have the form [f, g] = fh(x) where f is the function value and g
% is the gradient, both with the same size as the input vector x.
% x0 - The initial guess for the optimization solution.
% options - An optional structure containing the following fields:
% maxIter - The maximum number of iterations [default 10].
% optTol - The tolerance on the 2-norm of the gradient [default 1e-6].
% M - The history size [default 5].
% fid - The file ID for output [default 1].
% write - A flag indicating whether to save iterates to disk [default 0].
%
% OUTPUTS:
% xn - The final estimate of the optimization solution.
%
% Author: Tristan van Leeuwen
% Mathematical Institute, Utrecht University, The Netherlands
%
% Date: February 2012
if nargin < 3
options = [];
end
% Parse the options structure.
M = GetOptions(options, 'M', 5);
fid = GetOptions(options, 'fid', 1);
itermax = GetOptions(options, 'maxIter', 10);
tol = GetOptions(options, 'optTol', 1e-6);
write = GetOptions(options, 'write', 0);
init_step = GetOptions(options, 'init_step', 1);
% Initialize variables.
n = length(x0);
converged = 0;
iter = 0;
x = x0;
S = zeros(n, 0);
Y = zeros(n, 0);
% Perform initial evaluation.
[f, g] = fh(x);
nfeval = 1;
fprintf(fid, '# iter, # eval, stepsize, f(x), ||g(x)||_2\n');
fprintf(fid, '%6d, %6d, %1.2e, %1.5e, %1.5e\n', iter, nfeval, 1, f, norm(g));
if write
dlmwrite(['x_' num2str(iter) '.dat'], x);
end
% Main optimization loop.
while ~converged
% Compute search direction.
s = B(-g, S, Y);
p = -(s' * g) / (g' * g);
if (p < 0)
fprintf(fid,'Loss of descent, reset history\n');
S = zeros(n,0);
Y = zeros(n,0);
s = B(-g,S,Y);
end
% linesearch
[ft,gt,lambda,lsiter] = WolfeLineSearch(fh,x,f,g,s,init_step);
nfeval = nfeval + lsiter;
% update
xt = x + lambda*s;
S = [S (xt - x)];
Y = [Y (gt - g)];
if (size(S,2) > M)
S = S(:,end-M+1:end);
Y = Y(:,end-M+1:end);
end
f = ft;
g = gt;
x = xt;
iter = iter + 1;
fprintf(fid,'%6d, %6d, %1.2e, %1.5e, %1.5e\n',iter,nfeval,lambda,f,norm(g));
if write
dlmwrite(['x_' num2str(iter) '.dat'],x);
end
% check convergence
converged = (iter > itermax) || (norm(g) < tol) || (lambda < tol);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function z = B(x, S, Y)
% APPLY_LBFGS_INVERSE_HESSIAN_TO_VECTOR
%
% The following function applies the L-BFGS inverse Hessian to a given
% vector.
%
% INPUTS:
% x - Column vector of length n.
% S - Matrix of history of steps with size n x M.
% Y - Matrix of history of gradient differences with size n x M.
%
% OUTPUTS:
% z - Column vector of length n, obtained as the result of the application
% of the L-BFGS inverse Hessian to x.
M = size(S, 2);
% Initialize variables for calculation.
alpha = zeros(M, 1);
rho = zeros(M, 1);
% Calculate the values of rho.
for k = 1:M
rho(k) = 1 / (Y(:, k)' * S(:, k));
end
q = x;
% Perform the first recursion.
for k = M:-1:1
alpha(k) = rho(k) * S(:, k)' * q;
q = q - alpha(k) * Y(:, k);
end
% Apply the initial approximation of the Hessian.
if M > 0
a = (Y(:, end)' * S(:, end)) / (Y(:, end)' * Y(:, end));
else
a = 1 / norm(x, 1);
end
z = a * q;
% Perform the second recursion.
for k = 1:M
beta = rho(k) * (Y(:, k)' * z);
z = z + (alpha(k) - beta) * S(:, k);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ft,gt,lambda,lsiter] = WolfeLineSearch(FunctionHandle, ...
InitialGuess, InitialFunctionValue, InitialGradient, ...
SearchDirection, OptionalInitialStep)
% Implements the Simple Wolfe Line Search algorithm,
% adapted from the source
% (http://cs.nyu.edu/overton/mstheses/skajaa/msthesis.pdf, Algorithm 3).
lsiter = 0;
FirstConstant = 1e-2;
SecondConstant = 0.9;
TerminationIndicator = false;
LowerBound = 0;
UpperBound = Inf;
InitialLambda = 0.5;
if OptionalInitialStep
InitialLambda = 0.1 * InitialLambda * norm(InitialGuess) / ...
norm(SearchDirection);
end
while ~TerminationIndicator
if UpperBound < Inf
CurrentLambda = (UpperBound + LowerBound) / 2;
else
CurrentLambda = 2 * InitialLambda;
end
if lsiter < 10
[ft,gt] = FunctionHandle(InitialGuess + ...
(CurrentLambda * SearchDirection));
lsiter = lsiter + 1;
else
CurrentLambda = 0;
break;
end
if (ft > InitialFunctionValue + FirstConstant * CurrentLambda ...
* (InitialGradient' * SearchDirection))
UpperBound = CurrentLambda;
elseif ((gt' * SearchDirection) < SecondConstant * ...
(InitialGradient' * SearchDirection))
LowerBound = CurrentLambda;
else
TerminationIndicator = true;
end
end
lambda = CurrentLambda;
end |
github | RadioFreeAsia/RDacity-master | sndfile_save.m | .m | RDacity-master/lib-src/libsndfile/Octave/sndfile_save.m | 1,595 | utf_8 | e111c414a56ad9be6860d082f0de0cca | ## Copyright (C) 2002-2011 Erik de Castro Lopo
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2, or (at your option)
## any later version.
##
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this file. If not, write to the Free Software Foundation,
## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
## -*- texinfo -*-
## @deftypefn {Function File} {} sndfile_save (@var{filename, data, fs})
## Save the given @var{data} as audio data to the given at @var{fs}. Set
## the sample rate to @var{fs}.
## @end deftypefn
## Author: Erik de Castro Lopo <[email protected]>
## Description: Save data as a sound file
function sndfile_save (filename, data, fs)
if nargin != 3,
error ("Need three input arguments: filename, data and fs.") ;
endif
if (! isstr (filename)),
error ("First parameter 'filename' is must be a string.") ;
endif
if (max (size (fs)) > 1),
error ("Second parameter 'fs' must be a single value, not an array or matrix.") ;
endif
[nr nc] = size (data) ;
if (nr > nc),
data = data' ;
endif
samplerate = fs ;
wavedata = data ;
str = sprintf ("save -mat-binary %s samplerate wavedata", filename) ;
eval (str) ;
endfunction
|
github | RadioFreeAsia/RDacity-master | sndfile_play.m | .m | RDacity-master/lib-src/libsndfile/Octave/sndfile_play.m | 1,558 | utf_8 | 08c37ba08d4a75136216b4c844420b00 | ## Copyright (C) 2002-2011 Erik de Castro Lopo
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2, or (at your option)
## any later version.
##
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this file. If not, write to the Free Software Foundation,
## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
## -*- texinfo -*-
## @deftypefn {Function File} {} sndfile_play (@var{data, fs})
## Play @var{data} at sample rate @var{fs} using the sndfile-play
## program.
## @end deftypefn
## Author: Erik de Castro Lopo <[email protected]>
## Description: Play the given data as a sound file
function sndfile_play (data, fs)
if nargin != 2,
error ("Need two input arguments: data and fs.") ;
endif
if (max (size (fs)) > 1),
error ("Second parameter fs must be a single value.") ;
endif
[nr nc] = size (data) ;
if (nr > nc),
data = data' ;
endif
samplerate = fs ;
wavedata = data ;
filename = tmpnam () ;
cmd = sprintf ("save -mat-binary %s fs data", filename) ;
eval (cmd) ;
cmd = sprintf ("sndfile-play %s", filename) ;
[output, status] = system (cmd) ;
if (status),
disp (outout) ;
endif
endfunction
|
github | RadioFreeAsia/RDacity-master | sndfile_load.m | .m | RDacity-master/lib-src/libsndfile/Octave/sndfile_load.m | 1,483 | utf_8 | 06ed17568a7d51c3e166c6907a5e6ba9 | ## Copyright (C) 2002-2011 Erik de Castro Lopo
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2, or (at your option)
## any later version.
##
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this file. If not, write to the Free Software Foundation,
## 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
## -*- texinfo -*-
## @deftypefn {Function File} {} sndfile_load (@var{filename})
## Load data from the file given by @var{filename}.
## @end deftypefn
## Author: Erik de Castro Lopo <[email protected]>
## Description: Load the sound data from the given file name
function [data fs] = sndfile_load (filename)
if (nargin != 1),
error ("Need an input filename") ;
endif
samplerate = -1 ;
samplingrate = -1 ;
wavedata = -1 ;
eval (sprintf ('load -f %s', filename)) ;
if (samplerate > 0),
fs = samplerate ;
elseif (samplingrate > 0),
fs = samplingrate ;
else
error ("Not able to find sample rate.") ;
endif
if (max (size (wavedata)) > 1),
data = wavedata ;
else
error ("Not able to find waveform data.") ;
endif
endfunction
|
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