plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
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' }, ...
... |
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.tx... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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', ...
... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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 R... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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', ...
{ 'nnCostFunct... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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', ...
}, ..... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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 Fun... |
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] = p... |
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 Fa... |
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
%
% ... |
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-... |
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... |
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,... |
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)
im... |
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
... |
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: w... |
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 = ... |
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,... |
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
... |
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);
weightSu... |
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: w... |
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 NUMBE... |
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 'fi... |
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 pref... |
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
... |
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 wi... |
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] ... |
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] ... |
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... |
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... |
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]... |
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, ... |
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
% ---... |
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;
... |
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... |
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 = ce... |
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(... |
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,:));
... |
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 sh... |
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
... |
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 (c... |
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... |
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/');... |
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/');... |
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... |
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(... |
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(triangul... |
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
da... |
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... |
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... |
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(... |
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
% ... |
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)
%
% I... |
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)
%
% In... |
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:... |
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,:));
... |
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 (c... |
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,:));
... |
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(log... |
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);
... |
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*.
%
% LA... |
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)
%
% *****... |
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 ... |
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... |
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 distribute... |
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 distribute... |
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 distribute... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.