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 | shkabko/Machine-learning-Stanford-master | loadjson.m | .m | Machine-learning-Stanford-master/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | shkabko/Machine-learning-Stanford-master | loadubjson.m | .m | Machine-learning-Stanford-master/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | shkabko/Machine-learning-Stanford-master | saveubjson.m | .m | Machine-learning-Stanford-master/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | shkabko/Machine-learning-Stanford-master | submit.m | .m | Machine-learning-Stanford-master/ex5/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 | shkabko/Machine-learning-Stanford-master | submitWithConfiguration.m | .m | Machine-learning-Stanford-master/ex5/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 | shkabko/Machine-learning-Stanford-master | savejson.m | .m | Machine-learning-Stanford-master/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | shkabko/Machine-learning-Stanford-master | loadjson.m | .m | Machine-learning-Stanford-master/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | shkabko/Machine-learning-Stanford-master | loadubjson.m | .m | Machine-learning-Stanford-master/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | shkabko/Machine-learning-Stanford-master | saveubjson.m | .m | Machine-learning-Stanford-master/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex2/ex2/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 | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex4/ex4/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 | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | porterStemmer.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex6/ex6/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 | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex7/ex7/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 | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/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 | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | afdiaz/ml-stanford-coursera-master | submit.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | afdiaz/ml-stanford-coursera-master | submitWithConfiguration.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/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 | afdiaz/ml-stanford-coursera-master | savejson.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | afdiaz/ml-stanford-coursera-master | loadjson.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | afdiaz/ml-stanford-coursera-master | loadubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | afdiaz/ml-stanford-coursera-master | saveubjson.m | .m | ml-stanford-coursera-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | UCL-SML/gp-adf-master | minimize.m | .m | gp-adf-master/minimize.m | 11,198 | utf_8 | c8ef15d46aa4df5aa90002deeeaa0f64 | function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function using conjugate gradients.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to the funct... |
github | UCL-SML/gp-adf-master | sq_dist.m | .m | gp-adf-master/sq_dist.m | 1,967 | utf_8 | 4b47740ab9df8ebf0acd5ae2d557acef | % sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the fir... |
github | UCL-SML/gp-adf-master | hypCurb.m | .m | gp-adf-master/hypCurb.m | 2,378 | utf_8 | 2aa8259c63afdfece72ccc8a31ef5e82 | %% hypCurb.m
% *Summary:* Wrapper for GP training (via gpr.m), penalizing large SNR and
% extreme length-scales to avoid numerical instabilities
%
% function [f df] = hypCurb(lh, covfunc, x, y, curb)
%
% *Input arguments:*
%
% lh log-hyper-parameters [D+2 x E ]
% covfun... |
github | UCL-SML/gp-adf-master | covSEard.m | .m | gp-adf-master/covSEard.m | 1,734 | utf_8 | 1f400b4ffc10975b4572164e889e177c | %% covSEard.m
% Squared Exponential covariance function with Automatic Relevance Detemination
% (ARD) distance measure. The covariance function is parameterized as:
%
% k(x^p,x^q) = sf2 * exp(-(x^p - x^q)'*inv(P)*(x^p - x^q)/2)
%
% where the P matrix is diagonal with ARD parameters ell_1^2,...,ell_D^2, where
% D is the... |
github | UCL-SML/gp-adf-master | covSum.m | .m | gp-adf-master/covSum.m | 2,403 | utf_8 | bf6228b9460e36949e8a52e49b67666e | %% covSum.m
% *Summary:* Compose a covariance function as the sum of other covariance
% functions. This function doesn't actually compute very much on its own, it
% merely does some bookkeeping, and calls other covariance functions to do the
% actual work.
%
% function [A, B] = covSum(covfunc, logtheta, x, z)
%
% (... |
github | UCL-SML/gp-adf-master | eps2pdf.m | .m | gp-adf-master/eps2pdf.m | 10,709 | utf_8 | 1b8ff86caa2f79161dc72c9fa2b44320 | function [result,msg] = eps2pdf(epsFile,fullGsPath,orientation)
%EPS2PDF Converts an eps file to a pdf file using GhostScript (GS)
%
% [result,msg] = eps2pdf(epsFile,fullGsPath,orientation)
%
% - epsFile: eps file name to be converted to pdf file
% - fullGsPath: (optional) FULL GS path, including the file ... |
github | UCL-SML/gp-adf-master | solve_chol.m | .m | gp-adf-master/solve_chol.m | 993 | utf_8 | 50d81a361032ceb40d9102492db78fe9 | % solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in the C language ... |
github | UCL-SML/gp-adf-master | sim_scalar.m | .m | gp-adf-master/sim_scalar.m | 8,848 | utf_8 | dca73f0389c49278ed0af07c134c2b54 | function [sqmaha nllx nlly rmsex] = sim_scalar(flag1, flag2)
% several filters (EKF, UKF, GP-UKF, GP-ADF) tested on a scalar function
%
% inputs arguments (number of arguments counts, not the value)
% flag1: indicates whether figures shall be drawn
% flag2: indicates whether figures shall be printed
%
% return... |
github | UCL-SML/gp-adf-master | gpr.m | .m | gp-adf-master/gpr.m | 2,833 | utf_8 | 3f1c270a5d7daccb9115e4d97f87bb61 | %% gpr.m
% *Summary:* Gaussian process regression, with a named covariance function. Two
% modes are possible: training and prediction: if no test data are given, the
% function returns minus the log likelihood and its partial derivatives with
% respect to the hyperparameters; this mode is used to fit the hyperparamete... |
github | UCL-SML/gp-adf-master | covNoise.m | .m | gp-adf-master/covNoise.m | 1,086 | utf_8 | 40d4e24e7127f8134528c2c8f8772626 | %% covNoise.m
% Independent covariance function, ie "white noise", with specified variance.
% The covariance function is specified as:
%
% k(x^p,x^q) = s2 * \delta(p,q)
%
% where s2 is the noise variance and \delta(p,q) is a Kronecker delta function
% which is 1 iff p=q and zero otherwise. The hyperparameter is
%
% log... |
github | chuhang/HouseCraft-master | test_ann_class.m | .m | HouseCraft-master/ann_color/ann_wrapper/test_ann_class.m | 3,328 | utf_8 | 35e851aad5c31c4237b3b8f04d638f68 | function test_ann_class
fprintf(1,'start test...\n');
dbstop if error
for dim = 10:25:60
for n = 2:3
[anno pts Y] = make_ann(dim,10^n);
test_ksearch(anno, pts, Y,'ksearch');
test_ksearch(anno, pts, Y, 'prisearch');
test_frsearch(anno, pts, Y);
close(anno);
end
end
% loa... |
github | chuhang/HouseCraft-master | tsp_ga.m | .m | HouseCraft-master/proc_floorplan_label/travelling_salesman/tsp_ga.m | 9,854 | utf_8 | 2fcb1b7941456432f4cdab4bcda68c91 | %TSP_GA Traveling Salesman Problem (TSP) Genetic Algorithm (GA)
% Finds a (near) optimal solution to the TSP by setting up a GA to search
% for the shortest route (least distance for the salesman to travel to
% each city exactly once and return to the starting city)
%
% Summary:
% 1. A single salesman travels... |
github | chuhang/HouseCraft-master | boxesEval.m | .m | HouseCraft-master/asset_detector/edges/boxesEval.m | 5,118 | utf_8 | 92042e7eff2def2fcafd0202645b23c0 | function recall = boxesEval( varargin )
% Perform object proposal bounding box evaluation and plot results.
%
% boxesEval evaluates a set bounding box object proposals on the dataset
% specified by the 'data' parameter (which is generated by boxesData.m).
% The methods are specified by the vector 'names'. For each meth... |
github | chuhang/HouseCraft-master | edgesEvalDir.m | .m | HouseCraft-master/asset_detector/edges/edgesEvalDir.m | 5,852 | utf_8 | b708b92045eaa75fa68d09e169447bb6 | function varargout = edgesEvalDir( varargin )
% Calculate edge precision/recall results for directory of edge images.
%
% Enhanced replacement for boundaryBench() from BSDS500 code:
% http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/
% Uses same format for results and is fully compatible with boundary... |
github | chuhang/HouseCraft-master | edgeBoxesSweeps.m | .m | HouseCraft-master/asset_detector/edges/edgeBoxesSweeps.m | 3,411 | utf_8 | e5a9cecaa2b2e071c5d8729811f751dc | function edgeBoxesSweeps()
% Parameter sweeps for Edges Boxes object proposals.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar and Larry Zitnick, 2... |
github | chuhang/HouseCraft-master | edgesTrain.m | .m | HouseCraft-master/asset_detector/edges/edgesTrain.m | 13,669 | utf_8 | c29662f392dd5074db27a50767e39cef | function model = edgesTrain( varargin )
% Train structured edge detector.
%
% For an introductory tutorial please see edgesDemo.m.
%
% USAGE
% opts = edgesTrain()
% model = edgesTrain( opts )
%
% INPUTS
% opts - parameters (struct or name/value pairs)
% (1) model parameters:
% .imWidth - [32] width of i... |
github | chuhang/HouseCraft-master | spAffinities.m | .m | HouseCraft-master/asset_detector/edges/spAffinities.m | 4,227 | utf_8 | c8d1c1cc618a7266fee4b2d10651c8c2 | function [A,E,U] = spAffinities( S, E, segs, nThreads )
% Compute superpixel affinities and optionally corresponding edge map.
%
% Computes an m x m affinity matrix A where A(i,j) is the affinity between
% superpixels i and j. A has values in [0,1]. Only affinities between
% spatially nearby superpixels are computed; t... |
github | chuhang/HouseCraft-master | edgesSweeps.m | .m | HouseCraft-master/asset_detector/edges/edgesSweeps.m | 8,831 | utf_8 | c36ed011e7daa4ea08d83453e0cf8125 | function edgesSweeps()
% Parameter sweeps for structured edge detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar, 2014.
% Licensed under the ... |
github | chuhang/HouseCraft-master | imagesAlign.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/imagesAlign.m | 8,228 | utf_8 | 8c6fa5a3ec3fbd24e2f218b784c44861 | function [H,Ip] = imagesAlign( I, Iref, varargin )
% Fast and robust estimation of homography relating two images.
%
% The algorithm for image alignment is a simple but effective variant of
% the inverse compositional algorithm. For a thorough overview, see:
% "Lucas-kanade 20 years on A unifying framework,"
% S. B... |
github | chuhang/HouseCraft-master | opticalFlow.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/opticalFlow.m | 7,422 | utf_8 | fc064ac51401485beb8d870b14069b3f | function [Vx,Vy,reliab] = opticalFlow( I1, I2, varargin )
% Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck.
%
% Implemented 'type' of optical flow estimation:
% LK: http://en.wikipedia.org/wiki/Lucas-Kanade_method
% HS: http://en.wikipedia.org/wiki/Horn-Schunck_method
% SD: Simple block-based sum of ... |
github | chuhang/HouseCraft-master | seqWriterPlugin.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/seqWriterPlugin.m | 8,341 | utf_8 | 44ff47b4ce1791cca65cbab22d6cf032 | function varargout = seqWriterPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow writing of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (swp=seqWriterPlugin):
% h=swp('open',h,fName,info) % Open a seq file for writing ... |
github | chuhang/HouseCraft-master | kernelTracker.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/kernelTracker.m | 9,376 | utf_8 | 0655ea3b81f5fed79c8adf154822a846 | function [allRct, allSim, allIc] = kernelTracker( I, prm )
% Kernel Tracker from Comaniciu, Ramesh and Meer PAMI 2003.
%
% Implements the algorithm described in "Kernel-Based Object Tracking" by
% Dorin Comaniciu, Visvanathan Ramesh and Peter Meer, PAMI 25, 564-577,
% 2003. This is a fast tracking algorithm that utili... |
github | chuhang/HouseCraft-master | seqIo.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/seqIo.m | 17,080 | utf_8 | 8139c41a705720eb629b76c4325b38d6 | function out = seqIo( fName, action, varargin )
% Utilities for reading and writing seq files.
%
% A seq file is a series of concatentated image frames with a fixed size
% header. It is essentially the same as merging a directory of images into
% a single file. seq files are convenient for storing videos because: (1)
%... |
github | chuhang/HouseCraft-master | seqReaderPlugin.m | .m | HouseCraft-master/asset_detector/edges/toolbox/videos/seqReaderPlugin.m | 9,678 | utf_8 | 973331e143942056ecc0636c8d9a2ab0 | function varargout = seqReaderPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow reading of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (srp=seqReaderPlugin):
% h = srp('open',h,fName) % Open a seq file for reading ... |
github | chuhang/HouseCraft-master | pcaApply.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/pcaApply.m | 3,381 | utf_8 | efce97931d73a13b72fa17751a26ed8c | function varargout = pcaApply( X, U, mu, k )
% Companion function to pca.
%
% Use pca.m to retrieve the principal components U and the mean mu from a
% set of vectors x, then use pcaApply to get the first k coefficients of
% x in the space spanned by the columns of U. See pca for general usage.
%
% If x is large, pcaAp... |
github | chuhang/HouseCraft-master | forestTrain.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/forestTrain.m | 6,199 | utf_8 | b4c7a7f17f51981483f670ac2b68115f | function forest = forestTrain( data, hs, varargin )
% Train random forest classifier.
%
% Dimensions:
% M - number trees
% F - number features
% N - number input vectors
% H - number classes
%
% USAGE
% forest = forestTrain( data, hs, [varargin] )
%
% INPUTS
% data - [NxF] N length F feature vectors
% hs ... |
github | chuhang/HouseCraft-master | fernsRegTrain.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/fernsRegTrain.m | 5,975 | utf_8 | e051629ae1e43b9a068f81f165efd75c | function [ferns,ysPr] = fernsRegTrain( data, ys, varargin )
% Train boosted fern regressor.
%
% Boosted regression using random ferns as the weak regressor. See "Greedy
% function approximation: A gradient boosting machine", Friedman, Annals of
% Statistics 2001, for more details on boosted regression.
%
% A few notes ... |
github | chuhang/HouseCraft-master | rbfDemo.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/rbfDemo.m | 2,990 | utf_8 | 8256343c81d154e2647f0719fc658fe4 | function rbfDemo( dataType, noiseSig, scale, k, cluster, show )
% Demonstration of rbf networks for regression.
%
% See rbfComputeBasis for discussion of rbfs.
%
% USAGE
% rbfDemo( dataType, noiseSig, scale, k, cluster, show )
%
% INPUTS
% dataType - 0: 1D sinusoid
% 1: 2D sinusoid
% 2: ... |
github | chuhang/HouseCraft-master | pdist2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/pdist2.m | 5,223 | utf_8 | 2bf46042aaa820dd220332b5ccca85e1 | function D = pdist2( X, Y, metric )
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an n-by-p matrix representing another set of points in the same
% space. This function computes the m-by-n distance matrix D where D(i,j)
% is the ... |
github | chuhang/HouseCraft-master | pca.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/pca.m | 3,305 | utf_8 | 4c2386d1da5057dc997bd9a066a00140 | function [U,mu,vars] = pca( X )
% Principal components analysis (alternative to princomp).
%
% A simple linear dimensionality reduction technique. Use to create an
% orthonormal basis for the points in R^d such that the coordinates of a
% vector x in this basis are of decreasing importance. Instead of using all
% d bas... |
github | chuhang/HouseCraft-master | kmeans2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/classify/kmeans2.m | 5,312 | utf_8 | 5aef268971a7b6e1d0a5bd9efb8ba236 | function [ IDX, C, d ] = kmeans2( X, k, varargin )
% Fast version of kmeans clustering.
%
% Cluster the N x p matrix X into k clusters using the kmeans algorithm. It
% returns the cluster memberships for each data point in the N x 1 vector
% IDX and the K x p matrix of cluster means in C.
%
% This function is in some w... |
github | chuhang/HouseCraft-master | acfModify.m | .m | HouseCraft-master/asset_detector/edges/toolbox/detector/acfModify.m | 4,275 | utf_8 | 0bac51d0f27e677baef003eec3ea1262 | function detector = acfModify( detector, varargin )
% Modify aggregate channel features object detector.
%
% Takes an object detector trained by acfTrain() and modifies it. Only
% certain modifications are allowed to the detector and the detector should
% never be modified directly (this may cause the detector to be in... |
github | chuhang/HouseCraft-master | acfDetect.m | .m | HouseCraft-master/asset_detector/edges/toolbox/detector/acfDetect.m | 3,397 | utf_8 | 030501d52f67b27f79d776a06a40ac44 | function bbs = acfDetect( I, detector, fileName )
% Run aggregate channel features object detector on given image(s).
%
% The input 'I' can either be a single image (or filename) or a cell array
% of images (or filenames). In the first case, the return is a set of bbs
% where each row has the format [x y w h score] and... |
github | chuhang/HouseCraft-master | bbGt.m | .m | HouseCraft-master/asset_detector/edges/toolbox/detector/bbGt.m | 33,550 | utf_8 | df2a8d373e62446d12c97a9047e777b7 | function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives access to two types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
%
% The bb annotation sto... |
github | chuhang/HouseCraft-master | bbApply.m | .m | HouseCraft-master/asset_detector/edges/toolbox/detector/bbApply.m | 21,241 | utf_8 | 4a57b2cf1d518bf93570fb79d6a57c40 | function varargout = bbApply( action, varargin )
% Functions for manipulating bounding boxes (bb).
%
% A bounding box (bb) is also known as a position vector or a rectangle
% object. It is a four element vector with the fields: [x y w h]. A set of
% n bbs can be stores as an [nx4] array, most funcitons below can handle... |
github | chuhang/HouseCraft-master | imwrite2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/imwrite2.m | 5,147 | utf_8 | 90c5a5419876c07bb7d9b53d480ad966 | function I = imwrite2( I, mulFlag, imagei, path, ...
name, ext, nDigits, nSplits, spliti, varargin )
% Similar to imwrite, except follows a strict naming convention.
%
% Wrapper for imwrite that writes file to the filename:
% fName = [path name int2str2(i,nDigits) '.' ext];
% Using imwrite:
% imwrite( I, fName, wri... |
github | chuhang/HouseCraft-master | convnFast.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/convnFast.m | 9,163 | utf_8 | d452d471846d7eac7240f7fd9a80f23f | function C = convnFast( A, B, shape )
% Fast convolution, replacement for both conv2 and convn.
%
% See conv2 or convn for more information on convolution in general.
%
% This works as a replacement for both conv2 and convn. Basically,
% performs convolution in either the frequency or spatial domain, depending
% on wh... |
github | chuhang/HouseCraft-master | imMlGauss.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/imMlGauss.m | 5,735 | utf_8 | a89f64d4d726c0f7b564c2a60b0d5c2b | function varargout = imMlGauss( G, symmFlag, show )
% Calculates max likelihood params of Gaussian that gave rise to image G.
%
% Suppose G contains an image of a gaussian distribution. One way to
% recover the parameters of the gaussian is to threshold the image, and
% then estimate the mean/covariance based on the c... |
github | chuhang/HouseCraft-master | montage2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/montage2.m | 7,545 | utf_8 | 47a07c538e1bc84d016303aedaec7d96 | function varargout = montage2( IS, prm )
% Used to display collections of images and videos.
%
% Improved version of montage, with more control over display.
% NOTE: Can convert between MxNxT and MxNx3xT image stack via:
% I = repmat( I, [1,1,1,3] ); I = permute(I, [1,2,4,3] );
%
% USAGE
% varargout = montage2( IS, ... |
github | chuhang/HouseCraft-master | jitterImage.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/jitterImage.m | 5,313 | utf_8 | 516943a216826601a87d530a16bd34c1 | function IJ = jitterImage( I, varargin )
% Creates multiple, slightly jittered versions of an image.
%
% Takes an image I, and generates a number of images that are copies of the
% original image with slight translation, rotation and scaling applied. If
% the input image is actually an MxNxK stack of images then applie... |
github | chuhang/HouseCraft-master | movieToImages.m | .m | HouseCraft-master/asset_detector/edges/toolbox/images/movieToImages.m | 950 | utf_8 | 580ad7e5c0e5c39e780f04dbd8de506e | function I = movieToImages( M )
% Creates a stack of images from a matlab movie M.
%
% Repeatedly calls frame2im. Useful for playback with playMovie.
%
% USAGE
% I = movieToImages( M )
%
% INPUTS
% M - a matlab movie
%
% OUTPUTS
% I - MxNxT array (of images)
%
% EXAMPLE
% load( 'images.mat' ); [X,map]=gray2ind... |
github | chuhang/HouseCraft-master | toolboxUpdateHeader.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/toolboxUpdateHeader.m | 2,355 | utf_8 | 62304c9da6b4b485f2ab7dd9ca54e9e1 | function toolboxUpdateHeader
% Update the headers of all the files.
%
% USAGE
% toolboxUpdateHeader
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Image&Video Toolbox Version 3.25
% Copyright 2013 Piotr Dollar. [pdollar-at-caltech.edu]
% Please email me if you find bugs, or have suggestions or quest... |
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