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 | mahyarnajibi/SSH-master | evaluation.m | .m | SSH-master/lib/wider_eval_tools/evaluation.m | 3,654 | utf_8 | 1963726efb0cb4a054c23471317d67e8 | function evaluation(norm_pred_list,gt_dir,setting_name,setting_class,legend_name)
load(gt_dir);
if ~exist(sprintf('./plot/baselines/Val/%s/%s',setting_class,legend_name),'dir')
mkdir(sprintf('./plot/baselines/Val/%s/%s',setting_class,legend_name));
end
IoU_thresh = 0.5;
event_num = 61;
thresh_num = 1000;
or... |
github | mahyarnajibi/SSH-master | wider_eval.m | .m | SSH-master/lib/wider_eval_tools/wider_eval.m | 1,301 | utf_8 | 8613d27185c0343468233a87491b7fd0 | % WIDER FACE Evaluation
% Conduct the evaluation on the WIDER FACE validation set.
%
% Shuo Yang Dec 2015
% Changed the interface for compatibility with the SSH face detector code
%
function wider_eval(pred_dir,legend_name,plot_out_path)
addpath(genpath('./plot'));
%Please specify your prediction direc... |
github | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/linear-regression/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/linear-regression/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/linear-regression/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/k-means-clustering/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/k-means-clustering/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/k-means-clustering/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/k-means-clustering/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/k-means-clustering/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/k-means-clustering/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/neural-network/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | predict.m | .m | machine-learning-demos-master/neural-network/machine-learning-ex3/ex3/predict.m | 1,195 | utf_8 | 80bf0db5cf7c9d10096832739324a659 |
function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
% p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
% trained weights of a neural network (Theta1, Theta2)
% Useful values
m = size(X, 1);
num_labels = size(Theta2, 1);
% You need... |
github | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/neural-network/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/neural-network/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | porterStemmer.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/support-vector-machine/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/support-vector-machine/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/logistic-regression/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 | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/logistic-regression/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/logistic-regression/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/logistic-regression/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/logistic-regression/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/logistic-regression/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 | SamKirkiles/machine-learning-demos-master | submit.m | .m | machine-learning-demos-master/recommender-systems/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | SamKirkiles/machine-learning-demos-master | submitWithConfiguration.m | .m | machine-learning-demos-master/recommender-systems/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | SamKirkiles/machine-learning-demos-master | savejson.m | .m | machine-learning-demos-master/recommender-systems/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 | SamKirkiles/machine-learning-demos-master | loadjson.m | .m | machine-learning-demos-master/recommender-systems/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 | SamKirkiles/machine-learning-demos-master | loadubjson.m | .m | machine-learning-demos-master/recommender-systems/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 | SamKirkiles/machine-learning-demos-master | saveubjson.m | .m | machine-learning-demos-master/recommender-systems/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 | brandoneh/Gradient-Descent-ADAM-master | Adam_git.m | .m | Gradient-Descent-ADAM-master/Adam_git.m | 2,925 | utf_8 | 12d973ce0375f93557b0fa3953109a27 | % This algorithm utilises Adaptive Moment Estimation (ADAM) to find a
% local minimum of the landscape from a randomised starting point
function Adam % Initialisation
DrawComplexLandscape;
NumSteps=10; % Number of steps for gradient descent
LRate=0.2; % Learni... |
github | TristanJM/robot-behaviour-master | OpenEpuck.m | .m | robot-behaviour-master/library/matlab/matlab files/OpenEpuck.m | 570 | utf_8 | 191de89490ebde9e87d368a0c6843deb | %! \brief Open the communication with the e-puck
% \params port The port in wich the e-puck is paired to.
% it must be a string like that "COM11" if e-puck is paired
% on COM 11.
%/
function OpenEpuck(port)
global EpuckPort;
EpuckPort = serial(port,'BaudRate', 115200,'inputBuffersize',4096,'OutputBufferSize'... |
github | TristanJM/robot-behaviour-master | CloseEpuck.m | .m | robot-behaviour-master/library/matlab/matlab files/CloseEpuck.m | 159 | utf_8 | f769f239f7b77dbf4b648579595e83fe | %! \brief Close the communication with the e-puck
function CloseEpuck()
global EpuckPort;
fclose(EpuckPort);
clear EpuckPort;
clear global EpuckPort;
end |
github | uncledickHe/speechAD-master | htk_model2matlab.m | .m | speechAD-master/scripts/hmm_tools/matlab_htk_interface/htk_model2matlab.m | 4,989 | utf_8 | 5ff92f4f72300eb391d2e014b19eeaae | function model_struct=htk_model2matlab(filename)
% Reads the params of the htk-model found in filename into an appropriate
% structure htkModel
%
%
% model_struct = htk_model2matlab( filename)
%
if nargin < 1
filename = 'single_model_zero';
end
covkind_cell = {'DIAGC','INVDIAGC','FULLC','LLTC','XFORMC'};
durkin... |
github | uncledickHe/speechAD-master | msMmf2masv.m | .m | speechAD-master/scripts/hmm_tools/matlab_htk_interface/msMmf2masv.m | 7,000 | UNKNOWN | 137ffe51831f6ae4aba6698023e302f1 | function mmf_struct=msMmf2masv(filename)
% Reads the params of the htk-model found in filename into an appropriate
% structure mmf_struct. The htk model can be multi-stream
% mmf_struct = mmf2matlab(filename)
%
% CVS_Version_String = '$Id: mmf2matlab.m,v 1.4 2003/12/10 20:00:46 tuerk Exp $';
% CVS_Name_String = '$Na... |
github | uncledickHe/speechAD-master | models2mmf.m | .m | speechAD-master/scripts/hmm_tools/matlab_htk_interface/models2mmf.m | 2,693 | utf_8 | 7f69040189b4a895d7b0ea86a5ef9926 | function models2mmf(model_cell, mmfName)
% Function to write a list of models to the mmfName mmf file
mpath = fileparts(mmfName);
[s, m, m_id]=mkdir(mpath);
mmfId = fopen(mmfName, 'w');
n_models = length(model_cell);
for model_counter = 1:n_models
current_model = model_cell{model_counter};
current_htk_... |
github | uncledickHe/speechAD-master | metrop.m | .m | speechAD-master/scripts/hmm_tools/netlab/metrop.m | 4,976 | utf_8 | 53e05637fbfd2fcd95efaadd86e97ce9 | function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
% sample from the distribution P ~ EXP(-F), where F is the first
% argument to METROP. T... |
github | uncledickHe/speechAD-master | hmc.m | .m | speechAD-master/scripts/hmm_tools/netlab/hmc.m | 7,683 | utf_8 | 64c15e958297afe69787b8617dc1a56a | function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin)
%HMC Hybrid Monte Carlo sampling.
%
% Description
% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
% algorithm to sample from the distribution P ~ EXP(-F), where F is the
% first argument to HMC. The Markov chain starts at the poi... |
github | uncledickHe/speechAD-master | gtminit.m | .m | speechAD-master/scripts/hmm_tools/netlab/gtminit.m | 5,204 | utf_8 | ab76f6114a7e85375ade5e5889d5f6a7 | function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% I... |
github | uncledickHe/speechAD-master | mlphess.m | .m | speechAD-master/scripts/hmm_tools/netlab/mlphess.m | 1,633 | utf_8 | b91a15ca11b4886de6c1671c33a735d3 | function [h, hdata] = mlphess(net, x, t, hdata)
%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
%
% Description
% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding... |
github | uncledickHe/speechAD-master | glmhess.m | .m | speechAD-master/scripts/hmm_tools/netlab/glmhess.m | 4,024 | utf_8 | 2d706b82d25cb35ff9467fe8837ef26f | function [h, hdata] = glmhess(net, x, t, hdata)
%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
%
% Description
% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to t... |
github | uncledickHe/speechAD-master | rbfhess.m | .m | speechAD-master/scripts/hmm_tools/netlab/rbfhess.m | 3,138 | utf_8 | 0a6ef29c8be32e9991cacfe42bdfa0b3 | function [h, hdata] = rbfhess(net, x, t, hdata)
%RBFHESS Evaluate the Hessian matrix for RBF network.
%
% Description
% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second deriva... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | objdall1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/objdall1.m | 1,655 | utf_8 | fe0b9a2b006aac1db50ddb1e090982e7 | % objdall1 - objective function of DAL with L1 regularization
%
% Copyright(c) 2009-2011 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function varargout=objdall1(aa, info, prob, ww, uu, A, B, lambda, eta)
m = length(aa);
n = length(ww);
if isempty(info.ATaa)
info.ATaa=A.Ttime... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | set_defaults.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/set_defaults.m | 3,907 | utf_8 | 006370b4b4c3d2399e34f39e019f0a55 | function [opt, isdefault]= set_defaults(opt, varargin)
%[opt, isdefault]= set_defaults(opt, defopt)
%[opt, isdefault]= set_defaults(opt, field/value list)
%
% This functions fills in the given struct opt some new fields with
% default values, but only when these fields DO NOT exist before in opt.
% Existing field... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dalsqwl1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dalsqwl1.m | 2,637 | utf_8 | 21132a054ab8af6a797c718b4d5172ad | % dalsqwl1 - DAL with the weighted squared loss and the L1 regularization
%
% Overview:
% Solves the optimization problem:
% xx = argmin 0.5*sum(weight.*(A*x-bb).^2) + lambda*||x||_1
%
% Syntax:
% [xx,status]=dalsqwl1(xx, A, bb, lambda, weight, <opt>)
%
% Inputs:
% xx : initial solution ([nn,1])
% A : th... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dalsqgl.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dalsqgl.m | 2,937 | utf_8 | 9a16d566635bbc7d0402d4923ef0fd3c | % dalsqgl - DAL with squared loss and grouped L1 regularization
%
% Overview:
% Solves the optimization problem:
% xx = argmin 0.5||A*x-bb||^2 + lambda*||x||_G1
% where
% ||x||_G1 = sum(sqrt(sum(xx.^2)))
% (grouped L1 norm)
%
% Syntax:
% [xx,status]=dalsqgl(xx0, A, bb, lambda, <opt>)
%
% Inputs:
% xx0 : ini... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | spdiag.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/spdiag.m | 295 | utf_8 | c0d22e00533600ff01a1d7666bdb8857 | % spdiag - sparse diagonal matrix
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function D = spdiag(d)
if isempty(d)
D = [];
return;
end
if size(d,1)<size(d,2)
d = d';
end
D = spdiags(d,0,size(d,1),size(d,1)); |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | mvarfilter.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/mvarfilter.m | 484 | utf_8 | 346fef31c23fcfd976223fd915c1ff30 | % mvarfilter - Multivariate AR filter
%
% Example:
% H=randmvar(20,3,10);
% Z=mvarfilter(H0, 2/pi*log(tan(pi*rand(N,M)/2)))';
%
% Copyright(c) 2011 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function Y=mvarfilter(A, X)
[M1,M2,P]=size(A);
[N,M]=size(X);
if M1~=M2 || M1~=M
... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | objdall1n.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/objdall1n.m | 1,720 | utf_8 | 7aca684d4daf6cff938b12704fc52d13 | % objdall1n - objective function of DAL with non-negative L1 regularization
%
% Copyright(c) 2009-2011 Ryota Tomioka
% 2011 Shigeyuki Oba
% This software is distributed under the MIT license. See license.txt
function varargout=objdall1n(aa, info, prob, ww, uu, A, B, lambda, eta)
m = length(aa);
n = ... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dalsql1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dalsql1.m | 2,492 | utf_8 | b0862789d0daef46502b547e330befcd | % dalsql1 - DAL with the squared loss and the L1 regularization
%
% Overview:
% Solves the optimization problem:
% xx = argmin 0.5||A*x-bb||^2 + lambda*||x||_1
%
% Syntax:
% [xx,status]=dalsql1(xx, A, bb, lambda, <opt>)
%
% Inputs:
% xx : initial solution ([nn,1])
% A : the design matrix A ([mm,nn]) or a... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | loss_lrp.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/loss_lrp.m | 419 | utf_8 | debb2d715476d14a5b1787d9f05415d5 | % loss_lrp - logistic loss function
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [floss, gloss]=loss_lrp(zz, yy)
zy = zz.*yy;
z2 = 0.5*[zy, -zy];
outmax = max(z2,[],2);
sumexp = sum(exp(z2-outmax(:,[1,1])),2);
logpout = z2-(outmax+log(su... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | ds_softth.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/ds_softth.m | 762 | utf_8 | 2c4da40b882920caf0281d081453d696 | % ds_softth - soft threshold function for DS regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [vv,ss,info]=ds_softth(vv,lambda,info)
ss=zeros(sum(min(info.blks,[],2)),1);
ixs=0;
ixv=0;
for kk=1:size(info.blks,1)
blk=info.blks(kk,:);
... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | loss_hsp.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/loss_hsp.m | 445 | utf_8 | bb5ab7baf7e75b9ee81111490527ddf7 | % loss_hsp - hyperbolic secant loss function
%
% Copyright(c) 2009-2011 Ryota Tomioka
% 2009 Stefan Haufe
% This software is distributed under the MIT license. See license.txt
function [floss, gloss]=loss_hsp(zz, bb)
% floss = -sum(log(sech(bb-zz)./pi));
% gloss = tanh(zz-bb);
zz = zz-bb;
mz = abs(z... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | l1n_softth.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/l1n_softth.m | 354 | utf_8 | 815a804eca57e0d586b959f724bdc41c | % l1n_softth - soft threshold function for non-negative L1 regularization
%
% Copyright(c) 2009-2011 Ryota Tomioka
% 2011 Shigeyuki Oba
% This software is distributed under the MIT license. See license.txt
function [vv,ss]=l1n_softth(vv,lambda,info)
n = size(vv,1);
I=find(vv>lambda);
vv=sparse(I,1... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | en_dnorm.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/en_dnorm.m | 341 | utf_8 | b360a589f93eedfdd3e0c5f739049d8c | % en_dnorm - conjugate of the Elastic-net regularizer
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [nm,ishard]=en_dnorm(ww,lambda,theta)
if theta<1
ishard=0;
nm = 0.5*sum(max(0,abs(ww)-lambda*theta).^2)/(lambda*(1-theta));
else
ishard=1;
nm ... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | hessMultdalds.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/hessMultdalds.m | 1,632 | utf_8 | 3b780b4611bf0790d4b1393d2b48cd92 | % hessMultdalds - function that computes H*x for DAL with the
% dual spectral norm (trace norm) regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function bb = hessMultdalds(aa, A, eta, Hinfo)
info=Hinfo.info;
hloss=Hinfo.hloss;
la... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dal.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dal.m | 10,999 | utf_8 | 2123116099e6556eae8955b6f1e8fb8c | % dal - dual augmented Lagrangian method for sparse learaning/reconstruction
%
% Overview:
% Solves the following optimization problem
% xx = argmin f(x) + lambda*c(x)
% where f is a user specified (convex, smooth) loss function and c
% is a measure of sparsity (currently L1 or grouped L1)
%
% Syntax:
% [ww, uu, ... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dallrl1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dallrl1.m | 2,586 | utf_8 | 23fe3ceb4e6ff62f718590fd25c21ec7 | % dallrl1 - DAL with logistic loss and the L1 regularization
%
% Overview:
% Solves the optimization problem:
% [xx, bias] = argmin sum(log(1+exp(-yy.*(A*x+bias)))) + lambda*||x||_1
%
% Syntax:
% [ww,bias,status]=dallrl1(ww0, bias0, A, yy, lambda, <opt>)
%
% Inputs:
% ww0 : initial solution ([nn,1])
% bias0 :... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | l1_softth.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/l1_softth.m | 338 | utf_8 | b5cfb9ee5042e9a3ec5ee4f76bdc9934 | % l1_softth - soft threshold function for L1 regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [vv,ss]=l1_softth(vv,lambda,info)
n = size(vv,1);
Ip=find(vv>lambda);
In=find(vv<-lambda);
vv=sparse([Ip;In],1,[vv(Ip)-lambda;vv(In)+lambda],... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | objdalgl.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/objdalgl.m | 2,680 | utf_8 | 2e2a3317d137d233ef8b8c14763f8010 | % objdalgl - objective function of DAL with grouped L1 regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function varargout=objdalgl(aa, info, prob, ww, uu, A, B, lambda, eta)
nn=sum(info.blks);
if isempty(info.ATaa)
info.ATaa=A.Ttimes(aa);
end... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | hessMultdalgl.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/hessMultdalgl.m | 681 | utf_8 | bc168bca3a884574a4b2262ba694e29b | % hessMultdalgl - function that computes H*x for DAL with grouped
% L1 regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function yy = hessMultdalgl(xx, A, eta, Hinfo)
blks =Hinfo.blks;
hloss=Hinfo.hloss;
I =Hinfo.I;
vv =Hinfo... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dallren.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dallren.m | 2,309 | utf_8 | 0114b7779a5493a550be329c06d4aca9 | % dallren - DAL with logistic loss and the Elastic-net regularization
%
% Overview:
% Solves the optimization problem:
% [xx, bias] = argmin sum(log(1+exp(-yy.*(A*x+bias)))) + lambda*sum(theta*abs(x)+0.5*(1-theta)*x.^2)
%
% Syntax:
% [xx,bias,status]=dallren(xx, bias,A, yy, lambda, <opt>)
%
% Inputs:
% xx : in... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | gl_dnorm.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/gl_dnorm.m | 310 | utf_8 | 548ff135b603e34521e688eaf10f3709 | % gl_dnorm - conjugate of the grouped L1 regularizer
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [nm,ishard]=gl_dnorm(ww,blks)
nm=0;
ix0=0;
for kk=1:length(blks)
I=ix0+(1:blks(kk));
ix0=I(end);
nm=max(nm, norm(ww(I)));
end
ishard=1; |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | vec.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/vec.m | 197 | utf_8 | b1da0371244534faa076d4892c1f5bec | % vec - vectorize an array
%
% Copyright(c) 2009-2011 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function V=vec(M)
sz=size(M);
V=reshape(M, [prod(sz), 1]);
|
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dalsqds.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dalsqds.m | 2,795 | utf_8 | aaf16ed514999fd0313e7a1e3327c880 | % dalsqds - DAL with squared loss and the dual spectral norm
% (trace norm) regularization
%
% Overview:
% Solves the optimization problem:
% ww = argmin 0.5||A*x-bb||^2 + lambda*||w||_DS
%
% where ||w||_DS = sum(svd(w))
%
% Syntax:
% [ww,bias,status]=dalsqds(ww, bias, A, yy, lambda, <opt>)
%
% Inputs:... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | archive.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/archive.m | 284 | utf_8 | f6095975ffda71bd8c3d6d9aae23a25e | % archive - pack variables into a struct
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function S=archive(varargin)
S = [];
for i=1:length(varargin)
name =varargin{i};
S = setfield(S, name, evalin('caller', name));
end
|
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | loss_hsd.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/loss_hsd.m | 697 | utf_8 | 318eb18ee26cd792789ce9e7874a4f59 | % loss_hsd - conjugate hyperbolic secant loss function
%
% Syntax:
% [floss, gloss, hloss, hmin]=loss_hsd(aa, yy)
%
% Copyright(c) 2009-2011 Ryota Tomioka
% 2009 Stefan Haufe
% This software is distributed under the MIT license. See license.txt
function varargout=loss_hsd(zz, bb)
m=length(bb);
glos... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | en_spec.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/en_spec.m | 240 | utf_8 | 5c3c02603f633b327c2329792acd9f75 | % en_spec - spectrum function for the Elastic-net regularizer
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function nm=en_spec(ww,theta)
nm=theta*abs(ww)+0.5*(1-theta)*ww.^2;
|
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | l1n_spec.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/l1n_spec.m | 377 | utf_8 | f6ec361bb27d59f27d21647d93e32ec7 | % l1n_spec - spectrum function for the non-negative L1 regularizer
%
% Copyright(c) 2009-2011 Ryota Tomioka
% 2011 Shigeyuki Oba
% This software is distributed under the MIT license. See license.txt
function ss=l1n_spec(ww)
n=size(ww,1);
Ip=find(ww>0); lenp=length(Ip);
In=find(ww<0); lenn=length(I... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dallral1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dallral1.m | 2,891 | utf_8 | e6cc08d58e4cb17e58ea91d357969f47 | % dallral1 - DAL with logistic loss and the adaptive L1 regularization
%
% Overview:
% Solves the optimization problem:
% [xx, bias] = argmin sum(log(1+exp(-yy.*(A*x+bias)))) + ||pp.*x||_1
%
% Syntax:
% [ww,bias,status]=dallral1(ww0, bias0, A, yy, pp, <opt>)
%
% Inputs:
% ww0 : initial solution ([nn,1])
% bias... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | loss_sqpw.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/loss_sqpw.m | 263 | utf_8 | d5891fc0e2bd5020818a554d66963234 | % loss_sqpw - weighted squared loss function
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [floss, gloss]=loss_sqpw(zz, bb, weight)
gloss = weight.*(zz-bb);
floss = 0.5*sum(weight.*(zz-bb).^2); |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | loss_lrd.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/loss_lrd.m | 574 | utf_8 | f1fa72d1eff87af2af3f4fccf018fd9c | % loss_lrd - conjugate logistic loss function
%
% Syntax:
% [floss, gloss, hloss, hmin]=loss_lrd(aa, yy)
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function varargout = loss_lrd(aa, yy)
mm=length(aa);
gloss=nan*ones(mm,1);
ya = aa.*yy;
I = find(0<ya & ... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | dallrgl.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/dallrgl.m | 3,477 | utf_8 | 071af63f08df43fe71b5ee06e941cad2 | % dallrgl - DAL with logistic loss and grouped L1 regularization
%
% Overview:
% Solves the optimization problem:
% [xx,bias] = argmin sum(log(1+exp(-yy.*(A*x+bias)))) + lambda*||x||_G1
% where
% ||x||_G1 = sum(sqrt(sum(xx(Ii).^2)))
% (Ii is the index-set of the i-th group
%
% Syntax:
% [xx,bias,status]=dallrg... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | objdalds.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/objdalds.m | 1,484 | utf_8 | 670fbfb2f3845d2ffc874b8d01c0cddc | % objdalds - objective function of DAL with the dual spectral norm
% (trace norm) regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function varargout=objdalds(aa, info, prob, ww, uu, A, B, lambda, eta)
m = length(aa);
if isempty(info.... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | objdalen.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/objdalen.m | 1,758 | utf_8 | 47318933b36ad2e398faf5c57c25fd8f | % objdalen - objective function of DAL with the Elastic-net regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function varargout=objdalen(aa, info, prob, ww, uu, A, B, lambda, eta)
theta=info.theta;
m = length(aa);
n = length(ww);
if isempty(inf... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | ds_dnorm.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/ds_dnorm.m | 353 | utf_8 | 2989cfee52d30f7227684a727837bcb5 | % ds_dnorm - conjugate of the dual spectral norm regularizer
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function [nm,ishard]=ds_dnorm(ww,blks)
nm=0;
ix0=0;
for kk=1:size(blks,1)
blk=blks(kk,:);
I=ix0+(1:blk(1)*blk(2));
ix0=I(end);
nm=max(nm,norm(re... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | evalgap.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/evalgap.m | 594 | utf_8 | 2335b40b2884ed2677231e2068a39e40 | function gap = evalgap(fnc, fspec, dnorm, ww, uu, A, B, lambda)
[ff,gg]=evalloss(fnc,ww,uu,A,B);
fval = ff+lambda*sum(fspec(ww));
dval = evaldual(fnc,dnorm,-gg,A,B,lambda);
gap = (fval+dval)/fval;
function [fval,gg]=evalloss(fnc, ww, uu, A, B)
if ~isempty(uu)
zz=A*ww+B*uu;
else
zz=A*ww;
end
[fval, gg] =fnc.p(... |
github | jhwjhw0123/HSIC_Lasso_with_optimization-master | hessMultdall1.m | .m | HSIC_Lasso_with_optimization-master/DAL_opt/hessMultdall1.m | 421 | utf_8 | 0b74b26b2f1ac5be768d661e759c297b | % hessMultdall1 - function that computes H*x for DAL with L1
% regularization
%
% Copyright(c) 2009 Ryota Tomioka
% This software is distributed under the MIT license. See license.txt
function yy = hessMultdall1(xx, A, eta, Hinfo)
hloss=Hinfo.hloss;
AF=Hinfo.AF;
I=Hinfo.I;
n=Hinfo.n;
len=length(I);
yy... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.