plateform stringclasses 1
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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/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 | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | porterStemmer.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-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 | adeelz92/Machine-Learning-Coursera-master | computeCostMulti.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/computeCostMulti.m | 1,461 | utf_8 | 74d2711937f88e7075b71c33a41d1c05 | <<<<<<< HEAD
function J = computeCostMulti(X, y, theta)
%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
% parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = le... |
github | adeelz92/Machine-Learning-Coursera-master | plotData.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/plotData.m | 2,143 | utf_8 | 7e1361b61cd72cd55b594b638122a62d | <<<<<<< HEAD
function plotData(x, y)
%PLOTDATA Plots the data points x and y into a new figure
% PLOTDATA(x,y) plots the data points and gives the figure axes labels of
% population and profit.
figure; % open a new figure window
% ====================== YOUR CODE HERE ======================
% Instructions: Plot ... |
github | adeelz92/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/submit.m | 3,795 | utf_8 | fe4f4a3a4708e9c32d1dce843b89a601 | <<<<<<< HEAD
function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ '... |
github | adeelz92/Machine-Learning-Coursera-master | computeCost.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/computeCost.m | 1,383 | utf_8 | 80ae1a995245c710bb7a283f2af8bd98 | <<<<<<< HEAD
function J = computeCost(X, y, theta)
%COMPUTECOST Compute cost for linear regression
% J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
% parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = length(y); % number of training examples
... |
github | adeelz92/Machine-Learning-Coursera-master | gradientDescentMulti.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/gradientDescentMulti.m | 2,121 | utf_8 | 37bc1373b130af3c56c84d1078da96e1 | <<<<<<< HEAD
function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
% theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some u... |
github | adeelz92/Machine-Learning-Coursera-master | featureNormalize.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/featureNormalize.m | 2,761 | utf_8 | 43678d868c7ae0a55f1c9c62895781a6 | <<<<<<< HEAD
function [X_norm, mu, sigma] = featureNormalize(X)
%FEATURENORMALIZE Normalizes the features in X
% FEATURENORMALIZE(X) returns a normalized version of X where
% the mean value of each feature is 0 and the standard deviation
% is 1. This is often a good preprocessing step to do when
% working with... |
github | adeelz92/Machine-Learning-Coursera-master | gradientDescent.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/gradientDescent.m | 2,059 | utf_8 | 45aba8a27bdd93931d06ca14cbff0532 | <<<<<<< HEAD
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some useful values
m... |
github | adeelz92/Machine-Learning-Coursera-master | normalEqn.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/normalEqn.m | 1,397 | utf_8 | 7cab8da774577114fa7e8bc7dc8fa3da | <<<<<<< HEAD
function [theta] = normalEqn(X, y)
%NORMALEQN Computes the closed-form solution to linear regression
% NORMALEQN(X,y) computes the closed-form solution to linear
% regression using the normal equations.
theta = zeros(size(X, 2), 1);
% ====================== YOUR CODE HERE ======================
% I... |
github | adeelz92/Machine-Learning-Coursera-master | warmUpExercise.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/warmUpExercise.m | 1,083 | utf_8 | 47efd08256462a219f0d50a2d0b61bab | <<<<<<< HEAD
function A = warmUpExercise()
%WARMUPEXERCISE Example function in octave
% A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix
A = [];
% ============= YOUR CODE HERE ==============
% Instructions: Return the 5x5 identity matrix
% In octave, we return values by... |
github | adeelz92/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 11,167 | utf_8 | 15bb69805ac813a4d0eb1a49910d76ae | <<<<<<< HEAD
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
[ema... |
github | adeelz92/Machine-Learning-Coursera-master | makeValidFieldName.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/makeValidFieldName.m | 2,433 | utf_8 | 11b9115e9c2fbc891cff5e6a863b3c0c | <<<<<<< HEAD
function str = makeValidFieldName(str)
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is no... |
github | adeelz92/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 34,967 | utf_8 | 89ce645e1d285b3e2939ab41c954911e | <<<<<<< HEAD
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... |
github | adeelz92/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 37,507 | ibm852 | db2be62c64f4d30da5ab5ddff783ff6c | <<<<<<< HEAD
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 f... |
github | adeelz92/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 31,191 | utf_8 | 067220858f702669a9bc91b9faa0dabe | <<<<<<< HEAD
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... |
github | adeelz92/Machine-Learning-Coursera-master | mergestruct.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/mergestruct.m | 1,585 | utf_8 | eb44bed28d00ab8bc3ac745728b0e2d7 | <<<<<<< HEAD
function s=mergestruct(s1,s2)
%
% s=mergestruct(s1,s2)
%
% merge two struct objects into one
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% s1,s2: a struct object, s1 and s2 can not be arrays
%
% output:
% s: the merged struct object. fields in s1 and s2... |
github | adeelz92/Machine-Learning-Coursera-master | jsonopt.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/jsonopt.m | 1,805 | utf_8 | ec3c1295451ec6e1401d0bda4f76adfd | <<<<<<< HEAD
function val=jsonopt(key,default,varargin)
%
% val=jsonopt(key,default,optstruct)
%
% setting options based on a struct. The struct can be produced
% by varargin2struct from a list of 'param','value' pairs
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
%
% $Id: loadjson.m 371 2012-06-20 12:43:06... |
github | adeelz92/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 32,289 | utf_8 | 2bb826366ff33858889ef78b7e3a517e | <<<<<<< HEAD
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 stri... |
github | adeelz92/Machine-Learning-Coursera-master | varargin2struct.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m | 2,231 | utf_8 | 5a6083faf9fdfe3bddcedb2675feba87 | <<<<<<< HEAD
function opt=varargin2struct(varargin)
%
% opt=varargin2struct('param1',value1,'param2',value2,...)
% or
% opt=varargin2struct(...,optstruct,...)
%
% convert a series of input parameters into a structure
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% 'param... |
github | OmidS/PackMan-master | DepMatUpdate.m | .m | PackMan-master/source/DepMatUpdate.m | 1,170 | utf_8 | 93b7a6b01594d08a02d03cd14ffcf9bc | function DepMatUpdate(repoList, varargin)
% DepMatUpdate. Clones or updates all repositories in a DepMatRepo list
%
%
%
% Licence
% -------
% Part of DepMat. https://github.com/tomdoel/depmat
% Author: Tom Doel, 2015. www.tomdoel.com
% Distributed under... |
github | OmidS/PackMan-master | getDepList.m | .m | PackMan-master/source/getDepList.m | 558 | utf_8 | 3538b383dd3fd24e5307185cc4cb967d | %getDepList Returns an array of DepMatRepo objects representing the
%dependencies of this project
% Modify this if you want to add more dependencies to your project
function depList = getDepList
depList = [];
% Arguments for DepMatRepo: DepMatRepo(Name, Branch, Url, FolderName, Commit, GetLatest)
% Example:... |
github | OmidS/PackMan-master | installDeps.m | .m | PackMan-master/source/installDeps.m | 2,695 | utf_8 | f20281b879c8029054754861d1eaa1e2 | %installDeps Installs/updates dependencies of the project
% Inputs:
% - (1) depList (optional, default: call getDepList): An array of
% DepMatRepo objects containing info about each dependency.
% - (2) depSubDir (optional, default: 'external'): Name of subdirectory
% for dep... |
github | OmidS/PackMan-master | DepMatAddPaths.m | .m | PackMan-master/source/DepMatAddPaths.m | 1,737 | utf_8 | 8897a1f48e6613da5074a1d150fa57f4 | function DepMatAddPaths(baseFolderList, repoNameList, forceUpdate)
% DepMatAddPaths. Adds paths for all subfolders in given repositories
%
%
%
% Licence
% -------
% Part of DepMat. https://github.com/tomdoel/depmat
% Author: Tom Doel, 2015. www.tomdoel.com
%... |
github | OmidS/PackMan-master | testPackMan.m | .m | PackMan-master/source/tests/testPackMan.m | 6,917 | utf_8 | ad94475c6b557b6dc6420ab0d70c6e60 | %testPackMan Tests the PackMan class
% To run the tests:
% runtests('testPackMan');
%% Main function to generate tests
function tests = testPackMan
tests = functiontests(localfunctions);
end
%% Test Functions
function testThatPackManConstructorWorks(testCase)
% Test specific code
pm = PackMan();
... |
github | xiaominghero/ImageDehazing-master | estimate_airlight.m | .m | ImageDehazing-master/estimate_airlight.m | 6,967 | utf_8 | 305575c6ca57010c444b8dc1e55214d3 | function [ Aout ] = estimate_airlight( img, Amin, Amax, N, spacing, K, thres )
%Estimate airlight of an image, using a 3*2D Hough transform, where each
%point votes for a given location using a fixed set of angles.
%
% This is an implementation of our paper:
% Dana Berman, Tali Treibitz, Shai Avidan,
% "Air-light... |
github | raymondlouie/MPF-BML-master | myProcessOptions.m | .m | MPF-BML-master/3rd Party Code/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | raymondlouie/MPF-BML-master | helper_L1.m | .m | MPF-BML-master/3rd Party Code/helper_L1.m | 5,522 | utf_8 | f5ece941456aba08ac972943a77a202c | function [w] = helper_L1(funObj,w,ind_diag,ind_nodiag,options_MPF )
verbose = options_MPF.verbose;
optTol = options_MPF.optTol;
progTol = options_MPF.progTol;
maxIter = options_MPF.maxIter;
suffDec = options_MPF.suffDec;
memory = options_MPF.memory;
lambda_h = options_MPF.lambda_h;
lambda_J = options_MPF.lambda_J;
gam... |
github | raymondlouie/MPF-BML-master | helper_L1.m | .m | MPF-BML-master/Helper Functions/helper_L1.m | 5,540 | utf_8 | 8fdb81f2fcb399aafd67367443ab0b48 | function [w] = helper_L1(funObj,w,ind_diag,ind_nodiag,options_MPF )
verbose = options_MPF.verbose;
opt_tol = options_MPF.opt_tol;
prog_tol = options_MPF.prog_tol;
max_iter = options_MPF.max_iter;
suffDec = options_MPF.suffDec;
memory = options_MPF.memory;
lambda_h = options_MPF.lambda_h;
lambda_J = options_MPF.lambda_... |
github | geoffxiao/Phase-Field-Modeling-master | freezeColors.m | .m | Phase-Field-Modeling-master/(110)/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | geoffxiao/Phase-Field-Modeling-master | freezeColors.m | .m | Phase-Field-Modeling-master/(100)/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | geoffxiao/Phase-Field-Modeling-master | freezeColors.m | .m | Phase-Field-Modeling-master/(111)/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | wenbihan/salt_iccv2017-master | module_videoEnlarge.m | .m | salt_iccv2017-master/salt_tool/module_videoEnlarge.m | 2,070 | utf_8 | b134880afee23f85e8f18129d4c0971e | function [enlargedVideo, BMparam] = ...
module_videoEnlarge(video, BMparam)
%MODULE_IMAGEENLARGE Summary of this function goes here
% Goal: enlarge the image by symmetry for BM purpose
% Inputs:
% 1. video : [aa0, bb0, numFrame] size video (gray-scale)
% 2. BMparam : parameters for BM
% ... |
github | xllau/TRoM_annotation_v1.0-master | Mark_Road.m | .m | TRoM_annotation_v1.0-master/Mark_Road.m | 12,216 | utf_8 | bb8a558c9e6652953c3c9266b962897a | function varargout = Mark_Road(varargin)
% MARK_ROAD MATLAB code for Mark_Road.fig
% MARK_ROAD, by itself, creates a new MARK_ROAD or raises the existing
% singleton*.
%
% H = MARK_ROAD returns the handle to a new MARK_ROAD or the handle to
% the existing singleton*.
%
% MARK_ROAD('CALLBACK',hO... |
github | MORLab/sss-master | sss_gettingStarted.m | .m | sss-master/demos/sss_gettingStarted.m | 10,941 | utf_8 | c415a2268573b3fd8b1fe1cb313b004a | function sss_gettingStarted(Opts)
% SSS_GETTINGSTARTED - Introductory demo to sss toolbox
%
% Syntax:
% SSS_GETTINGSTARTED
%
% Description:
% This function can be executed as it is and will guide the
% user in the command window through an introductory journey in the
% capabilities and adv... |
github | MORLab/sss-master | init_dae2_so.m | .m | sss-master/src/extras/third-party/MESS/usfs/dae2_so/init_dae2_so.m | 4,755 | utf_8 | 6b710a7ef6ee096fd2e09e358245c01c | function [eqn,erg] = init_dae2_so(eqn, opts, flag1, flag2)
%% init_dae_2(eqn, flagA, flagE)
% return true or false if Data for A_ and E_ resp. flag1 and flag2 are
% availabe and correct in eqn.
%
% erg = init_dae_2(eqn,flag1);
% erg = init_dae_2(eqn,flag1,flag2);
%
% erg = init_dae_2(eqn,'A') (==init_dae_2(e... |
github | MORLab/sss-master | init_dae_so_1.m | .m | sss-master/src/extras/third-party/MESS/usfs/dae_so_1/init_dae_so_1.m | 4,638 | utf_8 | c08ff22f5ef926365e28de4cbdbf0848 | function [eqn,erg] = init_dae_so_1(eqn, opts, flag1, flag2)
%% init_dae_so_1(eqn, flagA, flagE)
% return true or false if Data for A and E resp. flag1 and flag2 are availabe
% and correct in eqn.
%
% erg = init_so_1(eqn,flag1);
% erg = init_so_1(eqn,flag1,flag2);
%
% erg = init_so_1(eqn,'A') (==init_so_1(eqn,... |
github | MORLab/sss-master | init_solveLse.m | .m | sss-master/src/extras/third-party/MESS/usfs/solveLse/init_solveLse.m | 3,756 | utf_8 | f5399dda5fb91a785fe890c1e90441b2 | function [eqn, erg] = init_solveLse(eqn, opts,flag1,flag2)
%
% Preprocessing of the system
% .
% x = A x + B u
% y = C x,
%
% where A is SPARSE.
%
% The preprocessing consists of a permutation of the state
%
% x <-- P * x
%
% with a permutation matrix P for bandwidth reduction, which
% results in... |
github | MORLab/sss-master | init_default.m | .m | sss-master/src/extras/third-party/MESS/usfs/default/init_default.m | 4,278 | utf_8 | 571e49a6b55ad0336af8ec70663e8ebc | function [eqn, erg] = init_default(eqn, opts,flag1,flag2)
% function [eqn, erg] = init_default(eqn, opts,flag1,flag2)
%
% The function returns true or false if data for A_ and E_ resp. flag1 and flag2 are availabe and corrects in structure eqn.
%
% Inputs:
%
% eqn structure with data
% opts ... |
github | MORLab/sss-master | init_so_2.m | .m | sss-master/src/extras/third-party/MESS/usfs/so_2/init_so_2.m | 4,749 | utf_8 | 2c6b584392ddccdc6ebec2ae71d63cf4 | function [eqn,erg] = init_so_2(eqn, opts,flag1,flag2)
%function [eqn,erg] = init_so_2(eqn, opts,flag1,flag2)
%
% The second order system
%
% M x"(t) + D x'(t) + K x(t) = B u(t)
% y(t) = C x(t)
%
% is transformed to the first order system
%
% E z'(t) = A z(t) + G u(t)
% y(t) = L ... |
github | MORLab/sss-master | init_so_1.m | .m | sss-master/src/extras/third-party/MESS/usfs/so_1/init_so_1.m | 4,557 | utf_8 | 9e38ab2389f8698435876fef6a722b39 | function [eqn,erg] = init_so_1(eqn, opts,flag1,flag2)
%function [eqn,erg] = init_so_1(eqn, opts,flag1,flag2)
%
% The second order system
%
% M x'' + D x' + K x = B u
% y = C x
%
% is transformed to the first order system
%
% E x' = A x + B u
%
% where
%
% |-K 0 |
% E= | 0 M | ,... |
github | MORLab/sss-master | init_dae3_so.m | .m | sss-master/src/extras/third-party/MESS/usfs/dae3_so/init_dae3_so.m | 4,754 | utf_8 | 371aadfaeba691155d3cebc8a6770303 | function [eqn,erg] = init_dae3_so(eqn, opts, flag1, flag2)
%% init_dae_2(eqn, flagA, flagE)
% return true or false if Data for A_ and E_ resp. flag1 and flag2 are
% availabe and correct in eqn.
%
% erg = init_dae_2(eqn,flag1);
% erg = init_dae_2(eqn,flag1,flag2);
%
% erg = init_dae_2(eqn,'A') (==init_dae_2(e... |
github | MORLab/sss-master | init_dae_1.m | .m | sss-master/src/extras/third-party/MESS/usfs/dae_1/init_dae_1.m | 5,203 | utf_8 | 44c3bccf49876c1f3cec9a8ada673286 | function [eqn,erg] = init_dae_1(eqn, opts, flag1, flag2)
%% init(eqn, flagA, flagE)
% return true or false if Data for A_ and E_ resp. flag1 and flag2 are a
% vailabe and correct in eqn.
%
% erg = init(eqn,flag1);
% erg = init(eqn,flag1,flag2);
%
% erg = init(eqn,'A') (==init(eqn,'A','A'));
% erg = init(eqn... |
github | MORLab/sss-master | init_dae_2.m | .m | sss-master/src/extras/third-party/MESS/usfs/dae_2/init_dae_2.m | 5,365 | utf_8 | 2d441fc89ac304f827cc4806c7f5140c | function [eqn,erg] = init_dae_2(eqn, opts, flag1, flag2)
%% init_dae_2(eqn, flagA, flagE)
% return true or false if Data for A_ and E_ resp. flag1 and flag2 are
% availabe and correct in eqn.
%
% erg = init_dae_2(eqn,flag1);
% erg = init_dae_2(eqn,flag1,flag2);
%
% erg = init_dae_2(eqn,'A') (==init_dae_2(eqn... |
github | MORLab/sss-master | spy.m | .m | sss-master/src/+sssFunc/spy.m | 2,486 | utf_8 | 1063767f890d201eb9ad49b473ddab66 | function spy(sys,name)
% SPY - Plot sparsity pattern of sss system
%
% Syntax:
% SPY(sys)
% SPY(sys,name)
%
% Description:
% This function plots the sparsity pattern of the E and A matrices of
% the sparse state-space system sys into a new figure.
%
% It is possible to p... |
github | MORLab/sss-master | zpk.m | .m | sss-master/src/@sss/zpk.m | 5,051 | utf_8 | 592ae41e2043565fd43d2a7aab710564 | function zpkData = zpk(sys,varargin)
% ZPK - Compute largest poles and zeros or zpk object of an LTI system
%
% Syntax:
% zpkData = ZPK(sys)
% zpkData = ZPK(sys,kP,typeP,kZ,typeZ)
% zpkData = ZPK(sys,k,typeP,typeZ)
% zpkData = ZPK(sys,kP,kZ,type)
% zpkData = ZPK(sys,kP,kZ)
% zpkData ... |
github | MORLab/sss-master | step.m | .m | sss-master/src/@sss/step.m | 11,990 | utf_8 | fe55117196ceae915e4b99c489d62ebd | function varargout = step(varargin)
% STEP - Computes and/or plots the step response of a sparse LTI system
%
% Syntax:
% STEP(sys)
% STEP(sys,t)
% STEP(sys,Tfinal)
% STEP(sys1, sys2, ..., t)
% STEP(sys1, sys2, ..., Tfinal)
% STEP(sys1,'-r',sys2,'--k',t);
% STEP(sys1,'-r',sys2,'--k',Tfinal)
% [h, t] = ... |
github | MORLab/sss-master | norm.m | .m | sss-master/src/@sss/norm.m | 8,316 | utf_8 | bbedc28462e661f71da8eef9b0bb370f | function [nrm, varargout] = norm(sys, varargin)
% NORM - Computes the p-norm of an sss LTI system
%
% Syntax:
% nrm = NORM(sys)
% nrm = NORM(sys,p)
% [nrm, hInfPeakfreq] = NORM(sys, inf)
% nrm = NORM(...,Opts)
%
% Description:
% This function computes the p-norm of an LTI system given
% ... |
github | MORLab/sss-master | truncate.m | .m | sss-master/src/@sss/truncate.m | 3,637 | utf_8 | 4d9996d5e1d20cb1f9dd9b8eba0705cd | function sys = truncate(sys, idxOut, idxIn)
% TRUNCATE - Truncates a sparse LTI system (sss)
%
% Syntax:
% sys = truncate(sys, idxOut, idxIn)
%
% Description:
% sys = truncate(sys, idxOut, idxIn) truncates the sparse state-space
% system sys by preserving only the indices defined by idxOut and
% ... |
github | MORLab/sss-master | freqresp.m | .m | sss-master/src/@sss/freqresp.m | 15,397 | utf_8 | 5251d1a3e2c3b0e4839f1c3f3495e9fc | function [varargout] = freqresp(varargin)
% FREQRESP - Frequency response of sparse state-space systems.
%
% Syntax:
% [G, omega] = freqresp(sys)
% G = freqresp(sys, omega)
% G = freqresp(sys, Opts)
% G = freqresp(sys, omega, Opts)
%
% Description:
% Evaluates complex transfer function of... |
github | MORLab/sss-master | impulse.m | .m | sss-master/src/@sss/impulse.m | 10,188 | utf_8 | e0753525daf8286f21893b10d3310f7b | function varargout = impulse(varargin)
% IMPULSE - Computes and/or plots the impulse response of a sparse LTI system
%
% Syntax:
% IMPULSE(sys)
% IMPULSE(sys,t)
% IMPULSE(sys,Tfinal)
% IMPULSE(sys1, sys2, ..., t)
% IMPULSE(sys1, sys2, ..., Tfinal)
% IMPULSE(sys1,'-r',sys2,'--k',t)
% IMPULSE(sys1,'-r',sys... |
github | MORLab/sss-master | disp.m | .m | sss-master/src/@sss/disp.m | 3,347 | utf_8 | 68c4a8644fbaf496c2c926c5b47163f8 | function infostr = disp(sys)
% DISP - Displays information about a sparse state-space model
%
% Syntax:
% DISP(sys)
% infostr = DISP(sys)
%
% Description:
% DISP(sys) displays information about a sparse state-space model:
%
% # SSS, DSSS or DAE
% # SISO, SIMO, MISO, MIMO
% # ... |
github | MORLab/sss-master | benchmarksCheck.m | .m | sss-master/test/benchmarksCheck.m | 7,994 | utf_8 | 9026ae5c9f58230598585b5cbaae9d83 | function benchmarksCheck(varargin)
% BENCHMARKSCHECK - Verify standard benchmarks
%
% Syntax:
% BENCHMARKSCHECK
% BENCHMARKSCHECK('sourcePath','/source/path/for/benchmarks/')
% BENCHMARKSCHECK('destinationPath','/destination/path/for/benchmarks/')
% BENCHMARKSCHECK('benchmarksList',cellArrayOfBenchmarks)
%
%... |
github | MORLab/sss-master | testImpulse.m | .m | sss-master/test/testScripts/testImpulse.m | 6,954 | utf_8 | 9b7c0df3c434715f8d9aecc0e5183e9d | classdef testImpulse < sssTest
% testImpulse - testing of impulse.m
%
% ------------------------------------------------------------------
% This file is part of sssMOR, a Sparse State Space, Model Order
% Reduction and System Analysis Toolbox developed at the Institute
% of Automatic C... |
github | MORLab/sss-master | testZpk.m | .m | sss-master/test/testScripts/testZpk.m | 1,468 | utf_8 | 00e4c6959f90e7fbcaa0265d2e8eefaf | classdef testZpk < sssTest
% testZpk - testing of zpk.m
methods(Test)
function testZpkObject(testCase)
for i=1:length(testCase.sysCell)
sys=testCase.sysCell{i};
% call zpk for the sys
kP = 8;
kZ = 8;
zpkData... |
github | MORLab/sss-master | testLyapchol.m | .m | sss-master/test/testScripts/testLyapchol.m | 3,359 | utf_8 | 4d707ecedad98808c29c451e7f345c71 | classdef testLyapchol < sssTest
methods(Test)
function testLyapchol1(testCase)
for i=1:length(testCase.sysCell)
sys=testCase.sysCell{i};
if ~sys.isDae
[S,R]=lyapchol(sys);
verification(testCase, sys, S, R);
... |
github | MORLab/sss-master | testBodemag.m | .m | sss-master/test/testScripts/testBodemag.m | 1,630 | utf_8 | 0b358f6a0c80c0d9b24fdb82888633ce | classdef testBodemag < sssTest
methods(Test)
function testCall(testCase)
for i=1:length(testCase.sysCell)
sys=testCase.sysCell{i};
if ~sys.isDae
bodemag(sys,1:100,'r--');
bodemag(sys,{10,100});
end
... |
github | MORLab/sss-master | testBode.m | .m | sss-master/test/testScripts/testBode.m | 4,352 | utf_8 | 202ecef8e13fb1e559a6d3ce1f69b6c6 | classdef testBode < sssTest
methods (Test)
function mainFunctionality(testCase)
% verify the correct execution compared to built-in for given
% frequencies
for i=1:length(testCase.sysCell)
sys_sss=testCase.sysCell{i};
if ~sys_sss.isD... |
github | MORLab/sss-master | testZero.m | .m | sss-master/test/testScripts/testZero.m | 5,362 | utf_8 | 34459eceb55156760254c437bd3d25df | classdef testZero < sssTest
% testZeros - testing of zero.m
methods(Test)
function testLM(testCase)
for i=1:length(testCase.sysCell)
sys=testCase.sysCell{i};
if ~sys.isDae
sys.Name
Opts.type='lm';
... |
github | MORLab/sss-master | testMinus.m | .m | sss-master/test/testScripts/testMinus.m | 3,860 | utf_8 | dde89a78157ad15ef3fc7e2b1ecb1a2c | classdef testMinus < sssTest
% testMinus - testing of minus.m
%
% Description:
% The function minus.m is tested (3 tests) on:
% + combination of two benchmark-systems.
% + combination of two random-systems that are equal.
% + combination of two random-systems that are different.
... |
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