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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | porterStemmer.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-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 | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submit.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | submitWithConfiguration.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | savejson.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadjson.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | loadubjson.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | mridulnagpal/Andrew-Ng-ML-Course-Assignments-master | saveubjson.m | .m | Andrew-Ng-ML-Course-Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | devraj89/Canonical-Correlation-and-its-Variants-master | cca.m | .m | Canonical-Correlation-and-its-Variants-master/cca.m | 1,404 | utf_8 | ade088dc6e270b320ffa9c6a7b7c01ab | % Modified version from David R. Hardoon
%
% http://www.davidroihardoon.com/Professional/Code_files/cca.m
%
% @article{hardoon:cca,
% author = {Hardoon, David and Szedmak, Sandor and {Shawe-Taylor}, John},
% title = {Canonical Correlation Analysis: An Overview with Application to Learning Methods},
% booktitle = {Neura... |
github | JerryWisdom/Caffe-windows-master | classification_demo.m | .m | Caffe-windows-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | 1988kramer/UTIAS-practice-master | animateMRCLAMdataSet.m | .m | UTIAS-practice-master/common/animateMRCLAMdataSet.m | 6,719 | utf_8 | 49bde0b6cf3366c8dfa03035dcc4ff2f | % UTIAS Multi-Robot Cooperative Localization and Mapping Dataset
% produced by Keith Leung (keith.leung@robotics.utias.utoronto.ca) 2009
% Matlab script animateMRCLAMdataSet.m
% Description: This scripts creates an animation using ground truth data.
% Run this script after loadMRCLAMdataSet.m and sampleMRCLAMdataSet.m
... |
github | 1988kramer/UTIAS-practice-master | sampleMRCLAMdataSet.m | .m | UTIAS-practice-master/common/sampleMRCLAMdataSet.m | 4,367 | utf_8 | e87c4cda2a0f697ef211f7d1d26b8fd2 | % UTIAS Multi-Robot Cooperative Localization and Mapping Dataset
% produced by Keith Leung (keith.leung@robotics.utias.utoronto.ca) 2009
% Matlab script animateMRCLAMdataSet.m
% Description: This scripts samples the dataset at fixed intervals
% (default is 0.02s). Odometry data is interpolated using the recorded time.... |
github | 1988kramer/UTIAS-practice-master | path_loss.m | .m | UTIAS-practice-master/common/path_loss.m | 427 | utf_8 | f626413a793d78d0f53e23000161b355 | % computes euclidean loss between robot's estimated path and ground truth
% ignores bearing error
function loss = path_loss(Robots, robot_num, start)
loss = 0;
for i = start:size(Robots{robot_num}.G,1)
x_diff = Robots{robot_num}.G(i,2) - Robots{robot_num}.Est(i,2);
y_diff = Robots{robot_num}.G(i... |
github | 1988kramer/UTIAS-practice-master | loadMRCLAMdataSet.m | .m | UTIAS-practice-master/common/loadMRCLAMdataSet.m | 2,240 | utf_8 | d74f623ab03b14b836499655dc8fa290 | % UTIAS Multi-Robot Cooperative Localization and Mapping Dataset
% produced by Keith Leung (keith.leung@robotics.utias.utoronto.ca) 2009
% Matlab script loadMRCLAMdataSet.m
% Description: This scripts parses the 17 text files that make up a
% dataset into Matlab arrays. Run this script within the the dataset
% d... |
github | 1988kramer/UTIAS-practice-master | kill_landmarks.m | .m | UTIAS-practice-master/feature-persistence/kill_landmarks.m | 683 | utf_8 | 83dafb1f6e2dceda8c2e066213394fcb | % returns an array of randomly selected times to kill the specified
% number of randomly chosen landmarks
% NOTE: death of certain, less used landmarks can go unnoticed
% should think of better way to select landmarks to kill
function times_of_death = kill_landmarks(n_landmarks, n_killed, t0, tmax, deltaT)
t... |
github | PurviAgrawal/Unsupervised_modFilt_CRBM-master-master | nvmex_helper.m | .m | Unsupervised_modFilt_CRBM-master-master/nvmex_helper.m | 8,678 | utf_8 | 95177d5818ea641df37a6453697b13b1 | function errorCode = nvmex_helper(varargin)
%MEX_HELPER is a helper function that contains the code that MEX.M (an
% autogenerated file) executes. It sets up the inputs to call mex.pl (on PC)
% and mex (on Unix).
%
% For information on how to use MEX see MEX help by typing "help mex" or
% "mex -h".
... |
github | imistyrain/SSH-Windows-master | evaluation.m | .m | SSH-Windows-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 | imistyrain/SSH-Windows-master | wider_eval.m | .m | SSH-Windows-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 | twhughes/Accelerator_Inverse_Design-master | textprogressbar.m | .m | Accelerator_Inverse_Design-master/dependencies/textprogressbar/textprogressbar.m | 9,929 | utf_8 | ae0af981548e7e074cce81ff5d0fb091 | function upd = textprogressbar(n, varargin)
% UPD = TEXTPROGRESSBAR(N) initializes a text progress bar for monitoring a
% task comprising N steps (e.g., the N rounds of an iteration) in the
% command line. It returns a function handle UPD that is used to update and
% render the progress bar. UPD takes a single argument... |
github | twhughes/Accelerator_Inverse_Design-master | cod_sparse.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/cod_sparse.m | 6,815 | utf_8 | 1a4ce0b1b8b582cce43c74ce5ac13213 | function [U, R, V, r] = cod_sparse (A, arg)
%COD_SPARSE complete orthogonal decomposition of a sparse matrix A = U*R*V'
%
% [U, R, V, r] = cod_sparse (A)
% [U, R, V, r] = cod_sparse (A, opts)
%
% The sparse m-by-n matrix A is factorized into U*R*V' where R is m-by-n and
% all zero except for R(1:r,1:r), which is up... |
github | twhughes/Accelerator_Inverse_Design-master | factorize.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/factorize.m | 11,934 | utf_8 | 21103fa9cb25c81819412ac23dcf8b00 | function F = factorize (A,strategy,burble)
%FACTORIZE an object-oriented method for solving linear systems
% and least-squares problems, and for representing operations with the
% inverse of a square matrix or the pseudo-inverse of a rectangular matrix.
%
% F = factorize(A) returns an object F that holds the factorizat... |
github | twhughes/Accelerator_Inverse_Design-master | factorization.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/factorization.m | 26,728 | utf_8 | 55ffea84aab117fa3c5b7553af5c25be | classdef factorization
%FACTORIZATION a generic matrix factorization object
% Normally, this object is created via the F=factorize(A) function. Users
% do not need to use this method directly.
%
% This is an abstract class that is specialized into 13 different kinds of
% matrix factorizations:
%
% factorization_chol... |
github | twhughes/Accelerator_Inverse_Design-master | test_factorize.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/Test/test_factorize.m | 14,450 | utf_8 | 9ac6e99f0fe01b551b2aa0cbdbdc17ed | function err = test_factorize (A, strategy)
%TEST_FACTORIZE test the accuracy of the factorization object
%
% Example
% test_factorize (A) ; % where A is square or rectangular, sparse or dense
% test_factorize (A, strategy) ; % forces a particular strategy;
% % works only if the matrix... |
github | twhughes/Accelerator_Inverse_Design-master | test_disp.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/Test/test_disp.m | 6,016 | utf_8 | 457e71942dd7611721712b8b00e42ac2 | function test_disp
%TEST_DISP test the display method of the factorize object
%
% Example
% test_disp
%
% See also factorize, test_all.
% Copyright 2011-2012, Timothy A. Davis, http://www.suitesparse.com
reset_rand ;
tol = 1e-10 ;
err = 0 ;
%-------------------------------------------------------------------------... |
github | twhughes/Accelerator_Inverse_Design-master | test_svd.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/Test/test_svd.m | 6,212 | utf_8 | 6302c745225348ed8b5a230f4441477b | function err = test_svd (A)
%TEST_SVD test factorize(A,'svd') and factorize(A,'cod') for a given matrix
%
% Example
% err = test_svd (A) ;
%
% See also test_all
% Copyright 2011-2012, Timothy A. Davis, http://www.suitesparse.com
fprintf ('.') ;
if (nargin < 1)
% has rank 3
A = magic (4) ;
end
[m, n] = si... |
github | twhughes/Accelerator_Inverse_Design-master | test_accuracy.m | .m | Accelerator_Inverse_Design-master/dependencies/Factorize/Test/test_accuracy.m | 2,952 | utf_8 | 5d41ae00bdefd11f286352bff8500c20 | function err = test_accuracy
%TEST_ACCURACY test the accuracy of the factorize object
%
% Example
% err = test_accuracy
%
% See also test_all, test_factorize.
% Copyright 2011-2012, Timothy A. Davis, http://www.suitesparse.com
fprintf ('\nTesting accuracy:\n') ;
reset_rand ;
A = [ 0.1482 0.3952 0.1783 1.1... |
github | unsky/FPN-master | classification_demo.m | .m | FPN-master/caffe-fpn/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | unsky/FPN-master | voc_eval.m | .m | FPN-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | david-perez/annu-master | funceuler.m | .m | annu-master/functions/funceuler.m | 174 | iso_8859_13 | 4a2e673c31a7714183c840a267f48e59 | % La ecuación diferencial x'(t) = x(t) tiene como solución x(t) = ce^t.
function f = funceuler(~, x, ~) % La t se declara como ~ porque no la usamos.
f = x(1);
end
|
github | mbrossar/FUSION2018-master | rukfUpdate.m | .m | FUSION2018-master/filters/rukfUpdate.m | 1,788 | utf_8 | 24325b68cd25a2d02a2b35e7dd34f88d | function [chi,omega_b,a_b,S] = rukfUpdate(chi,omega_b,a_b,...
S,y,param,R,ParamFilter)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = (1-W0)/(2*N_aug);
gamma =... |
github | mbrossar/FUSION2018-master | EsimatePosAmers.m | .m | FUSION2018-master/filters/EsimatePosAmers.m | 2,093 | utf_8 | 98a1882400a96d76ab9789928887664e | function [points3d, errors] = EsimatePosAmers(pointTracks, ...
camPoses, cameraParams)
numTracks = numel(pointTracks);
points3d = zeros(numTracks, 3);
numCameras = size(camPoses, 2);
cameraMatrices = containers.Map('KeyType', 'uint32', 'ValueType', 'any');
for i = 1:numCameras
id = camPoses(i).ViewId;
R =... |
github | mbrossar/FUSION2018-master | manageAmers.m | .m | FUSION2018-master/filters/manageAmers.m | 5,456 | utf_8 | bf85aa3a6e9c58d710fe3434db202d1f | function [S,PosAmers,ParamFilter,trackerBis,myTracks,PosAmersNew,...
IdxAmersNew,trackCov,pointsMain,validityMain] = manageAmers(S,...
PosAmers,ParamFilter,ParamGlobal,trackerBis,trajFilter,I,...
pointsMain,validityMain,IdxImage,myTracks,pointsBis)
PosAmersNew = [];
IdxAmersNew = [];
trackCov = [];
MaxAmer... |
github | mbrossar/FUSION2018-master | ukfRefUpdate.m | .m | FUSION2018-master/filters/ukfRefUpdate.m | 2,508 | utf_8 | 0ffab5e57239a98e26302370a16c3f8c | function [chi,v,PosAmers,omega_b,a_b,S,xidot] = ukfRefUpdate(chi,v,omega_b,a_b,...
S,y,param,R,ParamFilter,PosAmers,xidot)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_a... |
github | mbrossar/FUSION2018-master | ukfUpdate.m | .m | FUSION2018-master/filters/ukfUpdate.m | 1,933 | utf_8 | 0c034d87cb979ce37c640d5fdf9a74b4 | function [Rot,v,x,PosAmers,omega_b,a_b,S] = ukfUpdate(Rot,v,x,omega_b,a_b,...
S,y,param,R,ParamFilter,PosAmers)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = ... |
github | mbrossar/FUSION2018-master | lukfUpdate.m | .m | FUSION2018-master/filters/lukfUpdate.m | 1,788 | utf_8 | 9b390dc82185a8f43fa24167dcce1816 | function [chi,omega_b,a_b,S] = lukfUpdate(chi,omega_b,a_b,...
S,y,param,R,ParamFilter)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = (1-W0)/(2*N_aug);
gamma =... |
github | sremes/nonstationary-spectral-kernels-master | nlogp_kronecker.m | .m | nonstationary-spectral-kernels-master/matlab/nlogp_kronecker.m | 4,732 | utf_8 | 7045f460bbba9f2b83a5d9e1287f2885 | function [l,g,K] = nlogp_kronecker(hyp, u, x, hyp_kernel)
% Negative marginal likelihood and gradients for the generalized spectral
% mixture product (GSM-P) kernel using Kronecker inference on a multidimensional grid.
% x: cell array of length P containing the input points along all P axes
% u: P-dimensional array of... |
github | sremes/nonstationary-spectral-kernels-master | minimize_v2.m | .m | nonstationary-spectral-kernels-master/matlab/minimize_v2.m | 11,952 | utf_8 | d8aad9cf50639371a892fbcc202eed7c | % minimize.m - minimize a smooth differentiable multivariate function using
% LBFGS (Limited memory LBFGS) or CG (Conjugate Gradients)
% Usage: [X, fX, i] = minimize(X, F, p, other, ... )
% where
% X is an initial guess (any type: vector, matrix, cell array, struct)
% F is the objective function (function poi... |
github | sremes/nonstationary-spectral-kernels-master | init_inputdep.m | .m | nonstationary-spectral-kernels-master/matlab/init_inputdep.m | 1,722 | utf_8 | 38d3396e051fdbc067e65354747ad7bf | function hyp = init_inputdep(u,x,A,ell)
% Init the GSM kernel by fitting GMM's on the spectrogram of the data.
% u: signal values
% x: input points (regularly spaced!)
% A: number of mixture components in GSM
% ell: length-scale of gaussian kernel to be used for interpolating from spectrogram -> x
N = length(x); dt =... |
github | sremes/nonstationary-spectral-kernels-master | inputdep_gibbs.m | .m | nonstationary-spectral-kernels-master/matlab/inputdep_gibbs.m | 4,002 | utf_8 | f5db007b932baecce70edf9cf6df0838 | function [K,dhyp,dKdt] = inputdep_gibbs(x, y, hyp, hyp_kernels)
%% Generalized spectral mixture (GSM) kernel
% x, y: input points
% hyp: kernel hyperparameters (latent functions mu(x), ell(x) and sigma(x))
% hyp_kernels: kernels for latent functions mu(x), ell(x), sigma(x)
K = zeros(size(x,1),size(y,1));
A = length(hy... |
github | mitkof6/opensim-task-space-master | save2pdf.m | .m | opensim-task-space-master/matlab/printSimulationResults/save2pdf.m | 2,129 | utf_8 | c3cd2d01c93be3193fe80d7c4d72978c | %SAVE2PDF Saves a figure as a properly cropped pdf
%
% save2pdf(pdfFileName,handle,dpi)
%
% - pdfFileName: Destination to write the pdf to.
% - handle: (optional) Handle of the figure to write to a pdf. If
% omitted, the current figure is used. Note that handles
% are typically the fi... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/08_gui_equalization/gui.m | 3,839 | utf_8 | c0d9ae1e216da5ea81090b92e31d724a | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/13_gui_filters/gui.m | 5,331 | utf_8 | 7ea5de5ff311db43a42b861d7259b27d | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/16_prewitt_sobel_gui/gui.m | 4,381 | utf_8 | 8bfa0a4f98ac392c7af4aeb93a08cc4e | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/extras/gui_color_segmentation/gui.m | 6,785 | utf_8 | f09f8077984d0169e7cbde86110e315e | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/extras/final_project_01/gui.m | 6,901 | utf_8 | 2b583a90aba00a96b55959d2385c3ef5 | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | warborn/matlab-ai02-master | cursor.m | .m | matlab-ai02-master/extras/final_project_01/cursor.m | 7,266 | utf_8 | f430a0819efbdb99db2c78b8d5acf9fc | function varargout = cursor(varargin)
% CURSOR MATLAB code for cursor.fig
% CURSOR, by itself, creates a new CURSOR or raises the existing
% singleton*.
%
% H = CURSOR returns the handle to a new CURSOR or the handle to
% the existing singleton*.
%
% CURSOR('CALLBACK',hObject,eventData,handles,... |
github | warborn/matlab-ai02-master | gui.m | .m | matlab-ai02-master/10_gui_thresholding/gui.m | 6,249 | utf_8 | b8affc5df1d971111b01b820435d60a4 | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | Tympan/Tympan_Audio_Design_Tool-master | getCommentLines.m | .m | Tympan_Audio_Design_Tool-master/scripts/functions/getCommentLines.m | 2,436 | utf_8 | 20c215d91928c5dac08e8ee86e5f67a3 | function comment_lines = getCommentLines(all_lines,Iline)
%let's just grab the file header comment. That's simplest, though maybe wrong
comment_lines = grabFileHeaderComment(all_lines);
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function comment_lines = grabFileHeaderComment(all_lines)
%default... |
github | Tympan/Tympan_Audio_Design_Tool-master | parseAudioObjectHTML.m | .m | Tympan_Audio_Design_Tool-master/scripts/functions/parseAudioObjectHTML.m | 1,858 | utf_8 | 7ae025bcdaddf66d6b97b31101b60060 | function all_docs = parseAudioObjectHTML(fname,outpname);
if nargin < 2
outpname = 'NodeDocs\';
if nargin < 1
fname = 'Temp\node_docs.txt';
end
end
%% get the data
if iscell(fname)
% we're already given the text, so no need to load it
all_lines = fname;
else
% read file
fid=fopen(f... |
github | Tympan/Tympan_Audio_Design_Tool-master | createDefaultDoc.m | .m | Tympan_Audio_Design_Tool-master/scripts/functions/createDefaultDoc.m | 3,864 | utf_8 | 0172b39b5a95726f76418960ddbe6d81 | function all_lines = createEmptyDoc(name,class_comment_lines)
name = deblank(name);
all_lines={};
if ~isempty(class_comment_lines)
all_lines = addHelpText(name,class_comment_lines,all_lines);
end
all_lines = addTemplateText(name,all_lines);
return
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function all_lines = addHelpTex... |
github | Tympan/Tympan_Audio_Design_Tool-master | buildNewNodes.m | .m | Tympan_Audio_Design_Tool-master/scripts/functions/buildNewNodes.m | 12,891 | utf_8 | 1a7be649ce3469a7d5cdbc3f862882f6 | function [headings,new_node_data]=buildNewNodes(source_pname)
%Look into directory of objects and build node info from the contents
if nargin < 1
%source_pname = 'C:\Users\wea\Documents\Arduino\libraries\OpenAudio_ArduinoLibrary\';
source_pname = 'C:\Users\wea\Documents\Arduino\libraries\Tympan_Library\';
end... |
github | Tympan/Tympan_Audio_Design_Tool-master | parseNodeFile.m | .m | Tympan_Audio_Design_Tool-master/scripts/functions/parseNodeFile.m | 3,249 | utf_8 | 5dc42133c06e830772ea9f78b60bc88b | function all_data = parseNodeFile(fname)
if nargin < 1
fname = 'Temp\nodes.txt';
end
%% read file
fid=fopen(fname,'r');
all_lines=[];
tline=fgetl(fid);
while ischar(tline)
all_lines{end+1} = tline;
tline=fgetl(fid);
end
fclose(fid);
%% parse the file
all_data=[];
for Iline=1:length(all_lines)
data=[]... |
github | SFlannigan/Tensor_Network_Methods-master | ncon.m | .m | Tensor_Network_Methods-master/Kernel/ncon.m | 28,306 | utf_8 | 3a36e8cec73f13af312918d0f8daeae2 | function tensor = ncon(tensorList,legLinks,sequence,finalOrder)
% ncon v1.01 (c) R. N. C. Pfeifer, 2014.
% ==========
% Network CONtractor: NCON
% function A = ncon(tensorList,legLinks,sequence,finalOrder)
% Contracts a single tensor network.
%
% Supports disjoint networks, trivial (dimension 1) indices, 1D objects, t... |
github | SFlannigan/Tensor_Network_Methods-master | expv.m | .m | Tensor_Network_Methods-master/Kernel/expv.m | 4,863 | utf_8 | c8732ae90e0aa822b4d89d0835ebf115 | % [w, err, hump] = expv( t, A, v, tol, m )
% EXPV computes an approximation of w = exp(t*A)*v for a
% general matrix A using Krylov subspace projection techniques.
% It does not compute the matrix exponential in isolation but instead,
% it computes directly the action of the exponential operator on the
% operan... |
github | JunhuanLi/mowerautosaved-master | hampelf.m | .m | mowerautosaved-master/Automower/Code/Navigation/姿态解算_m/hampelf.m | 526 | utf_8 | 71b97d57044765b5ee65fbce7e8c4864 | % function [xfilt, xi, xmedian, xsigma] = hampel_my(x)
function xfilt = hampelf(x)
%#codegen
k = 3;
nsigma = 3;
x = x(:);
%filter size will be 2*k+1
[xmad,xmedian] = movmadf(x,k);
% % scale the MAD by ~1.4826 as an estimate of its standard deviation
scale = 1.482602218505602;
xsigma = scale*xmad;
% identify points t... |
github | JunhuanLi/mowerautosaved-master | mag_fitting_ellipse.m | .m | mowerautosaved-master/Automower/Code/Navigation/姿态解算_m/mag_fitting_ellipse.m | 628 | utf_8 | 2c56f832d5f052b629d2378ba3d17488 | % %ellipse fitting
function mag_body = mag_fitting_ellipse(ellipse_t,imu_mx,imu_my,imu_mz)
%mapping to circle
phi = ellipse_t.phi;
B = [ellipse_t.X0;ellipse_t.Y0];
Xf = max(1,ellipse_t.b/ellipse_t.a);
Yf = max(1,ellipse_t.a/ellipse_t.b);
T = diag([Xf,Yf]);
% T = diag([1,ellipse_t.a/ellipse_t.b]);
A = [cos(phi) -sin(... |
github | JunhuanLi/mowerautosaved-master | mag_calibration_ellipse.m | .m | mowerautosaved-master/Automower/Code/Navigation/mag_analysis/mag_calibration_ellipse.m | 650 | utf_8 | 226fa941d77be995f2666a1bf662f2bf | % %ellipse fitting
% ellipse_t = fit_ellipse(imu_mx,imu_my); %parameters
function mag_body = mag_calibration_ellipse(ellipse_t,imu_mx,imu_my,imu_mz)
%mapping to circle
phi = ellipse_t.phi;
B = [ellipse_t.X0;ellipse_t.Y0];
Xf = max(1,ellipse_t.b/ellipse_t.a);
Yf = max(1,ellipse_t.a/ellipse_t.b);
T = diag([Xf,Yf]); ... |
github | JunhuanLi/mowerautosaved-master | spherHarmonicEval.m | .m | mowerautosaved-master/Automower/Code/Navigation/磁偏角计算/spherHarmonicEval.m | 4,225 | utf_8 | da368cd1395fe2df1b4b0bdb9d1e0e6f | function [V,gradV]=spherHarmonicEval(C,S,point,a,c)
%#codegen
scalFactor=10^(-280);
fullyNormalized=true;
M = 12;
%If the coefficients are Schmidt-quasi-normalized, then convert them to
%fully normalized coefficients.
if(fullyNormalized==false)
%Duplicate the input coefficients so that when they are modified, the... |
github | luk036/ellcpp-master | ldlt.m | .m | ellcpp-master/ldlt.m | 921 | utf_8 | 1d3bdb083b615f5e3b10a8e2538df357 | %
% [L,D]=ldlt(A)
%
% This function computes the square root free Cholesky factorization
%
% A=L*D*L'
%
% where L is a lower triangular matrix with ones on the diagonal, and D
% is a diagonal matrix.
%
% It is assumed that A is symmetric and postive definite.
%
% Reference: Golub and Van Loan, "Matrix Computations... |
github | adelbibi/Tensor_CSC-master | sparse_code_update_ADMM_2D.m | .m | Tensor_CSC-master/Training/sparse_code_update_ADMM_2D.m | 3,409 | utf_8 | a5382aa76b33c22a49fa1a677ec840af | function [X,error_XZnorm,error_reg] = sparse_code_update_ADMM_2D(Dhat,Xhat,Yhat,n3,n4,K,N,lambda)
Xhat_per = permute(Xhat,[3,4, 1, 2]);
X_per = real(ifft2(Xhat_per))*sqrt(n3*n4);
X = permute(X_per,[3,4, 1, 2]);
Z = X;
U = Z;
%% Conj function parameters
pcg_tol = 1e-7;
%% ADMM updates parameters init
rho = 1;
gamma = 1e... |
github | adelbibi/Tensor_CSC-master | rconv2.m | .m | Tensor_CSC-master/Training/image_helpers/rconv2.m | 1,789 | utf_8 | 5e2a3b15d1c1fadc409d5cb3b6a5b4b7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% Convolution of two matrices, with boundaries handled via reflection
% about the edge pixels. Result will be of size of LARGER matrix.
%
% Further adapted for speed by Matthew Zeiler.
%
% @file
% @author Matthew Zeiler
% @author Eero Simonsce... |
github | adelbibi/Tensor_CSC-master | CreateImagesList.m | .m | Tensor_CSC-master/Training/image_helpers/CreateImagesList.m | 25,616 | utf_8 | 1c9fb0d1123e9dc41f5b3d446351b08e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% This takes all images from the input folder, converts them to the desired
% colorspace, removes mean/divides by standard deviations (if desired), and
% constrast normalizes the image (if desired). If the images are of different
% sizes, then ... |
github | adelbibi/Tensor_CSC-master | split_folders_files.m | .m | Tensor_CSC-master/Training/image_helpers/split_folders_files.m | 1,172 | utf_8 | e1d2fca2cb656660543772eb4e5468b0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% Return two struct arrays of just the folers and just the files of the input
% struct array.
%
% @file
% @author Matthew Zeiler
% @date Mar 11, 2010
%
% @fileman_file @copybrief split_folders_files.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | adelbibi/Tensor_CSC-master | check_imgs_path.m | .m | Tensor_CSC-master/Training/image_helpers/check_imgs_path.m | 1,977 | utf_8 | 6efa00d529b073702ab46ee25cb25b2e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% This is a helper function to check that there are valid image files or
% a .mat in the input path that can be used by CreateImages.m
%
% @file
% @author Matthew Zeiler
% @date Jun 28, 2011
%
% @image_file @copybrief check_imgs_path.m
%%%%%%%%... |
github | adelbibi/Tensor_CSC-master | CreateImage.m | .m | Tensor_CSC-master/Training/image_helpers/CreateImage.m | 24,712 | utf_8 | 4332dbe1daa9b2d1803b445a61363183 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% This takes all images from the input folder, converts them to the desired
% colorspace, removes mean/divides by standard deviations (if desired), and
% constrast normalizes the image (if desired). If the images are of different
% sizes, then ... |
github | adelbibi/Tensor_CSC-master | CreateImages.m | .m | Tensor_CSC-master/Training/image_helpers/CreateImages.m | 26,810 | utf_8 | a1ac1d9be68a54c241bf728f9ecc3c99 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%>
% This takes all images from the input folder, converts them to the desired
% colorspace, removes mean/divides by standard deviations (if desired), and
% constrast normalizes the image (if desired). If the images are of different
% sizes, then ... |
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