plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | ThanhChinhBK/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/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 | ThanhChinhBK/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | ThanhChinhBK/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | qboticslabs/ros_robotics_projects-master | teleop.m | .m | ros_robotics_projects-master/chapter_8_codes/Matlab/teleop.m | 10,043 | utf_8 | 7f1f1a8bd40b2cbd4fb318fe3d696aa9 | function varargout = teleop(varargin)
%Before starting this code, you have to launch any robotic simulation on
%ROS PC or start a ROS robot
% For example, you can test it using turtlebot simulation
% You can launch turtlebot simulation using following command
% $ roslaunch turtlebot_gazebo turtlebot_world.launch
% R... |
github | Oxtay/modulationclassification_matlab-master | fkmeans.m | .m | modulationclassification_matlab-master/k-means/fast k-means/fkmeans.m | 7,426 | utf_8 | 6b8627b1d5fb32be996ca6818b925363 | function [label, centroid, dis] = fkmeans(X, k, options)
% FKMEANS Fast K-means with optional weighting and careful initialization.
% [L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X
% into k (or, rarely, fewer) clusters by applying the well known batch
% K-means algorithm. Rows of X correspo... |
github | haidai/gtsam-master | ccolamd_test.m | .m | gtsam-master/gtsam/3rdparty/CCOLAMD/MATLAB/ccolamd_test.m | 11,944 | utf_8 | ab91fed9a7d6b40fa30544983b26cc7f | function ccolamd_test
%CCOLAMD_TEST extensive test of ccolamd and csymamd
%
% Example:
% ccolamd_test
%
% See also csymamd, ccolamd, ccolamd_make.
% Copyright 1998-2007, Timothy A. Davis, Stefan Larimore, and Siva Rajamanickam
% Developed in collaboration with J. Gilbert and E. Ng.
help ccolamd_test
global ccolamd... |
github | haidai/gtsam-master | geodarea.m | .m | gtsam-master/gtsam/3rdparty/GeographicLib/matlab/geodarea.m | 4,241 | utf_8 | a20b9abbe24d8781e0c053b3ddfd9f3a | function [A, P, N] = geodarea(lats, lons, ellipsoid)
%GEODAREA Surface area of polygon on an ellipsoid
%
% A = GEODAREA(lats, lons)
% [A, P, N] = GEODAREA(lats, lons, ellipsoid)
%
% calculates the surface area A of the geodesic polygon specified by the
% input vectors lats, lons (in degrees). The ellipsoid ve... |
github | haidai/gtsam-master | geoddistance.m | .m | gtsam-master/gtsam/3rdparty/GeographicLib/matlab/geoddistance.m | 17,333 | utf_8 | 3b8e33df114efbd010cafcfdd2b79868 | function [s12, azi1, azi2, S12, m12, M12, M21, a12] = geoddistance ...
(lat1, lon1, lat2, lon2, ellipsoid)
%GEODDISTANCE Distance between points on an ellipsoid
%
% [s12, azi1, azi2] = GEODDISTANCE(lat1, lon1, lat2, lon2)
% [s12, azi1, azi2, S12, m12, M12, M21, a12] =
% GEODDISTANCE(lat1, lon1, lat2, lo... |
github | haidai/gtsam-master | tranmerc_fwd.m | .m | gtsam-master/gtsam/3rdparty/GeographicLib/matlab/tranmerc_fwd.m | 5,674 | utf_8 | acff0226812f95bc17989337218cdde5 | function [x, y, gam, k] = tranmerc_fwd(lat0, lon0, lat, lon, ellipsoid)
%TRANMERC_FWD Forward transverse Mercator projection
%
% [X, Y] = TRANMERC_FWD(LAT0, LON0, LAT, LON)
% [X, Y, GAM, K] = TRANMERC_FWD(LAT0, LON0, LAT, LON, ELLIPSOID)
%
% performs the forward transverse Mercator projection of points (LAT,LON)... |
github | haidai/gtsam-master | tranmerc_inv.m | .m | gtsam-master/gtsam/3rdparty/GeographicLib/matlab/tranmerc_inv.m | 5,994 | utf_8 | 3ccf6b37ca13daed68a0ae8f166151ce | function [lat, lon, gam, k] = tranmerc_inv(lat0, lon0, x, y, ellipsoid)
%TRANMERC_INV Inverse transverse Mercator projection
%
% [LAT, LON] = TRANMERC_INV(LAT0, LON0, X, Y)
% [LAT, LON, GAM, K] = TRANMERC_INV(LAT0, LON0, X, Y, ELLIPSOID)
%
% performs the inverse transverse Mercator projection of points (X,Y) to
... |
github | haidai/gtsam-master | gtsamExamples.m | .m | gtsam-master/matlab/gtsam_examples/gtsamExamples.m | 5,664 | utf_8 | f2621b78fabdb370c4f63d5e0309b7e9 | function varargout = gtsamExamples(varargin)
% GTSAMEXAMPLES MATLAB code for gtsamExamples.fig
% GTSAMEXAMPLES, by itself, creates a new GTSAMEXAMPLES or raises the existing
% singleton*.
%
% H = GTSAMEXAMPLES returns the handle to a new GTSAMEXAMPLES or the handle to
% the existing singleton*.
%
% ... |
github | haidai/gtsam-master | VisualISAM_gui.m | .m | gtsam-master/matlab/gtsam_examples/VisualISAM_gui.m | 10,009 | utf_8 | ed501f5a7d855d179385d3bb29e65500 | function varargout = VisualISAM_gui(varargin)
% VisualISAM_gui: runs VisualSLAM iSAM demo in GUI
% Interface is defined by VisualISAM_gui.fig
% You can run this file directly, but won't have access to globals
% By running ViusalISAMDemo, you see all variables in command prompt
% Authors: Duy Nguyen Ta
% Last Mod... |
github | mathieuboudreau/B1Paper_Analysis-master | calculateVFAT1ErrorDueToB1.m | .m | B1Paper_Analysis-master/src/t1/calculateVFAT1ErrorDueToB1.m | 977 | utf_8 | 9c7412f74142f58c9b60e237392d9da6 | function [fittedT1, t1Error] = calculateVFAT1ErrorDueToB1(T1, TR, FAs, b1ErrorRange)
%CALCULATEVFAT1ERRORDUETOB1 Fit VFA data generated for a range of
%inaccurate B1 correction values
%--args--
% T1: scalar in ms
% TR: scalar in ms
% FAs: array in deg
% b1ErrorRange: array in relative amplitude. Accurate b1 = 1... |
github | mathieuboudreau/B1Paper_Analysis-master | getDataMag_niak_mb.m | .m | B1Paper_Analysis-master/src/t1/getDataMag_niak_mb.m | 1,716 | utf_8 | f9ed6f7541e1e4e284e842dc99d9e283 | % getData.m
%
% Generates the .mat from the DICOM images for GS T1 mapping.
%
% written by J. Barral, M. Etezadi-Amoli, E. Gudmundson, and N. Stikov, 2009
% (c) Board of Trustees, Leland Stanford Junior University
%
% Modifications:
% July 2013 - Mathieu Boudreau: Adapted code to use Niak
% ... |
github | mathieuboudreau/B1Paper_Analysis-master | generateStructB1T1Data.m | .m | B1Paper_Analysis-master/src/data_analysis/generateStructB1T1Data.m | 2,636 | utf_8 | 95ecddc64bc3ea4ac20228cf01692fe9 | function s = generateStructB1T1Data(b1Folder, t1Folder, b1Keys, t1Key)
%GENERATESTRUCTB1T1DATA Generate struct containing subdirectory locations
%of b1 and t1 files-of-interest.
%
% --args--
% b1Folder = char
% e.g. 'b1/' or 'b1_whole_brain/'
% t1Folder = char
% e.g. 't1/' or 't1_whole_brain/'
% b1K... |
github | mathieuboudreau/B1Paper_Analysis-master | dirs2cells.m | .m | B1Paper_Analysis-master/src/util/dirs2cells.m | 1,148 | utf_8 | a295e7f506510ecf03f115775ce07671 | function [cellsOfDirs] = dirs2cells(parentDir)
%DIRS2CELLS Generates a cell array containing the names of all the
%directories in the specified parent directory.
%
% --args--
% parentDir: String of parent directory to be scanned for list of dirs
%
% --return--
% cellsOfDirs: Cell array of strings of each direct... |
github | mathieuboudreau/B1Paper_Analysis-master | do_ratio_b1_single.m | .m | B1Paper_Analysis-master/src/b1/do_ratio_b1_single.m | 3,994 | utf_8 | 78ba1843a246873f35b1b3fc948d86b2 | function do_ratio_b1_single(img1, alpha, output)
%
% function do_ratio_b1_single(img1, alpha, output)
%
% img1 : minc file
% alpha : pulse angle for first image in degrees
% (second image is double this)
% output : name for output file
%
% Note: output is clamped t... |
github | BelaPlatform/bela-hardware-master | capeletlop.m | .m | bela-hardware-master/capelets/audio_expander/capeletlop.m | 1,321 | utf_8 | 099cf6c077d4217b83bdef215af7f283 | function capeletlop
% Frequency range for plot
f = 50:44100;
w = f*(2*pi);
ax = [-40, 10, f(1), f(end)];
% input second-order low pass
R1 = 27400;
R3 = 27400;
C2 = 330 * 1e-12;
C4 = 330 * 1e-12;
K = 1 + 16.2/27.4;
Hi = componentsToTransferFunction(R1, C2, R3, C4, K, w);
% Output second-order low pass
R1 = 28700;
R3 ... |
github | golden1232004/webrtc_new-master | rtpAnalyze.m | .m | webrtc_new-master/tools/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | golden1232004/webrtc_new-master | apmtest.m | .m | webrtc_new-master/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | golden1232004/webrtc_new-master | plot_neteq_delay.m | .m | webrtc_new-master/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | du0002in/stereovision-on-road-detection-master | get_stop_flag.m | .m | stereovision-on-road-detection-master/get_stop_flag.m | 328 | utf_8 | 213be72c5d43e54da7064f0e89fd19d5 | %flag=1 ==> keep on running
%flag=0 ==> stop the process
function flag=get_stop_flag
ele_no=0;
while(ele_no==0) %in case when the file is being written, it will read in nothing
fid=fopen('D:\Stereo\GUI\runningflag.txt','rt');
C=textscan(fid, '%d', 1);
ele_no=numel(C{1});
fclose(fid);
end
flag=... |
github | du0002in/stereovision-on-road-detection-master | ipm.m | .m | stereovision-on-road-detection-master/ipm.m | 785 | utf_8 | cd8a70ef324d6af24b57a321fbabf1e4 | % inverse perspective mapping
% img is the retified and half resoluted image, p is view angle Phi, H is cam height, focal is
% cam focal length, xxc and yyc are cam coordinate grid defined as
% following:
% [ximg,yimg]=meshgrid(1:640,1:2:480);
% yyc=-yimg+cc_y;
% xxc=-ximg+cc_x_left;
function ipm_img=ipm(img,p,H... |
github | du0002in/stereovision-on-road-detection-master | harris.m | .m | stereovision-on-road-detection-master/harris.m | 3,299 | utf_8 | 830c1ef3909f31f1af79dbe154fcd1b9 | % HARRIS - Harris corner detector
%
% Usage: [cim, r, c] = harris(im, sigma, thresh, radius, disp)
%
% Arguments:
% im - image to be processed.
% sigma - standard deviation of smoothing Gaussian. Typical
% values to use might be 1-3.
% thresh - thres... |
github | du0002in/stereovision-on-road-detection-master | smoothVector.m | .m | stereovision-on-road-detection-master/MapSimulation/smoothVector.m | 327 | utf_8 | 490ac18603d8ec915b25fbf22ead5442 | % 1D convolution by first repeating initial and final value as
% many times as half the filter size and then taking the central part.
% Filter length must be odd.
%
function res = smoothVector(v,filter)
s = length(filter);
tmp = conv( [v(1)*ones(s,1) ; v ; v(end)*ones(s,1) ], filter);
res = tmp( s+ (s+1)/2 : end-s-(s-... |
github | du0002in/stereovision-on-road-detection-master | stereo_img.m | .m | stereovision-on-road-detection-master/MapSimulation/stereo_img.m | 6,452 | utf_8 | fa58683c7fa09430d42087862c0e50cd | %Generate the simulated stereo images given the vehicle global cor. and its
%direction w.r.t global cor. +x axis (+x pointing to the right)
function [img_left, img_right]=stereo_img(X,Y,theta,phi,H,dist_left_right,L_wheel_cam,Tpix2cam_left, Tpix2cam_right)
% tic;
[Tcar2g,IDX,xc]=Car2Gloable(X,Y,theta);
[Tcam2car_l... |
github | du0002in/stereovision-on-road-detection-master | Pix2Cam.m | .m | stereovision-on-road-detection-master/MapSimulation/Pix2Cam.m | 269 | utf_8 | 6b7c724ec977cef9b1e7b8a9119e879a | %Cam intrinsic matrix
%[x_pix;y_pix;1]=Pix2Cam*[x_cam;y_cam;z_cam;1];
function [Tpix2cam_left, Tpix2cam_right]=Pix2Cam(focal,cc_x_left,cc_x_right,cc_y)
Tpix2cam_left=[-focal 0 cc_x_left;0 -focal cc_y;0 0 1];
Tpix2cam_right=[-focal 0 cc_x_right;0 -focal cc_y;0 0 1]; |
github | du0002in/stereovision-on-road-detection-master | Car2Gloable.m | .m | stereovision-on-road-detection-master/MapSimulation/Car2Gloable.m | 2,645 | utf_8 | 6b75af3a7393f76d5c6ad6af843fb073 | %take the vehicle global coordinate and the vehicle orintation w.r.t road tangent. Return
%the transformation matrix from car to global
%[x_car;y_car;z_car;1]=Tcar2g*[x_g;y_g;z_g;1];
function [Tcar2g,IDX,xc]=Car2Gloable(X,Y,theta)
load 'map_cordinate4_dotted';
ind_x=((map_x>=(X-2*simulated_lane_width))&(map_x<=(X+... |
github | du0002in/stereovision-on-road-detection-master | Cam2Car.m | .m | stereovision-on-road-detection-master/MapSimulation/Cam2Car.m | 734 | utf_8 | db3c0db205acb364ea5c98bc6ddca212 | %Take camera pitch and height as input
%Output the transform from cam to car coordinate or cam extrinsic matrix
%[x_cam;y_cam;z_cam;1]=Tcam2car*[x_car;y_car;z_car;1]
function [Tcam2car_left,Tcam2car_right]=Cam2Car(phi,H,dist_left_right,L_wheel_cam)
%rotate cam cor. around its x axis by -phi
R1=[1 0 0;0 cos(-phi) -... |
github | du0002in/stereovision-on-road-detection-master | stereo_img2.m | .m | stereovision-on-road-detection-master/MapSimulation/stereo_img2.m | 6,491 | utf_8 | cb28fe46192012d7e35d3fe00072607d | %Generate the simulated stereo images given the vehicle global cor. and its
%direction w.r.t global cor. +x axis (+x pointing to the right)
function [img_left, img_right]=stereo_img2(X,Y,theta,phi,H,dist_left_right,L_wheel_cam,Tpix2cam_left, Tpix2cam_right, ...
Xg,Yg,Zg)
% tic;
Tcar2g=Car2Gloable2(X,Y,theta);
... |
github | du0002in/stereovision-on-road-detection-master | parse_osm.m | .m | stereovision-on-road-detection-master/MapSimulation/johnyf-openstreetmap-bb37962/parse_osm.m | 3,177 | utf_8 | f5f8396dab411bd148176d70246eed77 | function [parsed_osm] = parse_osm(osm_xml)
%PARSE_OSM Parse into a structure a loaded OSM XML structure.
%
% parsed_osm = PARSE_OSM(osm_xml) takes as input a MATLAB structure
% osm_xml containing the XML data loaded from an OpenStreetMap file using
% function load_osm_xml, and returns another MATLAB structu... |
github | du0002in/stereovision-on-road-detection-master | plot_way.m | .m | stereovision-on-road-detection-master/MapSimulation/johnyf-openstreetmap-bb37962/plot_way.m | 2,786 | utf_8 | b6e1ee40605a0af54e9aaffcb6ac405e | function [] = plot_way(ax, parsed_osm, map_img_filename)
%PLOT_WAY plot parsed OpenStreetMap file
%
% usage
% PLOT_WAY(ax, parsed_osm)
%
% input
% ax = axes object handle
% parsed_osm = parsed OpenStreetMap (.osm) XML file,
% as returned by function parse_openstreetmap
% map_img_filen... |
github | du0002in/stereovision-on-road-detection-master | xml2struct_fex28518.m | .m | stereovision-on-road-detection-master/MapSimulation/johnyf-openstreetmap-bb37962/dependencies/xml2struct/xml2struct_fex28518.m | 4,550 | utf_8 | f323fc78b57ecf94e8e7ce38bd002d71 | function [ s ] = xml2struct_fex28518( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Ev... |
github | du0002in/stereovision-on-road-detection-master | Check_next_lane_position.m | .m | stereovision-on-road-detection-master/MPC/GA with Simple Map/Simulated Img/Check_next_lane_position.m | 8,984 | utf_8 | 4dd5cd4e90b517a3547259839a73abbf | %Generate the simulated stereo images given the vehicle global cor. and its
%direction w.r.t global cor. +x axis (+x pointing to the right)
function [xl,yl,xr,yr]=Check_next_lane_position(X,Y,theta,phi,H,abc,leftrightlane,lane_width,dist_left_right,L_wheel_cam,Tpix2cam_left, Tpix2cam_right,offset_vector)
[~, ~, ~,... |
github | du0002in/stereovision-on-road-detection-master | Car2Gloable2.m | .m | stereovision-on-road-detection-master/MPC/GA with Simple Map/Simulated Img/Car2Gloable2.m | 3,123 | utf_8 | a89f875688bff035b952809aa4439a7b | %take the vehicle global coordinate and the vehicle orintation w.r.t road tangent. Return
%the transformation matrix from car to global
%[x_car;y_car;z_car;1]=Tcar2g*[x_g;y_g;z_g;1];
function Tcar2g=Car2Gloable2(X,Y,theta)
%rotate vehicle cordinate by -(theta+pi/2) ard its y axis
R1=[cos(-theta-pi/2) 0 sin(-thet... |
github | du0002in/stereovision-on-road-detection-master | stereo_img_SL.m | .m | stereovision-on-road-detection-master/Vehicle Simulator/Straight Road Img/stereo_img_SL.m | 6,390 | utf_8 | 372fa09a80a36f7f5f388312bee9eaff | %Generate the simulated stereo images given the vehicle global cor. and its
%direction w.r.t global cor. +x axis
function [img_left, img_right]=stereo_img_SL(X,Y,theta,phi,H,dist_left_right,L_wheel_cam,Tpix2cam_left, Tpix2cam_right, map_x,map_y,map_z, Lane_marking_point_x, Lane_marking_point_y, Lane_marking_point_z)
... |
github | du0002in/stereovision-on-road-detection-master | Car2Gloable_SL.m | .m | stereovision-on-road-detection-master/Vehicle Simulator/Straight Road Img/Car2Gloable_SL.m | 2,764 | utf_8 | f127d96dab939f240c14591ed070377d | %take the vehicle global coordinate and the vehicle orintation w.r.t road tangent. Return
%the transformation matrix from car to global
%[x_car;y_car;z_car;1]=Tcar2g*[x_g;y_g;z_g;1];
function [Tcar2g,IDX,xc]=Car2Gloable_SL(X,Y,theta, map_x, map_y, map_z)
% load 'map_cordinate4_dotted';
simulated_lane_width=3.5;
i... |
github | jonreal/openWearable-master | heelStrikeCheck.m | .m | openWearable-master/utils/heelStrikeCheck.m | 3,164 | utf_8 | ad4ebabb60f91967f5a3b9c8da3b09bc | function rtn = heelStrikeCheck(trialName)
%
% Check HS detection
%
vspacing = 0.4;
rtn = embedded_process_data(trialName);
l_thr = 500;
l_d_thr = 50;
r_thr = 500;
r_d_thr = 50;
for i=1:numel(rtn.data.time)
heelForce = rtn.data.l_s3;
d_heelForce = rtn.data.l_d_s3;
if i==1
gpl(i) =... |
github | nejaz1/plotlib-master | convert.m | .m | plotlib-master/+plt/convert.m | 1,959 | utf_8 | acb3bb03d99da83048a70c4d8424710b | function convert(in,out,varargin)
%% Description
% Batch converts file(s) to the desired format and resolution
% Input
% in : name(s) of input files to be converted
% for more than one file, provide a string array
% out : name(s) to give output files
% needs as many names as input... |
github | nejaz1/plotlib-master | get_colours_alpha.m | .m | plotlib-master/+plt/+helper/get_colours_alpha.m | 686 | utf_8 | 3c83bda1c2609f304beed1255254f621 | function varargout = get_colours_alpha(c,lvl)
%% Description
% Synthetically create alpha value for provided colours based on the
% desired alpha lvl
%
% Author
% Naveed Ejaz (ejaz.naveed@gmail.com)
if ~iscell(c)
cAlpha = alphaHelper(c,lvl);
else
N = length(c);
cAlpha = cell(1,N);
for i=... |
github | nejaz1/plotlib-master | scatterplot.m | .m | plotlib-master/+plt/+helper/+dataframe/scatterplot.m | 12,891 | utf_8 | 447bf771f4e9d5d67a9f52d64de97e90 | function varargout=scatterplot(x,y,varargin)
% function scatterplot(x,y,varargin)
% Provides a scatterplot of the y-values against x-values
% INPUT:
% x: Nx1 vector of x-values
% y: Nx1 vector of y-values
% VARARGIN:
% Format options (for all symbols)
% 'markertype',{o s v ^...}
% 'marke... |
github | nejaz1/plotlib-master | lineplot.m | .m | plotlib-master/+plt/+helper/+dataframe/lineplot.m | 21,110 | utf_8 | 960d5c950fc55963847301d824b19f34 | function [x_coord,PLOT,ERROR]=lineplot(xvar,y,varargin)
% Synopsis
% [xcoord,PLOT,ERROR]=lineplot(xvar,y,varargin)
% Description
% xvar: independent variables [N*c], with c>1 a hierarchical grouping is used
% Y: dependent variable [N*1]
% if Y is a N*p varaible, then different lines are plotted for
% differe... |
github | nejaz1/plotlib-master | dotplot.m | .m | plotlib-master/+plt/+helper/+dataframe/dotplot.m | 12,236 | utf_8 | 396d74bf95951f1080701fc3b9bc8423 | function dotplot(group,y,varargin)
% dotplot(group,y,varargin)
% group: one or more variables defining the x-axis
% y: Data to be plotted (one vector)
% Plots a boxplot of the data
% The midline of the box is the median of the data
% The box itself spans from the 25th to 75th percentile (middle... |
github | nejaz1/plotlib-master | myboxplot.m | .m | plotlib-master/+plt/+helper/+dataframe/myboxplot.m | 12,539 | utf_8 | 3d9dd6b2a591966a8bdf8345f8e96fba | function myboxplot(group,y,varargin)
% myboxplot(group,y,varargin)
% group: one or more variables defining the x-axis
% y: Data to be plotted (one vector)
% Plots a boxplot of the data
% The midline of the box is the median of the data
% The box itself spans from the 25th to 75th percenti... |
github | cfo/TheiaSfM-master | flann_search.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_search.m | 3,564 | utf_8 | 7dfb2eee171a6fef9aa4adec527e3145 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | flann_load_index.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_load_index.m | 1,578 | utf_8 | f9bcc41fd5972c5c987d6a4d41bdc796 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | test_flann.m | .m | TheiaSfM-master/libraries/flann/src/matlab/test_flann.m | 10,328 | utf_8 | 151c22994b0192f8a071649ad26fbc6b | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | flann_free_index.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_free_index.m | 1,614 | utf_8 | 5d719d8d60539b6c90bee08d01e458b5 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | flann_save_index.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_save_index.m | 1,563 | utf_8 | 5a44d911827fba5422041529b3c01cf6 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | flann_set_distance_type.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_set_distance_type.m | 1,914 | utf_8 | a62dd85add564e04c01aefeb65083f5d | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | cfo/TheiaSfM-master | flann_build_index.m | .m | TheiaSfM-master/libraries/flann/src/matlab/flann_build_index.m | 2,299 | utf_8 | f4cdee51a1c9616f205dcc814c943903 | function [index, params, speedup] = flann_build_index(dataset, build_params)
%FLANN_BUILD_INDEX Builds an index for fast approximate nearest neighbors search
%
% [index, params, speedup] = flann_build_index(dataset, build_params) - Constructs the
% index from the provided 'dataset' and (optionally) computes the optima... |
github | duckietown-bunny/Software-master | split_annotation_by_files.m | .m | Software-master/catkin_ws/src/f1/anti_instagram/scripts/annotation/split_annotation_by_files.m | 1,029 | utf_8 | 9e01249f81282365698de21ae148473e | %
% splits annotation files by saving each annotations set from the map under
% XXX.mat, where XXX.jpg was the original filename.
%
% Usage: from the same directory as the annotator, use:
% split_annotation_by_files(joint_filename)
%
% to read in python, use for example:
% In [17]: res=scipy.io.loadmat('frame0000.mat')... |
github | duckietown-bunny/Software-master | road_annotation.m | .m | Software-master/catkin_ws/src/f1/anti_instagram/scripts/annotation/road_annotation.m | 7,109 | utf_8 | 55827a1a335095ea2e66033b78d1fbc2 | function varargout = road_annotation(varargin)
% ROAD_ANNOTATION MATLAB code for road_annotation.fig
% ROAD_ANNOTATION(image_file_list), where image_file_list is a cell array of string, creates a new ROAD_ANNOTATION or raises the existing
% singleton*.
%
% H = ROAD_ANNOTATION(image_file_list) returns the... |
github | duckietown-bunny/Software-master | road_annotation.m | .m | Software-master/catkin_ws/src/f1/annotation/road_annotation.m | 7,596 | utf_8 | dec80f0a6ab5c8a404e0d558487543eb | function varargout = road_annotation(varargin)
% ROAD_ANNOTATION MATLAB code for road_annotation.fig
% ROAD_ANNOTATION(image_file_list), where image_file_list is a cell array of string, creates a new ROAD_ANNOTATION or raises the existing
% singleton*.
%
% H = ROAD_ANNOTATION(image_file_list) returns the... |
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