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 | AndyWood91/experiment_programs-master | exptInstructions.m | .m | experiment_programs-master/Le Pelley/Reward Vs Predictiveness/functions/exptInstructions.m | 4,198 | utf_8 | 7ce4a16e26cf08582b2008b92b619970 |
function exptInstructions
global MainWindow white
global bigMultiplier smallMultiplier medMultiplier
global centOrCents
global instrCondition
global softTimeoutDuration
instructStr1 = 'The rest of this experiment is similar to the trials you have just completed. On each trial, you should move your eyes to the DIAMON... |
github | AndyWood91/experiment_programs-master | get_details.m | .m | experiment_programs-master/Wood/get_details.m | 26,972 | utf_8 | 7cd137d1077f3fb2d5fc3fe4960a40d7 | %% get_details
% identifying information (age, gender, hand) are stored separately from
% experiment information for anonymity.
% TODO: turn inputs into a class and make validation a method.
%% code
function [DATA] = get_details(title, conditions, sessions, bonus)
% variable declarations
start = datestr(n... |
github | AndyWood91/experiment_programs-master | update_details.m | .m | experiment_programs-master/Wood/update_details.m | 1,328 | utf_8 | 992cc4584616356ec0af9f87478edf5c | %% update_details
% input arguments
% experiment: Map container created by the get_details function
% bonus_session: optional float for performance bonus. Default is 0.
% outputs
% saves experiment Map to raw_data directory
%% code
function [] = update_details(experiment, bonus_session)
... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | porterStemmer.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/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 | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | submit.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | submitWithConfiguration.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | savejson.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | loadjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | loadubjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zlotus/Coursera_Machine_Learning_Exercises-master | saveubjson.m | .m | Coursera_Machine_Learning_Exercises-master/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | bvarga92/dsp-main | rboFilter.m | .m | dsp-main/rboFilter.m | 463 | utf_8 | fc1f543f31e339bd2223c358284d1293 | % Rekurziv lyukszuro.
% u: a szurendo jel
% f: az elnyomando relativ frekvenciak vektora (az 1 jelenti a mintavetel frekvenciat)
% alpha: batorsagi tenyezo
% e: a szurt jel
function e=rboFilter(u,f,alpha)
f=f(:)';
y=zeros(size(u));
x=zeros(length(f),1);
e=zeros(size(u));
for ii=1:length... |
github | bvarga92/dsp-main | interpolate_trig.m | .m | dsp-main/interp/interpolate_trig.m | 373 | utf_8 | f70c26cc947fa79dbe401c96276301fb | %Trigonometrikus interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_trig(y,x,x2)
N=length(y);
dx=x(2)-x(1);
Y=fft(y)/N;
yi=Y(1);
for ii=1:floor(N/2)
yi=yi+2*abs(Y... |
github | bvarga92/dsp-main | interpolate_ls.m | .m | dsp-main/interp/interpolate_ls.m | 436 | utf_8 | 9caf221b7fc2ec89e49913ab1fd1d80f | %Polinomialis Least Squares interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
%D: az illesztendo polinom fokszama
function yi=interpolate_ls(y,x,x2,D)
X=ones(length(y),D+1);
for ii=1:D
X(:,ii+... |
github | bvarga92/dsp-main | interpolate_spline.m | .m | dsp-main/interp/interpolate_spline.m | 675 | utf_8 | 2bc13272e2173b65c12530f75c3cb4d3 | %Harmadfoku, elsorendu spline interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_spline(y,x,x2)
dy=(y(2:end)-y(1:end-1))./(x(2:end)-x(1:end-1));
yi=[];
for ii=1:length(x)-2;
... |
github | bvarga92/dsp-main | interpolate_lin.m | .m | dsp-main/interp/interpolate_lin.m | 483 | utf_8 | 6f6ff83d5453067ba8974e73b6d9d5c4 | %Linearis interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_lin(y,x,x2)
yi=zeros(size(x2));
for ii=1:length(x2)
if x2(ii)>=x(end)
yi(ii)=y(end);
else
... |
github | bvarga92/dsp-main | interpolate_lag.m | .m | dsp-main/interp/interpolate_lag.m | 431 | utf_8 | 0be2b5c695229d8f268d6616abc3296b | %Lagrange-interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_lag(y,x,x2)
yi=zeros(size(x2));
for ii=1:length(x)
P=ones(size(x2));
for jj=1:length(x)
if jj... |
github | bvarga92/dsp-main | interpolate_herm.m | .m | dsp-main/interp/interpolate_herm.m | 596 | utf_8 | e7ff68035b3537fc20a35be76127a3bb | %Hermite-interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_herm(y,x,x2)
dy=(y(2:end)-y(1:end-1))./(x(2:end)-x(1:end-1));
N=length(x);
A=zeros(2*N-1,2*N-1);
b=zeros(2*N-1,1);... |
github | bvarga92/dsp-main | interpolate_spline_cr.m | .m | dsp-main/interp/interpolate_spline_cr.m | 582 | utf_8 | 136d9b6931245d55b2d2bc8be05f962e | %Catmull-Rom-spline interpolacio
%y: a jel mintainak vektora
%x: az idovektor (hossza megegyezik y hosszaval)
%x2: ezekben az idopontokban keressuk a jel erteket
function yi=interpolate_spline_cr(y,x,x2)
yi=zeros(size(x2));
for ii=1:length(x2)
idx=sum(x<=x2(ii));
if idx==1; contin... |
github | bvarga92/dsp-main | interpolate_lpf.m | .m | dsp-main/interp/interpolate_lpf.m | 273 | utf_8 | 5b0d5e42a35accda2433e82bcf569983 | %Interpolacio nullak beszurasaval es alulatereszto szuressel
%y: a jel mintainak vektora
%K: az interpolacios arany
function yi=interpolate_lpf(y,K)
y=y(:)';
yi=[y ; zeros(K-1,length(y))];
yi=yi(:)';
[B,A]=butter(6,0.8/K);
yi=filter(B,A,yi)*K;
|
github | pnanez/HyEQ_Toolbox-master | package_toolbox.m | .m | HyEQ_Toolbox-master/package_toolbox.m | 3,607 | utf_8 | 9ed8a028a1b093fa6898ef2333647090 | function package_toolbox()
% Create a MATLAB Toolbox package from the Hybrid Equations Toolbox source code.
%
% Before running this script to generate a released toolbox package,
% update the build number in HybridEquationsToolbox.prj.
%
% By Paul Wintz, 2021-2022.
% 'package_toolbox' is a function instead of script ... |
github | pnanez/HyEQ_Toolbox-master | HybridPlotBuilder_demo.m | .m | HyEQ_Toolbox-master/doc/HybridPlotBuilder_demo.m | 24,729 | utf_8 | 1a0d60b64339e8944e35bce9b108080b | %% Plotting Hybrid Arcs
% In this document, we describe how to generate plots of |HybridArc| objects
% using |HybridPlotBuilder|.
%% Setup
% First, we create several |HybridArc| solution objects to use as examples
import hybrid.examples.*
config = HybridSolverConfig('Refine', 15); % 'Refine' option makes the plots sm... |
github | pnanez/HyEQ_Toolbox-master | ExtractSimulinkFunctionBlocks.m | .m | HyEQ_Toolbox-master/doc/src/ExtractSimulinkFunctionBlocks.m | 8,509 | utf_8 | 1893bb4270a05abc83446f589798f9a9 | function ExtractSimulinkFunctionBlocks()
% Script to rebuild tex files from source files
disp('===== Generating .m files from source files =====')
warning('off','Simulink:Commands:LoadingOlderModel')
%% Split the main simulator script in functions
% Open the simulator source file
toolbox_root = hybrid.getFolderLoca... |
github | pnanez/HyEQ_Toolbox-master | HybridPlotBuilder.m | .m | HyEQ_Toolbox-master/matlab/HybridPlotBuilder.m | 53,871 | utf_8 | df1241ffa25feaf1269953efd0a3b8c7 | classdef HybridPlotBuilder < handle
% Class for plotting hybrid arcs with many configuration options.
%
% See also: <a href="matlab: hybrid.internal.openHelp('HybridPlotBuilder_demo')">Demo: Creating plots with HybridPlotBuilder</a>, and HybridArc/plotFlows, HybridArc/plotJumps, etc.
%
% Added in HyEQ Toolbox version ... |
github | pnanez/HyEQ_Toolbox-master | HybridSystem.m | .m | HyEQ_Toolbox-master/matlab/HybridSystem.m | 26,726 | utf_8 | 3e422b39d02a673a6d0d9a737fd50a53 | classdef (Abstract) HybridSystem < handle
% Abstract class for defining hybrid systems. A concrete hybrid system is defined by writing a subclass of HybridSystem.
%
% Added in HyEQ Toolbox version 3.0.
% Written by Paul K. Wintz, Hybrid Systems Laboratory, UC Santa Cruz (©2022).
properties% (SetAccess = immutabl... |
github | pnanez/HyEQ_Toolbox-master | HybridArc.m | .m | HyEQ_Toolbox-master/matlab/HybridArc.m | 13,663 | utf_8 | 0e2032e0c900b6774bc2317e77affa03 | classdef HybridArc
% A numerical representation of a hybrid arc.
%
% See also: HybridSolution, <a href="matlab: showdemo HybridSystem_demo">Demo: How to Implement and Solve a Hybrid System</a>.
% Written by Paul K. Wintz, Hybrid Systems Laboratory, UC Santa Cruz.
% © 2021.
properties(SetAccess = immutable)... |
github | pnanez/HyEQ_Toolbox-master | HybridSubsystemSolution.m | .m | HyEQ_Toolbox-master/matlab/HybridSubsystemSolution.m | 3,619 | utf_8 | b2202d5d283e9ee4294251f5f6e73687 | classdef HybridSubsystemSolution < HybridSolution
% Class of hybrid solutions that include input and output signals.
%
% See also: HybridSolution, HybridSubsystem, hybrid.CompositeHybridSolution.
%
% Added in HyEQ Toolbox version 3.0.
% Written by Paul K. Wintz, Hybrid Systems Laboratory, UC Santa Cruz (©2022).
... |
github | pnanez/HyEQ_Toolbox-master | HybridSubsystem.m | .m | HyEQ_Toolbox-master/matlab/HybridSubsystem.m | 19,571 | utf_8 | 3706724107ce0d53216f6cd77211e6ef | classdef (Abstract) HybridSubsystem < handle
% Class of hybrid subsystems with inputs and outputs, used in the construction of composite hybrid systems.
%
% See also: CompositeHybridSystem, HybridSystem, HybridSubsystemBuilder, hybrid.subsystems, <a href="matlab:
% hybrid.internal.openHelp('CompositeHybridSystem_demo'... |
github | pnanez/HyEQ_Toolbox-master | HybridSolution.m | .m | HyEQ_Toolbox-master/matlab/HybridSolution.m | 4,412 | utf_8 | b699cbf8a186abb396db28fdc7a663d6 | classdef HybridSolution < HybridArc
% Solution to a hybrid dynamical system, as generated by the function HybridSystem.solve().
% This class defines three properties in addition to those found in HybridArc:
% * x0: the initial state of the solution.
% * xf: the final state of the solution.
% * termination_cause: the r... |
github | pnanez/HyEQ_Toolbox-master | CompositeHybridSystem.m | .m | HyEQ_Toolbox-master/matlab/CompositeHybridSystem.m | 24,355 | utf_8 | 90720d602a7605ad4fa779657a0baf9f | classdef CompositeHybridSystem < HybridSystem
% This class models a hybrid system with one or more subsystem.
%
% The subsystems are provided as instances of
% HybridSubsystem with inputs generated by feedback functions
% stored in kappa_C and kappa_D. The kappa_C feedbacks are used during
% flows and the kappa_D feed... |
github | pnanez/HyEQ_Toolbox-master | HyEQsolver.m | .m | HyEQ_Toolbox-master/matlab/HyEQsolver.m | 16,217 | utf_8 | ae9d2f320e9831b64d0326e3484c751e | function [t, j, x] = HyEQsolver(f,g,C,D,x0,TSPAN,JSPAN,rule,options,solver,E,progress)
% Solves hybrid equations.
% Syntax: [t j x] = HyEQsolver(f,g,C,D,x0,TSPAN,JSPAN,rule,options,solver,E)
% computes solutions to the hybrid equations
%
% \dot{x} = f(x,t,j) x \in C x^+ = g(x,t,j) x \in D
%
% where x is the s... |
github | pnanez/HyEQ_Toolbox-master | HybridSystemBuilder.m | .m | HyEQ_Toolbox-master/matlab/HybridSystemBuilder.m | 6,847 | utf_8 | db45debbffe366ff6a12a26c14c951b9 | classdef HybridSystemBuilder < handle
% Construct, inline, a HybridSystem object.
%
% builder = HybridSystemBuilder() ...
% .flowMap(@(x, t) t*sin(x)) ...
% .jumpMap(@(x, t, j) -x) ...
% .flowSetIndicator(@(x) x <= 0) ...
% .jumpSetIndicator(@(x) x >= 0);... |
github | pnanez/HyEQ_Toolbox-master | HybridSubsystemBuilder.m | .m | HyEQ_Toolbox-master/matlab/HybridSubsystemBuilder.m | 14,187 | utf_8 | 9814e60c8c1fd6d91c7e1173c8fe0d22 | classdef HybridSubsystemBuilder < handle
% Construct, inline, a HybridSubsystem object.
%
% builder = HybridSubsystemBuilder() ...
% .flowMap(@(x, u) u*sin(x)) ...
% .jumpMap(@(x, u, t, j) -x) ...
% .flowSetIndicator(@(x) x <= 0) ...
% .jumpSetIndicator(@(x) x >= 0) ...
... |
github | pnanez/HyEQ_Toolbox-master | configureToolbox.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/configureToolbox.m | 6,803 | utf_8 | 915bcebc5ca8b922010e4c0b55bcc88a | function configureToolbox()
% Script for finishing the installation of the Hybrid Equations Toolbox.
% In particular, this script:
% 1. Checks that only one version of the toolbox is installed.
% 2. Prompts the user to run automated tests.
% 3. Enables autocomplete data for the MATLAB editor in supported versions of
% ... |
github | pnanez/HyEQ_Toolbox-master | PlotSettings.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/PlotSettings.m | 26,268 | utf_8 | fe13a71eb8be7a199fef8cd17bb2eaaf | classdef PlotSettings < matlab.mixin.Copyable
% Data object class containing settings used by HybridPlotBuilder.
%
% Added in HyEQ Toolbox version 3.0.
% Written by Paul K. Wintz, Hybrid Systems Laboratory, UC Santa Cruz (©2022).
properties
% Text
label_size
title_size
tick_label_... |
github | pnanez/HyEQ_Toolbox-master | HyEQsolverTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/HyEQsolverTest.m | 7,350 | utf_8 | 9f693cab3efaef73ea3bf3e6806db4c7 | classdef HyEQsolverTest < matlab.unittest.TestCase
methods (Test)
function testDefaultPriorityIsJumps(testCase)
f = @(x) 1e5; % This shouldn't be used.
g = @(x) 0;
C = @(x) 1;
D = @(x) 1;
x0 = 1;
tspan = [0, 100];
... |
github | pnanez/HyEQ_Toolbox-master | buildPlotDataArrayTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/buildPlotDataArrayTest.m | 1,858 | utf_8 | e2a98df025bf49959dd9003d69f609e3 | classdef buildPlotDataArrayTest < matlab.unittest.TestCase
properties
sol_1
sol_2
sol_3
sol_4
end
methods
function this = buildPlotDataArrayTest()
t = [linspace(0, 1, 50)'; linspace(1, 2, 50)'];
j = [zeros(50, 1); ones(50, 1)];
... |
github | pnanez/HyEQ_Toolbox-master | CheckHybridSolutionTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/CheckHybridSolutionTest.m | 4,043 | utf_8 | bcd98671d23f55cee5fe622354032117 | classdef CheckHybridSolutionTest < matlab.unittest.TestCase
methods (Test)
function testCorrectSolution(testCase)
import hybrid.tests.internal.*
dt = 0.1;
C_vals = [1, 1, 0, 0]';
D_vals = [1, 0, 1, 0]';
priority = hybrid.Pr... |
github | pnanez/HyEQ_Toolbox-master | VerifyHybridSolutionDomainTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/VerifyHybridSolutionDomainTest.m | 4,863 | utf_8 | 8cdbcac60457b71e3712a815e6d35af1 | classdef VerifyHybridSolutionDomainTest < matlab.unittest.TestCase
% This test class verifies that the function verifyHybridSolutionDomainTest
% correctly verifies a given hybrid time domain along with C_vals and
% D_vals.
methods (Test)
function testCorrectDomaInEmptyJumpSe... |
github | pnanez/HyEQ_Toolbox-master | CompositeHybridSolutionTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/CompositeHybridSolutionTest.m | 6,182 | utf_8 | 91b69c11d347c4f1b8754bea97d63924 | classdef CompositeHybridSolutionTest < matlab.unittest.TestCase
methods (Test)
function testReferencingSubsystemsWithoutNames(testCase)
dims = [1, 2, 3];
[sol, subsystems] = createCompositeSolution(dims);
testCase.assertEqual(sol.subsys_count, len... |
github | pnanez/HyEQ_Toolbox-master | HybridPlotBuilderTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/+slow_essential/HybridPlotBuilderTest.m | 32,357 | utf_8 | 1461ff1af645de3f869fa780787ca2fb | classdef HybridPlotBuilderTest < matlab.unittest.TestCase
properties
sol_1
sol_2
sol_3
sol_4
fig_cleanup
end
methods
function this = HybridPlotBuilderTest()
close all
t = [linspace(0, 1, 50)'; linspace(1, 2, 50)'];
... |
github | pnanez/HyEQ_Toolbox-master | ExamplesTest.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+tests/+slow_dev_only/ExamplesTest.m | 3,142 | utf_8 | a6f879a14f714d937acb58ffa245b1ad | classdef ExamplesTest < matlab.unittest.TestCase
methods(TestMethodSetup)
function disableWarningAboutOldSimulinkFiles(~)
warning('off','Simulink:Commands:LoadingOlderModel')
end
end
methods(TestMethodTeardown)
function reenableWarningAboutOldSimulinkFiles(~)
... |
github | pnanez/HyEQ_Toolbox-master | buildPlotDataArray.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/buildPlotDataArray.m | 5,062 | utf_8 | b6f985ad82a9b4c5145f0b5dfba47752 | function plot_data_array = buildPlotDataArray(axis_symbols, x_label_ndxs, hybrid_sol, plot_settings)
% Create a cell array of PlotData objects.
% axis_symbols: cell array containing 't', 'j', and 'x'
% x_label_ndxs: numeric row vector containing the state component indices to use for
% the label, legen... |
github | pnanez/HyEQ_Toolbox-master | openHelp.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/openHelp.m | 2,157 | utf_8 | b7d9fa65271f5e2a92bf077509b9863a | function openHelp(name)
% Open a help page stored in doc/html/ (which are published based on the
% MATLAB .m files stored in doc/. If no 'name' is given, then the root of
% the HyEQ Toolbox help is opened.
% Handle no input arguments.
if nargin == 0
name = 'TOC.html';
end
% Append ... |
github | pnanez/HyEQ_Toolbox-master | EZHybridSubsystem.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/EZHybridSubsystem.m | 2,179 | utf_8 | 25faed941aaa0bb762440f6ec20b262e | classdef EZHybridSubsystem < HybridSubsystem
properties(SetAccess = immutable)
f
g
C_indicator
D_indicator
end
methods
function obj = EZHybridSubsystem(f, g, C_indicator, D_indicator,...
state_dim, input_dim, output_dim, flow_output_fnc, jump... |
github | pnanez/HyEQ_Toolbox-master | evaluateInOrder.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/evaluateInOrder.m | 3,483 | utf_8 | 79c97bb2b0833c4367282cb2e28ed753 | function [us, ys] = evaluateInOrder(order, inputs, outputs, xs, t, js)
% Evaluates given input and output functions in a specified order.
%
% Arguments:
% order (char array): Contains a row for each entry in 'inputs' and each entry in
% * 'outputs'. A row that starts with 'u' indicates an input function and row that st... |
github | pnanez/HyEQ_Toolbox-master | EZHybridSystem.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/EZHybridSystem.m | 4,319 | utf_8 | a9290e8b44174891f390365c16fd39f1 | classdef EZHybridSystem < HybridSystem
% EZHybridSystem is an implementation of HybridSystem that takes
% the flow map, jump map, flow set indicator, and jump set indicator
% functions as function handles in the constructor. This allows for a
% HybridSystem to be quickly written in-line using anonymous functions
%... |
github | pnanez/HyEQ_Toolbox-master | sortInputAndOutputFunctionNames.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/sortInputAndOutputFunctionNames.m | 2,767 | utf_8 | 0f93a63999f0679b74de99d585f3eab2 | function sorted_names = sortInputAndOutputFunctionNames(inputs, outputs)
% SORTINPUTANDOUTPUTFUNCTIONNAMES
% Create a char array that indicates an order that the given input and output
% functions can be evaluated. For a system that is a composition of N
% subsystems, the input function of the ith system must have one ... |
github | pnanez/HyEQ_Toolbox-master | HybridUtils.m | .m | HyEQ_Toolbox-master/matlab/+hybrid/+internal/+experimental/HybridUtils.m | 3,112 | utf_8 | 00d105a8ffaa67e8662affe58f24b934 | classdef HybridUtils
% HYBRIDUTILS A collection of functions useful for working with hybrid systems.
methods(Static)
function t_end = timeOfNonconvergence(sol, dist_function, tol)
% Truncate the solution to the time where the solution has not
% yet converged.
if ~exist(... |
github | r-zemblys/irf-master | rdir.m | .m | irf-master/util_lib/I2MC-Dev/functions/helpers/rdir.m | 12,433 | utf_8 | 880aab8ba1581934d68c4e00a3db0172 | function [varargout] = rdir(rootdir,varargin)
% RDIR - Recursive directory listing
%
% D = rdir(ROOT)
% D = rdir(ROOT, TEST)
% D = rdir(ROOT, TEST, RMPATH)
% D = rdir(ROOT, TEST, 1)
% D = rdir(ROOT, '', ...)
% [D, P] = rdir(...)
% rdir(...)
%
%
% *Inputs*
%
% * ROOT
%
% rdir(ROOT) lists the spec... |
github | r-zemblys/irf-master | FileFromFolder.m | .m | irf-master/util_lib/I2MC-Dev/functions/helpers/FileFromFolder.m | 2,357 | utf_8 | 990dfb39666608ef14c50fba0806cc83 | function [file,nfile] = FileFromFolder(folder,mode,f_ext)
% [file,nfile] = FileFromFolder(folder,mode,ext)
%
% Returns struct with all files in directory FOLDER.
% MODE specifies whether an error is displayed when no directories are
% found (default). If MODE is 'silent', only a message will will be
% displayed i... |
github | r-zemblys/irf-master | kmeans2.m | .m | irf-master/util_lib/I2MC-Dev/functions/I2MC/kmeans2.m | 7,195 | utf_8 | 9a9145023b87c21836784584ef23b80f | function [idx, C] = kmeans2(X)
% n points in p dimensional space
n = size(X,1);
maxit = 100;
% case {'plus','kmeans++'}
% Select the first seed by sampling uniformly at random
C(1,:) = X(ceil(end*rand),:);
% Select the rest of the seeds by a probabilistic model
sampleProbability = (X(:,1) - C(1)).^2 + (... |
github | mbanani/attend-master | boxesEval.m | .m | attend-master/edges/boxesEval.m | 5,118 | utf_8 | 92042e7eff2def2fcafd0202645b23c0 | function recall = boxesEval( varargin )
% Perform object proposal bounding box evaluation and plot results.
%
% boxesEval evaluates a set bounding box object proposals on the dataset
% specified by the 'data' parameter (which is generated by boxesData.m).
% The methods are specified by the vector 'names'. For each meth... |
github | mbanani/attend-master | edgesEvalDir.m | .m | attend-master/edges/edgesEvalDir.m | 5,852 | utf_8 | b708b92045eaa75fa68d09e169447bb6 | function varargout = edgesEvalDir( varargin )
% Calculate edge precision/recall results for directory of edge images.
%
% Enhanced replacement for boundaryBench() from BSDS500 code:
% http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/
% Uses same format for results and is fully compatible with boundary... |
github | mbanani/attend-master | edgeBoxesSweeps.m | .m | attend-master/edges/edgeBoxesSweeps.m | 3,411 | utf_8 | e5a9cecaa2b2e071c5d8729811f751dc | function edgeBoxesSweeps()
% Parameter sweeps for Edges Boxes object proposals.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar and Larry Zitnick, 2... |
github | mbanani/attend-master | edgesTrain.m | .m | attend-master/edges/edgesTrain.m | 13,669 | utf_8 | c29662f392dd5074db27a50767e39cef | function model = edgesTrain( varargin )
% Train structured edge detector.
%
% For an introductory tutorial please see edgesDemo.m.
%
% USAGE
% opts = edgesTrain()
% model = edgesTrain( opts )
%
% INPUTS
% opts - parameters (struct or name/value pairs)
% (1) model parameters:
% .imWidth - [32] width of i... |
github | mbanani/attend-master | datasetBoxes.m | .m | attend-master/edges/datasetBoxes.m | 3,103 | utf_8 | d7f8c8ccdbd59bf107824e742c708306 | % Code is a very slight variation of the edgeBoxesDemo.m created by Piotr
% Dollar. All copyrights are given to him.
% Updated by: Mohamed El Banani
% Date: December 1, 2016
%
% A function to generate the bounding boxes from a folder containing a set
% of images. The output bounding boxes are written into a text file... |
github | mbanani/attend-master | spAffinities.m | .m | attend-master/edges/spAffinities.m | 4,227 | utf_8 | c8d1c1cc618a7266fee4b2d10651c8c2 | function [A,E,U] = spAffinities( S, E, segs, nThreads )
% Compute superpixel affinities and optionally corresponding edge map.
%
% Computes an m x m affinity matrix A where A(i,j) is the affinity between
% superpixels i and j. A has values in [0,1]. Only affinities between
% spatially nearby superpixels are computed; t... |
github | mbanani/attend-master | edgesSweeps.m | .m | attend-master/edges/edgesSweeps.m | 8,831 | utf_8 | c36ed011e7daa4ea08d83453e0cf8125 | function edgesSweeps()
% Parameter sweeps for structured edge detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar, 2014.
% Licensed under the ... |
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