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 | matthewmellor/UDP-Real-Time-Grapher-Desktop-App-master | udpGrapherV1.m | .m | UDP-Real-Time-Grapher-Desktop-App-master/udpGrapherV1.m | 37,265 | utf_8 | 249af030d986a4837f4e4eb07673dc57 | function varargout = udpGrapherV1(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @udpGrapherV1_OpeningFcn, ...
'gui_OutputFcn', @ud... |
github | matthewmellor/UDP-Real-Time-Grapher-Desktop-App-master | udptest.m | .m | UDP-Real-Time-Grapher-Desktop-App-master/udptest.m | 899 | utf_8 | 695e6024d74c3804248c77e2e392a9d2 | %UDP Logger Test
function udptest
%fclose(instrfindall); %May need to comment this out if first time running script
u = udp('18.111.54.180',2390, 'LocalPort', 5000); %Ip address of server
%2390 is the port of the server
%5000 is the local port
%Values above need to be changed to reflect the values o... |
github | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | porterStemmer.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submit.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | submitWithConfiguration.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | savejson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | loadubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | nladuo/ml-study-stuff-master | saveubjson.m | .m | ml-study-stuff-master/coursera-machine-learning/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 | filter-digital/TCAS-II-00594-2016-master | FIR_90TAPS_mlab.m | .m | TCAS-II-00594-2016-master/Altera_16.0/cores/FIR_90TAPS_sim/FIR_90TAPS_mlab.m | 6,222 | utf_8 | 5b3191eee588d8db69bb307d1346949c | %
%THIS IS A WIZARD GENERATED FILE. DO NOT EDIT THIS FILE!
%
%---------------------------------------------------------------------------------------------------------
%This is a filter with fixed coefficients.
%This Model Only Support Single Channel Input Data.
%Please input:
%data vector: stimulation(1:n)
% Thi... |
github | filter-digital/TCAS-II-00594-2016-master | FIR_90TAPS_mlab.m | .m | TCAS-II-00594-2016-master/Altera/cores/FIR_90TAPS_sim/FIR_90TAPS_mlab.m | 6,188 | utf_8 | 5ed3fcc40c097bca7eaff8164eceb7dc | %
%THIS IS A WIZARD GENERATED FILE. DO NOT EDIT THIS FILE!
%
%---------------------------------------------------------------------------------------------------------
%This is a filter with fixed coefficients.
%This Model Only Support Single Channel Input Data.
%Please input:
%data vector: stimulation(1:n)
% Thi... |
github | nchen9191/Eulers-Equation-Spectral-master | tau_mat3.m | .m | Eulers-Equation-Spectral-master/tau_mat3.m | 656 | utf_8 | ba580998da5aaa5039b2302ec0213cd0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matrix to compute tau's with pressure boundary conditions built in
% analytically
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | gradFFC.m | .m | Eulers-Equation-Spectral-master/gradFFC.m | 559 | utf_8 | 38ef2dcf39deebf5ef529201d789f78b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fourier-Fourier-Chebyshev calculation of gradient
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | iFFCT.m | .m | Eulers-Equation-Spectral-master/iFFCT.m | 543 | utf_8 | 3324b6ecac49c31b9cb153430dd2eea4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fourier-Fourier-Chebyshev transform to Physical-Physical-Physical
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | iChebTrans.m | .m | Eulers-Equation-Spectral-master/iChebTrans.m | 842 | utf_8 | db1a01892bdee277af67c2b9248a5b98 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fourier-Fourier-Chebyshev transform to Fourier-Fourier-Physical
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | Gen_green_euler.m | .m | Eulers-Equation-Spectral-master/Gen_green_euler.m | 4,575 | utf_8 | 088d4fae549f7eafc7882b73df8181c3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Precompute green functions for tau calculation
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | vorticityFFC.m | .m | Eulers-Equation-Spectral-master/vorticityFFC.m | 739 | utf_8 | 557b5d45d11211c64b5b7f68b13c68bc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fourier-Fourier-Chebyshev calculation of vorticity
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | FFCT.m | .m | Eulers-Equation-Spectral-master/FFCT.m | 636 | utf_8 | 234ca427b7eca9dde963ba0bc108faa7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Physical-Physical-Physical to Fourier-Fourier-Chebyshev transform
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | laplacianFFC.m | .m | Eulers-Equation-Spectral-master/laplacianFFC.m | 563 | utf_8 | 9ebfb349770ae58e7a7a3061b521ab6e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fourier-Fourier-Chebyshev transform to Physical-Physical-Physical
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | ddyCheb.m | .m | Eulers-Equation-Spectral-master/ddyCheb.m | 488 | utf_8 | c8867267452b8bbe5f14f6aa140b3f34 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 3D Chebyshev derivative, need to supply D matrix
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | twothird.m | .m | Eulers-Equation-Spectral-master/twothird.m | 698 | utf_8 | 5276fd68a54fb524ad689fad56b3d296 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 2/3 rule to de-alias nonlinear project
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | ChDiffnoBC.m | .m | Eulers-Equation-Spectral-master/ChDiffnoBC.m | 529 | utf_8 | 6c30614f453d46d5224cfb58c5a27371 | %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Building the Chebyshev derivative Matrix scaled by L
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | divFreeFFC.m | .m | Eulers-Equation-Spectral-master/divFreeFFC.m | 1,654 | utf_8 | 31c4e9dfaf42233957d60ed68ccc9ea2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Remove divergence from n+1/2 steps by calculating pressure and taus
% Update velocities mode by mode
% Analytically computed pressure boundary conditions in term of taus
%
% Nelson Chen
% University of California, Berkeley
% Computational Fl... |
github | nchen9191/Eulers-Equation-Spectral-master | crossProd.m | .m | Eulers-Equation-Spectral-master/crossProd.m | 516 | utf_8 | f50fa6661145b95844846e60d6eef6e9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute cross-product of two vectors in 3D arrays
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | nchen9191/Eulers-Equation-Spectral-master | ChebTrans.m | .m | Eulers-Equation-Spectral-master/ChebTrans.m | 811 | utf_8 | da88c4a636870043ebf5594732834511 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 3D Chebyshev (cosine) transform
%
% Nelson Chen
% University of California, Berkeley
% Computational Fluid Dynamics Lab
% nchen9191@berkeley.edu
% Last revision: 6/25/2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | hasauino/Implemented_RRTs-master | stlread.m | .m | Implemented_RRTs-master/stlread.m | 3,981 | utf_8 | f1c11b51cd13528daae6802cfcd3b539 | function varargout = stlread(file)
% STLREAD imports geometry from an STL file into MATLAB.
% FV = STLREAD(FILENAME) imports triangular faces from the ASCII or binary
% STL file idicated by FILENAME, and returns the patch struct FV, with fields
% 'faces' and 'vertices'.
%
% [F,V] = STLREAD(FILENAME) r... |
github | jiaxue1993/cnn-finetune-master | cnn_finetune_train.m | .m | cnn-finetune-master/cnn_finetune_train.m | 16,913 | utf_8 | f83e6c08355b21b71dd2f8bfe9f3db3f | function [net, info] = cnn_finetune_train(net, imdb, getBatch, varargin)
%CNN_FINETUNE_TRAIN A modified cnn_train
% Options added:
% 'maxIterPerEpoch'
% 'balancingFunction'
% Hang Su
%
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stocha... |
github | jiaxue1993/cnn-finetune-master | cnn_get_features.m | .m | cnn-finetune-master/cnn_get_features.m | 12,822 | utf_8 | 886b7ad48772af7bd17c21f9b37918b6 | function feats = cnn_get_features( imList, model, layers, varargin )
%CNN_GET_FEATURES Compute and save CNN activation features
%
% imList::
% mode 1: cell array of image paths
% mode 2: cell array of images OR stacked image tensor
% model:: 'imagenet-matconvnet-vgg-m'
% can be either string (mod... |
github | jiaxue1993/cnn-finetune-master | cnn_finetune_init.m | .m | cnn-finetune-master/cnn_finetune_init.m | 2,175 | utf_8 | d7183c559e2d627071a3a6486dbb2a1f | function net = cnn_finetune_init(imdb, net)
opts.weightInitMethod = 'xavierimproved' ;
opts.scale = 1;
if ~exist('net', 'var') || isempty(net),
net = 'imagenet-vgg-m';
end
if ischar(net),
net_path = fullfile('data','models',[net '.mat']);
if ~exist(net_path,'file'),
fprintf('Downloading model (%s) ...... |
github | jiaxue1993/cnn-finetune-master | cnn_finetune.m | .m | cnn-finetune-master/cnn_finetune.m | 4,303 | utf_8 | 6deb8f344a8d282c5366942291524c62 | function [net, info] = cnn_finetune(varargin)
setup;
opts.datasetName = 'new_wild';
opts.datafn = @setup_imdb_new_wild;
opts.expDir = fullfile('data','exp') ;
opts.baseNet = 'imagenet-matconvnet-vgg-verydeep-16';
opts.numEpochs = [5 5 10];
opts.numFetchThreads = 12 ;
opts.imdb = [];
opts.includeVal = false;... |
github | jiaxue1993/cnn-finetune-master | next_samples.m | .m | cnn-finetune-master/utils/next_samples.m | 1,619 | utf_8 | f4b40767091699ec75e98920212427ba | function [sample, sampleQueue] = next_samples(sample, sampleQueue, label, nSample, balancingFn)
%NEXT_SAMPLES Shuffling + sampling + balancing + clipping + caching
%
% Hang Su
if ~exist('sampleQueue', 'var') || isempty(sampleQueue),
sampleQueue = [];
end
if ~exist('label', 'var') || isempty(label),
label = one... |
github | jiaxue1993/cnn-finetune-master | dirfun.m | .m | cnn-finetune-master/utils/dirfun.m | 3,092 | utf_8 | 8b539042670f86f1a4042f9d9a5eb3b4 | function dirfun( dir_path, processFn, save_path, imreadFn, file_pattern, save_pattern, cnt_limit )
% dirfun Apply a function to each file in the directory
%
% dir_path: directory containing images, will be searched recursively
% processFn: function that will be applied to each image found
% save_path: (defa... |
github | jiaxue1993/cnn-finetune-master | setup_imdb_0131.m | .m | cnn-finetune-master/dataset/setup_imdb_0131.m | 1,990 | utf_8 | eb89c6c44eb7f340534fab2c366bf371 | function imdb = setup_imdb_0131(datasetDir, varargin)
suffix = '.jpg';
allframes_subdir = 'allFrames';
imdb.imageDir = datasetDir;
imdb.meta.sets = {'train', 'val', 'test'};
seqs =dir(datasetDir);
seqs = {seqs([seqs.isdir]).name};
seqs = setdiff(seqs, {'.', '..'});
classes = dir(fullfile(datasetDir, seqs{1}));
cl... |
github | jiaxue1993/cnn-finetune-master | setup_imdb_modelnet.m | .m | cnn-finetune-master/dataset/setup_imdb_modelnet.m | 8,584 | utf_8 | 94b6adaea3e771b6345b3a1aefc8fa7d | function imdb = setup_imdb_modelnet(datasetDir, varargin)
opts.seed = 0 ; % random seed generator
opts.ratio = [0.8 0.2]; % train:val ratio
opts.ext = '.png'; % extension of target files
opts.extmesh = '.off'; % extension of target mesh files
opts.useUprightAssumption = true; % if true, 1... |
github | crboth/LDPC_Decoder-master | ldpc_decoderQ.m | .m | LDPC_Decoder-master/MATLAB/ldpc_decoderQ.m | 1,355 | utf_8 | 04dca4de64081a1208e234711182884a | %Quantized simulation
function [out] = ldpc_decoderQ(llr, SpHenc, max_iterations, pad, pbd)
[row, col] = find(SpHenc);
[num_rows, ~] = size(SpHenc);
n = nnz(SpHenc);
k = 1:n;
rowi = 1:num_rows;
Q = zeros(1,n);
R = zeros(1,n);
i = 0;
P_v = llr;
sign_prod = zeros(1,num_rows);
... |
github | crboth/LDPC_Decoder-master | InitializeWiMaxLDPC.m | .m | LDPC_Decoder-master/MATLAB/InitializeWiMaxLDPC.m | 6,959 | utf_8 | 37e07fcc90355107bbea444142b3b9d7 | % File: InitializeWiMaxLDPC.m
%
% Description: Initializes the WiMax LDPC encoder/decoder
%
% The calling syntax is:
% [H_rows, H_cols, P] = InitializeWiMaxLDPC( rate, size, ind )
%
% Where:
% H_rows = a M-row matrix containing the indices of the non-zero rows of H excluding the dual-diagonal ... |
github | crboth/LDPC_Decoder-master | verigen.m | .m | LDPC_Decoder-master/verilog/verigen.m | 3,819 | utf_8 | 23d5438635961b981b8df94a1e967399 | %generates the top level verilog module of the LDPC decoder
function verigen(H, prec)
fID = fopen('VFiles/LDPC.v','w');
num_rows = 288;
num_cols = 576;
%could have passed these values in from the script
% num_con_each_Vnode
conV = sum(H,1); %sums all columns into row vector, total number of connections for a Var Node
... |
github | crboth/LDPC_Decoder-master | InitializeWiMaxLDPC.m | .m | LDPC_Decoder-master/verilog/InitializeWiMaxLDPC.m | 6,959 | utf_8 | 37e07fcc90355107bbea444142b3b9d7 | % File: InitializeWiMaxLDPC.m
%
% Description: Initializes the WiMax LDPC encoder/decoder
%
% The calling syntax is:
% [H_rows, H_cols, P] = InitializeWiMaxLDPC( rate, size, ind )
%
% Where:
% H_rows = a M-row matrix containing the indices of the non-zero rows of H excluding the dual-diagonal ... |
github | alshedivat/keras-gp-master | deriv_x.m | .m | keras-gp-master/kgp/backend/utils/deriv_x.m | 1,255 | utf_8 | acdd5accead3dc5f810cb2782ad4def4 | function dx = deriv_x(alpha, P, K, xg, mean, hyp, x, deg, h)
% deriv_x Compute dcovGrid_dx for given parameters.
% The function is taken from DKL framework.
if ~exist('h', 'var') h = 1e-5; end
if P == 1, tP = eye(size(x,2)); else tP = P; end
xP = x*P';
[M,dM] = covGrid('interp',xg,xP,deg); ... |
github | TheLegendAli/DeepLab-Context2-master | classification_demo.m | .m | DeepLab-Context2-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | TheLegendAli/DeepLab-Context2-master | MyVOCevalseg.m | .m | DeepLab-Context2-master/matlab/my_script/MyVOCevalseg.m | 5,377 | utf_8 | 29c4dbd42900bd0e204d178ec2c11f3e | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | TheLegendAli/DeepLab-Context2-master | MyVOCevalsegBoundary.m | .m | DeepLab-Context2-master/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | TheLegendAli/DeepLab-Context2-master | EvalSegResults.m | .m | DeepLab-Context2-master/matlab/my_script/EvalSegResults.m | 2,981 | utf_8 | c0a2767363891e5e68ccc2d218ab4c52 | %SetupEnv;
function EvalSegResults(post_folder, feature_name, model_name, testset, feature_type, dataset, id, trainset, is_mat, is_argmax, has_postprocess)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% You do not need to chage values below
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
VOC_root_folder = '{DATA_ROOT}';
ou... |
github | jchavanton/webrtc-master | apmtest.m | .m | webrtc-master/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 | jchavanton/webrtc-master | plot_neteq_delay.m | .m | webrtc-master/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 | kuka-isir/qpOASES-master | make.m | .m | qpOASES-master/qpOASES_svn/interfaces/simulink/make.m | 8,452 | utf_8 | 81d0673ce40d341bb2590ef6ad452336 | function [] = make( varargin )
%MAKE Compiles the Simulink interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | kuka-isir/qpOASES-master | qpOASES_options.m | .m | qpOASES-master/qpOASES_svn/interfaces/octave/qpOASES_options.m | 10,357 | utf_8 | f4676c178ac23389cbe6a19d89f60fb1 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2015 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | kuka-isir/qpOASES-master | make.m | .m | qpOASES-master/qpOASES_svn/interfaces/octave/make.m | 8,296 | utf_8 | daf76455fd9c1a6c9b91a28772175d6a | function [] = make( varargin )
%MAKE Compiles the octave interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | kuka-isir/qpOASES-master | qpOASES_auxInput.m | .m | qpOASES-master/qpOASES_svn/interfaces/octave/qpOASES_auxInput.m | 4,436 | utf_8 | 97652f7208f989e1af4467fce8498bd8 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2015 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | kuka-isir/qpOASES-master | qpOASES_options.m | .m | qpOASES-master/qpOASES_svn/interfaces/matlab/qpOASES_options.m | 10,357 | utf_8 | f4676c178ac23389cbe6a19d89f60fb1 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2015 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | kuka-isir/qpOASES-master | make.m | .m | qpOASES-master/qpOASES_svn/interfaces/matlab/make.m | 8,216 | utf_8 | c8bd69c0ca4ee6b90fd6ac42c52bb567 | function [] = make( varargin )
%MAKE Compiles the Matlab interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | kuka-isir/qpOASES-master | qpOASES_auxInput.m | .m | qpOASES-master/qpOASES_svn/interfaces/matlab/qpOASES_auxInput.m | 4,436 | utf_8 | 97652f7208f989e1af4467fce8498bd8 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2015 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | kuka-isir/qpOASES-master | runAllTests.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/runAllTests.m | 5,548 | utf_8 | bdff35e75080becaee269cf4f487d115 | function [ successFlag ] = runAllTests( doPrint )
if ( nargin < 1 )
doPrint = 0;
end
successFlag = 1;
curWarnLevel = warning;
warning('off');
% add sub-folders to Matlab path
setupTestingPaths();
clc;
%% run interface tests
fprintf(... |
github | kuka-isir/qpOASES-master | runInterfaceTest.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/tests/runInterfaceTest.m | 16,774 | utf_8 | 695fea461f4e440770b787dcafe361d2 | function [ successFlag ] = runInterfaceTest( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | kuka-isir/qpOASES-master | runRandomZeroHessian.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/tests/runRandomZeroHessian.m | 14,399 | utf_8 | 62c1efb1adf03ca9009d830d211f3ef8 | function [ successFlag ] = runRandomZeroHessian( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | kuka-isir/qpOASES-master | runInterfaceSeqTest.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/tests/runInterfaceSeqTest.m | 15,458 | utf_8 | 7fd12067642b559d3dd08130be565119 | function [ successFlag ] = runInterfaceSeqTest( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | kuka-isir/qpOASES-master | runRandomIdHessian.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/tests/runRandomIdHessian.m | 14,687 | utf_8 | 1bf0849e7175e73709bbae55057eeff5 | function [ successFlag ] = runRandomIdHessian( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | kuka-isir/qpOASES-master | isoctave.m | .m | qpOASES-master/qpOASES_svn/testing/matlab/auxFiles/isoctave.m | 508 | utf_8 | c857dec2b164c5835c0d5235cd7ad8f0 |
% ISOCTAVE True if the operating environment is octave.
% Usage: t=isoctave();
%
% Returns 1 if the operating environment is octave, otherwise
% 0 (Matlab)
%
% ---------------------------------------------------------------
function t=isoctave()
%ISOCTAVE True if the operating environment is octave.
% U... |
github | cipherlab-poly/crazyflie-public-master | quadrotor_plot.m | .m | crazyflie-public-master/MATLAB Files/quadrotor_plot.m | 7,392 | utf_8 | a2c8c7526185cb56faf3adc25c8c70af |
% Copyright (C) 1993-2014, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for Matlab (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Lic... |
github | janfreyberg/binocular-rivalry-master | rivalrytrials.m | .m | binocular-rivalry-master/trial-based-rivalry/rivalrytrials.m | 20,979 | utf_8 | 8783aeedf45d1a6562797a3832f8bf14 |
function rivalrytrials
try
clear all
%% Session Variables
commandwindow;
ID = input('Participant ID? ', 's');
scr_diagonal = 24; % in inches
scr_distance = 60; % in cm
% diagnosis = input('Diagnosis? ');
tstamp = clock;
if ~isdir( fullfile(pwd, 'Results', 'rivalry trials repetition') )
mkdir( fullfile(pwd, 'Resul... |
github | janfreyberg/binocular-rivalry-master | binocular_rivalry_ssvep.m | .m | binocular-rivalry-master/eeg-rivalry/binocular_rivalry_ssvep.m | 10,118 | utf_8 | d699bafe8b357223180ae3016bb67cf4 |
function binocular_ssvep
clearvars;
global pxsize frameWidth ycen xcen fixWidth scr l_key u_key d_key r_key...
esc_key stimRect fixLines fixPoint frameRect checkerboard_arrays...
checkerboard_alpha frequencies period_frames trialdur framedur...
breakdur currTrial
try
% get basic info, set filename.
rng(... |
github | somu15/Nonlinear_Modeling_Analysis-master | Wind_history_extraction.m | .m | Nonlinear_Modeling_Analysis-master/Hurricane_winds/Wind_history_extraction.m | 4,025 | utf_8 | 8f7b1b13f2f39cba68394d0117bb4d26 | % Function for extracting hurricane wind time history at a specified Lat -
% Long.
% Input parameters : .win file containing ADCIRC wind input data, requir -
% ed Lat and Long.
% Written by Somayajulu (02/26/2016)
function [Wind_U,Wind_V] = Wind_history_extraction()
prompt = 'Enter the file path along with file name ... |
github | jiegzhan/machine-learning-stanford-master | submit.m | .m | machine-learning-stanford-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 | jiegzhan/machine-learning-stanford-master | submitWithConfiguration.m | .m | machine-learning-stanford-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... |
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