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 | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/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 | jingconan/SBDet-master | convertf.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/convertf.m | 1,437 | utf_8 | 0b4046d80e4b28167468bb63068caa95 | %
% [A,b,c,K]=convertf(A,b,c,K)
%
% converts free variables in a SeDuMi problem into nonnegative LP variables.
%
function [A,b,c,K]=convertf(A,b,c,K)
%
% Get the number of constraints.
%
m=length(b);
%
% Deal with the following special case. If A is transposed, transpose
% it again so that it is of the right size.
%... |
github | jingconan/SBDet-master | writesol.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/writesol.m | 3,539 | utf_8 | 45c03d187e8ae5d6aa257199c7c80f75 | %
% writesol(fname,x,y,z,K)
%
% Writes out a solution file in the format used by CSDP and
% readsol.
%
% fname File name to read solution from.
% x,y,z Solution.
% K structure of the matrices.
%
%
%
function ret=writesol(fname,x,y,z,K);
%
% First, eliminate special cases that we don't handle.
%
... |
github | jingconan/SBDet-master | csdp.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/csdp.m | 5,693 | utf_8 | 8b2567074ddd136e22e0933ade5210d8 | %
% [x,y,z,info]=csdp(At,b,c,K,pars,x0,y0,z0)
%
% Uses CSDP to solve a problem in SeDuMi format.
%
% Input:
% At, b, c, K SDP problem in SeDuMi format.
% pars CSDP parameters (optional parameter.)
% x0,y0,z0 Optional starting point.
%
% Output:
%
% x, y, z ... |
github | jingconan/SBDet-master | readsol.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/readsol.m | 3,712 | utf_8 | 47653eea25fcde6b27811c335d3a899b | %
% [x,y,z]=readsol(fname,K,m)
%
% fname File name to read solution from.
% K structure of the matrices.
% m size of y vector.
%
% Modified 7/15/04, for greater MATLAB acceleration.
%
function [x,y,z]=readsol(fname,K,m)
%
% First, eliminate special cases that we don't handle.
%
%
% Check fo... |
github | jingconan/SBDet-master | writesdpa.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/writesdpa.m | 7,470 | utf_8 | 3fc52dd04326a97ae0a6c3355634a6ff | % This function takes a problem in SeDuMi MATLAB format and writes it out
% in SDPA sparse format.
%
% Usage:
%
% ret=writesdpa(fname,A,b,c,K,pars)
%
% fname Name of SDPpack file, in quotes
% A,b,c,K Problem in SeDuMi form
% pars Optional parameters.
% ... |
github | jingconan/SBDet-master | readsdpa.m | .m | SBDet-master/csdp6.1.0linuxp4/matlab/readsdpa.m | 3,062 | utf_8 | 1801da4521b127a71051894d685de254 | %
% [At,b,c,K]=readsdpa(fname)
%
% Reads in a problem in SDPA sparse format, and returns it in SeDuMi
% format.
%
% 7/20/07 Modified to handle comments and other cruft in the SDPA
% file. In particular,
%
% 1. Initial comment lines beginning with " or * are ignored.
% 2. In the first three lines, any extraneou... |
github | qdsclove/JustNoticeableDifference-master | func_lum_jnd.m | .m | JustNoticeableDifference-master/JND_matlab/func_lum_jnd.m | 712 | utf_8 | ed17c82aec451a3f61f3e1138a34d2a3 | function matout = func_lum_jnd(matin)
% estimate the background luminance distortion
if ~isa(matin, 'double')
matin = double(matin);
end
bg_lum = func_bg_lum(matin);
[col, row] = size(matin);
matout = zeros(col, row);
bg_jnd = lum_jnd;
for x = 1:col
for y = 1:row
matout(x,y) = bg_jnd( bg_lum(x,y)+1... |
github | aaronjridley/GITM-master | example_gitm_3dall_read.m | .m | GITM-master/srcMatlab/example_gitm_3dall_read.m | 2,570 | utf_8 | 49a67d31e0383a281c7b12617d84dd47 | % close all
% clear all
% clc
%% =======================================
%% =======================================
function [utime_all,lat_all,lon_all,alt_all,Tn_all,rho_all,VnUp_all]=GITM_3DALL_read(fpath)
% fpath='/Users/xianjing/Desktop/no_backup/Gitm/Runs/Variability/by-05+0.0/data/';
% % datasub includes all... |
github | novassdsu/VideoCallVoIP-master | echo_diagnostic.m | .m | VideoCallVoIP-master/VideoCallVoIP/submodules/externals/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | novassdsu/VideoCallVoIP-master | apmtest.m | .m | VideoCallVoIP-master/VideoCallVoIP/submodules/externals/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | novassdsu/VideoCallVoIP-master | plot_neteq_delay.m | .m | VideoCallVoIP-master/VideoCallVoIP/submodules/externals/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | novassdsu/VideoCallVoIP-master | exportfig.m | .m | VideoCallVoIP-master/VideoCallVoIP/submodules/externals/webrtc/modules/video_coding/codecs/test_framework/exportfig.m | 14,995 | utf_8 | d7427be6e56c37d4aec2f2c91c9a6341 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
github | novassdsu/VideoCallVoIP-master | plotBenchmark.m | .m | VideoCallVoIP-master/VideoCallVoIP/submodules/externals/webrtc/modules/video_coding/codecs/test_framework/plotBenchmark.m | 11,672 | utf_8 | a80ed712ca3895c1e7b6383d4cc07d38 | function plotBenchmark(fileNames, export)
%PLOTBENCHMARK Plots and exports video codec benchmarking results.
% PLOTBENCHMARK(FILENAMES, EXPORT) parses the video codec benchmarking result
% files given by the cell array of strings FILENAME. It plots the results and
% optionally exports each plot to an appropriatel... |
github | novassdsu/VideoCallVoIP-master | echo_diagnostic.m | .m | VideoCallVoIP-master/submodules/externals/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | novassdsu/VideoCallVoIP-master | apmtest.m | .m | VideoCallVoIP-master/submodules/externals/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | novassdsu/VideoCallVoIP-master | plot_neteq_delay.m | .m | VideoCallVoIP-master/submodules/externals/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | novassdsu/VideoCallVoIP-master | exportfig.m | .m | VideoCallVoIP-master/submodules/externals/webrtc/modules/video_coding/codecs/test_framework/exportfig.m | 14,995 | utf_8 | d7427be6e56c37d4aec2f2c91c9a6341 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
github | novassdsu/VideoCallVoIP-master | plotBenchmark.m | .m | VideoCallVoIP-master/submodules/externals/webrtc/modules/video_coding/codecs/test_framework/plotBenchmark.m | 11,672 | utf_8 | a80ed712ca3895c1e7b6383d4cc07d38 | function plotBenchmark(fileNames, export)
%PLOTBENCHMARK Plots and exports video codec benchmarking results.
% PLOTBENCHMARK(FILENAMES, EXPORT) parses the video codec benchmarking result
% files given by the cell array of strings FILENAME. It plots the results and
% optionally exports each plot to an appropriatel... |
github | neurodata/CAJAL-master | computeBlock.m | .m | CAJAL-master/packages/cubeCutout/computeBlock.m | 50,719 | utf_8 | e02614a20765972ebe400fd90003726a | function computeBlock(serverLocation, token, channel, resolution, xStart, xStop, yStart, yStop, zStart, zStop, xSpan, ySpan, zSpan, padX, padY, padZ, alignXY, alignZ, computeOptions, shuffleFilesFlag, cubeListFile, cubeOutputDir, mergeListFile, mergeOutputDir, print_flag)
% computeBlock function allows the user to crea... |
github | neurodata/CAJAL-master | imclipboard.m | .m | CAJAL-master/api/matlab/ramon/basic_viewer/imclipboard.m | 10,036 | utf_8 | 7f9d421bba2a740b6f4fecafaefb2eb8 | function varargout = imclipboard(clipmode, varargin)
%IMCLIPBOARD Copy and paste images to and from system clipboard.
%
% IMCLIPBOARD('copy', IMDATA) sets the clipboard content to the image
% represented by IMDATA. IMDATA must be MxN grayscale (double, uint8,
% uint16), MxN black and white (logical), MxNx3 true c... |
github | neurodata/CAJAL-master | brewermap.m | .m | CAJAL-master/api/matlab/ramon/basic_viewer/brewermap.m | 18,841 | utf_8 | 03014ffd7d2c8dc9f583d90160fe098a | function [map,num,typ] = brewermap(N,scheme)
% The complete selection of ColorBrewer colorschemes (RGB colormaps).
%
% (c) 2015 Stephen Cobeldick
%
% ### Function ###
%
% Returns any RGB colormap from the ColorBrewer colorschemes, especially
% intended for mapping and plots with attractive, distinguishable colo... |
github | neurodata/CAJAL-master | basicWrapperTestMFile.m | .m | CAJAL-master/api/matlab/wrapper/basicWrapperTestMFile.m | 1,231 | utf_8 | e6b2a9c4005c6c81feb3f5a787a07311 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright 2015 The Johns Hopkins University / Applied Physics Laboratory
% All Rights Reserved.
% Contact the JHU/APL Office of Technology Transfer for any additional rights.
% www.jhuapl.edu/ott
%
% Licensed under the Apache License, V... |
github | neurodata/CAJAL-master | TestRunDisplay.m | .m | CAJAL-master/test/matlab/xunit/TestRunDisplay.m | 9,772 | utf_8 | 5676f8f435e9c6ceeb16bd2b6bcdc6dd | classdef TestRunDisplay < TestRunMonitor
%TestRunDisplay Print test suite execution results.
% TestRunDisplay is a subclass of TestRunMonitor. If a TestRunDisplay
% object is passed to the run method of a TestComponent, such as a
% TestSuite or a TestCase, it will print information to the Command
% Window (or ... |
github | neurodata/CAJAL-master | rdir.m | .m | CAJAL-master/test/matlab/xunit/rdir.m | 11,648 | utf_8 | 3c03c1f9ef6a78541a530c5e776141ca | 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, '', ...)
% rdir(...)
%
%
% *Inputs*
%
% * ROOT
%
% rdir(ROOT) lists the specified files.
% ROOT can be a pathname,... |
github | neurodata/CAJAL-master | TestSuite.m | .m | CAJAL-master/test/matlab/xunit/TestSuite.m | 13,145 | utf_8 | a2f83a7c15ba0ad13fa330aa59d1451a | %TestSuite Collection of TestComponent objects
% The TestSuite class defines a collection of TestComponent objects.
%
% TestSuite methods:
% TestSuite - Constructor
% add - Add test component to test suite
% print - Display test suite summary to Comman... |
github | neurodata/CAJAL-master | runtests.m | .m | CAJAL-master/test/matlab/xunit/runtests.m | 4,841 | utf_8 | a24f89548b654b7bf0fc9ec25be2e2f2 | function out = runtests(varargin)
%runtests Run unit tests
% runtests runs all the test cases that can be found in the current directory
% and summarizes the results in the Command Window.
%
% Test cases can be found in the following places in the current directory:
%
% * An M-file function whose name start... |
github | neurodata/CAJAL-master | isTestCaseSubclass.m | .m | CAJAL-master/test/matlab/xunit/+xunit/+utils/isTestCaseSubclass.m | 894 | utf_8 | 5c7e3f1d9b1eb3e2148cad3cde93c5c7 | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_meta = meta.c... |
github | neurodata/CAJAL-master | arrayToString.m | .m | CAJAL-master/test/matlab/xunit/+xunit/+utils/arrayToString.m | 2,972 | utf_8 | 173fdeb3960985834f7a252c7916fa32 | function s = arrayToString(A)
%arrayToString Convert array to string for display.
% S = arrayToString(A) converts the array A into a string suitable for
% including in assertion messages. Small arrays are converted using disp(A).
% Large arrays are displayed similar to the way structure field values display
% ... |
github | neurodata/CAJAL-master | compareFloats.m | .m | CAJAL-master/test/matlab/xunit/+xunit/+utils/compareFloats.m | 4,376 | utf_8 | f77141ccf2a6b351c309eb9be4211065 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is either 'r... |
github | neurodata/CAJAL-master | testCubeCutout.m | .m | CAJAL-master/test/matlab/packages/testCubeCutout.m | 28,333 | utf_8 | 3ecb5434f0bdc9f4df1dbcc562965d43 | function test_suite= testCubeCutout%#ok<STOUT>
%testMatlabInit Unit test of the matlabInit wrapper script
% Init the test suite
initTestSuite;
end
function tempdummytest %#ok<*DEFNU>
assertEqual(1,1);
end
%% Cube Cutout Preprocess Basic tests
function testInputTypesBasic %#ok<*DEFNU>
asser... |
github | neurodata/CAJAL-master | testOCPQuery.m | .m | CAJAL-master/test/matlab/api/testOCPQuery.m | 14,430 | utf_8 | f28f8cff2f7e8ad3dd02cade5b7cb58d | function test_suite = testOCPQuery %#ok<STOUT>
%% TESTOCP Unit Test suite for the OCP api class
% Note: When writing unit tests the pwd is set to to path the tests are in
%% Init the test suite
initTestSuite;
end
%% Test field Errors
function testIncorrectFieldErros %#ok<*DEFNU>
asser... |
github | neurodata/CAJAL-master | testRAMONSeed.m | .m | CAJAL-master/test/matlab/api/testRAMONSeed.m | 6,941 | utf_8 | 94e6740250e6074dc5ac733f74d8d4fd | function test_suite= testRAMONSeed%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testTooManyArguments %#ok<*DEFNU>
% Create seed with too many arguments
assertExceptionThrown(@() RAMONSeed([50 23 12],eRAMONCubeOrientation.pos_z,124... |
github | neurodata/CAJAL-master | testOCP.m | .m | CAJAL-master/test/matlab/api/testOCP.m | 111,523 | utf_8 | 9785ec55c759a2c4a0cb65bba92a1b33 | function test_suite = testOCP %#ok<STOUT>
%% TESTOCP Unit Test suite for the OCP api class
global ocp_force_local
% This variable switches the database tokens to force to local database
% locations if server mapping is used on default tokens.
% You should leave this set to false unless you know... |
github | neurodata/CAJAL-master | testRAMONNeuron.m | .m | CAJAL-master/test/matlab/api/testRAMONNeuron.m | 6,040 | utf_8 | 556ed4a5c06c91fee8d56a3bc49ffa07 | function test_suite= testRAMONNeuron%#ok<STOUT>
%TESTNeuron Unit test of the neuron datatype
%% Init the test suite
initTestSuite;
end
function testTooManyArguments %#ok<*DEFNU>
% Create neuron with too many arguments
assertExceptionThrown(@() RAMONNeuron(1,32,.1,1,[{'test'},{'test2'}],'s... |
github | neurodata/CAJAL-master | testRAMONOrganelle.m | .m | CAJAL-master/test/matlab/api/testRAMONOrganelle.m | 12,278 | utf_8 | fd27fd84cccb333fc5e5dd8e5d3851ee | function test_suite= testRAMONOrganelle%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testDefaultRAMONOrganelle
% Create default seed
o1 = RAMONOrganelle();
assertEqual(o1.class, eRAMONOrganelleClass.unknown);
asse... |
github | neurodata/CAJAL-master | testRAMONSynapse.m | .m | CAJAL-master/test/matlab/api/testRAMONSynapse.m | 16,736 | utf_8 | 4ec25c6f38715c043852f4920b73a8b6 | function test_suite= testRAMONSynapse%#ok<STOUT>
%testRAMONSeed Unit test of the synapse datatype
%% Init the test suite
initTestSuite;
end
function testDefaultRAMONSynapse
% Create default seed
s1 = RAMONSynapse();
assertEqual(s1.synapseType, eRAMONSynapseType.unknown);
asse... |
github | neurodata/CAJAL-master | testOCPsingle.m | .m | CAJAL-master/test/matlab/api/testOCPsingle.m | 7,182 | utf_8 | f4df31cb5d87d048ca8c22a5f5794f38 | function test_suite = testOCPsingle %#ok<STOUT>
%% TESTOCP Unit Test suite for the OCP api class
global ocp_force_local
% This variable switches the database tokens to force to local database
% locations if server mapping is used on default tokens.
% You should leave this set to false unless yo... |
github | neurodata/CAJAL-master | testRAMONBase.m | .m | CAJAL-master/test/matlab/api/testRAMONBase.m | 6,502 | utf_8 | 47109b4369ecb25cf8edfeb608d87494 | function test_suite= testRAMONBase%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testTooManyArguments %#ok<*DEFNU>
% Create seed with too many arguments
assertExceptionThrown(@() RAMONBase([],.6,eRAMONAnnoStatus.ignored,[],'te... |
github | neurodata/CAJAL-master | testMatlabInit.m | .m | CAJAL-master/test/matlab/api/testMatlabInit.m | 8,356 | utf_8 | cf5ff383440a486120793c05db0ffe5f | function test_suite= testMatlabInit%#ok<STOUT>
%testMatlabInit Unit test of the matlabInit wrapper script
% NOTE: YOU MUST RUN THIS TEST FROM THE FRAMEWORK ROOT
% (it is recommended to always run from framework root regardless)
% Init the test suite
global gtestMatlabPath
gtestMatla... |
github | neurodata/CAJAL-master | testRAMONAttributedRegion.m | .m | CAJAL-master/test/matlab/api/testRAMONAttributedRegion.m | 228 | utf_8 | 61073bb9d40cd9c1347f5e064ed98b20 | function test_suite= testRAMONAttributedRegion%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testNothing %#ok<*DEFNU>
assertEqual(1,1);
end |
github | neurodata/CAJAL-master | testOCPHdf.m | .m | CAJAL-master/test/matlab/api/testOCPHdf.m | 7,515 | utf_8 | c13c96d6e0ab20fac81f0fa7dc7e7c0c | function test_suite= testOCPHdf%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
%% Error checks
function testNotRAMONObj %#ok<*DEFNU>
assertExceptionThrown(@() OCPHdf(34), 'OCPHdf:ArgError');
end
%% Seed
function testSeedFull
% Create seed
... |
github | neurodata/CAJAL-master | testRAMONGeneric.m | .m | CAJAL-master/test/matlab/api/testRAMONGeneric.m | 3,236 | utf_8 | 6609e24a064e3cc737c80c236d043ec3 | function test_suite= testRAMONGeneric%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testDefaultGenericAnnotation %#ok<*DEFNU>
% Create default seed
a1 = RAMONGeneric();
assertEqual(a1.id,[]);
assertEqual(a1.confidence,1... |
github | neurodata/CAJAL-master | testOCPDistributedSemaphore.m | .m | CAJAL-master/test/matlab/api/testOCPDistributedSemaphore.m | 2,910 | utf_8 | 5011015033c7096cd0fe27db00bfa383 | function test_suite = testOCPDistributedSemaphore %#ok<STOUT>
%% TESTOCP Unit Test suite for the OCP api class
%Note: Uses redis DB 1
%% Init the test suite
initTestSuite;
% shut of warnings (comment this out to test warning if desired)
warning('off','OCP:BatchWriteError');
warning... |
github | neurodata/CAJAL-master | testRAMONVolume.m | .m | CAJAL-master/test/matlab/api/testRAMONVolume.m | 8,653 | utf_8 | 969e931da9abd9c0e1c04e107a61a468 | function test_suite= testRAMONVolume%#ok<STOUT>
%TESTSEED Unit test of the seed datatype
%% Init the test suite
initTestSuite;
end
function testTooManyArguments %#ok<*DEFNU>
% Create volumeobject with too many arguments
d = magic(10);
assertExceptionThrown(@() RAMONVolume(d,eRAM... |
github | neurodata/CAJAL-master | testRAMONSegment.m | .m | CAJAL-master/test/matlab/api/testRAMONSegment.m | 9,594 | utf_8 | ad4e45535670f7bb7394b8027c11a524 | function test_suite= testRAMONSegment%#ok<STOUT>
%testRAMONSegment Unit test of the synapse datatype
%% Init the test suite
initTestSuite;
end
function testDefaultRAMONSegment
% Create default segment
s1 = RAMONSegment();
assertEqual(s1.data, []);
assertEqual(s1.xyzOffset, []);
assertEqual(s1.resolution([]),[]);
ass... |
github | invenia/matpy-master | py.m | .m | matpy-master/py.m | 3,700 | utf_8 | a9d8ba868f454eba19975b2215fa4ba0 | % A way to interact with python from matlab.
%
% Inputs:
% 1) the type of command, which can be any of the following:
% a. 'eval' this will run the second parameter as python
% b. 'set' this will export the third value by name of second parameter to python
% c. 'get' this will import the second parameter from ... |
github | invenia/matpy-master | TestMatpy.m | .m | matpy-master/Tests/TestMatpy.m | 10,208 | utf_8 | 4d322df65c6d092e0d4596e6d031dde6 | function suite = TestMatpy
initTestSuite;
end
function Setup
stmt = sprintf(['tmp = -1\n']);
py('eval', stmt);
end
function TearDown
stmt = sprintf(['tmp = -1\n']);
py('eval', stmt);
end
%% Test String Export and Import
function TestStringImport
symbols = ['a':'z' 'A':'Z' '0':'9'];
MAX_ST_L... |
github | friend0/vrepMatlab-master | quadClient.m | .m | vrepMatlab-master/quadClient.m | 5,058 | utf_8 | aa43715f0c67522c2e38c8c4164da479 | function quadClient()
pause on;
disp('Program started');
% Connection parameters
IPADDRESS = '127.0.0.1';
PORT = 20146;
WAIT_UNTIL_CONNECTED = true;
DO_NOT_RECONNECT_ONCE_DISCONNECTED = true;
TIME_OUT_IN_MSEC ... |
github | friend0/vrepMatlab-master | bubbleRobClient.m | .m | vrepMatlab-master/bubbleRobClient.m | 5,903 | utf_8 | 1f59d18cc081dac3c6b5caa4a8da8210 | function bubbleRobClient(actionFunction, functionInput)
% Connection parameters
IPADDRESS = '127.0.0.1';
PORT = 19999;
WAIT_UNTIL_CONNECTED = true;
DO_NOT_RECONNECT_ONCE_DISCONNECTED = true;
TIME_OUT_IN_MSEC = 5000;
COM... |
github | friend0/vrepMatlab-master | simpleSynchronousTest.m | .m | vrepMatlab-master/simpleSynchronousTest.m | 2,377 | utf_8 | 627bc93425edef1ec9436f7bc91c8eac | % Copyright 2006-2015 Coppelia Robotics GmbH. All rights reserved.
% marc@coppeliarobotics.com
% www.coppeliarobotics.com
%
% -------------------------------------------------------------------
% THIS FILE IS DISTRIBUTED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
% WARRANTY. THE USER WILL USE IT AT HIS/HER OWN RIS... |
github | friend0/vrepMatlab-master | robotTest.m | .m | vrepMatlab-master/robotTest.m | 2,360 | utf_8 | cbb736566152e0ec619280ca6954cb05 | function robotTest()
pause on;
disp('Program started');
vrep=remApi('remoteApi'); % using the prototype file (remoteApiProto.m)
vrep.simxFinish(-1); % just in case, close all opened connections
clientID=vrep.simxStart('127.0.0.1',19999,true,true,5000,5);
if clientID == -1
disp('Failed conn... |
github | q-bits/osm-scripts-master | find_missing_roads.m | .m | osm-scripts-master/roads import/find_missing_roads.m | 28,743 | utf_8 | c77081308057d588bb149d3202cbf1a5 | function find_missing_roads
% FIND_MISSING_ROADS
%
% See: https://wiki.openstreetmap.org/wiki/Import/South_Australian_Roads
%
% Assumes you have already run the following in the current directory:
% readnodes2('roads WGS84.osm')
%
% and assumes you have already run the following in the subdirecory "existi... |
github | q-bits/osm-scripts-master | process_processed.m | .m | osm-scripts-master/water/waterbodies import/process_processed.m | 9,622 | utf_8 | 39b23e1693618d5b430b4acf8ff8b980 | function process_processed
% PROCESS_PROCESSED
%
% See https://wiki.openstreetmap.org/wiki/Import/South_Australian_Waterbodies
%
% Assumes you have already run the following in the current directory:
% readnodes2('processed.osm')
%
% Henry Haselgrove
load nodes.mat %nids lats lons
load tags.mat... |
github | foucart/HTP-master | SHTP.m | .m | HTP-master/HTPCode/SHTP.m | 3,217 | utf_8 | baef792cb495221f23d5a208e6bedae1 | % SHTP.m
% Basic implementation of the Simultaneous Hard Thresholding Pursuit algorithm
% Find the jointly sparse solutions of the K underdetermined mXN linear system Ax_1=y_1,...,Ax_K=y_K
%
% Usage: [X,S,NormRes,NbIter] = SHTP(Y,A,s,MaxNbIter,mu,X0,TolRes,Warnings,Eps)
%
% Y: mxK matrix whose K columns are measuremen... |
github | foucart/HTP-master | SHTP_.m | .m | HTP-master/HTPCode/SHTP_.m | 3,165 | utf_8 | 676202b54f2aee34ae30938b8808b02c | % SHTP_.m
% Basic implementation of the Simultaneous Hard Thresholding Pursuit
% algorithm after normalization of the measurement matrix
% Find the jointly sparse solutions of the K underdetermined mXN linear system Ax_1=y_1,...,Ax_K=y_K
%
% Usage: [X,S,NormRes,NbIter] = SHTP_(Y,A,s,MaxNbIter,mu,X0,TolRes,Warnings,Eps... |
github | foucart/HTP-master | HTP_.m | .m | HTP-master/HTPCode/HTP_.m | 3,219 | utf_8 | 7f70a02a1d1ac825efdccd1c16592ddf | % HTP_.m
% Basic implementation of the Hard Thresholding Pursuit algorithm after
% normalisation of the measurement matrix
% Find the s-sparse solution of the underdetermined mXN linear system Ax=y
%
% Usage: [x,S,NormRes,NbIter] = HTP_(y,A,s,MaxNbIter,mu,x0,TolRes,Warnings,Eps)
%
% y: mx1 measurement vector
% A: mxN ... |
github | foucart/HTP-master | HTP.m | .m | HTP-master/HTPCode/HTP.m | 3,269 | utf_8 | 6d292e3f30f5134b978030586831b557 | % HTP.m
% Basic implementation of the Hard Thresholding Pursuit algorithm
% Find the s-sparse solution of the underdetermined mXN linear system Ax=y
%
% Usage: [x,S,NormRes,NbIter] = HTP(y,A,s,MaxNbIter,mu,x0,TolRes,Warnings,Eps)
%
% y: mx1 measurement vector
% A: mxN measurement matrix
% s: sparsity level
% MaxNbIter... |
github | foucart/HTP-master | FHTP_.m | .m | HTP-master/HTPCode/FHTP_.m | 3,729 | utf_8 | 812bb6b1f809244e23e7edb8a0840376 | % FHTP_.m
% Basic implementation of the Fast Hard Thresholding Pursuit algorithm
% after normalization of the measurement matrix
% Find the s-sparse solution of the underdetermined linear system Ax=y
%
% Usage: [x,S,NormRes,NbIter] = FHTP_(y,A,s,MaxNbIter,mu,NbDesc,t,x0,TolRes,Warnings)
%
% y: mx1 measurement vector
%... |
github | foucart/HTP-master | FHTP.m | .m | HTP-master/HTPCode/FHTP.m | 3,675 | utf_8 | 65ce5c017dcfbecbcc4a458693f51160 | % FHTP.m
% Basic implementation of the Fast Hard Thresholding Pursuit algorithm
% Find the s-sparse solution of the underdetermined linear system Ax=y
%
% Usage: [x,S,NormRes,NbIter] = FHTP(y,A,s,MaxNbIter,mu,NbDesc,t,x0,TolRes,Warnings)
%
% y: mx1 measurement vector
% A: mxN measurement matrix
% s: sparsity level
% M... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2zSzDc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2zSzDc.m | 26,790 | utf_8 | 1aae66d9d9b41a9b7af847ddc8330162 | function [varargout] = NeuroFIT_2zSzDc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | bradmonk/NeuroFIT-master | NeuroFIT.m | .m | NeuroFIT-master/GUI/NeuroFIT.m | 37,072 | utf_8 | c2738c976f79fc48827b8daea8ebc36e | function [varargout] = NeuroFIT(varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; %close all; %clear all;
% Change the current folder to the folder of this m-file.
if(~isdeployed); cd(fileparts(which(mfilename))); end
%% -- SETTING UP MAIN FIGURE
% close all
% GUIfh = figure(1);
% set(GUIfh,'OuterPositi... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2iDiDc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2iDiDc.m | 26,790 | utf_8 | 703c3e343261381e1e46fde16c0734cb | function [varargout] = NeuroFIT_2iDiDc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2zDzSc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2zDzSc.m | 26,790 | utf_8 | af04b593670ec59b9e533e0cdb9031e8 | function [varargout] = NeuroFIT_2zDzSc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | bradmonk/NeuroFIT-master | NeuroFIT_1z.m | .m | NeuroFIT-master/GUI/NeuroFIT_1z.m | 26,786 | utf_8 | e6309dae71d38c96ee7a8293c4a63a2c | function [varargout] = NeuroFIT_1z(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],'Str... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2iDiSc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2iDiSc.m | 26,790 | utf_8 | e27dd59d8a25d5f7b1b092cbde45ce1d | function [varargout] = NeuroFIT_2iDiSc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | bradmonk/NeuroFIT-master | NeuroFIT_1i.m | .m | NeuroFIT-master/GUI/NeuroFIT_1i.m | 29,116 | utf_8 | 85f868b7308cb930178e4fedce1b4670 | function [varargout] = NeuroFIT_1i(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
%% -- SETTING UP MAIN FIGURE & MESSAGE CON
clc; close all; %clear all;
Fh1 = figure(1);
set(Fh1,'OuterPosition',[550 400 1100 700],'Color',[1,1,1],'Tag','GUIfh')
hax1 = axes('Position',[.05 .22 .44 .7],'Color','none',... |
github | bradmonk/NeuroFIT-master | NeuroN3D.m | .m | NeuroFIT-master/GUI/NeuroN3D.m | 28,970 | utf_8 | 8bf9079c837230d1a52626a9239a9ac1 | function [varargout] = NeuroN3D(varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; close all;
% Change the current folder to the folder of this m-file.
% if(~isdeployed)
% cd(fileparts(which(mfilename)));
% end
% -- DEAL INPUT ARGS
if exist('varargin','var') && nargin > 0
[STRs, DODs, NUM... |
github | bradmonk/NeuroFIT-master | NeuroFIT_GUI.m | .m | NeuroFIT-master/GUI/NeuroFIT_GUI.m | 44,804 | utf_8 | 8482e7e7a581a3dc9db30d3c1d3a9271 | function varargout = NeuroFIT_GUI(varargin)
%NEUROFIT_GUI M-file for NeuroFIT_GUI.fig
% NEUROFIT_GUI, by itself, creates a new NEUROFIT_GUI or raises the existing
% singleton*.
%
% H = NEUROFIT_GUI returns the handle to a new NEUROFIT_GUI or the handle to
% the existing singleton*.
%
% NEUROFIT... |
github | bradmonk/NeuroFIT-master | NeuroT3D.m | .m | NeuroFIT-master/GUI/NeuroT3D.m | 27,879 | utf_8 | aabcb68ea227564d8029d6253148301a | function [varargout] = NeuroT3D(varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; close all;
% Change the current folder to the folder of this m-file.
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
%% -- DEAL INPUT ARGS
if exist('varargin','var') && nargin > 0
[STRs, DODs, NUMs, GHA... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2iSiDc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2iSiDc.m | 26,790 | utf_8 | 58b5fcfa11aacb8b25a4d7116afaea61 | function [varargout] = NeuroFIT_2iSiDc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | bradmonk/NeuroFIT-master | NeuroFIT_2zDzDc.m | .m | NeuroFIT-master/GUI/NeuroFIT_2zDzDc.m | 26,790 | utf_8 | 614db3e784c4b8c0cd3652583104f1c4 | function [varargout] = NeuroFIT_2zDzDc(GUIfh,varargin)
%% NeuroFIT - Neuro FLUORESCENT IMAGING TOOLBOX
clc; % close all; %clear all;
spfN=sprintf(' ');spf1=sprintf('>>'); spf2=sprintf('>>');spf3=sprintf('>>');spf4=sprintf('>>');
str = {spfN, spf1,spf2,spf3,spf4};
ft = annotation(GUIfh,'textbox', [0.02,0.02,0.7,0.1],... |
github | jp-sglab/Spherical_Hashing-master | SphericalHashing.m | .m | Spherical_Hashing-master/Src_Matlab/SphericalHashing.m | 2,325 | utf_8 | a1494991429b4d5c57652afe550f7230 | function [centers, radii] = SphericalHashing(data, bit)
[N, D] = size(data);
% initialize center positions
centers = random_center(data, bit);
[O1, O2, radii, avg, stddev] = compute_statistics(data, centers);
iter = 1;
while true
% force computation based on inters... |
github | ksmet1977/MCRI-master | MCRIm.m | .m | MCRI-master/MCRIm.m | 99,416 | utf_8 | c7af66d85b36a468136a89537088f6d7 | function [Rm,Rmi,Sa,Si]=MCRIm(spd,DD,dispyn,lampname,rescaletype);
%Memory colour rendering index
%spd=spectrum (380nm-780nm in 5nm steps)
%DD= degree of adaptation if DD<=1; else DD = illuminance in lux; if unknown: 0.90 gives good general results!
%dispyn = 0,1,2: display indices and plot spiderweb or not; [2,2]; dis... |
github | ohadf/isomatch-master | isomatch.m | .m | isomatch-master/isomatch.m | 4,292 | utf_8 | 4ae77d24b3a867f3c424c7aa7f684da3 | function [reverse_assignment, obj_val_fin, obj_val_init] = ...
isomatch(d_matrix, varargin)
% isomatch -- assign elements to locations while preserving distances
%
% [reverse_assignment, obj_val_fin, obj_val_init] = isomatch(d_matrix, varargin)
% Calculate the assignment according to distance matrix d_matrix.
% ... |
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