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 | dzarkadas/MFCC-Text-Independent-Speaker-Recognition-master | login.m | .m | MFCC-Text-Independent-Speaker-Recognition-master/Matlab_Codes/login.m | 6,925 | utf_8 | e05f5fd223dfa4769a7bdb3645c5c5de | function [ varargout ] = login(varargin)
% LOGIN MATLAB code for login.fig
% LOGIN, by itself, creates a new LOGIN or raises the existing
% singleton*.
%
% H = LOGIN returns the handle to a new LOGIN or the handle to
% the existing singleton*.
%
% LOGIN('CALLBACK',hObject,eventData,handles,...)... |
github | dzarkadas/MFCC-Text-Independent-Speaker-Recognition-master | main_menu.m | .m | MFCC-Text-Independent-Speaker-Recognition-master/Matlab_Codes/main_menu.m | 3,836 | utf_8 | 7622d23c1e50279ddd6e8a8a27198953 | function [ varargout ] = main_menu(varargin)
% MAIN_MENU MATLAB code for main_menu.fig
% MAIN_MENU, by itself, creates a new MAIN_MENU or raises the existing
% singleton*.
%
% H = MAIN_MENU returns the handle to a new MAIN_MENU or the handle to
% the existing singleton*.
%
% MAIN_MENU('CALLBACK... |
github | maggi3wang/NMPC_cont-master | ChebyshevDiffMatrix.m | .m | NMPC_cont-master/ChebyshevDiffMatrix.m | 1,983 | utf_8 | d1bfa19bb44afedb50a8b5b87dfced50 | % ChebyshevMatrices.m: form cosine-spaced vector of independent variable,
% Chebyshev differentiation and integration matrices
%
% Ross Allen, ASL, Stanford University
% Michael Colonno, ADL, Stanford University
%
% Started: 6/27/11
% Last Updated: Feb 13, 2014
%
% Input... |
github | maggi3wang/NMPC_cont-master | clencurt.m | .m | NMPC_cont-master/clencurt.m | 652 | utf_8 | 937bd512cfc8c804fd85dda251031864 | % CLENCURT nodes x (Chebyshev points) and weights w
% for Clenshaw-Curtis quadrature
%
% Reference: L. Trfethen, "Spectral Methods in MATLAB", SIAM 2000,
% Page 128
function [x,w_net] = clencurt(K)
theta = pi*(0:K)/K;
x = cos(theta); %1 ---> -1 (t)
x = fliplr(x); % -1 ---> 1 (t')
if mod(... |
github | gantech/fastv8DriverProgram-master | ReadSubDynSummary.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/ReadSubDynSummary.m | 4,674 | utf_8 | 4d38ea8de3904ead0cc56644d86c5ddf | function [data] = ReadSubDynSummary(fileName)
% [data] = ReadSubDynSummary(fileName)
% fileName is the SubDyn summary file to read
% data is a data structure containing the values from the summary file
fid = fopen( fileName );
if ( fid <= 0 )
error(['Could not open the summary file: ' fileName ]);
else... |
github | gantech/fastv8DriverProgram-master | SetFASTPar.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/SetFASTPar.m | 580 | utf_8 | 6d4ea195461856611eb8b6104ac1af03 | % Function for getting fast parameter
% In: FASTPar - Fast parameter structure
% Par - Parameter string
% Value - Value to set parameter to
% Out: Result - Function call result (1+ Success, -1 Failure)
%
% Knud A. Kragh %edited by Paul Fleming
function FASTParOut=... |
github | gantech/fastv8DriverProgram-master | ReadFASTMotionMeshes.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/ReadFASTMotionMeshes.m | 4,566 | utf_8 | 7167e67917477a9b8f252af982b15369 | function [Mesh] = ReadFASTMotionMeshes(fileName,plotMeshes)
%%
% fileName = ...
% ['C:\Users\bjonkman\Documents\DATA\DesignCodes\simulators\FAST\SVNdirectory\branches\BJonkman\CertTest\' ...
% 'Test21.gra'];
%%
if nargin < 2
plotMeshes = false;
end
%% read the file and header:
[... |
github | gantech/fastv8DriverProgram-master | ReadFASTmappingData.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/ReadFASTmappingData.m | 774 | utf_8 | bdf28c62072e523ef51601d46e7d5786 | function [MotionMap, LoadMap, Aug, lumpSrc, lumpDest] = ReadFASTmappingData(fid)
MotionMap = ReadFASTmatrix(fid);
LoadMap = ReadFASTmatrix(fid);
M = fread( fid, 3, 'int32');
if M(1) ~= 0
Aug = ReadFASTmesh(fid);
else
Aug = [];
end
if M(2) ~= 0
... |
github | gantech/fastv8DriverProgram-master | CompareMatrices.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/CompareMatrices.m | 2,080 | utf_8 | 633b29cfad77e5c4c0e199ded47f15e2 | function CompareMatrices(MatTitle,CellMat,caseDescr)
% function CompareMatrices(MatTitle,CellMat,caseDescr)
% this plots up to 2 dimensions of matrices stored in cell array Mat
% (CellMat can also be a structure with field MatTitle)
%%
refMat = 1;
nMat = length(CellMat); % number of matrices to compare/plot
... |
github | gantech/fastv8DriverProgram-master | GetFASTPar.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/GetFASTPar.m | 611 | utf_8 | 77cbd50df0556311e1bad2e01f9da094 | % Function for getting FAST parameter
% In: FASTPar - FAST parameter structure
% Par - Parameter string
% Out: OutData - Value of input parameter
%
% Knud A. Kragh
function [OutData,err,Indx]=GetFASTPar(FASTPar,Par)
err = false;
OutData=[];
Indx=0;
if isfield(FASTPar, 'Labe... |
github | gantech/fastv8DriverProgram-master | ReadFASTLinear.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Utilities/ReadFASTLinear.m | 3,854 | utf_8 | aa141286aef5dd93222ad1bdba6585d9 | function [data] = ReadFASTLinear(fileName)
fid=fopen(fileName);
if (fid == -1)
error(['Linearization file "',fileName,'" could not be opened.']);
end
% generic header:
fgetl(fid); % skip a blank line
data.ver{1,1} = fgetl(fid); % FAST version info
data.ve... |
github | gantech/fastv8DriverProgram-master | SD2Matlab.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/FAST2MATLAB/SD2Matlab.m | 10,717 | utf_8 | e0c2921ea394ea82b726c40c1d3753de | function DataOut = SD2Matlab(HD_file,hdrLines,DataOut)
%% HD2Matlab
% Function for reading HydroDyn input files in to a MATLAB struct.
%
%
%This function returns a structure DataOut, which contains the following
% cell arrays:
%.Val An array of values
%.Label An array of matching labels... |
github | gantech/fastv8DriverProgram-master | FAST2Matlab.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/FAST2MATLAB/FAST2Matlab.m | 13,034 | utf_8 | 4b2c96567c8b9134650129028969e70e | function DataOut = Fast2Matlab(FST_file,hdrLines,DataOut)
%% Fast2Matlab
% Function for reading FAST input files in to a MATLAB struct.
%
%
%This function returns a structure DataOut, which contains the following
% cell arrays:
%.Val An array of values
%.Label An array of matching label... |
github | gantech/fastv8DriverProgram-master | HD2Matlab.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/FAST2MATLAB/HD2Matlab.m | 16,125 | utf_8 | a6b776a593576eea38b63cdf43e98384 | function DataOut = HD2Matlab(HD_file,hdrLines,DataOut)
%% HD2Matlab
% Function for reading HydroDyn input files in to a MATLAB struct.
%
%
%This function returns a structure DataOut, which contains the following
% cell arrays:
%.Val An array of values
%.Label An array of matching labels... |
github | gantech/fastv8DriverProgram-master | Matlab2SD.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/MATLAB2FAST/Matlab2SD.m | 16,468 | utf_8 | 090148e1d3e2e575acad94e8903281c3 | % Matlab2SD
% Function for creating a new SD file given:
% 1) An input template file
% 2) A SD parameter structure
%
% In: SDPar - A SD parameter list
% TemplateFile - A .dat file to use as a template
% OutputFilename - Desired filename of output .fst file
%
% Paul Fleming, JUNE... |
github | gantech/fastv8DriverProgram-master | GenStepWindFile.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/MATLAB2FAST/GenStepWindFile.m | 1,136 | utf_8 | 8916e1297eecfd16e3d0f7c2a1e19e0f | % GenStepWindFile
% Function for generating a step wind speed file given:
% 1) A base filename
% 2) An array of windspeeds
% - The generated file will have a name BaseName_Windspeed_MPS.wnd
%
%In: fileName - filename for step file
% windSpeedArray - array of wind speeds in m/s
%
%
% P... |
github | gantech/fastv8DriverProgram-master | Matlab2FAST.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/MATLAB2FAST/Matlab2FAST.m | 16,230 | utf_8 | 270a3952cd5932257d959545389a29a8 | % Matlab2FAST(FastPar,TemplateFile,OutputFilename,hdrLines)
% Function for creating a new FAST file given:
% 1) An input template file
% 2) A FAST parameter structure
%
% In: FastPar - A FAST parameter list
% TemplateFile - A .fst file to use as a template
% OutputFilename - Desir... |
github | gantech/fastv8DriverProgram-master | GenConstWindFile.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/MATLAB2FAST/GenConstWindFile.m | 1,477 | utf_8 | 9174e59aa551d8a51957a760d35c08e0 | % GenConstWindFile
% Function for generating a constant wind speed file given:
% 1) A base filename
% 2) A windspeed
% - The generated file will have a name BaseName_Windspeed_MPS.wnd
%
%In: baseName - directory and basename of generated wind file
% windSpeed - wind speed in mps
%
... |
github | gantech/fastv8DriverProgram-master | Matlab2HD.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/MATLAB2FAST/Matlab2HD.m | 21,687 | utf_8 | d1b8143508bd4f9e5274665e433affef | % Matlab2HD
% Function for creating a new HD file given:
% 1) An input template file
% 2) A HD parameter structure
%
% In: HDPar - A HD parameter list
% TemplateFile - A .dat file to use as a template
% OutputFilename - Desired filename of output .fst file
%
% Paul Fleming, JUNE... |
github | gantech/fastv8DriverProgram-master | PlotFASToutput.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Plots/PlotFASToutput.m | 30,642 | utf_8 | bcb4ce0d0429abacf59a3237f6e25262 | function [outData]=PlotFASToutput(FASTfiles,FASTfilesDesc,ReferenceFile,Channels,ShowLegend,CustomHdr,PlotPSDs,OnePlot)
%..........................................................................
%function [timeSeriesData] = PlotFASToutput(FASTfiles,FASTfilesDesc,ReferenceFile,Channels)
%function [timeSeriesData] = ... |
github | gantech/fastv8DriverProgram-master | PlotFASTIOMeshes.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Plots/PlotFASTIOMeshes.m | 9,058 | utf_8 | 7220999eca90546272e8197f09576771 | function [Mesh1_I,Mesh1_O,Mesh2_I,Mesh2_O, f] ...
= PlotFASTIOMeshes( Mesh1_Input, Mesh1_Output,Mesh2_Input,Mesh2_Output, exportFigure )
%%
%
DisplacedOnly = true;
if nargin < 5
exportFigure = 0;
end
if exportFigure ~= 0
fig_start = 1;
fig_end = 2;
else
fig_start = 2;
fig_end... |
github | gantech/fastv8DriverProgram-master | PlotFASTIOMeshes_mapping.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Plots/PlotFASTIOMeshes_mapping.m | 3,211 | utf_8 | aec77054e1976dc83aaebf37995e0bf9 | function [Mesh1_I,Mesh1_O,Mesh2_I,Mesh2_O, f1, MotionMap, LoadMap] ...
= PlotFASTIOMeshes_mapping( MeshInputFile )
%% -----------------
% Read file:
[fid, message] = fopen( MeshInputFile );
if fid < 1
error(['PlotFASTIOMeshes:: Error opening file: ' MeshInputFile '. ' message]);
end... |
github | gantech/fastv8DriverProgram-master | PlotFASTMesh.m | .m | fastv8DriverProgram-master/fastv8/Utilities/MATLAB_Toolbox/Plots/PlotFASTMesh.m | 3,899 | utf_8 | 2a5a2937ad47385333c4833a5e9dde09 | function PlotFASTMesh( MeshFileName, figure_id, MarkerColor )
%
%
Mesh = ReadFASTmesh(MeshFileName);
if nargin < 2
figure_id = figure;
MarkerColor = 'c';
end
FieldNames = {'RefOrientation','Orientation','TranslationDisp',...
'TranslationVel','RotationVel',...
'Translati... |
github | gantech/fastv8DriverProgram-master | PlotCertTestResults.m | .m | fastv8DriverProgram-master/fastv8/CertTest/PlotCertTestResults.m | 26,846 | utf_8 | 20415be42ae9440d9e1c9b5acccee429 | function PlotCertTestResults( newPath, oldPath, PlotFAST, PlotAdams, PlotSimulink )
% FAST-ADAMS-Simulink CertTest comparisons:
%function PlotCertTestResults( newPath, oldPath, PlotFAST, PlotAdams, PlotSimulink )
% This function plots the FAST, ADAMS, and/or FAST_SFunc CertTest results,
% comparing .out/.plt, .elm... |
github | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/ex1/lib/submitWithConfiguration.m | 3,845 | utf_8 | 1d5c995c41f688757a6f9636ed87a79d | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex1-linear-regression/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex8-anomaly-recommender/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex7-k-means-pca/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex4-neural-network-learn/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex5-regularized-linear-regression/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | porterStemmer.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex6-svm/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/ex2/lib/submitWithConfiguration.m | 3,846 | utf_8 | 892d191478526bce32c13baca4ce5c1c | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex2-logistic-regression/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 | xiaominghu/demo-code-master | submit.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/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 | xiaominghu/demo-code-master | submitWithConfiguration.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | xiaominghu/demo-code-master | savejson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/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 | xiaominghu/demo-code-master | loadjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/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 | xiaominghu/demo-code-master | loadubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/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 | xiaominghu/demo-code-master | saveubjson.m | .m | demo-code-master/ml/octave/Coursera/CS229/ex3-neural-network/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 | xiaominghu/demo-code-master | learn_perceptron.m | .m | demo-code-master/ml/octave/Coursera/NeuralNetworsForMachineLearning/1-backpropagation/learn_perceptron.m | 6,069 | utf_8 | b1c01c7f0ac821c476cacea8f1f256c9 | %% Learns the weights of a perceptron and displays the results.
function [w] = learn_perceptron(neg_examples_nobias,pos_examples_nobias,w_init,w_gen_feas)
%%
% Learns the weights of a perceptron for a 2-dimensional dataset and plots
% the perceptron at each iteration where an iteration is defined as one
% full pass th... |
github | xiaominghu/demo-code-master | plot_perceptron.m | .m | demo-code-master/ml/octave/Coursera/NeuralNetworsForMachineLearning/1-backpropagation/plot_perceptron.m | 3,409 | utf_8 | 808099ac46c6f636fa74de07abbcc8bb | %% Plots information about a perceptron classifier on a 2-dimensional dataset.
function plot_perceptron(neg_examples, pos_examples, mistakes0, mistakes1, num_err_history, w, w_dist_history)
%%
% The top-left plot shows the dataset and the classification boundary given by
% the weights of the perceptron. The negative ex... |
github | Engineering-Course/caffe_ssl-master | classification_demo.m | .m | caffe_ssl-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 | Engineering-Course/caffe_ssl-master | MyVOCevalseg.m | .m | caffe_ssl-master/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %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 | Engineering-Course/caffe_ssl-master | MyVOCevalsegBoundary.m | .m | caffe_ssl-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 | Engineering-Course/caffe_ssl-master | MyVOCevalseg.m | .m | caffe_ssl-master/matlab/human_script/MyVOCevalseg.m | 4,834 | utf_8 | 9270ca70dce5f2bfa045cc78d4a7b337 | %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 | Engineering-Course/caffe_ssl-master | MyVOCevalsegBoundary.m | .m | caffe_ssl-master/matlab/human_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 | erilyth/Window-Detection-master | bilateral_filter.m | .m | Window-Detection-master/bilateral_filter.m | 1,265 | utf_8 | 9917c768dc256d78d6f119611958ba88 | % www.allontutorials.com
% This is a free software, have fun with it.
% Refer to the website for more info and parameter description
function Out = bilateral_filter(In,N,sigma_d,sigma_r)
% Allocate memory for the output image
Len = size(In);
Out = zeros(Len);
% Pre-compute the first gaussian from the formu... |
github | martinResearch/MatlabSVG-master | saveSVG.m | .m | MatlabSVG-master/saveSVG.m | 2,916 | utf_8 | d93889e6edd5b11e8799f49d2a7079f3 | function saveSVG(filename,svg)
fid=fopen(filename,'w');
fprintf(fid,'<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n');
fprintf(fid,'<!-- Created with Matlab -->\n');
fprintf(fid,'<svg\n');
fprintf(fid,' xmlns:dc="http://purl.org/dc/elements/1.1/"\n');
fprintf(fid,' xmlns:cc="http://creativecommons.org/... |
github | martinResearch/MatlabSVG-master | loadSVG.m | .m | MatlabSVG-master/loadSVG.m | 5,567 | utf_8 | 74c4aae5359a1691820b05a3836d3688 | %% License FreeBSD:
%
% Copyright (c) 2016 Martin de La Gorce
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above ... |
github | martinResearch/MatlabSVG-master | plotSVG.m | .m | MatlabSVG-master/plotSVG.m | 3,707 | utf_8 | 90da9f022abe2824afd5275a8e0141d7 | % License FreeBSD:
%
% Copyright (c) 2016 Martin de La Gorce
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above... |
github | sp9103/DeepHandPoseRecognizer-master | classification_demo.m | .m | DeepHandPoseRecognizer-master/Caffe/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 | leratojeffrey/OptiSDR-Compiler-master | get_state.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/rl/get_state.m | 1,320 | utf_8 | 2f5a995632e60991d5e19b41db0d1e03 | % This function returns a discretized value (a number) for a continuous
% state vector. Currently x is divided into 3 "boxes", x_dot into 3,
% theta into 6 and theta_dot into 3. A finer discretization produces a
% larger state space, but allows a better policy.
function [state] = get_state(x, x_dot, theta, theta_dot)
... |
github | leratojeffrey/OptiSDR-Compiler-master | show_cart.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/rl/show_cart.m | 551 | utf_8 | 8309ee25613425b7b4f7ce9618cc0850 | % This function displays the "animation"
function [] = show_cart(x, x_dot, theta, theta_dot, pause_time)
set(gcf,'DoubleBuffer','on');
length = 3;
plotx(1) = x;
ploty(1) = 0;
%plotx(2) = x + length * cos(theta + pi/2.0);
%ploty(2) = length * sin(theta + pi/2.0);
plotx(2) = x + length * sin(theta);
ploty(2) = lengt... |
github | leratojeffrey/OptiSDR-Compiler-master | read_graph_lr.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/read_graph_lr.m | 736 | utf_8 | 6a8af97778b59a610f526d46a1fb8661 | % Parse an entire edge file representing a graph (e.g. onlyedges-stanford).
function [read_edges, node_ids, edges] = read_graph_lr(filename, max_node_num, num_edges)
node_ids = zeros(max_node_num, 1);
edges = zeros(num_edges, 2);
% second pass: build graph
edge_index = 1;
fid = fopen(filename);
while ~feof... |
github | leratojeffrey/OptiSDR-Compiler-master | lbp.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/lbp.m | 3,908 | utf_8 | 512a1503cf4ce7497dc439baa287734f | function exectime = lbp(edgefile, printfile)
max_iter = 50;
converge_thresh = 0.001;
last_fraction_not_converged = 0.0;
last_avg_dist = 0.0;
last_max_dist = 999.0;
%%% Load data
% first pass for edges
[num_nodes, num_edges, max_line_size, max_node_num] = get_graph_values(edgefile);
fprintf('num nodes: ... |
github | leratojeffrey/OptiSDR-Compiler-master | get_graph_values.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/get_graph_values.m | 770 | utf_8 | fee57ddb856ff9121201812b3b7e1156 | % Parse pertinent statistics from an edge file representing a graph (e.g. onlyedges-stanford)
function [num_nodes, num_edges, max_line_size, max_node_num] = get_graph_values(filename)
num_nodes = 0;
num_edges = 0;
max_line_size = 0;
max_node_num = 0;
cur_line_size = 0;
node_num_index = 0;
% first pass: fin... |
github | leratojeffrey/OptiSDR-Compiler-master | read_graph_lr.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/struct/read_graph_lr.m | 736 | utf_8 | 6a8af97778b59a610f526d46a1fb8661 | % Parse an entire edge file representing a graph (e.g. onlyedges-stanford).
function [read_edges, node_ids, edges] = read_graph_lr(filename, max_node_num, num_edges)
node_ids = zeros(max_node_num, 1);
edges = zeros(num_edges, 2);
% second pass: build graph
edge_index = 1;
fid = fopen(filename);
while ~feof... |
github | leratojeffrey/OptiSDR-Compiler-master | lbp.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/struct/lbp.m | 3,936 | utf_8 | f347960f56b459df63ebf42e91659c80 | function exectime = lbp(edgefile, printfile)
max_iter = 50;
converge_thresh = 0.001;
last_fraction_not_converged = 0.0;
last_avg_dist = 0.0;
last_max_dist = 999.0;
%%% Load data
% first pass for edges
[num_nodes, num_edges, max_line_size, max_node_num] = get_graph_values(edgefile);
fprintf('num nodes: ... |
github | leratojeffrey/OptiSDR-Compiler-master | get_graph_values.m | .m | OptiSDR-Compiler-master/framework/apps/matlab/lbp/struct/get_graph_values.m | 770 | utf_8 | fee57ddb856ff9121201812b3b7e1156 | % Parse pertinent statistics from an edge file representing a graph (e.g. onlyedges-stanford)
function [num_nodes, num_edges, max_line_size, max_node_num] = get_graph_values(filename)
num_nodes = 0;
num_edges = 0;
max_line_size = 0;
max_node_num = 0;
cur_line_size = 0;
node_num_index = 0;
% first pass: fin... |
github | leratojeffrey/OptiSDR-Compiler-master | accpercent.m | .m | OptiSDR-Compiler-master/framework/accresults/accpercent.m | 198 | utf_8 | 452fd228b14a27415ae6d0c3cac3e60d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function s = accpercent(x,y)
s = 0;
for i = 1:length(x)
if (x(i)==y(i))
s = s + 1;
endif;
endfor;
s = s/length(x);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
github | johndpope/Codes-master | computeH.m | .m | Codes-master/Other Projects/Computer Vision/Augmented reality/computeH.m | 398 | utf_8 | 49bdcf8583a2f105d34bd391d2a35ca6 | % 3.2.1
function [H2to1] = computeH(p1, p2)
% img1 = imread('../data/cv_cover.jpg');
% img2 = imread('../data/cv_desk.png');
% [p1,p2]=MatchPics(img1,img2) ;
for i=1:size(p1,1)
A(2*i-1,:)=[-p1(i,1) -p1(i,2) -1 0 0 0 p1(i,1)*p2(i,1) p1(i,2)*p2(i,1) p2(i,1)] ;
A(2*i,:)=[0 0 0 -p1(i,1) -p1(i,2) -1 p1(i,1)*p2(i,2... |
github | johndpope/Codes-master | MatchPics.m | .m | Codes-master/Other Projects/Computer Vision/Augmented reality/MatchPics.m | 1,031 | utf_8 | 30ae4c0890be76bdc1d13d2b79ad1a25 | % Q3.1.4
function [I1_matched_pts,I2_matched_pts] = MatchPics(I1, I2)
% img1=imread('../data/cv_cover.jpg') ;
% img2=imread('../data/cv_desk.png') ;
img1=I1;
img2=I2;
if size(img1,3) == 3
i1_gr = rgb2gray(img1);
else
i1_gr = img1 ;
end
if size(img2,3) == 3
i2_gr = rgb2gray(img2);
else
i2_gr = img2;
en... |
github | johndpope/Codes-master | computeH_norm.m | .m | Codes-master/Other Projects/Computer Vision/Augmented reality/computeH_norm.m | 593 | utf_8 | 5b21708e71be825abdcdd787f6d3698f | % Q3.2.2
function [H2to1] = computeH_norm(p1, p2)
% img1 = imread('../data/cv_desk.png');
% img2 = imread('../data/cv_cover.jpg');
% [p1,p2]=MatchPics(img1,img2) ;
mu=mean(p1);
T1 = [1/max(p1(1)) 0 -mu(1)/max(p1(1)) ; 0 1/max(p1(2)) -mu(2)/max(p1(2)) ; 0 0 1] ;
p1_n(:,1) = (p1(:,1) - mu(1))/max(p1(1));
p1_n(:,2... |
github | johndpope/Codes-master | computeH_ransac.m | .m | Codes-master/Other Projects/Computer Vision/Augmented reality/computeH_ransac.m | 1,882 | utf_8 | c702c1f5c344f5b77c4d7b106de6028d | % Q3.2.3
function [bestH2to1, inliers] = computeH_ransac(locs1, locs2)
p1=locs2;
p2=locs1;
thr = 50 ;
% img1 = imread('../data/cv_desk.png');
% img2 = imread('../data/cv_cover.jpg');
% [p1,p2]=MatchPics(img1,img2) ;
% mu=mean(p1);
% p1_n(:,1) = p1(:,1) - mu(1);
% p1_n(:,2) = p1(:,2) - mu(2);
% p1_nor = p1_n/((... |
github | johndpope/Codes-master | MatchP.m | .m | Codes-master/Other Projects/Computer Vision/Augmented reality/MatchP.m | 862 | utf_8 | 2d97439876caa13e091caa2a034ed121 | % Q3.1.4
function [I1_matched_pts,I2_matched_pts] = MatchP(I1, I2)
img1=I1;
img2=I2;
% img2=imread('../data/cv_cover.jpg') ;
% img1=imread('../data/cv_desk.png') ;
if size(img1,3) == 3
i1_gr = rgb2gray(img1);
else
i1_gr = img1 ;
end
if size(img2,3) == 3
i2_gr = rgb2gray(img2);
else
i2_gr = img2;
end... |
github | johndpope/Codes-master | refineF.m | .m | Codes-master/Other Projects/Computer Vision/3D scene reconstruction/refineF.m | 1,154 | utf_8 | 139b5e2f09b2504492454cf1a25cc8f1 | %function F = refineF(F,pts1,pts2)
% Non-linear refinement of fundamental matrix using simplex
% pts1 and pts2 are Nx2 matrices, first row of pts1 being
% x coordinate from first image, second row of pts1 being
% y coordinate from first image
function F = refineF(F,pts1,pts2)
X=[pts1(:,1) pts2(:,1)];
Y=[pts1(:,2) pts... |
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