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 | sofacoustics/SOFAtoolbox-master | SOFAmerge.m | .m | SOFAtoolbox-master/SOFAtoolbox/SOFAmerge.m | 6,261 | utf_8 | 477c29fbdc672d2b317473efa3d94575 | function [C, log] = SOFAmerge(A,B)
%SOFAmerge - Merge two SOFA objects
% Usage: [C, log] = SOFAmerge(A,B)
%
% C = SOFAmerge(A, B) merges SOFA objects A and B to the single one object C.
%
% A and B must be of the same SOFA conventions.
%
% [C, log] = SOFAmerge(A, B) returns a log string describing the process
... |
github | sofacoustics/SOFAtoolbox-master | SOFAconvertTUBerlinBRIR2SOFA.m | .m | SOFAtoolbox-master/SOFAtoolbox/converters/SOFAconvertTUBerlinBRIR2SOFA.m | 3,465 | utf_8 | fcaa4540bfd1196edcae953b293e8441 | function Obj=SOFAconvertTUBerlinBRIR2SOFA(irs)
%SOFAconvertTUBerlinBRIR2SOFA - converts from irs (TU-Berlin format) to SOFA format, using MultiSpeakerBRIR Convention
% OBJ=SOFAconvertTUBerlinBRIR2SOFA(irs)
%
% SOFAconvertTUBerlinBRIR2SOFA(irs) converts the HRTFs described in irs (see TU-Berlin HRTF format) to a SO... |
github | sofacoustics/SOFAtoolbox-master | SOFAhrtf2dtf.m | .m | SOFAtoolbox-master/SOFAtoolbox/converters/SOFAhrtf2dtf.m | 6,971 | utf_8 | 90e621361efd8a90c428edf74507cd5c | function [dtf,ctf]=SOFAhrtf2dtf(hrtf,varargin)
%SOFAHRTF2DTF - splits HRTFs into directional transfer functions (DTFs) and common transfer functions (CTFs)
% Usage: [dtf,ctf]=SOFAhrtf2dtf(hrtf)
% [dtf,ctf]=SOFAhrtf2dtf(hrtf,f1,f2)
%
% Input parameters:
% hrtf : SOFA object with SimpleFreeFiel... |
github | sofacoustics/SOFAtoolbox-master | test_SOFAdbURL.m | .m | SOFAtoolbox-master/SOFAtoolbox/test/test_SOFAdbURL.m | 12,545 | utf_8 | 45f79456f534c2656997f64a49aa49a7 | %test_SOFAdbURL - Test script, using all SOFA files from SOFAdbURL database.
%
% test_SOFAdbURL scans all [SOFAdbURL '/database/'] subfolders for *.sofa files.
% The SOFA files are downloaded to the local path [SOFAdbPath '\urlDatabase'], loaded to a SOFA object, and saved to a temporary file.
% All warnings and... |
github | mhlabCodingTeam/SegEM-master | installer.m | .m | SegEM-master/installer.m | 7,981 | utf_8 | 53e8c7f4543a2bcfd1f639b43c5538d3 | function varargout = installer(varargin)
% INSTALLER MATLAB code for installer.fig
% INSTALLER, by itself, creates a new INSTALLER or raises the existing
% singleton*.
%
% H = INSTALLER returns the handle to a new INSTALLER or the handle to
% the existing singleton*.
%
% INSTALLER('CALLBACK',hO... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewTestComparisonRetinaVsCortex.m | .m | SegEM-master/retina/segmentation/visualization/visualizeOverviewTestComparisonRetinaVsCortex.m | 7,028 | utf_8 | 54ffc9611e923afcf118037b067d60a9 | function visualizeOverviewTestComparisonRetinaVsCortex( paramRetinaTest,paramCortexTest )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
load(paramRetinaTest.cmSource);
display('Overview Training vs. Test Comparison');
figure('position', [1 1 1600 785]);
hold on;
% DEBUG
% ... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewSamplingSeries.m | .m | SegEM-master/retina/segmentation/visualization/visualizeOverviewSamplingSeries.m | 7,649 | utf_8 | a88daf95f4a56a5d78607a7cfa72ba08 | function visualizeOverviewSamplingSeries( paramRetinaTest,paramCortexTest )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
for sk=1:length(paramRetinaTest.skel)
paramRetinaTest.nrNodes(sk) = size(paramRetinaTest.skel{sk}.nodes,1);
end
paramRetinaTest.density = paramRetinaTest.totalP... |
github | mhlabCodingTeam/SegEM-master | visualizeOverview_4.m | .m | SegEM-master/retina/segmentation/visualization/visualizeOverview_4.m | 2,255 | utf_8 | a15eec7dda71a3fbd40d71918310a3e5 | function visualizeOverview_4( param,paramTest )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
load(param.cmSource);
if ~exist([param.dataFolder param.figureSubfolder '/'], 'dir')
mkdir([param.dataFolder param.figureSubfolder '/']);
end
display('Overview Training vs. Te... |
github | mhlabCodingTeam/SegEM-master | distinguishable_colors.m | .m | SegEM-master/retina/segmentation/visualization/distinguishable_colors.m | 5,905 | utf_8 | f9b1b0ee1a9278b1ee46f1c6def38baf | function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more ... |
github | mhlabCodingTeam/SegEM-master | makeSegmentList.m | .m | SegEM-master/auxiliaryMethods/makeSegmentList.m | 1,449 | utf_8 | 68de0abda4bddf226f81fe797c5aee1b | function listS=makeSegmentList(nml,overWriteEdges,emphasizeNodes)
a=cell(1);
a{1,1}=nml;
if overWriteEdges
a{1,1}.edges=zeros(size(a{1,1}.nodes,1)-1,2);
for i=1:size(a{1,1}.edges,1)
if emphasizeNodes
a{1,1}.edges(i,:)=[1,i+1];
else
a{1,1}.edges(i,:)=[i,i+1];
... |
github | mhlabCodingTeam/SegEM-master | distinguishable_colors.m | .m | SegEM-master/auxiliaryMethods/distinguishable_colors.m | 5,905 | utf_8 | f9b1b0ee1a9278b1ee46f1c6def38baf | function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more ... |
github | mhlabCodingTeam/SegEM-master | equalizeSkeletons.m | .m | SegEM-master/cortex/segmentation/equalizeSkeletons.m | 2,220 | utf_8 | c4566949376fe4168bfcc192b24a60b0 | function skel = equalizeSkeletons(skel)
% Determine skeleton with maximal inter-node distance
interNodeDistance = calculateInterNodeDistance(skel);
[maxVal, maxIdx] = max(interNodeDistance);
toEqualize = setdiff(1:length(skel),maxIdx);
% Actual equalization
for i=1:length(toEqualize)
%... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewWithThreeLinesAndZeroHits.m | .m | SegEM-master/cortex/segmentation/visualization/visualizeOverviewWithThreeLinesAndZeroHits.m | 13,401 | utf_8 | bbf8f7f929d387ef5667fed8d5d51b5f | function visualizeOverviewWithThreeLinesAndZeroHits( param,paramTest,nodeSize )
% Sorry for bad form, was done in a hurry! :) like most of this stuff xD
if nargin == 2
nodeSize = 1;
end
if ~exist([param.dataFolder param.figureSubfolder '/'], 'dir')
mkdir([param.dataFolder param.figureSubfolder '/']);
end
disp... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewNodeSizeControl.m | .m | SegEM-master/cortex/segmentation/visualization/visualizeOverviewNodeSizeControl.m | 6,920 | utf_8 | c5a879951f0759cc40b61f33a74f8026 | function visualizeOverviewNodeSizeControl( param, paramTest)
if ~exist([param.dataFolder param.figureSubfolder '/'], 'dir')
mkdir([param.dataFolder param.figureSubfolder '/']);
end
display('Control Plot Node Size Threshold, see reviewer 2 comment 9');
figure('position', [1 1 1600 785]);
hold on;
[a, b, c, keptSamp... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewNew.m | .m | SegEM-master/cortex/segmentation/visualization/visualizeOverviewNew.m | 3,645 | utf_8 | 2dbaa34befc319b508a1e3ee55191d79 | function visualizeOverviewNew( param, paramTest )
% Pass param and paramTest from mainSeg.m
if ~exist([param.dataFolder param.figureSubfolder '/'], 'dir')
mkdir([param.dataFolder param.figureSubfolder '/']);
end
display('Overview: Split-merger segmentation parameter grid search');
figure('position', [1 1 1600 785]... |
github | mhlabCodingTeam/SegEM-master | visualizeOverviewComparison.m | .m | SegEM-master/cortex/segmentation/visualization/visualizeOverviewComparison.m | 2,315 | utf_8 | 72179864e89657c4f2dcfd9c5c05a724 | function visualizeOverviewComparison( param,paramTest,nodeSize )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
if nargin == 2
nodeSize = 1;
end
if ~exist([param.dataFolder param.figureSubfolder '/'], 'dir')
mkdir([param.dataFolder param.figureSubfolder '/']);
end... |
github | mhoward3210/ccl-master | ccl_data_genlwr.m | .m | ccl-master/Matlab_Version/Functions/data_generation/ccl_data_genlwr.m | 6,155 | utf_8 | 52846ed2bab9870ea1ef9b6d5a82dc87 | function dataset = ccl_data_genlwr (settings)
% dataset = ccl_data_genlwr (settings)
%
% Generate Constraint Consistent Learning (CCL) data for a 7 Dof simulated robot arm
%
% Input:
%
% settings Task related parameters (details see comments)
%
% Output:
%
% dataset Generated ... |
github | mhoward3210/ccl-master | ccl_learnv_ncl.m | .m | ccl-master/Matlab_Version/Functions/learn_nullspace_component/ccl_learnv_ncl.m | 2,587 | utf_8 | f53457e1b20de19519885b3f0beabfad | function model = ccl_learnv_ncl(X, Y, model)
% model = ccl_learnv_ncl(X, Y, model)
%
% Learn the nullspace component of the input data X
% Input:
%
% X Observed states
% Y Observed actions of the form Y = A(X)'B(X) + N(X) F(X) where N and F
% ... |
github | mhoward3210/ccl-master | ccl_math_solve_lm.m | .m | ccl-master/Matlab_Version/Functions/subfunctions/ccl_math_solve_lm.m | 4,483 | utf_8 | 1594e718832a26ee30a38ad140569cce | function [xf, S, msg] = ccl_math_solve_lm (varargin)
% [xf, S, msg] = ccl_math_solve_lm (varargin)
%
% Solve nonlinear least squared problem using Levenberg-Maquardt algoritm
%
% Input
% FUN Objective fnction
% xc Initial guesses of solution
% O... |
github | drbenvincent/delay-discounting-analysis-master | addSubFoldersToPath.m | .m | delay-discounting-analysis-master/ddToolbox/addSubFoldersToPath.m | 1,375 | utf_8 | 8514579d3e8825b803b03952848f245a | function addSubFoldersToPath()
blacklist={'.git','.ignore','.graffle'}; % TODO: inject this rather than define it here
allSubpaths = getAllSubpaths();
pathsToAdd = filterPaths(allSubpaths,blacklist);
add2path(pathsToAdd);
end
function allSubpaths = getAllSubpaths()
pathOfThisFunction = mfilename('fullpath');
[... |
github | drbenvincent/delay-discounting-analysis-master | WAIC.m | .m | delay-discounting-analysis-master/ddToolbox/WAIC.m | 5,292 | utf_8 | 4f79ce567129ff800f1a627500082c3d | classdef WAIC
%WAIC WAIC object
% The WAIC object is intended to help conduct Bayesian model
% comparison.
%
% Step 1: Create a WAIC object for each model we have. We do this by
% creating a WAIC instance, calling it with a table of log likeliood
% values. Each column corresponds to an MCMC sample, and ... |
github | drbenvincent/delay-discounting-analysis-master | create_subplots.m | .m | delay-discounting-analysis-master/ddToolbox/utils-plot/create_subplots.m | 869 | utf_8 | dc747a0b7f6e9fb19adb60161f453b3d | function subplot_handles = create_subplots(N, facetStyle)
%CREATE_SUBPLOTS
% This function creates a spatial layout of N subplots with the facetStyle
% provided. It returns a set of handles.
% `facetStyle` = {'row' | 'col' | 'square'}
assert(isscalar(N))
assert(ischar(facetStyle))
subplot_handles = zeros(N,1);
switc... |
github | drbenvincent/delay-discounting-analysis-master | my_shaded_errorbar_zone_UL.m | .m | delay-discounting-analysis-master/ddToolbox/utils-plot/my_shaded_errorbar_zone_UL.m | 815 | utf_8 | 80d01e22a7a6d8e5cb0c40501ff8200c | function [h]=my_shaded_errorbar_zone_UL(x,upper,lower,col)
% Plots a shaded region of error
%
% my_shaded_errorbar_zone_UL([-10:0.1:10],[-10:0.1:10]+1,[-10:0.1:10]-1,[0.7 0.7 0.7])
%
% eg, my_shaded_errorbar_zone([-10:0.1:10],x,abs(randn(size(x)))+2,[0 0 1])
%
%handle=patch([x max(x)-x+min(x)],[y+e flipud(y'-e')... |
github | drbenvincent/delay-discounting-analysis-master | checkGitHubDependencies.m | .m | delay-discounting-analysis-master/ddToolbox/utils/checkGitHubDependencies.m | 2,550 | utf_8 | ab875d65462fce322c34c10dc73bf238 | function checkGitHubDependencies(dependencies)
% This function takes a cell array of url's to github repositories, loop through
% them and ensure they exist on the path, or clone them to your local machine.
%
% Example input:
%
% dependencies={...
% 'https://github.com/drbenvincent/mcmc-utils-matlab',...
% 'https://g... |
github | drbenvincent/delay-discounting-analysis-master | ensureColumnsPresentInTable.m | .m | delay-discounting-analysis-master/ddToolbox/utils/ensureColumnsPresentInTable.m | 483 | utf_8 | 1b6d2df560f2d81494277b0e5e8282e3 | function T = ensureColumnsPresentInTable(T, nameValuePairs)
assert(istable(T))
assert(iscell(nameValuePairs))
for n = 1:2:numel(nameValuePairs)-1
colName = nameValuePairs{n};
val = nameValuePairs{n+1};
if ~isColumnPresent(T, colName)
newColumn = table(val.*ones( height(T), 1),...
'VariableNames',{colName});
... |
github | drbenvincent/delay-discounting-analysis-master | HT_BayesFactor.m | .m | delay-discounting-analysis-master/ddToolbox/hypothesis-testing/HT_BayesFactor.m | 3,747 | utf_8 | 58c065769fe8b32cc4d538a878cf8f6b | function HT_BayesFactor(priorSamples, posteriorSamples, hypothesis, testValue)
%warning('This code only implements the hypothesis x<0')
%import mcmc.*
if testValue~=0
error('not yet implemented for testValue ~= 0')
end
switch hypothesis
case{'>'}
% Discard samples >testValue
% in order to evaluate the order-... |
github | drbenvincent/delay-discounting-analysis-master | DF2.m | .m | delay-discounting-analysis-master/ddToolbox/DeterministicFunction/DF2.m | 3,001 | utf_8 | 71de5567bf140a04e91f3bbbbd29770a | classdef (Abstract) DF2 < DiscountFunction
%DF2
methods (Access = public)
function obj = DF2(varargin)
obj = obj@DiscountFunction(varargin{:});
end
% TODO: contents of this function is NOT generic to all 2D discount
% surfaces. It contains lots of code specific to Hyperbolic +
% magnitude disc... |
github | drbenvincent/delay-discounting-analysis-master | convergenceSummary.m | .m | delay-discounting-analysis-master/ddToolbox/CODA/convergenceSummary.m | 2,719 | utf_8 | 7fbcf82d3464349bc957249308bb6c45 | function convergenceSummary(Rhat, savePath, IDnames)
% TODO: export Rhat stats in the form of a Table. Would need a
% table for participant-level variables. This would now include
% a "group" unobserved participant. But we may also have other
% group-level parameters in addition to this, and these might
% have to go in... |
github | drbenvincent/delay-discounting-analysis-master | calcMode.m | .m | delay-discounting-analysis-master/ddToolbox/CODA/calcMode.m | 310 | utf_8 | 5609145c4c3e7c22f9acd81783c7f94a | % TODO: rename... this is for samples from UNIVARIATE distributions
function mode = calcMode(x)
% calculate the mode of a set of samples from some arbitrary univariate distribution.
assert(isvector(x))
% TODO: don't use Matlab's slow ksdensity
[F, XI] = ksdensity( x );
[~, ind] = max(F);
mode = XI(ind);
end
|
github | drbenvincent/delay-discounting-analysis-master | computeStats.m | .m | delay-discounting-analysis-master/ddToolbox/CODA/computeStats.m | 4,336 | utf_8 | 9694d1c0af64bb90e5bdd47b7c4ac42e | function stats = computeStats(all_samples)
% For each variable (field in the all_samples structure), compute a series
% of statistics (which will be fields of stats). The only complexity is
% that we have to be sensitive to whether each variable is a scalar,
% vector, or 2D matrix. Higher-dimensional variables are not ... |
github | drbenvincent/delay-discounting-analysis-master | generateFigure4.m | .m | delay-discounting-analysis-master/reproduce-figures/generateFigure4.m | 1,582 | utf_8 | 92de54b28f735bbe7eea68e0f7e983b7 | function generateFigure4
%close all
opts.maxlogB = 10000;
opts.maxD = 365;
figure(1), clf
% %% Parameter space
% subplot(1,2,1)
% title('a.')
% hold on
% xlabel('m')
% ylabel('c')
% axis square
% axis([-4 1 -5 5])
%% EXAMPLE 1
m=-0.5; c= -1;
subplot(2,2,1)
internalPlotMagEffect(m,c)
title('a.')
add_text_to_figur... |
github | jadonk/machinekit-master | accel_err_jump.m | .m | machinekit-master/tests/trajectory-planner/circular-arcs/octave/accel_err_jump.m | 921 | utf_8 | cd231e7a05ae557f1f7e7c437fad44a3 | ## Copyright (C) 2014 Robert Ellenberg
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program is di... |
github | rcrowder/nupic.audio-master | runSpringerSegmentationAlgorithm.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/runSpringerSegmentationAlgorithm.m | 2,719 | utf_8 | 3992cddf5ce19695b582ab458a27e675 | % function assigned_states = runSpringerSegmentationAlgorithm(audio_data, Fs, B_matrix, pi_vector, total_observation_distribution, figures)
%
% A function to assign states to a PCG recording using a duration dependant
% logisitic regression-based HMM, using the trained B_matrix and pi_vector
% trained in "trainSpringer... |
github | rcrowder/nupic.audio-master | getHeartRateSchmidt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/getHeartRateSchmidt.m | 3,744 | utf_8 | 1ed442555a04142f56a009968f3ce1bb | % function [heartRate systolicTimeInterval] = getHeartRateSchmidt(audio_data, Fs, figures)
%
% Derive the heart rate and the sytolic time interval from a PCG recording.
% This is used in the duration-dependant HMM-based segmentation of the PCG
% recording.
%
% This method is based on analysis of the autocorrelation fun... |
github | rcrowder/nupic.audio-master | get_duration_distributions.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/get_duration_distributions.m | 3,838 | utf_8 | 73e1d0a24c75a37aaec86578d7edde9d | % function [d_distributions max_S1 min_S1 max_S2 min_S2 max_systole min_systole max_diastole min_diastole] = get_duration_distributions(heartrate,systolic_time)
%
% This function calculates the duration distributions for each heart cycle
% state, and the minimum and maximum times for each state.
%
%% Inputs:
% heartrat... |
github | rcrowder/nupic.audio-master | butterworth_low_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/butterworth_low_pass_filter.m | 2,910 | utf_8 | b7646abeb8d9ef0bef5bb0390bf19e89 | % function low_pass_filtered_signal = butterworth_low_pass_filter(original_signal,order,cutoff,sampling_frequency, figures)
%
% Low-pass filter a given signal using a forward-backward, zero-phase
% butterworth low-pass filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the... |
github | rcrowder/nupic.audio-master | getDWT.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/getDWT.m | 2,523 | utf_8 | e3cb331767f9a35456289a7afdfebf3b | % function [cD cA] = getDWT(X,N,Name)
%
% finds the discrete wavelet transform at level N for signal X using the
% wavelet specified by Name.
%
%% Inputs:
% X: the original signal
% N: the decomposition level
% Name: the wavelet name to use
%
%% Outputs:
% cD is a N-row matrix containing the detail coefficie... |
github | rcrowder/nupic.audio-master | default_Springer_HSMM_options.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/default_Springer_HSMM_options.m | 1,639 | utf_8 | c60c752c26e15c49182f51bf63b927b0 | % function springer_options = default_Springer_HSMM_options()
%
% The default options to be used with the Springer segmentation algorithm.
% USAGE: springer_options = default_Springer_HSMM_options
%
% Developed for use in the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEE... |
github | rcrowder/nupic.audio-master | viterbiDecodePCG_Springer.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/viterbiDecodePCG_Springer.m | 15,252 | utf_8 | 1506ed816b87a08f7bb7b54bd5811f35 | % function [delta, psi, qt] = viterbiDecodePCG_Springer(observation_sequence, pi_vector, b_matrix, total_obs_distribution, heartrate, systolic_time, Fs, figures)
%
% This function calculates the delta, psi and qt matrices associated with
% the Viterbi decoding algorithm from:
% L. R. Rabiner, "A tutorial on hidden Mark... |
github | rcrowder/nupic.audio-master | schmidt_spike_removal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/schmidt_spike_removal.m | 4,383 | utf_8 | 04f80bbfe14d805f7ea10c8f7274427e | % function [despiked_signal] = schmidt_spike_removal(original_signal, fs)
%
% This function removes the spikes in a signal as done by Schmidt et al in
% the paper:
% Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J.
% (2010). Segmentation of heart sound recordings by a duration-dependent
% hidden ... |
github | rcrowder/nupic.audio-master | butterworth_high_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/butterworth_high_pass_filter.m | 2,906 | utf_8 | 624230f9365a7fe3319f4a0afd0d5fab | % function high_pass_filtered_signal = butterworth_high_pass_filter(original_signal,order,cutoff,sampling_frequency)
%
% High-pass filter a given signal using a forward-backward, zero-phase
% butterworth filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the filter (1,2,3,... |
github | rcrowder/nupic.audio-master | expand_qt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/expand_qt.m | 2,231 | utf_8 | 28c77e8d3bbf9c89ffe6238e2ced0522 | % function expanded_qt = expand_qt(original_qt, old_fs, new_fs, new_length)
%
% Function to expand the derived HMM states to a higher sampling frequency.
%
% Developed by David Springer for comparison purposes in the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEEE... |
github | rcrowder/nupic.audio-master | get_PSD_feature_Springer_HMM.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/get_PSD_feature_Springer_HMM.m | 2,514 | utf_8 | 329381fe8300078046020de4e189d634 | %cfunction [psd] = get_PSD_feature_Springer_HMM(data, sampling_frequency, frequency_limit_low, frequency_limit_high, figures)
%
% PSD-based feature extraction for heart sound segmentation.
%
%% INPUTS:
% data: this is the audio waveform
% sampling_frequency is self-explanatory
% frequency_limit_low is the lower-bound o... |
github | rcrowder/nupic.audio-master | Hilbert_Envelope.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/Hilbert_Envelope.m | 1,915 | utf_8 | 119ac28becc0e1f2b547f1d697ae22d0 | % function [hilbert_envelope] = Hilbert_Envelope(input_signal, sampling_frequency,figures)
%
% This function finds the Hilbert envelope of a signal. This is taken from:
%
% Choi et al, Comparison of envelope extraction algorithms for cardiac sound
% signal segmentation, Expert Systems with Applications, 2008
%
%% Input... |
github | rcrowder/nupic.audio-master | getSpringerPCGFeatures.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/getSpringerPCGFeatures.m | 4,180 | utf_8 | 22cdb4b7ac7bacae0d4a401b0caf9d13 | % function [PCG_Features, featuresFs] = getSpringerPCGFeatures(audio_data, Fs, figures)
%
% Get the features used in the Springer segmentation algorithm. These
% features include:
% -The homomorphic envelope (as performed in Schmidt et al's paper)
% -The Hilbert envelope
% -A wavelet-based feature
% -A PSD-based featu... |
github | rcrowder/nupic.audio-master | Homomorphic_Envelope_with_Hilbert.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/Homomorphic_Envelope_with_Hilbert.m | 3,501 | utf_8 | 9abf07de13b4d0c6371582de783989be | % function homomorphic_envelope = Homomorphic_Envelope_with_Hilbert(input_signal, sampling_frequency,lpf_frequency,figures)
%
% This function finds the homomorphic envelope of a signal, using the method
% described in the following publications:
%
% S. E. Schmidt et al., ?Segmentation of heart sound recordings by a
% ... |
github | rcrowder/nupic.audio-master | normalise_signal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/nupic2016/normalise_signal.m | 1,457 | utf_8 | b36a6f26a53581643f4de858bf85ad9f | % function [normalised_signal] = normalise_signal(signal)
%
% This function subtracts the mean and divides by the standard deviation of
% a (1D) signal in order to normalise it for machine learning applications.
%
%% Inputs:
% signal: the original signal
%
%% Outputs:
% normalised_signal: the original signal, ... |
github | rcrowder/nupic.audio-master | getHeartRateSchmidt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/getHeartRateSchmidt.m | 3,376 | utf_8 | 18fd6afb407725662c4a91cf4c5558ec | % function [heartRate systolicTimeInterval] = getHeartRateSchmidt(audio_data, Fs)
%
% Derive the heart rate and the sytolic time interval from a PCG recording.
% This is used in the duration-dependant HMM-based segmentation of the PCG
% recording.
%
% This method is based on analysis of the autocorrelation function, an... |
github | rcrowder/nupic.audio-master | get_duration_distributions.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/get_duration_distributions.m | 3,825 | utf_8 | 0f6a2282f8381cf405c89bbe5a6254c0 | % function [d_distributions max_S1 min_S1 max_S2 min_S2 max_systole min_systole max_diastole min_diastole] = get_duration_distributions(heartrate,systolic_time)
%
% This function calculates the duration distributions for each heart cycle
% state, and the minimum and maximum times for each state.
%
%% Inputs:
% heartrat... |
github | rcrowder/nupic.audio-master | butterworth_low_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/butterworth_low_pass_filter.m | 2,910 | utf_8 | b7646abeb8d9ef0bef5bb0390bf19e89 | % function low_pass_filtered_signal = butterworth_low_pass_filter(original_signal,order,cutoff,sampling_frequency, figures)
%
% Low-pass filter a given signal using a forward-backward, zero-phase
% butterworth low-pass filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the... |
github | rcrowder/nupic.audio-master | schmidt_spike_removal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/schmidt_spike_removal.m | 4,383 | utf_8 | 04f80bbfe14d805f7ea10c8f7274427e | % function [despiked_signal] = schmidt_spike_removal(original_signal, fs)
%
% This function removes the spikes in a signal as done by Schmidt et al in
% the paper:
% Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J.
% (2010). Segmentation of heart sound recordings by a duration-dependent
% hidden ... |
github | rcrowder/nupic.audio-master | viterbiDecodePCG.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/viterbiDecodePCG.m | 13,027 | utf_8 | acbc4b03da3daa1b2aa8d3bd3c1b5f06 | % function [delta psi qt] = viterbiDecodePCG(observation_sequence, pi_vector, b_matrix,heartrate, systolic_time, Fs)
%
% This function calculates the delta and psi matrices associated with the
% duration-dependant Viterbi decoding algorithm. This algorithm is outlined
% in:
% L. R. Rabiner, ?A tutorial on hidden Markov... |
github | rcrowder/nupic.audio-master | runSchmidtSegmentationAlgorithm.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/runSchmidtSegmentationAlgorithm.m | 2,603 | utf_8 | 77c3e2a6a1a125cc9f13103290fe3f34 | % function assigned_states = runSchmidtSegmentationAlgorithm(audio_data, Fs, B_matrix, pi_vector, figures)
%
% A function to assign states to a PCG recording based on a trained
% duration-dependant HMM
%
%% INPUTS:
% audio_data: The raw audio data from the PCG recording
% Fs: the sampling frequency of the audio recordi... |
github | rcrowder/nupic.audio-master | butterworth_high_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/butterworth_high_pass_filter.m | 2,906 | utf_8 | 624230f9365a7fe3319f4a0afd0d5fab | % function high_pass_filtered_signal = butterworth_high_pass_filter(original_signal,order,cutoff,sampling_frequency)
%
% High-pass filter a given signal using a forward-backward, zero-phase
% butterworth filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the filter (1,2,3,... |
github | rcrowder/nupic.audio-master | default_Schmidt_HSMM_options.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/default_Schmidt_HSMM_options.m | 1,713 | utf_8 | 4094109ab8ca4d42d20896194e507259 | % function schmidt_options = default_Schmidt_HSMM_options()
%
% The default options to be used with the Schmidt segmentation algorithm.
% USAGE: schmidt_options = default_Schmidt_HSMM_options
%
% This code is derived from the paper:
% S. E. Schmidt et al., "Segmentation of heart sound recordings by a
% duration-depende... |
github | rcrowder/nupic.audio-master | expand_qt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/expand_qt.m | 2,231 | utf_8 | 28c77e8d3bbf9c89ffe6238e2ced0522 | % function expanded_qt = expand_qt(original_qt, old_fs, new_fs, new_length)
%
% Function to expand the derived HMM states to a higher sampling frequency.
%
% Developed by David Springer for comparison purposes in the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEEE... |
github | rcrowder/nupic.audio-master | getSchmidtPCGFeatures.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/getSchmidtPCGFeatures.m | 2,892 | utf_8 | 42add36b2fdf0331a87e43c5302bc344 | % function [PCG_Features, featuresFs] = getSchmidtPCGFeatures(audio, Fs, figures)
%
% Get the features used in the Schmidt segmentation algorithm. This is only
% the homomorphic envelope of the signal, downsampled to 50hz
%
%% INPUTS:
% audio_data: array of data from which to extract features
% Fs: the sampling frequen... |
github | rcrowder/nupic.audio-master | labelPCGStates.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/labelPCGStates.m | 7,091 | utf_8 | 33fb766537cb928c1a10cebf6385feab | % function states = labelPCGStates(envelope,s1_positions, s2_positions, samplingFrequency, figures)
%
% This function assigns the state labels to a PCG record.
% This is based on ECG markers, dervied from the R peak and end-T wave locations.
%
%% Inputs:
% envelope: The PCG recording envelope (found in getSchmidtPCGFe... |
github | rcrowder/nupic.audio-master | trainBandPiMatricesSchmidt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/trainBandPiMatricesSchmidt.m | 3,415 | utf_8 | 985ff337d1f7a9075d428e9be08c8585 | %function [B_matrix, pi_vector] = trainBandPiMatricesSchmidt(state_observation_values)
%
% Train the B matrix and pi vector for the HMM.
% The pi vector is the initial state probability, while the B matrix are
% the observation probabilities. In the case of Schmidt's algorith, the
% observation probabilities are based ... |
github | rcrowder/nupic.audio-master | Homomorphic_Envelope_with_Hilbert.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/Homomorphic_Envelope_with_Hilbert.m | 3,501 | utf_8 | 9abf07de13b4d0c6371582de783989be | % function homomorphic_envelope = Homomorphic_Envelope_with_Hilbert(input_signal, sampling_frequency,lpf_frequency,figures)
%
% This function finds the homomorphic envelope of a signal, using the method
% described in the following publications:
%
% S. E. Schmidt et al., ?Segmentation of heart sound recordings by a
% ... |
github | rcrowder/nupic.audio-master | normalise_signal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/normalise_signal.m | 1,457 | utf_8 | b36a6f26a53581643f4de858bf85ad9f | % function [normalised_signal] = normalise_signal(signal)
%
% This function subtracts the mean and divides by the standard deviation of
% a (1D) signal in order to normalise it for machine learning applications.
%
%% Inputs:
% signal: the original signal
%
%% Outputs:
% normalised_signal: the original signal, ... |
github | rcrowder/nupic.audio-master | trainSchmidtSegmentationAlgorithm.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016b/trainSchmidtSegmentationAlgorithm.m | 3,940 | utf_8 | d0f9ff806f80be8e855c29f2ff23f57c | % function [B_matrix, pi_matrix] = trainSchmidtSegmentationAlgorithm(PCGCellArray, annotationsArray, Fs, figures)
%
% Training the emissions matrix, B_matrix, and initial distribution,
% pi_vector, for the Schmidt HMM segmentation algorithm.
%
%% Inputs:
% PCGCellArray: A 1XN cell array of the N audio signals. For eval... |
github | rcrowder/nupic.audio-master | runSpringerSegmentationAlgorithm.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/runSpringerSegmentationAlgorithm.m | 2,719 | utf_8 | 3992cddf5ce19695b582ab458a27e675 | % function assigned_states = runSpringerSegmentationAlgorithm(audio_data, Fs, B_matrix, pi_vector, total_observation_distribution, figures)
%
% A function to assign states to a PCG recording using a duration dependant
% logisitic regression-based HMM, using the trained B_matrix and pi_vector
% trained in "trainSpringer... |
github | rcrowder/nupic.audio-master | getHeartRateSchmidt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/getHeartRateSchmidt.m | 3,744 | utf_8 | 1ed442555a04142f56a009968f3ce1bb | % function [heartRate systolicTimeInterval] = getHeartRateSchmidt(audio_data, Fs, figures)
%
% Derive the heart rate and the sytolic time interval from a PCG recording.
% This is used in the duration-dependant HMM-based segmentation of the PCG
% recording.
%
% This method is based on analysis of the autocorrelation fun... |
github | rcrowder/nupic.audio-master | get_duration_distributions.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/get_duration_distributions.m | 3,838 | utf_8 | 73e1d0a24c75a37aaec86578d7edde9d | % function [d_distributions max_S1 min_S1 max_S2 min_S2 max_systole min_systole max_diastole min_diastole] = get_duration_distributions(heartrate,systolic_time)
%
% This function calculates the duration distributions for each heart cycle
% state, and the minimum and maximum times for each state.
%
%% Inputs:
% heartrat... |
github | rcrowder/nupic.audio-master | butterworth_low_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/butterworth_low_pass_filter.m | 2,910 | utf_8 | b7646abeb8d9ef0bef5bb0390bf19e89 | % function low_pass_filtered_signal = butterworth_low_pass_filter(original_signal,order,cutoff,sampling_frequency, figures)
%
% Low-pass filter a given signal using a forward-backward, zero-phase
% butterworth low-pass filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the... |
github | rcrowder/nupic.audio-master | getDWT.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/getDWT.m | 2,523 | utf_8 | e3cb331767f9a35456289a7afdfebf3b | % function [cD cA] = getDWT(X,N,Name)
%
% finds the discrete wavelet transform at level N for signal X using the
% wavelet specified by Name.
%
%% Inputs:
% X: the original signal
% N: the decomposition level
% Name: the wavelet name to use
%
%% Outputs:
% cD is a N-row matrix containing the detail coefficie... |
github | rcrowder/nupic.audio-master | default_Springer_HSMM_options.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/default_Springer_HSMM_options.m | 1,639 | utf_8 | c60c752c26e15c49182f51bf63b927b0 | % function springer_options = default_Springer_HSMM_options()
%
% The default options to be used with the Springer segmentation algorithm.
% USAGE: springer_options = default_Springer_HSMM_options
%
% Developed for use in the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEE... |
github | rcrowder/nupic.audio-master | viterbiDecodePCG_Springer.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/viterbiDecodePCG_Springer.m | 15,252 | utf_8 | 1506ed816b87a08f7bb7b54bd5811f35 | % function [delta, psi, qt] = viterbiDecodePCG_Springer(observation_sequence, pi_vector, b_matrix, total_obs_distribution, heartrate, systolic_time, Fs, figures)
%
% This function calculates the delta, psi and qt matrices associated with
% the Viterbi decoding algorithm from:
% L. R. Rabiner, "A tutorial on hidden Mark... |
github | rcrowder/nupic.audio-master | schmidt_spike_removal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/schmidt_spike_removal.m | 4,383 | utf_8 | 04f80bbfe14d805f7ea10c8f7274427e | % function [despiked_signal] = schmidt_spike_removal(original_signal, fs)
%
% This function removes the spikes in a signal as done by Schmidt et al in
% the paper:
% Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J.
% (2010). Segmentation of heart sound recordings by a duration-dependent
% hidden ... |
github | rcrowder/nupic.audio-master | butterworth_high_pass_filter.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/butterworth_high_pass_filter.m | 2,906 | utf_8 | 624230f9365a7fe3319f4a0afd0d5fab | % function high_pass_filtered_signal = butterworth_high_pass_filter(original_signal,order,cutoff,sampling_frequency)
%
% High-pass filter a given signal using a forward-backward, zero-phase
% butterworth filter.
%
%% INPUTS:
% original_signal: The 1D signal to be filtered
% order: The order of the filter (1,2,3,... |
github | rcrowder/nupic.audio-master | expand_qt.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/expand_qt.m | 2,231 | utf_8 | 28c77e8d3bbf9c89ffe6238e2ced0522 | % function expanded_qt = expand_qt(original_qt, old_fs, new_fs, new_length)
%
% Function to expand the derived HMM states to a higher sampling frequency.
%
% Developed by David Springer for comparison purposes in the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEEE... |
github | rcrowder/nupic.audio-master | get_PSD_feature_Springer_HMM.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/get_PSD_feature_Springer_HMM.m | 2,514 | utf_8 | 329381fe8300078046020de4e189d634 | %cfunction [psd] = get_PSD_feature_Springer_HMM(data, sampling_frequency, frequency_limit_low, frequency_limit_high, figures)
%
% PSD-based feature extraction for heart sound segmentation.
%
%% INPUTS:
% data: this is the audio waveform
% sampling_frequency is self-explanatory
% frequency_limit_low is the lower-bound o... |
github | rcrowder/nupic.audio-master | Hilbert_Envelope.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/Hilbert_Envelope.m | 1,915 | utf_8 | 119ac28becc0e1f2b547f1d697ae22d0 | % function [hilbert_envelope] = Hilbert_Envelope(input_signal, sampling_frequency,figures)
%
% This function finds the Hilbert envelope of a signal. This is taken from:
%
% Choi et al, Comparison of envelope extraction algorithms for cardiac sound
% signal segmentation, Expert Systems with Applications, 2008
%
%% Input... |
github | rcrowder/nupic.audio-master | getSpringerPCGFeatures.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/getSpringerPCGFeatures.m | 4,180 | utf_8 | 22cdb4b7ac7bacae0d4a401b0caf9d13 | % function [PCG_Features, featuresFs] = getSpringerPCGFeatures(audio_data, Fs, figures)
%
% Get the features used in the Springer segmentation algorithm. These
% features include:
% -The homomorphic envelope (as performed in Schmidt et al's paper)
% -The Hilbert envelope
% -A wavelet-based feature
% -A PSD-based featu... |
github | rcrowder/nupic.audio-master | Homomorphic_Envelope_with_Hilbert.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/Homomorphic_Envelope_with_Hilbert.m | 3,501 | utf_8 | 9abf07de13b4d0c6371582de783989be | % function homomorphic_envelope = Homomorphic_Envelope_with_Hilbert(input_signal, sampling_frequency,lpf_frequency,figures)
%
% This function finds the homomorphic envelope of a signal, using the method
% described in the following publications:
%
% S. E. Schmidt et al., ?Segmentation of heart sound recordings by a
% ... |
github | rcrowder/nupic.audio-master | normalise_signal.m | .m | nupic.audio-master/CinC2016Challenge_PCG/examples/sample2016/normalise_signal.m | 1,457 | utf_8 | b36a6f26a53581643f4de858bf85ad9f | % function [normalised_signal] = normalise_signal(signal)
%
% This function subtracts the mean and divides by the standard deviation of
% a (1D) signal in order to normalise it for machine learning applications.
%
%% Inputs:
% signal: the original signal
%
%% Outputs:
% normalised_signal: the original signal, ... |
github | liubenyuan/STSBL-FM-master | STSBL_FM.m | .m | STSBL-FM-master/STSBL_FM.m | 13,450 | utf_8 | 76a479d7e1c45e5eb4075fcf8b88648f | function Result = STSBL_FM(PHI,Y,blkStartLoc,LearnLambda,varargin)
%------------------------------------------------------------------
% The code for FMLM optimized Spatio-Temporal Sparse Recovery
%
% Coded by: Liu Benyuan < liubenyuan AT gmail DOT com >
% Date : 2013-12-12
%
% optimise :
% 1. replac... |
github | thecoldviews/He-or-She-master | UI_main.m | .m | He-or-She-master/MATLAB Implementation/UI_main.m | 12,666 | utf_8 | 084b539caf32dd6e970f65ffb9cb0e01 | function varargout = UI_main(varargin)
% UI_MAIN MATLAB code for UI_main.fig
% UI_MAIN, by itself, creates a new UI_MAIN or raises the existing
% singleton*.
%
% H = UI_MAIN returns the handle to a new UI_MAIN or the handle to
% the existing singleton*.
%
% UI_MAIN('CALLBACK',hObject,eventData,... |
github | ArashAkbarinia/ColourCategorisation-master | FitPointsToSlices.m | .m | ColourCategorisation-master/matlab/src/geometry/FitPointsToSlices.m | 2,175 | utf_8 | e6255f6522461bb3def8b87b9641970e | function slices = FitPointsToSlices(points, centre, plotme)
%FitPointsToSlices fits a set of points two a slice (two lines).
%
% inputs
% points the polar coordinates of the points.
% centre the polar coordinates of the centre, default [0, 0].
% plotme if true the points and the slice is plotted.
%
% outputs
... |
github | ArashAkbarinia/ColourCategorisation-master | ColourNamingComputeError.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/ColourNamingComputeError.m | 1,492 | utf_8 | 04e4ebc1db53a033b1c7296b552cb7f8 | function contingency = ColourNamingComputeError(ImageMask, NamingImage, ColourName)
if strcmp(ColourName, 'all')
ColourNames = {'green', 'blue', 'purple', 'pink', 'red', 'orange', 'yellow', 'brown', 'grey', 'white', 'black'};
contingency = struct();
for i = 1:numel(ColourNames)
CurrentImageMask = ImageMask(:... |
github | ArashAkbarinia/ColourCategorisation-master | ColourNamingContingencyTable.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/ColourNamingContingencyTable.m | 1,567 | utf_8 | 7e885f33419909a33e9f8fe492d68608 | function [] = ColourNamingContingencyTable(DirPath, method)
if nargin < 2
DirPath = '/home/arash/Software/Repositories/neurobit/data/dataset/ColourNameDataset/ebay/';
method = 'our';
end
method = lower(method);
disp(['Reading results for method ', method]);
SubFolders = GetSubFolders(DirPath);
nSubFolder = leng... |
github | ArashAkbarinia/ColourCategorisation-master | PlotColourNamingDifferences.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/reports/PlotColourNamingDifferences.m | 916 | utf_8 | 69c515292a36aa6cd42c1a9d0cfbeedb | function h = PlotColourNamingDifferences(ResBelonging, GtBelonging)
%PlotColourNamingDifferences Summary of this function goes here
% Detailed explanation goes here
ColouredBelongingImage = belonging2naming(ResBelonging);
[ErrorIndsB, GtIndsB] = CompareResultGroundTruth(ColouredBelongingImage, belonging2naming(GtBe... |
github | ArashAkbarinia/ColourCategorisation-master | SigmoidAngle.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/SigmoidAngle.m | 1,737 | utf_8 | 0b97d6cdd565615ea25430a0b6397cd8 | % SigmoidAngle: Computes the oriented 2D-sigmoid values at each sample in vector s
% s - Input samples
% tx, ty - Column vectors of translations from the origin
% alfa - Column vector of rotation angles with respect to the axis
% b - Column vector of beta parameters of the Sigmoid function
% u - V... |
github | ArashAkbarinia/ColourCategorisation-master | TripleSigmoid_E.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/TripleSigmoid_E.m | 916 | utf_8 | e808170125bf637ec759fb111904918a | % TripleSigmoid_E: Computes the value of the TSE function (Triple Sigmoid with
% Elliptical centre) at each value in vector s
% s - Input samples
% tx, ty - Column vectors of translations from the origin
% alfa_x, alfa_y - Column vectors of rotation angles with respect to x an... |
github | ArashAkbarinia/ColourCategorisation-master | Sigmoid.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/Sigmoid.m | 238 | utf_8 | d897f4187667139af5c9b7f111927d75 | % Sigmoid: Computes the sigmoid value at each of the 1-dimensional values in s
% s - Input samples
% t - Translation from the origin
% b - Beta parameter of the sigmoid function
function y=Sigmoid(s,t,b)
y=1./(1+exp(-b*(s-t)));
|
github | ArashAkbarinia/ColourCategorisation-master | ImColorNamingTSELab.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/ImColorNamingTSELab.m | 3,943 | utf_8 | 30a2c0a0bfc004aaf8d393366ab54cc2 | % [imaRes,imaIndex,CD]=ImColorNamingTSELab(ima,parFileName1,parFileName2,parFileName3)
%
% Given an image in sRGB format, applies the color naming model and returns:
% imaRes - image with each pixel painted the representative RGB colour of
% the maximum membership value given by the color naming model
... |
github | ArashAkbarinia/ColourCategorisation-master | ColorName2rgb.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/ColorName2rgb.m | 937 | utf_8 | 78776356dc580d2346e7b2ded5c04734 | % ColorName2rgb: Given a matrix with a set of color names, returns the RGB values representing each color name
function RGB=ColorName2rgb(colorNames)
n=size(colorNames,1);
RGB=zeros(n,3);
RGB=repmat(strcmp(colorNames,'Red'),1,3).*repmat([1.0 0.0 0.0],n,1) + repmat(strcmp(colorNames,'Orange'),1,3).*repmat([1.0 0.6... |
github | ArashAkbarinia/ColourCategorisation-master | EllipticalSigmoid.m | .m | ColourCategorisation-master/matlab/src/evaluation/colournaming/others/RobertColourNaming/EllipticalSigmoid.m | 1,874 | utf_8 | 5e3a8023621c834c901067c4e6bbcbe5 | % EllipticalSigmoid: Computes the value of an elliptic sigmoid at location s=[x,y]
% s - Input samples
% tx, ty - Column vectors of translations from the origin
% be - Column vector of beta values of the Elliptic Sigmoid
% ex, ey - Column vectors of semi-minor and semi-major axis of the Elliptic Sigmoi... |
github | ArashAkbarinia/ColourCategorisation-master | SegmentedColourPoints.m | .m | ColourCategorisation-master/matlab/src/imagefactory/SegmentedColourPoints.m | 2,601 | utf_8 | 359eb605e80d432edad8e423fc75d9df | function [ColourBoxesImage, GroundTruthImage] = SegmentedColourPoints(DirPath, nLimistPoitns)
if nargin < 1
DirPath = '/home/arash/Software/Repositories/neurobit/data/dataset/ColourNameDataset/ebay/';
end
if isempty(strfind(DirPath, '.mat'))
if nargin < 2
nLimistPoitns = 1000;
end
ColourPoints = EmptyColo... |
github | ArashAkbarinia/ColourCategorisation-master | XYZ2sRGB.m | .m | ColourCategorisation-master/matlab/src/transformations/colourspaces/XYZ2sRGB.m | 2,911 | utf_8 | 20716b9e48b1f422ed0a9ae4d5909dc6 | function outpic = XYZ2sRGB(inpic, gammacorr, refwhite)
%The option gammacorr determines whether or not the results will be linearised
%or gammacorrected for presentation on a non-linear monitor
%gammacorr = 1 : results ready to be presented on a non-linear monitor;
%The default reference white is D65 and it will ... |
github | ArashAkbarinia/ColourCategorisation-master | FitExperimentPointsToEllipsoid.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/fitting/FitExperimentPointsToEllipsoid.m | 13,033 | utf_8 | 7c0de4af49fbcc52267cb81c3a2f0a96 | function ColourEllipsoids = FitExperimentPointsToEllipsoid(WhichColours, plotme, saveme)
if nargin < 1
WhichColours = {'c'};
end
if nargin < 2
plotme = 1;
saveme = 1;
end
if strcmpi(WhichColours{1}, 'c')
WhichColours = {'G', 'B', 'Pp', 'Pk', 'R', 'O', 'Y', 'Br'};
elseif strcmpi(WhichColours{1}, 'a... |
github | ArashAkbarinia/ColourCategorisation-master | FitColourPointsToEllipsoid.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/fitting/FitColourPointsToEllipsoid.m | 6,481 | utf_8 | 48513527c99a21418c2f702d10ba3fd6 | function ColourEllipsoids = FitColourPointsToEllipsoid(ColourSpace, WhichColours, plotme, saveme)
%FitColourPointsToEllipsoid Summary of this function goes here
% Detailed explanation goes here
if nargin < 1
ColourSpace = 'lab';
end
if nargin < 2
WhichColours = {'a'};
end
if nargin < 3
plotme = 0;
saveme = 1... |
github | ArashAkbarinia/ColourCategorisation-master | ReadExperimentResults.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/fitting/ReadExperimentResults.m | 7,074 | utf_8 | 4d7fe0e259415708c2680a277822e0ce | function [ColourFrontiers, borders] = ReadExperimentResults(FilePath, ColourFrontiers, borders, XYZ2lsYChoise, BackgroundType)
%ReadExperimentResults maps the colour frontiers result to its object.
%
% inputs
% FilePath the path to the mat file.
% ColourFrontiers previous list of colour frontiers, by defa... |
github | ArashAkbarinia/ColourCategorisation-master | OrganiseExperimentFrontiers.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/fitting/OrganiseExperimentFrontiers.m | 23,155 | utf_8 | a7900d751619c50019215f7c6cd39e27 | function [ColourFrontiers, ColourFrontiersCentre] = OrganiseExperimentFrontiers(FilePath, plotme, XYZ2lsYChoise, BackgroundType)
%OrganiseExperimentFrontiers the colour frontier experiment data.
%
% inputs
% FilePath the path to the original experiment.
% plotme if true it is plotted.
% XYZ... |
github | ArashAkbarinia/ColourCategorisation-master | PostProcessBelongingImage.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/belonging/PostProcessBelongingImage.m | 4,280 | utf_8 | 0bf9d89511fdc94dabfc905d8da2ae4d | function PostProcessedImage = PostProcessBelongingImage(ImageRGB, BelongingImage, rows, cols, plotme)
%PostProcessBelongingImage Summary of this function goes here
% Detailed explanation goes here
if nargin < 3
rows = [];
cols = [];
end
if nargin < 5
plotme = 0;
end
if ~isempty(rows) && ~isempty(cols)
[~, c... |
github | ArashAkbarinia/ColourCategorisation-master | rgb2belonging.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/belonging/rgb2belonging.m | 16,688 | utf_8 | 2858793e119f8b4edfbeffeec50864e7 | function BelongingImage = rgb2belonging(ImageRGB, ConfigsMat, plotme, GroundTruth, DoAdaptEllipsoids)
%RGB2BELONGING labels each pixel in the image as one of the focal eleven
% colours.
if nargin < 2
ConfigsMat = 'lab';
end
if ischar(ConfigsMat)
ColourSpace = ConfigsMat;
ConfigsMat = [];
else
Co... |
github | ArashAkbarinia/ColourCategorisation-master | PlotAllChannels.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/plots/PlotAllChannels.m | 1,796 | utf_8 | 5e364df7264fb53b8cf2713d018c0dd3 | function FigureHandler = PlotAllChannels(ImageRGB, BelongingImage, EllipsoidsTitles, EllipsoidsRGBs, FigureTitle)
%PlotAllChannels plots all the channels of the belonging image.
if nargin < 3
EllipsoidsTitles = [];
EllipsoidsRGBs = [];
FigureTitle = [];
end
if isempty(EllipsoidsTitles)
FunctionLocalPath = 'm... |
github | ArashAkbarinia/ColourCategorisation-master | PlotImagePixelGrid.m | .m | ColourCategorisation-master/matlab/src/algorithms/colourcategorisation/plots/PlotImagePixelGrid.m | 18,062 | utf_8 | 19621a76872e1f3958359794368bcbfe | function h = PlotImagePixelGrid(InputImage, gts)
%PlotImagePixelGrid plotting the results with gts on top
%
% inputs
% InputImage the labelled imaged.
% gts {berlin, sturge}.
%
% outputs
% h the figure number.
%
InputImage([1, 10], 2:end, :) = 128;
figure;
h = imshow(InputImage, 'InitialMagnification'... |
github | ArashAkbarinia/ColourCategorisation-master | BerlinKayColourBoundries.m | .m | ColourCategorisation-master/matlab/src/experiments/wcs/BerlinKayColourBoundries.m | 4,230 | utf_8 | 64a0939b375c65be5d89c6ab8294a690 | function ChipsTable = BerlinKayColourBoundries(ConvertToEllipsoidColours)
%BerlinKayColourBoundries Summary of this function goes here
% Detailed explanation goes here
if nargin < 1
ConvertToEllipsoidColours = false;
end
FunctionLocalPath = 'matlab/src/experiments/wcs/BerlinKayColourBoundries';
FunctionPath = mfi... |
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