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github | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | htm-community/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 | mcimpoi/deep-fbanks-master | get_rcnn_features.m | .m | deep-fbanks-master/get_rcnn_features.m | 4,011 | utf_8 | 586325647d966b0bd1ae73d0aa3ad3a8 | function code = get_rcnn_features(net, im, regions, varargin)
% GET_RCNN_FEATURES
% This function gets the fc7 features for an image region,
% extracted from the provided mask.
opts.regionBorder = 0.05 ;
opts.batchSize = 96 ;
opts = vl_argparse(opts, varargin) ;
if ~iscell(im)
im = {im} ;
regions = {regions... |
github | mcimpoi/deep-fbanks-master | os_seg_test.m | .m | deep-fbanks-master/os_seg_test.m | 10,608 | utf_8 | f48b5e324fc53c305baf609207c91cf2 | function os_seg_test(varargin)
% note: this function runs multiple setups (feature combinations) at the
% same time
% load stuff
[opts, imdb] = os_setup(varargin{:}) ;
classes = find(imdb.meta.inUse) ;
figure(200) ; clf ;
cmap = plot_legend(imdb) ;
for s = 1:numel(opts.suffix)
vl_xmkdir(opts.segPublishDir{s});
pr... |
github | mcimpoi/deep-fbanks-master | get_dcnn_features.m | .m | deep-fbanks-master/get_dcnn_features.m | 10,563 | utf_8 | 6c92f6c9504c3b6fdcc0805530ebf5b3 | function [code, codeLoc] = get_dcnn_features(net, im, regions, varargin)
% GET_DCNN_FEATURES Get convolutional features for an image region
% This function extracts the DCNN (CNN+FV) for one or more regions in an image.
% These can be used as SIFT replacement in e.g. a Fisher Vector.
%
% MASK should be an array ... |
github | mcimpoi/deep-fbanks-master | os_setup.m | .m | deep-fbanks-master/os_setup.m | 5,711 | utf_8 | b724df33a9179e1285f6ba54e9a8c43d | function [opts, imdb] = os_setup(varargin)
global is_iasonas
if (exist('is_iasonas', 'var') ||isempty(is_iasonas) || ~is_iasonas)
setup ;
end
opts.transf = {};
opts.seed = 1 ;
opts.batchSize = 128 ;
opts.useGpu = true ;
opts.regionBorder = 0.05 ;
opts.numDCNNWords = 64 ;
opts.numDSIFTWords = 256 ;
opts.numSamples... |
github | mcimpoi/deep-fbanks-master | os_train.m | .m | deep-fbanks-master/os_train.m | 14,871 | utf_8 | e94677e9b799e6be657e5a1a5a4c2b56 | function os_train(varargin)
opts.writeResults = 0;
[opts, imdb] = os_setup(varargin{:}) ;
% -------------------------------------------------------------------------
% Train encoders and compute codes
% -------------------------------------------------------------------------
... |
github | mcimpoi/deep-fbanks-master | distinguishable_colors.m | .m | deep-fbanks-master/distinguishable_colors.m | 5,746 | utf_8 | 231fe241da0889007418b11f860d1114 | 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 than ... |
github | mcimpoi/deep-fbanks-master | fix_vl_argparse.m | .m | deep-fbanks-master/compatibility/fix_vl_argparse.m | 3,075 | utf_8 | 59992c10c156509b6431042af22bf62f | function [opts, args] = fix_vl_argparse(opts, args)
% VL_ARGPARSE Parse list of parameter-value pairs
% OPTS = VL_ARGPARSE(OPTS, ARGS) updates the structure OPTS based on
% the specified parameter-value pairs ARGS={PAR1, VAL1, ... PARN,
% VALN}. The function produces an error if an unknown parameter name
% is ... |
github | mcimpoi/deep-fbanks-master | fix_vl_simplenn_display.m | .m | deep-fbanks-master/compatibility/fix_vl_simplenn_display.m | 11,035 | utf_8 | dd865a4d94ebbacf80047e0ec90656bf | function info = fix_vl_simplenn_display(net, varargin)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO=VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the network NET.
%
% The func... |
github | lawrennd/hsvargplvm-master | hsvargplvmStaticImageVisualise.m | .m | hsvargplvm-master/matlab/hsvargplvmStaticImageVisualise.m | 2,172 | utf_8 | 6e4e7bfc946c0263a136676c20f27068 | %{
dataType = 'image';
varargs{1} = [16 16];
varargs{2} = 1;
varargs{3} = 1;
varargs{4} = 1;
hsvargplvmStaticImageVisualise(mm2, Y, [dataType 'Visualise'], 0.03, varargs{:});
varargin = varargs;
visualiseFunction = 'imageVisualise';
axesWidth = 0.03;
Y = Ytr{1};
%}
function hsvargplvmStaticImageVisualise(mm2, Y, re... |
github | lawrennd/hsvargplvm-master | hsvargplvmRestorePrunedModel.m | .m | hsvargplvm-master/matlab/hsvargplvmRestorePrunedModel.m | 3,206 | utf_8 | 5c4693df976c6449d6e213100f1cb62b | function model = hsvargplvmRestorePrunedModel(model, Ytr, onlyData, options)
% SVARGPLVMRESTOREPRUNEDMODEL Restore a pruned shared var-GPLVM model.
% FORMAT
% DESC restores a svargplvm model which has been pruned and it brings it in
% the same state that it was before prunning.
% ARG model: the model to be restored
% A... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikeGradientsPar.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikeGradientsPar.m | 13,575 | utf_8 | c499255e28ba7a51adba8815b76d785b |
% Function identical to hsvargplvmLogLikeGradients but optimised for
% parallel computation w.r.t the submodels in each layer.
function g = hsvargplvmLogLikeGradientsPar(model)
g_leaves = hsvargplvmLogLikeGradientsLeaves(model.layer{1});
[g_nodes g_sharedLeaves] = hsvargplvmLogLikeGradientsNodes(model);
% Amen... |
github | lawrennd/hsvargplvm-master | hsvargplvmPropagateField.m | .m | hsvargplvm-master/matlab/hsvargplvmPropagateField.m | 1,378 | utf_8 | c4c07567c65532e483d40f9c871dfe91 | % HSVARGPLVMPROPAGATEFIELD Set the value for a (potentially not yet existing) field for every sub-model of the main hsvargplvm.
% DESC Set the value for a (potentially not yet existing) field for every sub-model of the main hsvargplvm.
% ARG model: the hsvargplvm model (containing the submodels) for which the
% we have... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikeGradientsOLD.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikeGradientsOLD.m | 12,084 | utf_8 | 1b44cb628a64e46c291f18b47939b31b | function g = hsvargplvmLogLikeGradients(model)
try
pool_open = matlabpool('size')>0;
catch e
pool_open = 0;
end
if pool_open && (isfield(model,'parallel') && model.parallel)
% Run the parallel version of the current function.
g=hsvargplvmLogLikeGradientsPar(model);
return
end
g_leav... |
github | lawrennd/hsvargplvm-master | hsvargplvmOptions.m | .m | hsvargplvm-master/matlab/hsvargplvmOptions.m | 2,138 | utf_8 | 3f207df9da08330607f93b0ffdf2b4bb |
% See also: hsvargplvm_init
function [options, optionsDyn] = hsvargplvmOptions(globalOpt, timeStampsTraining, labelsTrain)
if nargin < 2
timeStampsTraining = [];
end
if nargin < 3
labelsTrain = [];
end
%-- One options structure where there are some parts shared for all
% models/layers and some parts specif... |
github | lawrennd/hsvargplvm-master | hsvargplvmSampleLayer.m | .m | hsvargplvm-master/matlab/hsvargplvmSampleLayer.m | 1,389 | utf_8 | 3c8fbfc618f6db7ea341511016d7f7e7 | % Sample points from the vardistr. of the layer "lInp" and find outputs in
% layer "lOut" for the outputs "ind".
function [X, mu, sigma] = hsvargplvmSampleLayer(model, lInp, lOut, ind, dim,X, startingPoint)
if nargin <7 || isempty(startingPoint)
% This point will be initially drawn. Then, we will sample and alte... |
github | lawrennd/hsvargplvm-master | hsvargplvmAddParentPrior.m | .m | hsvargplvm-master/matlab/hsvargplvmAddParentPrior.m | 1,049 | utf_8 | 99c9c7e57ccc9468992a5f556d21c6c4 | % Takes a hsvargplvm model and adds a non-(standard normal) prior on the
% parent (see: addDynamics functions in the other vargplvm-related
% packages).
% See also: svargplvmAddDynamics.m
function model = hsvargplvmAddParentPrior(model, globalOpt, optionsDyn)
modelParent = model.layer{model.H};
modelParent.comp{1}.v... |
github | lawrennd/hsvargplvm-master | hsvargplvmPosteriorMeanVarSimple.m | .m | hsvargplvm-master/matlab/hsvargplvmPosteriorMeanVarSimple.m | 3,720 | utf_8 | 592eff8a301a83a9b52439779474ce26 | % This function is used for predicting in the hierarchical model.
% All intermediate mu and sigma should be returned, (TODO!!!) or
% the user can give extra arguments to define which outputs to get.
% ARG model: the hsvargplvm model
% ARG X: the test latent points of the TOP layer (parent)
% ARG varX: the var... |
github | lawrennd/hsvargplvm-master | hsvargplvmClusterScales.m | .m | hsvargplvm-master/matlab/hsvargplvmClusterScales.m | 3,993 | utf_8 | 95f17dcd5ba2f76e9b4b6a391953ac8d | % Here, a single layer is passed as a model
function [clu, clu2, clu3] = hsvargplvmClusterScales(model, noClusters, labelType, exclDims)
if nargin < 4
exclDims = [];
end
if nargin < 3
labelType = [];
end
if nargin < 2 || isempty(noClusters), noClusters = 2; end
% for compatibility with svargplv... |
github | lawrennd/hsvargplvm-master | truesize.m | .m | hsvargplvm-master/matlab/truesize.m | 12,445 | utf_8 | 52473d0e2b50e2a0f717f6b8a18a79f1 | function truesize(varargin)
%TRUESIZE Adjust display size of image.
% TRUESIZE(FIG,[MROWS NCOLS]) adjusts the display size of an
% image. FIG is a figure containing a single image or a single
% image with a colorbar. [MROWS NCOLS] is a 1-by-2 vector that
% specifies the requested screen area (in pixels) t... |
github | lawrennd/hsvargplvm-master | hsvargplvmCreateToyData2.m | .m | hsvargplvm-master/matlab/hsvargplvmCreateToyData2.m | 11,658 | utf_8 | fb394c8f847806bf48868de78a4297a4 | % Create toy data. Give [] as an argument if the default value is to be
% used for the corresponding parameter.
%
% Note: This is like hsvargplvmCreateToyData.m but the noise and the
% mapping to higher dimensions is added in the end only once (same effect,
% simple code)
%
% TODO: fix numSharedDims, numHierDims... |
github | lawrennd/hsvargplvm-master | multvargplvmClassVisualise.m | .m | hsvargplvm-master/matlab/multvargplvmClassVisualise.m | 6,326 | utf_8 | bdf164f22520fce503b9ce045d588d91 | function multvargplvmClassVisualise(call)
% LVMCLASSVISUALISE Callback function for visualising data.
% FORMAT
% DESC contains the callback functions for visualizing points from the
% latent space in the higher dimension space.
% ARG call : either 'click', 'move', 'toggleDynamics',
% 'dynamicsSliderChange'
%
... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikeGradients.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikeGradients.m | 14,432 | utf_8 | 6ef6a26ae4be67614f97675cffea35cf | function g = hsvargplvmLogLikeGradients(model)
%%%%%TEMP: to test the parallel function
%g=hsvargplvmLogLikeGradientsPar(model);
%return
%%%%%%
try
pool_open = matlabpool('size')>0;
catch e
pool_open = 0;
end
if pool_open && (isfield(model,'parallel') && model.parallel)
% Run the pa... |
github | lawrennd/hsvargplvm-master | stackedvargplvmClassVisualise.m | .m | hsvargplvm-master/matlab/stackedvargplvmClassVisualise.m | 7,980 | utf_8 | a9420d21d7834c13ed57be3c04b3641e | function stackedvargplvmClassVisualise(call, layer)
% LVMCLASSVISUALISE Callback function for visualising data.
% FORMAT
% DESC contains the callback functions for visualizing points from the
% latent space in the higher dimension space.
% ARG call : either 'click', 'move', 'toggleDynamics',
% 'dynamicsSlider... |
github | lawrennd/hsvargplvm-master | hsvargplvmClassVisualise.m | .m | hsvargplvm-master/matlab/hsvargplvmClassVisualise.m | 7,505 | utf_8 | 7b547b73fa433a77e35a53729e170166 | function hsvargplvmClassVisualise(call)
% LVMCLASSVISUALISE Callback function for visualising data.
% FORMAT
% DESC contains the callback functions for visualizing points from the
% latent space in the higher dimension space.
% ARG call : either 'click', 'move', 'toggleDynamics',
% 'dynamicsSliderChange'
%
% COPYRIGHT... |
github | lawrennd/hsvargplvm-master | hsvargplvmUpdateStats.m | .m | hsvargplvm-master/matlab/hsvargplvmUpdateStats.m | 5,978 | utf_8 | eb24292e659183e6550ebf86db703f70 | function model = hsvargplvmUpdateStats(model)
jitter = 1e-6;
for h=1:model.H
if h==model.H & isfield(model.layer{h}, 'dynamics') & ~isempty(model.layer{h}.dynamics)
model.layer{model.H} = vargplvmDynamicsUpdateStats(model.layer{model.H});
end
for m=1:model.layer{h}.M
if ... |
github | lawrennd/hsvargplvm-master | hsvargplvmPruneModel.m | .m | hsvargplvm-master/matlab/hsvargplvmPruneModel.m | 809 | utf_8 | 8a5b3e4520fc9e807607e9764bd5707b | % HSVARGPLVMPRUNEMODEL Prune a shared var-GPLVM model.
% FORMAT
% DESC prunes a Shared VAR-GPLVM model by removing some fields which can later be
% reconstructed based on what is being kept. Used when storing a model.
% ARG model: the model to be pruned
% ARG onlyData: only prune the data parts. Useful when saving a mo... |
github | lawrennd/hsvargplvm-master | hsvargplvmClassVisualiseORIG.m | .m | hsvargplvm-master/matlab/hsvargplvmClassVisualiseORIG.m | 7,132 | utf_8 | 68dce6d54290466b231c99c5949b8de4 | function hsvargplvmClassVisualise(call)
% LVMCLASSVISUALISE Callback function for visualising data.
% FORMAT
% DESC contains the callback functions for visualizing points from the
% latent space in the higher dimension space.
% ARG call : either 'click', 'move', 'toggleDynamics',
% 'dynamicsSliderChange'
%
% COPYRIGHT... |
github | lawrennd/hsvargplvm-master | vargpCovGrads.m | .m | hsvargplvm-master/matlab/vargpCovGrads.m | 2,189 | utf_8 | 848d9c60a04e93638d3e3ffeb8467009 |
function [gK_uu, gPsi0, gPsi1, gPsi2, g_Lambda, gBeta, tmpV] = vargpCovGrads(model)
gPsi1 = model.beta * model.m * model.B';
gPsi1 = gPsi1'; % because it is passed to "kernVardistPsi1Gradient" as gPsi1'...
gPsi2 = (model.beta/2) * model.T1;
gPsi0 = -0.5 * model.beta * model.d;
gK_uu = 0.5 * (model.T1 - (... |
github | lawrennd/hsvargplvm-master | hsvargplvmPosteriorMeanVar.m | .m | hsvargplvm-master/matlab/hsvargplvmPosteriorMeanVar.m | 6,136 | utf_8 | 9de5dd60e14366702e99574dad53e6e3 | % This function is used for predicting in the hierarchical model.
% All intermediate mu and sigma should be returned, (TODO!!!) or
% the user can give extra arguments to define which outputs to get.
% ARG model: the hsvargplvm model
% ARG X: the test latent points of the TOP layer (parent)
% ARG varX: the variance ... |
github | lawrennd/hsvargplvm-master | hierSetPlotNoVar.m | .m | hsvargplvm-master/matlab/hierSetPlotNoVar.m | 6,273 | utf_8 | b823df03c36f3d75f30af100c836a825 | function hierSetPlotNoVar(lvmClassVisualiseFunc, curLayer)
% LVMSETPLOTNOVAR A copy of lvmSetPlot where the variance in the input
% space is not plotted (making it faster for high-dimensional data)
%
% SEEALSO lvmSetPlot
%
% MLTOOLS
global visualiseInfo
if nargin <2
visualiseInfo = localFunc(lvmCla... |
github | lawrennd/hsvargplvm-master | hsvargplvmCreateToyData.m | .m | hsvargplvm-master/matlab/hsvargplvmCreateToyData.m | 3,713 | utf_8 | 7c2f20dc4ed3e12899410b51cf302e77 | % Create toy data. Give [] as an argument if the default value is to be
% used for the corresponding parameter.
function [Yall, dataSetNames, Z] = hsvargplvmCreateToyData(type, N, D, numSharedDims, numHierDims, noiseLevel, hierSignalStrength)
if nargin < 7 || isempty(hierSignalStrength), hierSignalStrength = 0.6;... |
github | lawrennd/hsvargplvm-master | hsvargplvmCreateOptions.m | .m | hsvargplvm-master/matlab/hsvargplvmCreateOptions.m | 2,307 | utf_8 | 096d0d9e4f2f5331c50e0cbec5fccc20 |
% Some options are given in globalOpt as cell arrays, some are given as
% single values, meaning that they be propagated for each layer. This
% function builds a complete struct with options. After this function is
% run, the field options will have a 2-D cell array:
% options.F{h}{m}
% for every field F which changes... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikeGradientsParOLD.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikeGradientsParOLD.m | 9,628 | utf_8 | 544da1509bdfe83e5c3199da7c7a0b1e |
% Function identical to hsvargplvmLogLikeGradients but optimised for
% parallel computation w.r.t the submodels in each layer.
function g = hsvargplvmLogLikeGradientsPar(model)
g_leaves = hsvargplvmLogLikeGradientsLeaves(model.layer{1});
[g_nodes g_sharedLeaves] = hsvargplvmLogLikeGradientsNodes(model);
% Amen... |
github | lawrennd/hsvargplvm-master | loadMocapData.m | .m | hsvargplvm-master/matlab/loadMocapData.m | 4,961 | utf_8 | 577ff818311334f68a7e9dcd095ca12f | function [Y,skel, channels] = loadMocapData()
%YA = vargplvmLoadData('hierarchical/demHighFiveHgplvm1',[],[],'YA');
%{
curDir = pwd;
cd ../../../vargplvmDEPENDENCIES/DATASETS0p1371/mocap/cmu/02/
fileNameAsf='02.asf';
fileNameAmc='02_05.amc';
skel = acclaimReadSkel(fileNameAsf);
[channels, skel] = acclaimLoadChannels(f... |
github | lawrennd/hsvargplvm-master | hsvargplvmOptimiseModel.m | .m | hsvargplvm-master/matlab/hsvargplvmOptimiseModel.m | 4,211 | utf_8 | a9a736b81cedeb2c5f61b2767555b6e5 |
% model, pruneModel, saveModel, globalOpt, {initVardistIters, itNo}
% (the last two arguments override the globalOpt values)
function [model, modelPruned, modelInitVardist] = hsvargplvmOptimiseModel(model, varargin)
modelInitVardist = [];
pruneModel = true;
saveModel = true;
if isfield(model, 'saveName')
if str... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikeGradientsParOLD2.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikeGradientsParOLD2.m | 12,807 | utf_8 | 4bdf91681f50b8010743b57f697d3c09 |
% Function identical to hsvargplvmLogLikeGradients but optimised for
% parallel computation w.r.t the submodels in each layer.
function g = hsvargplvmLogLikeGradientsPar(model)
g_leaves = hsvargplvmLogLikeGradientsLeaves(model.layer{1});
[g_nodes g_sharedLeaves] = hsvargplvmLogLikeGradientsNodes(model);
% Amen... |
github | lawrennd/hsvargplvm-master | lvmVisualiseHierarchical.m | .m | hsvargplvm-master/matlab/lvmVisualiseHierarchical.m | 5,555 | utf_8 | 2a78eb7f9b45224e9917b8fc36bde4ad | function lvmVisualiseHierarchical(model, YLbls, ...
visualiseFunction, visualiseModify, showVariance, varargin)
% LVMVISUALISEGENERAL Visualise the manifold.
% This is a copy of lvmVisualise where the classVisualise function depends on the
% model type. Additionally, there is a flag showVariance which, when se... |
github | lawrennd/hsvargplvm-master | hsvargplvmReconstructInputs.m | .m | hsvargplvm-master/matlab/hsvargplvmReconstructInputs.m | 762 | utf_8 | 3e3684db3c5fa7ecef1c0242d1e9b587 | % Try to reconstruct the training inputs of layer lInp to the oututs of
% layer lOut. A well-trained model should return outputs very close to the
% real outputs Y.
function mu = hsvargplvmReconstructInputs(model, Y, lInp, lOut, ind)
if nargin <2 || isempty(Y)
Y = multvargplvmJoinY(model.layer{lOut});
end
if na... |
github | lawrennd/hsvargplvm-master | kmeans.m | .m | hsvargplvm-master/matlab/kmeans.m | 25,447 | utf_8 | 6754ef7b6dffbfa95759447de5748333 | function [idx, C, sumD, D] = kmeans(X, k, varargin)
%KMEANS K-means clustering.
% IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix
% X into K clusters. This partition minimizes the sum, over all
% clusters, of the within-cluster sums of point-to-cluster-centroid
% distances. Rows of X corres... |
github | lawrennd/hsvargplvm-master | hsvargplvmLogLikelihood.m | .m | hsvargplvm-master/matlab/hsvargplvmLogLikelihood.m | 2,300 | utf_8 | e8947ee9bb6b4eb122d6cadcc0aa1501 | function ll = hsvargplvmLogLikelihood(model)
F_leaves = hsvargplvmLogLikelihoodLeaves(model.layer{1});
F_nodes = hsvargplvmLogLikelihoodNode(model);
F_entropies = hsvargplvmLogLikelihoodEntropies(model);
% This refers to the KL quantity of the top node. The likelihood part is
% computed in hsvargplvmLogLikelihoo... |
github | lawrennd/hsvargplvm-master | hsvargplvmOptimiseModel.m | .m | hsvargplvm-master/matlab/demos/hsvargplvmOptimiseModel.m | 4,026 | utf_8 | 4bfa09d3304b921c17147349ed57d2ab |
% model, pruneModel, saveModel, globalOpt, {initVardistIters, itNo}
% (the last two arguments override the globalOpt values)
function [model, modelPruned] = hsvargplvmOptimiseModel(model, varargin)
pruneModel = true;
saveModel = true;
if isfield(model, 'saveName')
if strcmp(model.saveName, 'noSave')
sav... |
github | emlleger/par-scem-master | scem_slave.m | .m | par-scem-master/scem_slave.m | 837 | utf_8 | 138dcc23c94377571577ea4cbf0b66b2 | % Wait for data, process it and return it.
% Termination?
function scem_slave()
global world; global tag; global nodes; global rank; global SCEMPar; global Extra; global ModelName; global Measurement;
global scratch_dir = strcat('/Users/admin/workspace/ParSCEM/scem_ua',num2str(rank));
global model_dir = '$MODELDIR ... |
github | emlleger/par-scem-master | divvy.m | .m | par-scem-master/divvy.m | 914 | utf_8 | 8d979b4c50f790027d9a0695da130683 | ## Divide pigeons across pigeonholes as fairly as possible.
## Maintainer: Breanndán Ó Nualláin <bon@science.uva.nl>
## [lhs rhs] = divvy(n,m,i) divides n objects among m containers and
## returns the interval of n which is assigned to the ith container.
## All arguments should be positive integers.
## Need to a... |
github | jlizier/jidt-master | runHeartBreathRateKraskovMI.m | .m | jidt-master/tutorial/sampleExerciseSolutions/matlabOctave/runHeartBreathRateKraskovMI.m | 2,766 | utf_8 | aa7e985da8d13cdc9e9e2e74d6d30a51 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | runHeartBreathRateKraskovMIWithLags.m | .m | jidt-master/tutorial/sampleExerciseSolutions/matlabOctave/runHeartBreathRateKraskovMIWithLags.m | 4,425 | utf_8 | e3d926baabc0e310c37e41e24c6d8f71 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | octaveToJavaDoubleArray.m | .m | jidt-master/demos/octave/octaveToJavaDoubleArray.m | 2,045 | utf_8 | 5b5d01da08bc602fdecf28c26bb9ccf4 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | octaveToJavaIntMatrix.m | .m | jidt-master/demos/octave/octaveToJavaIntMatrix.m | 2,265 | utf_8 | cf6f1a9ff3b53bc56f0f1e30e738db4f | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | javaMatrixToOctave.m | .m | jidt-master/demos/octave/javaMatrixToOctave.m | 2,917 | utf_8 | b097cb8826df498cbe055b9d8b43c7d4 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | octaveToJavaDoubleMatrix.m | .m | jidt-master/demos/octave/octaveToJavaDoubleMatrix.m | 2,031 | utf_8 | 1df0c6b72db7c5af939e9fda67334a6a | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | octaveToJavaIntArray.m | .m | jidt-master/demos/octave/octaveToJavaIntArray.m | 2,212 | utf_8 | 4ffc6e0358698a9f284a69fc82f75566 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | runHeartBreathRateKernel.m | .m | jidt-master/demos/octave/SchreiberTransferEntropyExamples/runHeartBreathRateKernel.m | 5,499 | utf_8 | d8fba1de902be5f032d57b2054e85dff | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | runTentMap.m | .m | jidt-master/demos/octave/SchreiberTransferEntropyExamples/runTentMap.m | 3,829 | utf_8 | f8f456701c1801cd65bcb980279abf67 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | runUlamMap.m | .m | jidt-master/demos/octave/SchreiberTransferEntropyExamples/runUlamMap.m | 4,906 | utf_8 | 0291aab99b3fdc3641f965e236d42d46 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | runHeartBreathRateKraskov.m | .m | jidt-master/demos/octave/SchreiberTransferEntropyExamples/runHeartBreathRateKraskov.m | 6,973 | utf_8 | 34d8281d1833738fe073fc260737e5a7 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | activeInfoStorageHeartBreathRatesKraskov.m | .m | jidt-master/demos/octave/SchreiberTransferEntropyExamples/activeInfoStorageHeartBreathRatesKraskov.m | 4,939 | utf_8 | 9de505cdbc0c21fb84440341da000b23 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | greedyInferParents.m | .m | jidt-master/demos/octave/EffectiveNetworkInference/greedyInferParents.m | 16,419 | utf_8 | 639749ce29114f114d98ed6dc334f0fd | %%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2021, Joseph T. Lizier
% Distributed under GNU General Public License v3
%
% Infer the parent source variables to a given target, using the greedy/iterative/multivariate algorithm with TE.
% This a simplistic implementation of the full algorithm implemented in IDTxl - https://github... |
github | jlizier/jidt-master | runCA.m | .m | jidt-master/demos/octave/CellularAutomata/runCA.m | 9,137 | utf_8 | b6f101974e1ee95e05d228d0ee16f0f4 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% ... |
github | jlizier/jidt-master | prepareColourmap.m | .m | jidt-master/demos/octave/CellularAutomata/prepareColourmap.m | 3,099 | utf_8 | 0652bb4f5db1b87920b013f7eb383c0c | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | saveCA.m | .m | jidt-master/demos/octave/CellularAutomata/saveCA.m | 478 | utf_8 | 543ace1d386dfd29f2de9f2b51783e4a | %
% Utility function to save raw CA data to a text file format.
% Unfortunately MAtlab's -ascii format only writes full doubles, making much larger files than we need.
% So I wrote this.
function saveCA(filename, caStates)
warning('off','MATLAB:Java:DuplicateClass');
javaaddpath('../../../infodynamics.jar');
addpa... |
github | jlizier/jidt-master | plotLocalInfoValues.m | .m | jidt-master/demos/octave/CellularAutomata/plotLocalInfoValues.m | 8,767 | utf_8 | 9a653964a66404de617c202c47a63747 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | plotRawCa.m | .m | jidt-master/demos/octave/CellularAutomata/plotRawCa.m | 3,242 | utf_8 | 74dec26f660ac56972c620405986d2e0 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | plotLocalInfoMeasureForCA.m | .m | jidt-master/demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m | 18,473 | utf_8 | 14b05300d89fa3977b533f44adcf3245 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | coupledLogisticMap.m | .m | jidt-master/demos/octave/DetectingInteractionLags/coupledLogisticMap.m | 9,132 | utf_8 | 4dfb9e66160d652c16bc2e8d0ee9478b | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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 3 of the License, or
%% (at yo... |
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