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 | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchTimeAmdf.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchTimeAmdf.m | 1,552 | utf_8 | 65dde60df05593db4894a92eceb41a2f | % ======================================================================
%> @brief computes the lag of the average magnitude difference function
%> called by ::ComputePitch
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: samp... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolMidi2Freq.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolMidi2Freq.m | 416 | utf_8 | e1917fd1c5c45fdfc9232914eec5bb78 | % ======================================================================
%> @brief converts MIDI pitch to frequency
%>
%> @param p: MIDI pitch
%> @param f_A4: tuning frequency
%>
%> @retval f frequency
% ======================================================================
function [f] = ToolMidi2Freq(p, f_A4)... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralFlatness.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralFlatness.m | 622 | utf_8 | a52ca9ca858575a68fc854c99be6cd0d | % ======================================================================
%> @brief computes the spectral flatness from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vtf spectral... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ComputeBeatHisto.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ComputeBeatHisto.m | 1,675 | utf_8 | 02b3d1db0174da66b2b6f824f4259cd2 | % ======================================================================
%> @brief computes a simple beat histogram
%>
%> supported novelty measures are:
%> 'Flux',
%> 'Laroche',
%> 'Hainsworth'
%>
%> @param afAudioData: time domain sample data, dimension channels X samples
%> @param f_s: sample rate of aud... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolFreq2Bark.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolFreq2Bark.m | 909 | utf_8 | e34ced91b2a5b04d0671e05e2b40f2bf | % ======================================================================
%> @brief converts frequency to bark
%>
%> @param fInHz: frequency
%> @param cModel: 'Schroeder','Terhardt', 'Zwicker', or 'Traunmuller'
%>
%> @retval bark value
% ======================================================================
func... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | NoveltyFlux.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/NoveltyFlux.m | 491 | utf_8 | f5b1dfff85b32deab877253b85e5ef3b | % ======================================================================
%> @brief computes the novelty measure per Spectral Flux
%> called by ::ComputeNoveltyFunction
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval d_flux novelty m... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralCentroid.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralCentroid.m | 692 | utf_8 | 239d65a7715a8229facf1af8a2d9b76a | % ======================================================================
%> @brief computes the spectral centroid from the (squared) magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data
%>
%> @retval v spectral... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchSpectralAcf.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchSpectralAcf.m | 1,091 | utf_8 | c37b95b8172eb6e25a3999089904b85f | % ======================================================================
%> @brief computes the maximum of the spectral autocorrelation function
%> called by ::ComputePitch
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data
%>
%> @retval f acf maximum (in H... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimePeakEnvelope.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimePeakEnvelope.m | 2,156 | utf_8 | 0f82d365a7ea43848612cb1643ca9a9c | % ======================================================================
%> @brief computes two peak envelope measures for a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sam... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ComputeFeature.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ComputeFeature.m | 3,364 | utf_8 | 06dcf47fdf59ed554e6afb1cfac51fb0 | % ======================================================================
%> @brief computes a feature from the audio data
%>
%> supported features are:
%> 'SpectralCentroid',
%> 'SpectralCrest',
%> 'SpectralDecrease',
%> 'SpectralFlatness',
%> 'SpectralFlux',
%> 'SpectralKurtosis',
%> 'SpectralMfccs',
... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralSpread.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralSpread.m | 953 | utf_8 | 8831fbc6ab35d8deb4e185fc149010a0 | % ======================================================================
%> @brief computes the spectral spread from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data
%>
%> @retval v spectral spread (in ... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolSimpleDtw.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolSimpleDtw.m | 1,344 | utf_8 | c9b3468af70d4a2f8d46acc096c6ff17 | % ======================================================================
%> @brief computes path through a distance matrix with simple Dynamic Time
%> Warping
%>
%> @param D: distance matrix
%>
%> @retval p path with matrix indices
%> @retval C cost matrix
% =====================================================... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimeAcfCoeff.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimeAcfCoeff.m | 1,478 | utf_8 | d90beacf9c22cc97256bdbb662941963 | % ======================================================================
%> @brief computes the ACF coefficients of a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rat... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralRolloff.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralRolloff.m | 843 | utf_8 | edcb5408db52d4c4f975a1f2f9abec99 | % ======================================================================
%> @brief computes the spectral rolloff from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data
%>
%> @retval v spectral rolloff (i... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ComputeKey.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ComputeKey.m | 2,870 | utf_8 | 79a5a12fd18d524b2087df42c2b14b93 | % ======================================================================
%> @brief computes the key of the input audio (super simple variant)
%>
%> @param afAudioData: time domain sample data, dimension channels X samples
%> @param f_s: sample rate of audio data
%> @param afWindow: FFT window of length iBlockLengt... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimeZeroCrossingRate.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimeZeroCrossingRate.m | 1,116 | utf_8 | efc21d82543ca863791c55f1ab0d4b0f | % ======================================================================
%> @brief computes the zero crossing rate from a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimeRms.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimeRms.m | 1,216 | utf_8 | c1f8fc763f1442f2016bfa1c88c4f044 | % ======================================================================
%> @brief computes the RMS of a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rate of audio da... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralKurtosis.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralKurtosis.m | 1,364 | utf_8 | f233b6e3873aa07d9fcc63b21510566f | % ======================================================================
%> @brief computes the spectral kurtosis from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vsk spectral... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralCrest.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralCrest.m | 574 | utf_8 | 21d1355f45a5dfc44375543e2e05d820 | % ======================================================================
%> @brief computes the spectral crest from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vtsc spectral c... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralDecrease.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralDecrease.m | 747 | utf_8 | 3d3829bf8545bf40fa27f0ceb482bf3b | % ======================================================================
%> @brief computes the spectral decrease from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vsk spectral... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralSkewness.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralSkewness.m | 1,315 | utf_8 | 53cd6c9a18cbc0098dd9535e10488c36 | % ======================================================================
%> @brief computes the spectral skewness from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval v spectral s... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchTimeAuditory.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchTimeAuditory.m | 2,205 | utf_8 | 804a4f06051befb96a86deb17b74d487 | % ======================================================================
%> @brief computes the f_0 via a simple "auditory" approach
%> called by ::ComputePitch
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rate of a... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolGammatoneFb.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolGammatoneFb.m | 3,760 | utf_8 | 2f0c4b6aad6654b2475d9d37a8055972 | % ======================================================================
%> @brief computes a gammatone filterbank
%> see function MakeERBFilters.m from Slaneys Auditory Toolbox
%>
%> @param afAudioData: time domain sample data, dimension channels X samples
%> @param f_s: sample rate of audio data
%> @param iNumB... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchSpectralHps.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchSpectralHps.m | 873 | utf_8 | 39e8283ca14af17e9afb48be9aefe843 | % ======================================================================
%> @brief computes the maximum of the Harmonic Product Spectrum
%> called by ::ComputePitch
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data
%>
%> @retval f HPS maximum (in Hz)
% ==... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolFreq2Mel.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolFreq2Mel.m | 774 | utf_8 | e7663626f20021eaee38ccba7940be2a | % ======================================================================
%> @brief converts frequency to mel
%>
%> @param fInHz: frequency
%> @param cModel: 'Fant','Shaughnessy', or 'Umesh'
%>
%> @retval mel pitch value
% ======================================================================
function [mel] = To... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralPitchChroma.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralPitchChroma.m | 1,609 | utf_8 | 352abb63c41ede6ff1fb16862d87d6e3 | % ======================================================================
%> @brief computes the pitch chroma from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vpc pitch chroma
... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchTimeZeroCrossings.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchTimeZeroCrossings.m | 1,170 | utf_8 | b3a83c7803ce5a995da89d28f09183b2 | % ======================================================================
%> @brief computes f_0 through zero crossing distances
%> called by ::ComputePitch
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rate of audio ... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | scanMetadata.m | .m | Predominant_Instrument_Recognition-master/Scanning/scanMetadata.m | 97 | utf_8 | 60b718268ee37eb666b7b7562fb9951d | % Get metadata for a given file.
function metadata = scanMetadata(filename)
metadata = [];
end
|
github | cplaguna-audio/Predominant_Instrument_Recognition-master | scanIRMASTrainingData.m | .m | Predominant_Instrument_Recognition-master/Scanning/scanIRMASTrainingData.m | 1,371 | utf_8 | 03093fa951d4124a6adc90426a3775f2 | % Reads the IRMAS dataset into
function [audio_filenames, labels, metadata] = ...
scanIRMASTrainingData(root_path, QUICK_AND_DIRTY)
% Data for each instrument is stored in its own subdirectory. First, find
% the correct paths.
subdirs = dir(root_path);
subdirs(~[subdirs.isdir]) = [];
num_subdirs = size(subdirs,... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | confusionMatrix.m | .m | Predominant_Instrument_Recognition-master/Metrics/confusionMatrix.m | 353 | utf_8 | b820a5acc67914c516a89431ca2dc33c |
function confusion = confusionMatrix(predictions, ground_truth)
N = size(predictions, 1);
if(size(ground_truth,1) ~= N)
disp('Must have same the number of predictions and ground truth.');
end
count_correct = 0;
for(i=1:N)
if(predictions(i) == ground_truth(i))
count_correct = count_correct + 1;
end
end
c... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | crossPredict.m | .m | Predominant_Instrument_Recognition-master/Metrics/crossPredict.m | 870 | utf_8 | e868e7b31e0f7520fb1a5382c15b89e8 |
function [predictions, sorted_labels] = crossPredict(labels, features, n_fold)
predictions = [];
sorted_labels = [];
% Proportional distributions of classes among folds.
folds = cvpartition(labels, 'KFold', n_fold);
% Evaluate one fold at a time.
for (fold = 1:n_fold)
% Grab the test data.
test_indices = folds.... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | calculateAccuracy.m | .m | Predominant_Instrument_Recognition-master/Metrics/calculateAccuracy.m | 353 | utf_8 | ee12c05d87c171fe869a5cf2bdf08f94 |
function accuracy = calculateAccuracy(ground_truth, predictions)
N = size(predictions, 1);
if(size(ground_truth,1) ~= N)
disp('Must have same the number of predictions and ground truth.');
end
count_correct = 0;
for(i=1:N)
if(predictions(i) == ground_truth(i))
count_correct = count_correct + 1;
end
end
... |
github | jmtyszka/atlaskit-master | fs_roi_overlay.m | .m | atlaskit-master/fs_roi_overlay.m | 3,220 | utf_8 | 454f86b85de39247c0b8e62bf34a1af7 | function fs_roi_overlay(roi_csv)
% FS_ROI_OVERLAY Create a surface overlay from ROI results
% Map scalar values for each label ROI in the Destrieux atlas
% to a Freesurfer curvature overlay for use by Freeview
%
% AUTHOR : Mike Tyszka
% PLACE : Caltech
% DATES : 2019-07-16 JMT From scratch
% 2019-07-18 J... |
github | ycaihua/vchat-master | echo_diagnostic.m | .m | vchat-master/third_party/speex/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | luis-esteve/gnss-sdr-master | gnss_sdr_tcp_connector_tracking_start.m | .m | gnss-sdr-master/src/utils/simulink/SingleThread/gnss_sdr_tcp_connector_tracking_start.m | 7,441 | utf_8 | 7f7b1bb14cd772177cd54113bfdaea78 | % This MATLAB function builds and configures a simulink model
% for interacting with the GNSS-SDR platform through a TCP communication.
% \author David Pubill, 2012. dpubill(at)cttc.es
%
% ----------------------------------------------------------------------
%
% Copyright (C) 2010-2012 (see AUTHORS file for a l... |
github | luis-esteve/gnss-sdr-master | gnss_sdr_galileo_e1_tcp_connector_tracking_start.m | .m | gnss-sdr-master/src/utils/simulink/SingleThread/gnss_sdr_galileo_e1_tcp_connector_tracking_start.m | 8,026 | utf_8 | a608d70e225a329e93b06acf23c54ebe | %
% This MATLAB function builds and configures a simulink model
% for interacting with the GNSS-SDR platform through a TCP communication.
% \author David Pubill, 2012. dpubill(at)cttc.es
%
% ----------------------------------------------------------------------
%
% Copyright (C) 2010-2012 (see AUTHORS file for... |
github | luis-esteve/gnss-sdr-master | gnss_sdr_tcp_connector_parallel_tracking_start.m | .m | gnss-sdr-master/src/utils/simulink/MultiThread/gnss_sdr_tcp_connector_parallel_tracking_start.m | 8,658 | utf_8 | e3ec30dcf9bb095b8e35032b73eb648a | % This MATLAB function builds and configures a Simulink model
% for interacting with the GNSS-SDR platform through a TCP
% communication. Parallel Computing version.
% \author David Pubill, 2012. dpubill(at)cttc.es
%
% ----------------------------------------------------------------------
%
% Copyright (C) 2010-... |
github | luis-esteve/gnss-sdr-master | plot_acq_grid_gsoc.m | .m | gnss-sdr-master/src/utils/matlab/plot_acq_grid_gsoc.m | 2,211 | utf_8 | 7375e29426fb70ee3ebe65aa2471b1a1 | % Reads GNSS-SDR Acquisition dump binary file using the provided
% function and plots acquisition grid of acquisition statistic of PRN sat
%
% This function analyzes a experiment performed by Luis Esteve in the framework
% of the Google Summer of Code (GSoC) 2012, with the collaboration of Javier Arribas
% and Carl... |
github | luis-esteve/gnss-sdr-master | plot_acq_grid_gsoc_e5.m | .m | gnss-sdr-master/src/utils/matlab/plot_acq_grid_gsoc_e5.m | 3,329 | utf_8 | b70420403afc98166ac365604ee87d84 | % Reads GNSS-SDR Acquisition dump binary file using the provided
% function and plot acquisition grid of acquisition statistic of PRN sat.
% CAF input must be 0 or 1 depending if the user desires to read the file
% that resolves doppler ambiguity or not.
%
% This function analyzes a experiment performed by Marc Sales i... |
github | luis-esteve/gnss-sdr-master | plot_acq_grid_gsoc_glonass.m | .m | gnss-sdr-master/src/utils/matlab/plot_acq_grid_gsoc_glonass.m | 2,226 | utf_8 | e4d9f590dd79c888edd1ac2e21c63bf6 | % Reads GNSS-SDR Acquisition dump binary file using the provided
% function and plots acquisition grid of acquisition statistic of PRN sat
%
% This function analyzes a experiment performed by Luis Esteve in the framework
% of the Google Summer of Code (GSoC) 2012, with the collaboration of Javier Arribas
% and Car... |
github | luis-esteve/gnss-sdr-master | dll_pll_veml_read_tracking_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/dll_pll_veml_read_tracking_dump.m | 6,385 | utf_8 | 2d4a70a99f440d1be389537e5da11861 | % Usage: dll_pll_veml_read_tracking_dump (filename, [count])
%
% Read GNSS-SDR Tracking dump binary file into MATLAB.
% Opens GNSS-SDR tracking binary log file .dat and returns the contents
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System ... |
github | luis-esteve/gnss-sdr-master | read_complex_short_binary.m | .m | gnss-sdr-master/src/utils/matlab/libs/read_complex_short_binary.m | 889 | utf_8 | 02bb6b77371bbf54dd0aa385df3d450b | % Usage: read_complex_binary (filename, [count])
%
% Opens filename and returns the contents as a column vector,
% treating them as 32 bit complex numbers
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% Thi... |
github | luis-esteve/gnss-sdr-master | plotNavigation.m | .m | gnss-sdr-master/src/utils/matlab/libs/plotNavigation.m | 6,285 | utf_8 | 3996c4847e3c4c56708c5e3b3efc6d56 | % Function plots variations of coordinates over time and a 3D position
% plot. It plots receiver coordinates in UTM system or coordinate offsets if
% the true UTM receiver coordinates are provided.
%
% plotNavigation(navSolutions, settings)
%
% Inputs:
% navSolutions - Results from navigation solution functi... |
github | luis-esteve/gnss-sdr-master | gps_l1_ca_read_pvt_raw_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/gps_l1_ca_read_pvt_raw_dump.m | 2,037 | utf_8 | c6c231733a8cce01bdb3577882f94442 | % -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% This file is part of GNSS-SDR.
%
% SPDX-FileCopyrightText: Javier Arribas 2011
% SPDX-License-Identifier: GPL-3.0-or-later
%
% -----------------------------------... |
github | luis-esteve/gnss-sdr-master | read_complex_binary.m | .m | gnss-sdr-master/src/utils/matlab/libs/read_complex_binary.m | 1,088 | utf_8 | 2614a8ffccefeb376b2963ba0326873c | % Usage: read_complex_binary (filename, [count], [start_sample])
%
% Opens filename and returns the contents as a column vector,
% treating them as 32 bit complex numbers
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined ... |
github | luis-esteve/gnss-sdr-master | read_true_sim_observables_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/read_true_sim_observables_dump.m | 3,194 | utf_8 | 1de8b55b40f747e9c508b4dced7ad3d7 | % Usage: read_true_sim_observables_dump (filename, [count])
%
% Opens gnss-sdr-sim observables dump and reads all chennels
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% This file is part of GNSS-SDR.
%
% S... |
github | luis-esteve/gnss-sdr-master | read_complex_char_binary.m | .m | gnss-sdr-master/src/utils/matlab/libs/read_complex_char_binary.m | 887 | utf_8 | d18e4dd38faaf5ef61207a4deafd6329 | % Usage: read_complex_binary (filename, [count])
%
% Opens filename and returns the contents as a column vector,
% treating them as 32 bit complex numbers
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% Thi... |
github | luis-esteve/gnss-sdr-master | gps_l1_ca_dll_pll_read_tracking_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/gps_l1_ca_dll_pll_read_tracking_dump.m | 8,609 | utf_8 | bc90b2b8ca27132c1e2b9534b03413f6 | % Usage: gps_l1_ca_dll_pll_read_tracking_dump (filename, [count])
%
% Read GNSS-SDR Tracking dump binary file into MATLAB.
% Opens GNSS-SDR tracking binary log file .dat and returns the contents
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite Sy... |
github | luis-esteve/gnss-sdr-master | read_hybrid_observables_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/read_hybrid_observables_dump.m | 3,608 | utf_8 | f8573bcccf9f2a881b089ba9b8bed475 | % Opens GNSS-SDR tracking binary log file .dat and returns the contents
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% This file is part of GNSS-SDR.
%
% SPDX-FileCopyrightText: Javier Arribas 2011
% SPDX-L... |
github | luis-esteve/gnss-sdr-master | quantize_signal.m | .m | gnss-sdr-master/src/utils/matlab/libs/quantize_signal.m | 2,164 | utf_8 | 04e2fb05717749361579d470f670c5cc | % This function opens a binary file using the ibyte format, reads and
% quantizes the signal using the most significant nbits, and stores the
% quantized signal into an output file. The output file also uses the
% ibyte format.
%
% Usage: quantize_signal (infile, outfile, nbits)
%
% Inputs:
% infile - ... |
github | luis-esteve/gnss-sdr-master | gps_l1_ca_read_telemetry_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/gps_l1_ca_read_telemetry_dump.m | 2,034 | utf_8 | 3f906fa5e6039660ff55bb1c21e2bb82 | % -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% This file is part of GNSS-SDR.
%
% SPDX-FileCopyrightText: Javier Arribas 2011
% SPDX-License-Identifier: GPL-3.0-or-later
%
% -----------------------------------... |
github | luis-esteve/gnss-sdr-master | gps_l1_ca_pvt_read_pvt_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/gps_l1_ca_pvt_read_pvt_dump.m | 3,684 | utf_8 | 760ca494d33fb2ce26c39786e5037bbb | % Read GNSS-SDR PVT lib dump binary file into MATLAB. The resulting
% structure is compatible with the K.Borre MATLAB-based receiver.
%
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
% This file is part of GNSS-... |
github | luis-esteve/gnss-sdr-master | gps_l1_ca_kf_read_tracking_dump.m | .m | gnss-sdr-master/src/utils/matlab/libs/gps_l1_ca_kf_read_tracking_dump.m | 6,197 | utf_8 | 7203b11b0c42fc79c07c48b12b5b9039 | % Usage: gps_l1_ca_kf_read_tracking_dump (filename, [count])
%
% Read GNSS-SDR Tracking dump binary file into MATLAB.
% Opens GNSS-SDR tracking binary log file .dat and returns the contents
% -------------------------------------------------------------------------
%
% GNSS-SDR is a Global Navigation Satellite System ... |
github | NCAR/bspline-master | basis.m | .m | bspline-master/Design/basis.m | 2,706 | utf_8 | e4fe790c14de2c27cb8aed3b37df757a |
global dx = 1;
function y = basis (x)
y = 0;
global dx;
x = x / dx;
if (abs(x) < 2)
if (x < 0)
y = 0.25 * (2 + x)^3;
else
y = 0.25 * (2 - x)^3;
endif
if (abs(x) < 1)
if (x < 0)
y = y - (1 + x)^3;
else
y = y - (1 - x)^3;
endif
endif
endif
endfunction
function y = dbasis (x)
y = ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | kushagraagrawal/Machine_Learning_Assignments-master | porterStemmer.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submit.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | kushagraagrawal/Machine_Learning_Assignments-master | submitWithConfiguration.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kushagraagrawal/Machine_Learning_Assignments-master | savejson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
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