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 | hipercog/ctap-master | create_event_db.m | .m | ctap-master/ctap/src/utils/IO/bwrc_data_structure/create_event_db.m | 1,803 | utf_8 | f560ae5025bd2cf8ef4009f4d7dd621d | % Author: Andreas Henelius <andreas.henelius@ttl.fi>
%
%
function msg = create_event_db(dbfile, table_name, event_labels, event_types, clean)
%% Open database connection
dbid = mksqlite('open', dbfile);
%% Create the database
Tables = mksqlite('show tables');
if isempty(Tables)
Tables.tablename = '';
end
%% Tabl... |
github | hipercog/ctap-master | export_eeg_events.m | .m | ctap-master/ctap/src/utils/IO/bwrc_data_structure/export_eeg_events.m | 6,332 | utf_8 | d08c12899838c1bf5e1231825cfd7fd9 | % Export EEG events
% Arguments:
% datapath_eeg : Directory containing EEGLAB .set files
% datpath_db : Full path to a file where the database (sqlite) is
% The contents of the file will be deleted!
% event_type : The type of event to export
% event_type_zero : the event in... |
github | hipercog/ctap-master | lab_write_edf.m | .m | ctap-master/ctap/src/utils/IO/data_writers/lab_write_edf.m | 10,496 | utf_8 | b69d2267db096c46709431f5c145c724 | % lab_write_edf(filename, data, header)
%
% Original Code:
% Stefan Klanke 2010
%
% Modifications:
% 2011-02-03: 10:48:53Z roboos $
% 2012-04-26: F. Hatz Neurology Basel (added support for EDF+)
%
% data = matrix (channels x timeframes)
% header - structured information about the read eeg data
% header.... |
github | hipercog/ctap-master | unpackCellStr.m | .m | ctap-master/ctap/src/utils/strings/unpackCellStr.m | 392 | utf_8 | 61fbd17ee80f80d5b554866c1251670a | % obtain a non-nested cell array of strings (char arrays)
function strarr = unpackCellStr(input)
test = cellfun(@iscell, input);
strarr = [];
if any(test)
for i = 1:numel(test)
if test(i), strarr = [strarr unpackCellStr(input{i}(:)')];
else strarr = [strarr input{i}];
... |
github | hipercog/ctap-master | eeglab_psd.m | .m | ctap-master/ctap/src/utils/eeglabutils/eeglab_psd.m | 11,347 | utf_8 | 1ad8f58039dc61ad563f341aa9a388b9 | function Psd = eeglab_psd(EEG, csegEvent, varargin)
%% EEGLAB_PSD - Estimate PSD from segmented EEG (EEGLAB compatible)
%
% Description:
% Estimates Power Spectrum Density (PSD) from _segmented_ EEGLAB EEG
% dataset.
% A segmented EEG dataset has some events in EEG.event that can
% be used as calculation... |
github | hipercog/ctap-master | electrodeProximity.m | .m | ctap-master/ctap/src/utils/eeglabutils/chanlocs/electrodeProximity.m | 2,071 | utf_8 | a5e8339dd6468a53eef78bcc768f59f3 | function [epmap, rownm, colnm] = electrodeProximity( EEG )
%ELECTRODEPROXIMITY calculates Euclidean distance between all pairs of given
% electrodes
%
% Description:
% The function disregards "non-EEG" channels. These are taken as specified
% in the channel location structure/file (anything not labeled EE... |
github | hipercog/ctap-master | classify_events.m | .m | ctap-master/ctap/src/utils/ERP/erp_evmod/classify_events.m | 13,644 | utf_8 | 9ef4c8c95ce32336fadbb52d6ec5d464 | function [csm, fsm, nasm, crm, frm] =...
classify_events(evarr, stim, cresp, fresp, noresp, varargin)
%CLASSIFY_EVENTS - Interpret event code vector
%
% Description:
% Searches matching stimulus-response pairs from a vector of event codes.
% Can be used for example to interpret event log from test such a... |
github | hipercog/ctap-master | search_response.m | .m | ctap-master/ctap/src/utils/ERP/erp_evmod/search_response.m | 6,111 | utf_8 | 566169338f6d6042f8667afc41d93fe2 | function [respTargetMatch, norespTargetMatch,...
correctRespMatch, falseRespMatch] = search_response(evarr,...
target_code, response_code, standard_code_arr, varargin)
%SEARCH_RESPONSE - Detects responses to targets from an event array
%
% NOTE: This function has been using up to 15.12.2014 strArrayFind() ... |
github | hipercog/ctap-master | read_measinfo_sqlite.m | .m | ctap-master/ctap/src/utils/measurement_config/read_measinfo_sqlite.m | 4,640 | utf_8 | 240770b35884132dde80a49197953f1c | function measurement_config = read_measinfo_sqlite(mc_source, varargin)
%READ_MEASINFO_SQLITE Load measurement configuration (MC) data
%
% Description
%
% Inputs
%
% Variable input arguments:
%
% Output
%
% Dependencies
%
% ========================================================================
% COPYRIGHT NOTICE
% ... |
github | hipercog/ctap-master | reject_component_spectrum.m | .m | ctap-master/ctap/src/utils/bad_data_detect/reject_component_spectrum.m | 4,197 | utf_8 | c7a78cb42f9e36435689a51ad205244d | function [rejval, rejbin] = reject_component_spectrum(EEG, varargin)
% REJECT_COMPONENT_SPECTRUM reject components based on spectral properties.
%
% Syntax:
% [rejval, rejbin] = reject_component_spectrum(EEG, ...)
%
% Inputs:
% 'EEG' struct, EEG-file to process
%
% varargin Keyword-value pairs
... |
github | hipercog/ctap-master | recufast_badness_detector.m | .m | ctap-master/ctap/src/utils/bad_data_detect/recufast_badness_detector.m | 10,128 | utf_8 | c7d97ecc5fba80e1064c392d6d911178 | function result = recufast_badness_detector(...
ineeg, result, index, bounds, recuLim, datatype, varargin)
%RECUFAST_BADNESS_DETECTOR recursively looks for bad channels/epochs/components
%
% Description:
% takes an input EEG data struct and calculates the bad data, for either
% channels, epochs or Independent ... |
github | hipercog/ctap-master | OLD_eeg_detect_blink.m | .m | ctap-master/ctap/src/utils/bad_data_detect/blink/OLD_eeg_detect_blink.m | 12,063 | utf_8 | 43790ef2dd393483c49dceb68af10259 | function [peakLatArr, BlinkData] = OLD_eeg_detect_blink(veog, fs, varargin)
%EEG_DETECT_BLINK - EEG blink detection using filtering, derivatives and a fancy metric
%
% Description:
% This function implements main ideas from the paper:
% http://www.jemr.org/online/8/2/1
%
% This implementation applies several c... |
github | hipercog/ctap-master | eeg_detect_blink.m | .m | ctap-master/ctap/src/utils/bad_data_detect/blink/eeg_detect_blink.m | 12,351 | utf_8 | 745afaa29a69948817a88f22ab2da057 | function [peakLatArr, BlinkData] = eeg_detect_blink(veog, fs, varargin)
%EEG_DETECT_BLINK - EEG blink detection using filtering, derivatives and a fancy metric
%
% Description:
% This function implements main ideas from the paper:
% http://www.jemr.org/online/8/2/1
%
% This implementation applies several class... |
github | hipercog/ctap-master | CTAP_branchedpipe_looper.m | .m | ctap-master/ctap/src/pipeline/CTAP_branchedpipe_looper.m | 16,538 | utf_8 | 7d14391e2b7747cb1c6db6bf611d3bd3 | function CTAP_branchedpipe_looper(Cfg, varargin)
%CTAP_branchedpipe_looper - Loops over the functions defined in Cfg.pipe.stepSets
%
% Description:
% Input structs are documented in Google Docs:
% https://docs.google.com/document/d/1nexEgDg0JzumWbl3-KJRUnq2_vf6Iiiei_wAd6zM9vo/
%
% Syntax:
% CTAP_pipeline_looper(C... |
github | hipercog/ctap-master | CTAP_pipeline_looper.m | .m | ctap-master/ctap/src/pipeline/CTAP_pipeline_looper.m | 15,593 | utf_8 | 065d7537734ab5f53298e1833983335b | function CTAP_pipeline_looper(Cfg, varargin)
%CTAP_pipeline_looper - Loops over the functions defined in Cfg.pipe.stepSets
%
% Description:
% Input structs are documented in Google Docs:
% https://docs.google.com/document/d/1nexEgDg0JzumWbl3-KJRUnq2_vf6Iiiei_wAd6zM9vo/
%
% Syntax:
% CTAP_pipeline_looper(Cfg, vara... |
github | hipercog/ctap-master | CTAP_pipeline_brancher.m | .m | ctap-master/ctap/src/pipeline/CTAP_pipeline_brancher.m | 20,618 | utf_8 | 6b91878c0bd2bee385ac564d1e102550 | function CTAP_pipeline_brancher(Cfg, pipeArr, varargin)
%CTAP_pipeline_brancher - Branches the pipes defined in pipeArr
%
% Description:
% Input structs are documented in Google Docs:
% https://docs.google.com/document/d/1nexEgDg0JzumWbl3-KJRUnq2_vf6Iiiei_wAd6zM9vo/
%
% Syntax:
% CTAP_pipeline_brancher(Cfg, Filt,... |
github | hipercog/ctap-master | CTAP_postproc_brancher.m | .m | ctap-master/ctap/src/pipeline/CTAP_postproc_brancher.m | 3,731 | utf_8 | b7002c7dfebeae41ec7d205117db2fa3 | % TODO: UPDATE ARGUMENTS TO TAKE RUNPIPES, NOT FIRST/LAST
function CTAP_postproc_brancher(Cfg, dynFunc, dfArgs, pipeArr, varargin)
%CTAP_postproc_brancher - Applies a post-processing function to pipes in pipeArr
%
% Description:
%
% Syntax:
% CTAP_postproc_brancher(Cfg, dynFunc, dfArgs, pipeArr, first, last, dbg)
%
%... |
github | hipercog/ctap-master | ctap_get_bestpipe.m | .m | ctap-master/ctap/src/pipeline/compare_branches/ctap_get_bestpipe.m | 7,525 | utf_8 | a7de5f8cc56ab6f117fdef76b037e7c6 | function [bestpipe, bestpipeTab] =...
ctap_get_bestpipe(treeStats, treeRej, oud, plvls, varargin)
%CTAP_GET_BESTPIPE combines stats and rejections information to judge the
% best performing pipe from a set of competing branches
%
% Description: takes output (structs of stats & rejecti... |
github | hipercog/ctap-master | ctap_auto_config.m | .m | ctap-master/ctap/src/pipeline/manage_Cfg/ctap_auto_config.m | 13,969 | utf_8 | 3842de2bd207767ac0033205d3db9673 | function Cfg = ctap_auto_config(Cfg, fun_args)
%CTAP_AUTO_CONFIG - Processes configuration file/struct with respect to the
% desired analysis pipe.
%
% Does the following:
% * adds canonical locations to Cfg
% * adds some CTAP conventions (default settings)
% * assigns pipeline function parameters to Cfg.ctap
%... |
github | hipercog/ctap-master | ctaptest_generate_data.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_generate_data.m | 2,574 | utf_8 | b2d0d4b82205b6f827e07e0721089bb5 | % Generates a synthetic EEG based on the BCICIV data file.
%
% Args:
% datafile <string>: path to an EEGLAB set-file
% ch_file <string>: path to a channel location file for the target EEG
% eeg_length <double>: length of the target EEG (in seconds)
% srate <double>: desired sampling rate for the target EEG
% ... |
github | hipercog/ctap-master | ctaptest_add_blink.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_add_blink.m | 2,183 | ibm852 | a8dbee976bf9b11bb30fbf621fb87877 | % Add a blink artifact to EEG data
% eeg = ctaptest_add_blink(eeg,ampl,t_start,dur)
% Args:
% eeg: EEG struct
% ampl <double>: blink amplitude (in µV)
% t_start <double>: blink start time (in s)
% dur <double>: blink duration (in s)
% Returns:
% eeg: EEG struct with blink added to data
function eeg = ctap... |
github | hipercog/ctap-master | eeglab_seedgen_synthetic_data.m | .m | ctap-master/ctap/src/ctaptest/eeglab_seedgen_synthetic_data.m | 2,625 | utf_8 | 0b0d816f08e12927539c1141c803f3ca | % Generates a synthetic EEG based on seed EEG dataset
%
% todo: similar to ctaptest_generate_syndata.m but different interface.
% Merge somehow.
%
% Args:
% seedEEG <struct>: Seed EEGLAB dataset with channel locations in place
% ch <struct>: EEGLAB channel locations struct for the new dataset, ch = readlocs(ch_file... |
github | hipercog/ctap-master | eegviz.m | .m | ctap-master/ctap/src/ctaptest/eegviz.m | 663 | utf_8 | 05a54f466d5acab0cbe1077051bc9786 | % Dead simple EEG visualization function
%
% Jari Torniainen
% 2015
% Brain Work Research Centre, Finnish Institute of Occupational Health
% MIT License
function eegviz(dataset, varargin)
if isstruct(dataset)
dataset = dataset.data';
end
if length(varargin) == 1
scale = varargin{1};
indices = 1:size(dataset... |
github | hipercog/ctap-master | ctaptest_add_emg.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_add_emg.m | 4,360 | utf_8 | 2221fda450c09f2557d34139dec0300c | % Adds a burst of simulated EMG contamination to the EEG data
% function eeg = ctaptest_add_EMG(eeg,ampl,t_start,dur,loc,rad,F)
%
% Args:
% eeg: EEG struct
% ampl <double>: EMG signal amplitude from 0 to max
% t_start <double>: start time of the artifact (in seconds)
% dur <double>: duration of the burst (in ... |
github | hipercog/ctap-master | ctaptest_convert_bci.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_convert_bci.m | 1,031 | utf_8 | ed21b85a521b70c802daaa4d1927ed2f | % Converts a BCICIV dataset to a set-format
% Note: Only works for this specific dataset due to hardcoded values
%
% Arga:
% filename <string>: path to target file
%
% Returns:
% eeg: EEG struct
function eeg = ctaptest_convert_bci(filename)
try
load(filename)
catch ME
error('ctaptest_convert_bci:... |
github | hipercog/ctap-master | ctaptest_modify_variance.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_modify_variance.m | 542 | utf_8 | 8083c7ea36f06b396a51d6550f18748f | % Modifies variance of channels
%
% Args:
% eeg: EEG struct
% ch <mat>: vector of channel indices
% multiplier <double>: multiplying coeficient
%
function eeg = ctaptest_modify_variance(eeg, ch, multiplier)
if isempty(ch)
ch = 1:size(eeg.data,1);
end
eeg.data(ch,:)=eeg.data(ch,:)*sqrt(multiplier);
variance_pa... |
github | hipercog/ctap-master | ctaptest_add_ctap.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_add_ctap.m | 1,473 | utf_8 | c965218ce7e18464de19839aad94d34f | % Adds correct CTAP-fields to the dataset so it passes analysis steps
% TODO: Check if this is still needed
%
function eeg = ctaptest_add_ctap(eeg)
eeg.CTAP.subject = char(randi([65 90], 1, 10));
eeg.CTAP.measurement.casename = eeg.CTAP.subject;
eeg.CTAP.files.eegFile = 'synthetic';
eeg.CTAP.files.chann... |
github | hipercog/ctap-master | ctaptest_load_hydra.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_load_hydra.m | 239 | utf_8 | 04cb46bd8dbef6ed06ba374294000e36 | % This thing loads the hydra splash screen logo
% Currently deprecated
function load_hydra()
fid = fopen('hydra.txt');
str = fgetl(fid);
while ischar(str)
fprintf(1, '%s\n', str);
str = fgetl(fid);
end
fclose(fid);
pause(2);
end
|
github | hipercog/ctap-master | ctaptest_datagen.m | .m | ctap-master/ctap/src/ctaptest/ctaptest_datagen.m | 2,055 | utf_8 | c0b7d2a096c83494625250a86ab72f44 | % Generates a synthetic EEG based on the BCICIV data file.
%
% Args:
% datafile = a .mat file containing the BCICIV data
% chfile = channel location file
% mdl_order = model order for the synthetic model
% Returns:
% eeg = EEG-struct containing synthetic data
%
% todo: almost identical to ctap... |
github | hipercog/ctap-master | export_features_CTAP2.m | .m | ctap-master/ctap/src/core/export_features_CTAP2.m | 7,681 | utf_8 | 71c7ca7a0660d70d069dd0ab55403f86 | function export_features_CTAP2(id, featureIDArr, measFilt, MC, Cfg)
%CTAP_export_features2 - Export of CTAP study-level features into a text file
%
% Description:
% Generates a list of result files based on 'featureIDArr' and
% 'measFilt'. Reads data from these files, formats it and saves the
% result into a text... |
github | hipercog/ctap-master | CTAP_clear_results.m | .m | ctap-master/ctap/src/core/CTAP_clear_results.m | 1,614 | utf_8 | 0ead3c47770df89d3a967684ded8efc0 | function CTAP_clear_results(Cfg)
%CTAP_clear_results - Clear results
%
% Description:
% Removes intermediate data, logs, crashlogs, features, etc.
%
% Syntax:
% CTAP_clear_results(Cfg);
%
% Inputs:
% Cfg : CTAP configuration structure, must contain this field:
% Cfg.env.paths.analysisRoot : the path to clear ... |
github | hipercog/ctap-master | CTAP_reject_data.m | .m | ctap-master/ctap/src/core/CTAP_reject_data.m | 11,355 | utf_8 | 8f016723c2f092f4cdb64b9b12d6b2eb | function [EEG, Cfg] = CTAP_reject_data(EEG, Cfg)
%CTAPEEG_reject_data - Rejects detected bad channels, epochs, components or segments
%
% Description:
% After calling CTAP_detect_*() this function can be used to reject the
% detected channels, IC components, epochs or segments.%
%
% Syntax:
% [EEG, Cfg] = CTAP_re... |
github | hipercog/ctap-master | CTAP_detect_bad_channels.m | .m | ctap-master/ctap/src/core/CTAP_detect_bad_channels.m | 8,622 | utf_8 | fcf4c7542557d0459e6324b4ff0c559b | function [EEG, Cfg] = CTAP_detect_bad_channels(EEG, Cfg)
%CTAP_detect_bad_channels - Autodetect bad quality channels
%
% Description:
% Requires channel locations and types. These can be added using
% CTAP_load_chanlocs().
%
% Syntax:
% [EEG, Cfg] = CTAP_detect_bad_channels(EEG, Cfg);
%
% Inputs:
% EEG ... |
github | hipercog/ctap-master | CTAP_plot_ERP.m | .m | ctap-master/ctap/src/core/CTAP_plot_ERP.m | 4,186 | utf_8 | a1329266c608b4d56291ff4c23ac190e | function [EEG, Cfg] = CTAP_plot_ERP(EEG, Cfg)
%CTAP_plot_erp - Plot ERP of epoched data and export data to HDF5
%
% Description:
%
%
% Syntax:
% [EEG, Cfg] = CTAP_plot_ERP(EEG, Cfg)
%
% Inputs:
% EEG struct, EEGLAB structure
% Cfg struct, CTAP configuration structure
%
% Outputs:
% EEG s... |
github | hipercog/ctap-master | CTAP_detect_bad_comps.m | .m | ctap-master/ctap/src/core/CTAP_detect_bad_comps.m | 4,839 | utf_8 | 128f029974d4d7d02e8dae74a27223c5 | function [EEG, Cfg] = CTAP_detect_bad_comps(EEG, Cfg)
%CTAP_detect_bad_comps - Autodetect bad quality components
%
% Description:
%
% Syntax:
% [EEG, Cfg] = CTAP_detect_bad_comps(EEG, Cfg);
%
% Inputs:
% EEG struct, EEGLAB structure
% Cfg struct, CTAP configuration structure
% Cfg.ctap.detect_ba... |
github | hipercog/ctap-master | ctapeeg_erp_features.m | .m | ctap-master/ctap/src/generic/ctapeeg_erp_features.m | 7,821 | UNKNOWN | 55bcdca8268f543b94d98c1bb5130837 | function [FACTORS, ERP, ERPAREA] = ctapeeg_erp_features(EEG, search_limits, erp_direction, labels, blocks, varargin)
%PEAK_STATS_STACKED - Calculate ERP variables and assing into data structures
%
% Description:
% Calculates ERP peak statistics from epoched EEG data and stores the
% results in ATTK data struc... |
github | hipercog/ctap-master | ctapeeg_detect_bad_comps.m | .m | ctap-master/ctap/src/generic/ctapeeg_detect_bad_comps.m | 11,120 | utf_8 | fce62da99667ca8c3698c5b7ca30eae2 | function [EEG, varargout] = ctapeeg_detect_bad_comps(EEG, varargin)
%CTAPEEG_DETECT_BAD_COMPS use some given method to find bad ICA components
%
% Syntax:
% [EEG, varargout] = ctapeeg_detect_bad_comps(EEG, varargin)
%
% Input:
% 'EEG' EEG file to process
%
% varargin:
% 'outdir' output directory
% ... |
github | hipercog/ctap-master | ctap_manu2_oddball_erps.m | .m | ctap-master/ctap/templates/Frontiers_manuscript_examples/ctap_manu2_oddball_erps.m | 2,061 | utf_8 | 48be0ba95638bc45ec5e79c2ff37db6a | %% Plot ERPs of saved .sets
function [ERPS, ERP] = ctap_manu2_oddball_erps(Cfg, varargin)
p = inputParser;
p.addRequired('Cfg', @isstruct)
p.addParameter('loc_label', '', @ischar)
p.addParameter('PLOT', true, @islogical)
p.parse(Cfg, varargin{:});
Arg = p.Results;
setpth = fullfile(Cfg.env.paths.analysisRoot, Cfg.pip... |
github | hipercog/ctap-master | runctap_manu2_hydra.m | .m | ctap-master/ctap/templates/Frontiers_manuscript_examples/runctap_manu2_hydra.m | 7,335 | utf_8 | dc28525b13df4e5a5b4c2c65aea48ab7 | %% Parameter-sweeping CTAP script to clean SCCN data
% As referenced in the second CTAP article:
% Cowley BU and Korpela J (2018) Computational Testing for Automated
% Preprocessing 2: Practical Demonstration of a System for Scientific
% Data-Processing Workflow Management for High-Volume EEG.
% Front. Neurosci. 12:... |
github | hipercog/ctap-master | runctap_manu2_basic.m | .m | ctap-master/ctap/templates/Frontiers_manuscript_examples/runctap_manu2_basic.m | 6,987 | utf_8 | 987943ddb2f3819d9221124294ded315 | %% Linear CTAP script to clean SCCN data
% As referenced in the second CTAP article:
% Cowley BU and Korpela J (2018) Computational Testing for Automated
% Preprocessing 2: Practical Demonstration of a System for Scientific
% Data-Processing Workflow Management for High-Volume EEG.
% Front. Neurosci. 12:236. doi: 10... |
github | hipercog/ctap-master | runctap_manu2_branch.m | .m | ctap-master/ctap/templates/Frontiers_manuscript_examples/runctap_manu2_branch.m | 8,258 | utf_8 | 0e63336009148ab0bde79851b30115ac | %% Branching CTAP script to clean SCCN data
% As referenced in the second CTAP article:
% Cowley BU and Korpela J (2018) Computational Testing for Automated
% Preprocessing 2: Practical Demonstration of a System for Scientific
% Data-Processing Workflow Management for High-Volume EEG.
% Front. Neurosci. 12:236. doi:... |
github | hipercog/ctap-master | generate_synthetic_data_demo.m | .m | ctap-master/ctap/templates/minimalistic_example/generate_synthetic_data_demo.m | 4,996 | utf_8 | 914f9d3d36f6352f501bdee3bf744152 | function generate_synthetic_data_demo(SRCDIR, OUTDIR)
globalStream = RandStream.getGlobalStream;
reset(globalStream);
if ~isdir(OUTDIR), mkdir(OUTDIR); end;
% Data generation parameters
CH_FILE = 'chanlocs128_biosemi_withEOG.elp';
SRATE = 100;
EEG_LEN_MIN = 1;
EEG_LENGTH = 60 * EEG_LEN_MIN; %in seconds
MODEL_ORDER ... |
github | hipercog/ctap-master | generate_synthetic_data_manuscript.m | .m | ctap-master/ctap/templates/PeerJ_manuscript_example/generate_synthetic_data_manuscript.m | 5,740 | utf_8 | 488c0c92657c1b75238691cb23438a22 | function generate_synthetic_data_manuscript(SRCDIR, OUTDIR)
% Reproducable results:
%globalStream = RandStream.getGlobalStream;
%reset(globalStream);
rng('default');
rng(42);
% puts the settings of the random number generator used by
% RAND, RANDI, and RANDN to their default values so that they produce the
% same ra... |
github | hipercog/ctap-master | HYDRA_run_test.m | .m | ctap-master/ctap/templates/HYDRA_branching_example/HYDRA_run_test.m | 10,223 | utf_8 | ea433713701c52f3539eea0cee185b92 | %% CTAP HYDRA analysis batchfile
%
% To run this, you need:
% * Matlab R2016b or newer
% * EEGLAB, latest version,
% git clone https://github.com/sccn/eeglab.git
% * CTAP
% git clone https://github.com/bwrc/ctap.git
%
% Make sure your working directory is the CTAP root i.e. the folder with
% 'ctap' ... |
github | hipercog/ctap-master | cleanSCCNdataHYDRA.m | .m | ctap-master/ctap/templates/HYDRA_branching_example/cleanSCCNdataHYDRA.m | 5,253 | utf_8 | 56f1f762f803afbebd2ece9a0b6bb5d9 | %% Clean SCCN data CTAP script
% Runtime options for CTAP:
STOP_ON_ERROR = true;
OVERWRITE_OLD_RESULTS = true;
%% Setup
FILE_ROOT = mfilename('fullpath');
REPO_ROOT = FILE_ROOT(1:strfind(FILE_ROOT, fullfile(...
'ctap', 'templates', 'hydra_branch_example', 'cleanSCCNdata' )) - 1);
data_dir_in =fullfile... |
github | hipercog/ctap-master | cleanSCCNdata.m | .m | ctap-master/ctap/templates/paramsweep_example/example_data_prepro_scripts/cleanSCCNdata.m | 4,896 | utf_8 | c5ddd7662e2ee95fd388ec4953f518b4 | %% Clean SCCN data CTAP script
% Runtime options for CTAP:
STOP_ON_ERROR = true;
OVERWRITE_OLD_RESULTS = true;
%% Setup
FILE_ROOT = mfilename('fullpath');
REPO_ROOT = FILE_ROOT(1:strfind(FILE_ROOT, fullfile(...
'ctap', 'templates', 'paramsweep_example', 'example_data_prepro_scripts','cleanSCCNdata')) - 1);
data_... |
github | hipercog/ctap-master | reshape_import_UniBonn_data.m | .m | ctap-master/ctap/templates/paramsweep_example/example_data_prepro_scripts/reshape_import_UniBonn_data.m | 1,808 | utf_8 | eea92c333db73bd5d9f3913b150e00de | % Convert UniBonn data
% UNPACK 100 DATA FILES TO SOME VALID DIRECTORY
% GIVE indir=DIRECTORY-NAME AND inpth=PATH-TO-indir
% INTERMEDIATE .mat FILE WILL BE SAVED TO inpth
% OUTPUT DIRECTORY outdir IS SET TO BE SOURCE OF SYNTH-
% GENERATED DATA FILES - SEE param_sweep_setup.m
% ------------------------------------------... |
github | hipercog/ctap-master | pdftops.m | .m | ctap-master/dependencies/export_fig/pdftops.m | 5,528 | utf_8 | 1042ce73e979a940784d5ea5f57f1ffc | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | hipercog/ctap-master | crop_borders.m | .m | ctap-master/dependencies/export_fig/crop_borders.m | 5,133 | utf_8 | b744bf935914cfa6d9ff82140b48291e | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating ... |
github | hipercog/ctap-master | isolate_axes.m | .m | ctap-master/dependencies/export_fig/isolate_axes.m | 4,851 | utf_8 | 611d9727e84ad6ba76dcb3543434d0ce | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The o... |
github | hipercog/ctap-master | im2gif.m | .m | ctap-master/dependencies/export_fig/im2gif.m | 6,234 | utf_8 | 8ee74d7d94e524410788276aa41dd5f1 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function c... |
github | hipercog/ctap-master | read_write_entire_textfile.m | .m | ctap-master/dependencies/export_fig/read_write_entire_textfile.m | 961 | utf_8 | 775aa1f538c76516c7fb406a4f129320 | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fn... |
github | hipercog/ctap-master | pdf2eps.m | .m | ctap-master/dependencies/export_fig/pdf2eps.m | 1,522 | utf_8 | 4c8f0603619234278ed413670d24bdb6 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | hipercog/ctap-master | print2array.m | .m | ctap-master/dependencies/export_fig/print2array.m | 9,613 | utf_8 | e398a6296734121e6e1983a45298549a | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figur... |
github | hipercog/ctap-master | append_pdfs.m | .m | ctap-master/dependencies/export_fig/append_pdfs.m | 2,759 | utf_8 | 9b52be41aff48bea6f27992396900640 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the o... |
github | hipercog/ctap-master | using_hg2.m | .m | ctap-master/dependencies/export_fig/using_hg2.m | 1,100 | utf_8 | 47ca10d86740c27b9f6b397373ae16cd | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for i... |
github | hipercog/ctap-master | eps2pdf.m | .m | ctap-master/dependencies/export_fig/eps2pdf.m | 8,624 | utf_8 | 24048681d3f737f221497896307fd2f1 | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, de... |
github | hipercog/ctap-master | ghostscript.m | .m | ctap-master/dependencies/export_fig/ghostscript.m | 7,902 | utf_8 | ff62a40d651197dbea5d3c39998b3bad | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path... |
github | hipercog/ctap-master | fix_lines.m | .m | ctap-master/dependencies/export_fig/fix_lines.m | 6,441 | utf_8 | ffda929ebad8144b1e72d528fa5d9460 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% scre... |
github | hipercog/ctap-master | pcamat.m | .m | ctap-master/dependencies/fastica_25/pcamat.m | 12,075 | utf_8 | bcb1117d4132558d0d54d8b7b616a902 | function [E, D] = pcamat(vectors, firstEig, lastEig, s_interactive, ...
s_verbose);
%PCAMAT - Calculates the pca for data
%
% [E, D] = pcamat(vectors, firstEig, lastEig, ...
% interactive, verbose);
%
% Calculates the PCA matrices for given data (row) vectors. Returns
% the eigenvector (E) and diag... |
github | hipercog/ctap-master | icaplot.m | .m | ctap-master/dependencies/fastica_25/icaplot.m | 13,259 | utf_8 | dde3e6d852f657a3c1eaacbd03f5dcc7 | function icaplot(mode, varargin);
%ICAPLOT - plot signals in various ways
%
% ICAPLOT is mainly for plottinf and comparing the mixed signals and
% separated ica-signals.
%
% ICAPLOT has many different modes. The first parameter of the function
% defines the mode. Other parameters and their order depends on the
% mode. ... |
github | hipercog/ctap-master | computeSED_NOnorm.m | .m | ctap-master/dependencies/adjust_plugin/computeSED_NOnorm.m | 3,505 | utf_8 | 9495f0e1cbe1c4000f8c263599b1fb44 |
% computeSED_NOnorm() - Computes Spatial Eye Difference feature
% without normalization
%
% Usage:
% >> [out,medie_left,medie_right]=computeSED_NOnorm(topog,chanlocs,n);
%
% Inputs:
% topog - topographies vector
% chanlocs - EEG.chanlocs struct
% n - number of ICs
% nchannels - number of ch... |
github | hipercog/ctap-master | trim_and_max.m | .m | ctap-master/dependencies/adjust_plugin/trim_and_max.m | 2,047 | utf_8 | 283e6b2c0e1d21dfa144458795ebc3f1 |
% trim_and_max() - Computes maximum value from vector 'vettore'
% after removing the top 1% of the values
% (to be outlier resistant)
%
% Usage:
% >> valore=trim_and_max(vettore);
%
% Inputs:
% vettore - row vector
%
% Outputs:
% valore - result
%
%
% Author: Andrea Mognon, Center for Mind/Bra... |
github | hipercog/ctap-master | compute_GD_feat.m | .m | ctap-master/dependencies/adjust_plugin/compute_GD_feat.m | 2,230 | utf_8 | d3e10a3161558231306ab931ce3b89ed | % compute_GD_feat() - Computes Generic Discontinuity spatial feature
%
% Usage:
% >> res = compute_GD_feat(topografie,canali,num_componenti);
%
% Inputs:
% topografie - topographies vector
% canali - EEG.chanlocs struct
% num_componenti - number of components
%
% Outputs:
% res - GDSF values
% Co... |
github | hipercog/ctap-master | pop_prop_ADJ.m | .m | ctap-master/dependencies/adjust_plugin/pop_prop_ADJ.m | 19,436 | utf_8 | fbf99c13671e7f1a951b998774d40db9 | % pop_prop_ADJ() - overloaded pop_prop() for ADJUST plugin.
% plot the properties of a channel or of an independent component.
% ADJUST feature values are also shown (normalized wrt threshold).
%
%
% Usage:
% >> com = pop_prop_ADJ(EEG, typecomp, numcompo, winhandle, is_H, is_V... |
github | hipercog/ctap-master | computeSAD.m | .m | ctap-master/dependencies/adjust_plugin/computeSAD.m | 3,149 | utf_8 | ab49a7f8bb670b1a87eaad9585d7d006 |
% computeSAD() - Computes Spatial Average Difference feature
%
% Usage:
% >> [rapp,var_front,var_back,mean_front,mean_back]=computeSAD(topog,chanlocs,n);
%
% Inputs:
% topog - topographies vector
% chanlocs - EEG.chanlocs struct
% n - number of ICs
% nchannels - number of channels
%
% Outpu... |
github | hipercog/ctap-master | trim_and_mean.m | .m | ctap-master/dependencies/adjust_plugin/trim_and_mean.m | 1,262 | utf_8 | cbccb6fcc2ba3f6280554dd255f98def |
% trim_and_mean() - Computes average value from vector 'vettore'
% after removing the top .1% of the values
% (to be outlier resistant)
%
% Usage:
% >> valore=trim_and_mean(vettore);
%
% Inputs:
% vettore - row vector
%
% Outputs:
% valore - result
%
% Copyright (C) 2009 Andrea Mognon and Marc... |
github | hipercog/ctap-master | EM.m | .m | ctap-master/dependencies/adjust_plugin/EM.m | 5,419 | utf_8 | 6fbb243367ee762bb2d647b3fc5fe8a2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% EM - ADJUST package
%
% Performs automatic threshold on the digital numbers
% of the input vector 'vec'; based on Expectation - Maximization algorithm
% Reference paper:
% Bruzzone, L., Prieto, D.F., 2000. Automatic analysis of the diffe... |
github | hipercog/ctap-master | ADJUST.m | .m | ctap-master/dependencies/adjust_plugin/ADJUST.m | 12,655 | utf_8 | 8c696aef92d0ae22bfa01e93025dfdda |
% ADJUST() - Automatic EEG artifact Detector
% with Joint Use of Spatial and Temporal features
%
% Usage:
% >> [art, horiz, vert, blink, disc,...
% soglia_DV, diff_var, soglia_K, meanK, soglia_SED, SED, soglia_SAD, SAD, ...
% soglia_GDSF, GDSF, soglia_V, nuovaV]=ADJUST(EEG,out);
%
% Inputs:
% EEG ... |
github | hipercog/ctap-master | gridLegend.m | .m | ctap-master/dependencies/gridLegend/gridLegend.m | 11,996 | utf_8 | 302f1f84fe937eff949d62f5a0d60d1f | % gridLegend : plots a legend in a multi column format
%
% On a plot with a lot of traces the standard legend will often scroll off the bottom or the side of the figure,
% this function is intended to overcome this by allowing the user to define a multi column format for the
% legend.
%
% Usage : legHdl = gridL... |
github | hipercog/ctap-master | closest.m | .m | ctap-master/dependencies/epoch2continuous/closest.m | 1,155 | utf_8 | 384e908b71a9f556c8018a9fc4c04a75 | % Returns the closest value from a list
%
% Syntax
%
% c = closest(a,b); returns the closest value c in a for each value in b
% [c,i] = closest(a,b); returns the index i of the closest value in a for each value in b
% [c,i,d] = closest(a,b); returns the difference d of the closest value in a for each value in b... |
github | hipercog/ctap-master | epoch2continuous.m | .m | ctap-master/dependencies/epoch2continuous/epoch2continuous.m | 2,622 | utf_8 | 987f14535a2787d85ffb2e493341cd22 | % PURPOSE: converts an epoched dataset into a continuous one.
% Data segments are concatenated using a 'boundary' events.
%
% FORMAT
%
% EEG = epoch2continuous(EEG);
%
% Author: Javier Lopez-Calderon
% Center for Mind and Brain
% University of California, Davis,
% Davis, CA
% July 7, 2011
%
% Feedback would be... |
github | hipercog/ctap-master | h_epoch_interp_spl.m | .m | ctap-master/dependencies/faster_plugin/h_epoch_interp_spl.m | 5,579 | utf_8 | b3f72af0ca562a5a7d2b9cd9eca573ff | % Edit to the EEGLAB interpolation function to interpolate different
% channels within each epoch
% Cleaned up and removed irrelevant sections.
%
% Additions Copyright (C) 2010 Hugh Nolan, Robert Whelan and Richard Reilly, Trinity College Dublin,
% Ireland
%
% Based on:
%
% eeg_interp() - interpolate data channels
%
% ... |
github | hipercog/ctap-master | hurst_exponent.m | .m | ctap-master/dependencies/faster_plugin/hurst_exponent.m | 1,324 | utf_8 | f2772282eef28b3eeec78642c5fd889e | % The Hurst exponent
%--------------------------------------------------------------------------
% This function does dispersional analysis on a data series, then does a
% Matlab polyfit to a log-log plot to estimate the Hurst exponent of the
% series.
%
% This algorithm is far faster than a full-blown implementation... |
github | hipercog/ctap-master | rda.m | .m | ctap-master/dependencies/LIBRA/rda.m | 22,872 | utf_8 | a348826014862f9e306b03118334c320 | function result=rda(x,group,varargin)
%RDA performs linear and quadratic robust discriminant analysis
% on the data matrix x with known group structure. It is based on the
% MCD estimator (see mcdcov.m), hence it has to be applied to
% low-dimensional data.
%
% The Robust Discriminant method is describ... |
github | hipercog/ctap-master | cdq.m | .m | ctap-master/dependencies/LIBRA/cdq.m | 11,121 | utf_8 | 10abe71caf5c75fab99031d36495e5f4 | function result = cdq(x,y,c,varargin)
%CDQ computes Censored Depth Quantiles for regression, as described in
%
% Debruyne, M., Hubert, M., Portnoy, S., Vanden Branden, K. (2008),
% "Censored depth quantiles",
% Computational Statistics and Data Analysis, 52, 1604-1614.
%
% Required input arguments:
% x : D... |
github | hipercog/ctap-master | daplot.m | .m | ctap-master/dependencies/LIBRA/daplot.m | 2,771 | utf_8 | 324422792487173e708332f9c888023d | function daplot(x,group,center,covar,classic,method)
%DAPLOT plots 97.5% tolerances ellipses of the bivariate data set x, which
% consists of several groups defined by the input argument 'group'.
% Center and covar are estimates of the center and covariance matrix of each group,
% obtained with a classical ('CD... |
github | hipercog/ctap-master | madc.m | .m | ctap-master/dependencies/LIBRA/madc.m | 1,629 | utf_8 | 9c087e0664bf8b7631dbd19461fb0994 | function result=madc(x)
%MADC is a scale estimator given by the Median Absolute Deviation
% with finite sample correction factor.
% It is defined as
% mad(x)= b_n 1.4826 med(|x_i - med(x)|)
% with b_n a small sample correction factor to make the mad unbiased at the
% normal distribution. It can resi... |
github | hipercog/ctap-master | rstep.m | .m | ctap-master/dependencies/LIBRA/rstep.m | 3,363 | utf_8 | 2d01b8c1e79cf4b4fc51ceda617d61e8 | function [S,P,t,kmax,med]= rstep(X,k,center,r);
%RSTEP is an auxiliary function for 'rapca.m'.
%
% This function is part of LIBRA: the Matlab Library for Robust Analysis,
% available at:
% http://wis.kuleuven.be/stat/robust.html
%
% Created by Sabine Verboven and Mia Hubert (October 2000)
% Part of the ... |
github | hipercog/ctap-master | DetMCD.m | .m | ctap-master/dependencies/LIBRA/DetMCD.m | 21,488 | utf_8 | 4778b65a6928abcec3d15f06cd1a5075 | function [rew,raw,hsetsfull]=DetMCD(x,varargin)
% DetMCD computes the MCD estimator of a multivariate data set in a deterministic way.
% This estimator is given by the subset of h observations with smallest
% covariance determinant. The MCD location estimate is then the mean of those h points,
% and the MCD scatte... |
github | hipercog/ctap-master | mcdcov.m | .m | ctap-master/dependencies/LIBRA/mcdcov.m | 61,919 | utf_8 | 46c306a12918df588b62aaec312ba981 | function [rew,raw]=mcdcov(x,varargin)
%MCDCOV computes the MCD estimator of a multivariate data set. This
% estimator is given by the subset of h observations with smallest covariance
% determinant. The MCD location estimate is then the mean of those h points,
% and the MCD scatter estimate is their covariance ... |
github | hipercog/ctap-master | rpcr.m | .m | ctap-master/dependencies/LIBRA/rpcr.m | 20,257 | utf_8 | 2067019382035899e215c1a444bdc897 | function result=rpcr(x,y,varargin)
%RPCR is a 'Robust Principal Components Regression' method based on ROBPCA.
% It can be applied to both low and high-dimensional predictor variables x,
% and to one or multiple response variables y. It is resistant to outliers
% in the data. First, a robust principal components... |
github | hipercog/ctap-master | makeplot.m | .m | ctap-master/dependencies/LIBRA/makeplot.m | 63,407 | utf_8 | 2fdd826268ed2d3c51039e377ed80f73 | function makeplot(out,varargin)
%MAKEPLOT makes plots for the main functions. These figures can also be obtained
% by setting 'plots = 1' in those functions.
%
% Required input:
% out = a structure containing the output of one of the following classes:
% MCDCOV, LS, LTS, MLR, MCDREG, CPCA,CPCR, CSIMPL... |
github | hipercog/ctap-master | cvRpcr.m | .m | ctap-master/dependencies/LIBRA/cvRpcr.m | 18,747 | utf_8 | a14cb41edb54f75b144de45bd61d58c2 | function result = cvRpcr(x,y,kmax,rmsecv,h,k)
%CVRPCR calculates the robust RMSECV (root mean squared error of cross-validation) curve
% for RPCR or the robust RMSEP (root mean squared error of prediction) value in a fast way.
% The R-RMSECV curve can be used to make a selection of the optimal number of
% compo... |
github | hipercog/ctap-master | tree.m | .m | ctap-master/dependencies/LIBRA/tree.m | 15,091 | utf_8 | 052cfb801b338a8e44b3d3fc3e229c1c | function tree(objectorder,heights)
%TREE creates a tree in which the leaves represent
% objects. The vertical coordinate of the junction
% of two branches is the dissimilarity between the
% corresponding clusters (maximal 30 objects allowed).
%
% The algorithm is fully described in:
% Kaufman, L. and ... |
github | hipercog/ctap-master | removeObsMcd.m | .m | ctap-master/dependencies/LIBRA/removeObsMcd.m | 2,846 | utf_8 | b24cb0f0eeed12ca35bd354587fa17db | function result = removeObsMcd(data,i,inputH0,inputFull,csteps);
%REMOVEOBSMCD is an auxiliary function to perform cross-validation with MCD
% (see cvMcd.m).
%
% The input:
% data : the original data
% i : the index of the observation that has to be removed.
% inputH0 : a structure that contai... |
github | hipercog/ctap-master | ltsregres.m | .m | ctap-master/dependencies/LIBRA/ltsregres.m | 46,460 | utf_8 | a033384e25e2fe6c38bb946ee78b64fd | function [rew,raw] = ltsregres(x,y,varargin)
%LTSREGRES carries out least trimmed squares (LTS) regression, introduced in
%
% Rousseeuw, P.J. (1984), "Least Median of Squares Regression,"
% Journal of the American Statistical Association, Vol. 79, pp. 871-881.
%
% The LTS regression method minimizes the... |
github | hipercog/ctap-master | bagplot.m | .m | ctap-master/dependencies/LIBRA/bagplot.m | 59,890 | utf_8 | 20440b4e0147085a25e7cbe676b8ae0b | function result=bagplot(x,varargin)
%BAGPLOT draws a bagplot, which is a generalisation of the univariate boxplot
% to bivariate data. The original bagplot is described in
%
% Rousseeuw, P.J., Ruts, I. and Tukey, J.W. (1999),
% "The bagplot: a bivariate boxplot", The American Statistician, 53, 382-387.
%
% The... |
github | hipercog/ctap-master | cvRobpca.m | .m | ctap-master/dependencies/LIBRA/cvRobpca.m | 6,053 | utf_8 | 1fc58fa7a5955441f6951cd1a44106a8 | function result = cvRobpca(data,kmax,resrob,rawres,h,csteps)
%CVROBPCA calculates the robust cross-validated PRESS (predicted residual error sum of squares) curve
% for ROBPCA in a fast way. This curve can be used to make a selection of the optimal number of
% components. The function is used in robpca.m.
%
% ... |
github | hipercog/ctap-master | csimca.m | .m | ctap-master/dependencies/LIBRA/csimca.m | 19,833 | utf_8 | 45faec1e88a85543e10e27d178633027 | function result = csimca(x,group,varargin);
%CSIMCA performs the SIMCA method. This is a classification
% method on a data matrix x with a known group structure. On each group a
% robust PCA analysis is performed. Afterwards a classification
% rule is developped to determine the assignment of new observations.
... |
github | hipercog/ctap-master | fanny.m | .m | ctap-master/dependencies/LIBRA/fanny.m | 5,663 | utf_8 | 99f7c3c9819f14e4ea5e9bef562cadaf | function result = fanny(x,kclus,vtype,metric,plots)
%FANNY is a fuzzy clustering algorithm. It returns a list representing a fuzzy clustering of the data
% into kclus clusters.
%
% The algorithm is fully described in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cl... |
github | hipercog/ctap-master | robpca.m | .m | ctap-master/dependencies/LIBRA/robpca.m | 31,286 | utf_8 | 107eb86258ea2ad64b2abaad9fdebd7d | function result=robpca(x,varargin)
%ROBPCA is a 'ROBust method for Principal Components Analysis'.
% It is resistant to outliers in the data. The robust loadings are computed
% using projection-pursuit techniques and the MCD method.
% Therefore ROBPCA can be applied to both low and high-dimensional data sets.
... |
github | hipercog/ctap-master | rsimca.m | .m | ctap-master/dependencies/LIBRA/rsimca.m | 26,934 | utf_8 | 451c4be7f2bc7bb9a299e0b9957dbc79 | function result = rsimca(x,group,varargin)
%RSIMCA performs a robust version of the SIMCA method. This is a classification
% method on a data matrix x with a known group structure. On each group a
% robust PCA analysis (ROBPCA) is performed. Afterwards a classification
% rule is developped to determine the assig... |
github | hipercog/ctap-master | cda.m | .m | ctap-master/dependencies/LIBRA/cda.m | 21,184 | utf_8 | aca25c8539f79c874462980d0ab33ee7 | function result=cda(x,group,varargin)
%CDA performs linear and quadratic classical discriminant analysis
% on the data matrix x with known group structure.
%
% Required input arguments:
% x : training data set (matrix of size n by p).
% group : column vector containing the group numbers of the... |
github | hipercog/ctap-master | rrmse.m | .m | ctap-master/dependencies/LIBRA/rrmse.m | 4,071 | utf_8 | 1e06e95943b8cca120d615d706484bc3 | function result=rrmse(x,y,h,kmax,attrib,plots,k,weight,res)
%RRMSE calculates the robust RMSECV and/or the robust RMSEP-value
% for RPCR and RSIMPLS.
%
% The robust RMSECV is described in:
%
% Engelen, S., Hubert, M. (2005),
% "Fast model selection for robust calibration methods",
% Analytica Chimica Ac... |
github | hipercog/ctap-master | pam.m | .m | ctap-master/dependencies/LIBRA/pam.m | 8,753 | utf_8 | 74d2663795680d9bc09304f4a4d9177b | function result = pam(x,kclus,vtype,stdize,metric,plots)
%PAM is the Partitioning Around Medoids clustering algorithm.
% It returns a list representing a clustering of the data into kclus
% clusters based on the search for kclus representative objects or medoids among the observations of
% the data set.
%
% The... |
github | hipercog/ctap-master | simcaplot.m | .m | ctap-master/dependencies/LIBRA/simcaplot.m | 5,146 | utf_8 | 303fe3c5fb8883843f0ab2918c77ed19 | function simcaplot(result);
%SIMCAPLOT plots a scatter plot with the boundaries defined by the SIMCA method.
% It is based on the results from a simca analysis (see rsimca.m or csimca.m).
%
% For technical reasons, 6 different groups can be plotted (with different symbols).
% In case there are more groups, pl... |
github | hipercog/ctap-master | cvRsimpls.m | .m | ctap-master/dependencies/LIBRA/cvRsimpls.m | 15,188 | utf_8 | 4a983d5d1b52531c1cbf9e07bd875013 | function result = cvRsimpls(x,y,kmax,rmsecv,h,k)
%CVRIMPLS calculates the robust RMSECV (root mean squared error of cross-validation) curve
% for RSIMPLS or the robust RMSEP(root mean squared error of prediction) value in a fast way.
% The R-RMSECV curve can be used to make a selection of the optimal number of
... |
github | hipercog/ctap-master | l1median.m | .m | ctap-master/dependencies/LIBRA/l1median.m | 2,419 | utf_8 | e4c2b80a6d003717bfe8ec7802fd4ac0 | function result=L1median(x,tol);
%L1MEDIAN is an orthogonally equivariant location estimator,
% also known as the spatial median. It is defined as the point which
% minimizes the sum of the Euclidean distances to all observations in the
% data matrix x. It can resist 50% outliers.
%
% Reference (for the algori... |
github | hipercog/ctap-master | clusplot.m | .m | ctap-master/dependencies/LIBRA/clusplot.m | 8,426 | utf_8 | f2c5973d424819de23a3ecd7d432462c | function clusplot(x,ncluv,span,xlabels)
%CLUSPLOT creates a bivariate plot visualizing a partition (clustering)
% of the data. All observations are represented by points in the plot,
% using principal components or multidimensional scaling. Around each
% cluster an ellipse is drawn.
%
%The algorithm is fully de... |
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