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github | mortonne/aperture-master | pat_group_freqs.m | .m | aperture-master/patterns/operations/pat_group_freqs.m | 2,437 | utf_8 | 9f659ee4d65b7444ffa14701096b7544 | function [n, labels] = pat_group_freqs(subj, pat_name, dim, bins, ...
labels, filter)
%PAT_GROUP_FREQS Frequency of different group types in patterns.
%
% Use this to see how frequent different groups (defined by any pattern
% dimension) are for a set of subjects.
%
% [n, lab... |
github | mortonne/aperture-master | princomp_pattern.m | .m | aperture-master/patterns/operations/princomp_pattern.m | 4,811 | utf_8 | b4ac004935f5ea73188d17241872943f | function pat = princomp_pattern(pat, varargin)
%PRINCOMP_PATTERN Get principal components of a pattern.
%
% pat = princomp_pattern(pat, ...)
%
% INPUTS:
% pat: input pattern object.
%
% OUTPUTS:
% pat: modified pattern object.
%
% PARAMS:
% These options may be specified using parameter, value pairs... |
github | mortonne/aperture-master | modify_pattern.m | .m | aperture-master/patterns/operations/modify_pattern.m | 13,708 | utf_8 | 468e351c2ff358bb242729e8709ea97f | function pat = modify_pattern(pat, params, pat_name, res_dir)
%MODIFY_PATTERN Modify an existing pattern.
%
% *** DEPRECATED ***
% Functionality now split into bin_pattern, filter_pattern,
% reject_artifacts, zscore_pattern, and princomp_pattern.
%
% pat = modify_pattern(pat, params, pat_name, res_dir)
%
% Use t... |
github | mortonne/aperture-master | baseline_pattern.m | .m | aperture-master/patterns/operations/baseline_pattern.m | 2,917 | utf_8 | 6d31088af1716a62860225a8cec0b0e4 | function pat = baseline_pattern(pat, baselineMS, varargin);
%BASELINE_PATTERN Apply baseline correction to a pattern.
%
% Subtracts the mean of the baseline period from each event in a
% pattern. Used to remove the effect of slow signal drifts.
%
% pat = baseline_pattern(pat, baselineMS, ...)
%
% INPUTS:
% ... |
github | mortonne/aperture-master | bin_pattern.m | .m | aperture-master/patterns/operations/bin_pattern.m | 5,957 | utf_8 | f7c817feebbc122a92cc6a8d3c07f448 | function pat = bin_pattern(pat, varargin)
%BIN_PATTERN Average over bins of a pattern.
%
% Average over arbitrary bins along one or more dimensions of a
% pattern. For example, you can average over subsets of events,
% average within frequency bands or average over all channels in a
% region.
%
% Can also handle... |
github | mortonne/aperture-master | filter_pattern.m | .m | aperture-master/patterns/operations/filter_pattern.m | 5,720 | utf_8 | 203f75617e08d5f08d75d57cd70b64db | function pat = filter_pattern(pat, varargin)
%FILTER_PATTERN Get a subset of a pattern.
%
% pat = filter_pattern(pat, ...)
%
% INPUTS:
% pat: input pattern object.
%
% OUTPUTS:
% pat: filtered pattern object, with updated pattern matrix and
% associated metadata.
%
% PARAMS:
% These option... |
github | mortonne/aperture-master | diff_pattern.m | .m | aperture-master/patterns/operations/diff_pattern.m | 5,020 | utf_8 | cdb4d6e733300bf8393fbeb9252ed988 | function pat = diff_pattern(pat, varargin)
%DIFF_PATTERN Take differences between elements of a pattern.
%
% pat = diff_pattern(pat, ...)
%
% Currently, only takes differences between channels. Later, can expand
% to take pairs of indices (or vals?) along any dimension.
%
% INPUTS:
% pat: input pattern obje... |
github | mortonne/aperture-master | create_broadband_corr_pattern.m | .m | aperture-master/patterns/operations/create_broadband_corr_pattern.m | 3,761 | utf_8 | db0e65aeb4924b95dabaab68549cf2df | function pat = create_broadband_corr_pattern(pat, stat_name, varargin)
%CREATE_BROADBAND_CORR_PATTERN Subtract broadband power.
%
% Remove broadband power, based on the broadband regression estimates.
% Requires as an input the stat object created by broadband_regression.
%
% pat = create_broadband_corr_pattern(pa... |
github | mortonne/aperture-master | create_broadband_pattern.m | .m | aperture-master/patterns/operations/create_broadband_pattern.m | 2,515 | utf_8 | 617653f5577a937ec5c9cc6bb20712e1 | function pat = create_broadband_pattern(pat, stat_name, varargin)
%CREATE_BROADBAND_PATTERN Create a pattern with broadband power estimates.
%
% Create a broadband power pattern based on the broadband regression
% estimates created by broadband_regression.
%
% pat = create_broadband_pattern(pat, stat_name, ...)
%
... |
github | mortonne/aperture-master | rotate_pattern.m | .m | aperture-master/patterns/operations/rotate_pattern.m | 3,483 | utf_8 | 6b03aae4a17a7077c7ddac61983cdbe7 | function subj = rotate_pattern(subj, pca_pat_name, pat_name, varargin)
%ROTATE_PATTERN Rotate a pattern to match another pattern's PCA.
%
% subj = rotate_pattern(subj, pca_pat_name, pat_name, ...)
%
% INPUTS:
% subj: subject object.
%
% pca_pat_name: name of an attached pattern of principal components.
... |
github | mortonne/aperture-master | pat_event_freqs.m | .m | aperture-master/patterns/operations/pat_event_freqs.m | 1,516 | utf_8 | aa36a2b8ed1d43e920aa49cd3aee375b | function [n, labels] = pat_event_freqs(subj, pat_name, event_bins, ...
event_filter)
%PAT_EVENT_FREQS Frequency of different event types in patterns.
%
% Use this to see how frequent different types of events are for
% a set of subjects.
%
% [n, labels] = pat_event_freqs(subj... |
github | mortonne/aperture-master | remove_broadband.m | .m | aperture-master/patterns/operations/remove_broadband.m | 4,048 | utf_8 | 96d265be706ecef9cd591193047d64c4 | function pat = remove_broadband(pat, varargin)
%REMOVE_BROADBAND Subtract broadband power from a pattern.
%
% pat = remove_broadband(pat, ...)
%
% INPUTS:
% pat: input pattern object containing log-transformed power
% values.
%
% OUTPUTS:
% pat: pattern with broadband power subtracted.
%
% ... |
github | mortonne/aperture-master | reject_artifacts.m | .m | aperture-master/patterns/operations/reject_artifacts.m | 5,523 | utf_8 | 4373171e65eb3ab18efa73ef5cb80e24 | function pat = reject_artifacts(pat, varargin)
%REJECT_ARTIFACTS Remove artifacts from a pattern.
%
% pat = reject_artifacts(pat, ...)
%
% INPUTS:
% pat: input pattern object.
%
% OUTPUT:
% pat: output pattern object.
%
% PARAMS:
% These options may be specified using parameter, value pairs or by
% ... |
github | mortonne/aperture-master | time_zscore_pattern.m | .m | aperture-master/patterns/operations/time_zscore_pattern.m | 2,495 | utf_8 | 99640f623b3fc8e05fba70d12e1d05af | function pat = time_zscore_pattern(pat, varargin)
%TIME_ZSCORE_PATTERN Z-score a pattern over time.
%
% If the standard deviation for a timeseries is 0, the zscores for that
% timeseries will be NaN.
%
% pat = time_zscore_pattern(pat, ...)
%
% INPUTS:
% pat: a pattern object.
%
% OUTPUTS:
% pat: modi... |
github | mortonne/aperture-master | zscore_pattern.m | .m | aperture-master/patterns/operations/zscore_pattern.m | 6,005 | utf_8 | fd0cb4b5374b12ee9f2d75e518506692 | function subj = zscore_pattern(subj, pat_name, base_pat_name, varargin)
%ZSCORE_PATTERN Z-score a pattern compared to a baseline.
%
% To z-score a pattern, first define a baseline pattern and add it to
% the subj object. Baseline statistics (mean and std. dev.) will be
% calculated from the baseline pattern for ea... |
github | mortonne/aperture-master | patsize.m | .m | aperture-master/patterns/dimensions/patsize.m | 1,917 | utf_8 | 9b62cd3d1d1b6cc4d4db0cf105d41ca5 | function D = patsize(dim_info, dim)
%PATSIZE Get the size of a pattern from its dim structure.
%
% D = patsize(dim_info, dim)
%
% INPUTS:
% dim_info: structure containing information about the dimensions of a
% pattern.
%
% dim: optional; the dimension to return. If omitted, an array
% ... |
github | mortonne/aperture-master | get_bad_chans.m | .m | aperture-master/patterns/dimensions/get_bad_chans.m | 3,213 | utf_8 | e80b166577ef7eafcc3f4a5da19e135d | function [bad_chans,eeg_ind] = get_bad_chans(eeg_files,bad_chan_files)
%GET_BAD_CHANS Get a list of bad channels for given EEG files.
%
% [bad_chans, eeg_ind] = get_bad_chans(eeg_files, bad_chan_files)
%
% INPUTS:
% eeg_files: a cell array of roots for EEG filenames.
% e.g. {session_0/eeg/eeg.reref/L... |
github | mortonne/aperture-master | power_pattern.m | .m | aperture-master/patterns/creation/power_pattern.m | 6,723 | utf_8 | a12e1f7fab8f87b94dad1a27e1a58dc1 | function [pattern, params] = power_pattern(events, channels, varargin)
%POWER_PATTERN Create a pattern matrix of oscillatory power.
%
% [pattern, params] = power_pattern(events, channels, ...)
%
% Calculate oscillatory power using Morlet wavelets for all events in
% an events structure.
%
% INPUTS:
% events: ... |
github | mortonne/aperture-master | amp_pattern.m | .m | aperture-master/patterns/creation/amp_pattern.m | 5,631 | utf_8 | 1d2ced52d5e3e4290d95057d977e6cd3 | function [pattern, opt] = amp_pattern(events, channels, varargin)
%AMP_PATTERN Create a pattern matrix of oscillatory power.
%
% [pattern, params] = amp_pattern(events, channels, ...)
%
% Calculate amplitude at a frequency band, using the Hilbert transform.
%
% INPUTS:
% events: an events structure. Must have ... |
github | mortonne/aperture-master | sessPower.m | .m | aperture-master/patterns/creation/sessPower.m | 10,486 | utf_8 | def22c3313c15335385d0fbf7ed7a697 | function [pattern, params] = sessPower(events, channels, params, ...
base_events, bins)
%SESSPOWER Create a pattern of oscillatory power for one session.
%
% [pattern, params] = sessPower(events, channels, params, base_events, bins)
%
% Calculate oscillatory power using Morlet... |
github | mortonne/aperture-master | sessVoltage.m | .m | aperture-master/patterns/creation/sessVoltage.m | 8,021 | utf_8 | e19833b6d23a5fb71589784a1181f5d8 | function [pattern, params] = sessVoltage(events, channels, params, ...
base_events, bins)
%SESSVOLTAGE Create a voltage pattern for one session.
%
% [pattern, params] = sessVoltage(events, channels, params, base_events, bins)
%
% Voltage is calculated for one session at a tim... |
github | mortonne/aperture-master | eeglab2pat.m | .m | aperture-master/patterns/creation/eeglab2pat.m | 1,268 | utf_8 | 9a4c21de1433890fe86aef5b52f22790 | function pat = eeglab2pat(eeg)
%EEGLAB2PAT Import data from EEGLAB.
%
% pat = eeglab2pat(eeg)
% metadata
pat_name = eeg.setname;
pat_file = '';
subj_id = eeg.subject;
% dimensions
events = convert_events(eeg.epoch);
chan = convert_chans(eeg.chanlocs);
time = init_time(eeg.times);
freq = init_freq();
% initialize ... |
github | mortonne/aperture-master | pat_topoplot_fieldtrip.m | .m | aperture-master/patterns/fieldtrip/pat_topoplot_fieldtrip.m | 16,587 | utf_8 | 18ea9fb882b85966766e27ca2c29763f | function pat = pat_topoplot_fieldtrip(pat, fig_name, varargin)
%PAT_TOPOPLOT_FIELDTRIP Make topoplots and print them to disk.
%
% pat = pat_topoplot_fieldtrip(pat, fig_name, varargin)
%
% Create a topographical plot for each [event X time X frequency] in a
% pattern. Requires EEGLAB for the plotting functions, as... |
github | mortonne/aperture-master | headplot_fieldtrip.m | .m | aperture-master/patterns/fieldtrip/headplot_fieldtrip.m | 38,802 | utf_8 | 82b597bfca4be249abc82b7a3f4e37ee | % headplot_fieldtrip() - plot a spherically-splined EEG field map on a semi-realistic
% 3-D head model. Can 3-D rotate the head image using the left
% mouse button.
% Example:
% >> headplot example % show an example spherical 'eloc_angles' file
% >> headplot cartesian % show an example... |
github | mortonne/aperture-master | fieldtrip_head_report_subj.m | .m | aperture-master/patterns/fieldtrip/fieldtrip_head_report_subj.m | 5,425 | utf_8 | 52c04398724a709d441960a259e8f078 | function pdf_file = fieldtrip_head_report_subj(exp, pat_name, varargin)
%FIELDTRIP_HEAD_REPORT_SUBJ Create headplot report with fieldtrip
% clusters for every subject.
%
% pdf_file = fieldtrip_head_report_subj(exp, ...)
%
% INPUTS:
% exp: experiment object
% pat_name: pattern ... |
github | mortonne/aperture-master | fieldtrip_voltage_subj.m | .m | aperture-master/patterns/fieldtrip/fieldtrip_voltage_subj.m | 9,956 | utf_8 | 6df916b2e50ebefb5b560b4927e92fad | function exp = fieldtrip_voltage_subj(exp, varargin)
%FIELDTRIP_VOLTAGE_SUBJ Run fieldtrip on individual subjects for voltage erp.
%
% exp: = fieldtrip_voltage_subj(exp, ...)
%
% INPUTS:
% exp: experiment object.
%
% OUTPUTS:
% exp: experiment object with fieldstat objects
%
% PARAMS:
% These opt... |
github | mortonne/aperture-master | topoplot_fieldtrip.m | .m | aperture-master/patterns/fieldtrip/topoplot_fieldtrip.m | 85,727 | utf_8 | 9c839c7fed9930f9a90333d26513fb68 | % topoplot_fieldtrip() - plot a topographic map of a scalp data field in a 2-D circular view
% (looking down at the top of the head) using interpolation on a fine
% cartesian grid. Can also show specified channnel location(s), or return
% an interpolated value at an arbitrary s... |
github | mortonne/aperture-master | create_perf_pattern.m | .m | aperture-master/patclass/create_perf_pattern.m | 13,879 | utf_8 | ae9a390827c4fd4938e5aadf058be936 | function pat = create_perf_pattern(pat, stat_name, varargin)
%CREATE_PERF_PATTERN Create a pattern from classifier outputs.
%
% From the results of pattern classification, get a pattern containing
% a measure of classifier performance. The classifier output can then
% be manipulated and plotted in all of the ways ... |
github | mortonne/aperture-master | pcorr_perm_test.m | .m | aperture-master/patclass/pcorr_perm_test.m | 5,789 | utf_8 | 32f36f0f496860aaa2549017fe69009d | function pat = pcorr_perm_test(pat, stat_name, varargin)
%PCORR_PERM_TEST Run a permutation test on classifier percent correct.
%
% pat = pcorr_perm_test(pat, stat_name, ...)
%
% INPUTS:
% pat: pattern object.
%
% stat_name: name of the stat object that will be created.
%
% OUTPUTS:
% pat: pattern ... |
github | mortonne/aperture-master | classify_pat2pat.m | .m | aperture-master/patclass/classify_pat2pat.m | 12,935 | utf_8 | 298c61e924537ae8ed7ca68e70c4b3f2 | function subj = classify_pat2pat(subj, train_pat_name, test_pat_name, ...
stat_name, varargin)
%CLASSIFY_PAT2PAT Train a classifier on one pattern and test on another.
%
% Train a classifier on a pattern and test on another pattern. For each
% dimension of the patterns (except event... |
github | mortonne/aperture-master | class_confusion.m | .m | aperture-master/patclass/class_confusion.m | 1,826 | utf_8 | b9520fbc8ff83b265c86ba677b19fd44 | function confmat = class_confusion(res, conf_type)
%CLASS_CONFUSION Calculate confusion matrices from classifier output.
%
% confmat = class_confusion(res, conf_type)
%
% INPUTS:
% res: a results structure from pattern classification.
%
% conf_type: confusion measure to calculate:
% 'rate' ... |
github | mortonne/aperture-master | test_libsvm.m | .m | aperture-master/patclass/test_libsvm.m | 1,814 | utf_8 | e01559f3eafd8e6bda0299e947491ca4 | function [acts scratchpad] = test_libsvm(testpats,testtargs,scratchpad)
% Generates predictions using a trained logistic regression model
%
% [ACTS SCRATCHPAD] = TEST_RIDGE(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% License:
%=====================================================================
%
% This is part of the Princet... |
github | mortonne/aperture-master | perf_perm_test.m | .m | aperture-master/patclass/perf_perm_test.m | 4,357 | utf_8 | 8dcc0511a1628930fe63728d08e960f9 | function exp = perf_perm_test(exp, stat_path, stat_name, varargin)
%PERF_PERM_TEST Test significance of a permutated performance metric.
%
% stat = perf_perm_test(subj, stat_path, stat_name, ...)
%
% INPUTS:
% subj: vector of subject objects.
%
% stat_path: cell array of obj_type, obj_name pairs giving the... |
github | mortonne/aperture-master | group_onoff_perm_test.m | .m | aperture-master/patclass/group_onoff_perm_test.m | 4,201 | utf_8 | b5c8863b57c5a829b784afd09ea5e8a1 | function exp = group_onoff_perm_test(exp, pat_name, stat_name, varargin)
%GROUP_ONOFF_PERM_TEST Run a permutation test of significant OnOff.
%
% exp = group_onoff_perm_test(exp, pat_name, stat_name, ...)
%
% PARAMS:
% n_perms - number of times to permute the columns of the cross-
% correlation matri... |
github | mortonne/aperture-master | run_classifier.m | .m | aperture-master/patclass/run_classifier.m | 3,868 | utf_8 | 559346d632754f0c30b1eae6914cb527 | function [class,err,posterior] = run_classifier(trainpat,trainreg,testpat,testreg,classifier,params)
%RUN_CLASSIFIER Train and test a classifier using standard data formats.
%
% ******************
% -DEPRECATED-
% Used by the old version of classify_pat. Keeping it around for now
% since it has interfaces with... |
github | mortonne/aperture-master | train_libsvm.m | .m | aperture-master/patclass/train_libsvm.m | 2,681 | utf_8 | 34bfdf49022bcc84a421e67cde56415e | function [scratchpad] = train_libsvm(trainpats,traintargs,in_args,cv_args)
% Uses logistic regression with regularization to predict your regressors
%
% [SCRATCHPAD] = TRAIN_RIDGE(TRAINPATS,TRAINTARGS,IN_ARGS,CV_ARGS)
%
% Logistic regression, but penalises small weights (like weight
% regularization in backprop), so i... |
github | mortonne/aperture-master | create_events.m | .m | aperture-master/events/creation/create_events.m | 5,433 | utf_8 | e56d851488be04197c38362869a85662 | function subj = create_events(subj, fcn_handle, fcn_input, varargin)
%CREATE_EVENTS Create events for all sessions of a subject.
%
% Update the events for every session in a subj structure. For each
% session, if an events.mat file does not exist, events will be created
% and saved. If input files are specified, a... |
github | mortonne/aperture-master | create_pat_report.m | .m | aperture-master/plotting/reports/create_pat_report.m | 7,201 | utf_8 | d51f048cb2dc9569630c4a3972a26fd3 | function [table, header] = create_pat_report(pat, dim, fig_names, ...
fig_labels, varargin)
%CREATE_PAT_REPORT Create a PDF report of figures derived from a pattern.
%
% [table, header] = create_pat_report(pat, dim, fig_names, fig_labels, ...)
%
% Use this function to pr... |
github | mortonne/aperture-master | pdflatex.m | .m | aperture-master/plotting/reports/pdflatex.m | 3,023 | utf_8 | d82953100f083ffb75d19d6116fb75e9 | function pdf_file = pdflatex(latex_file,compile_method)
%PDFLATEX Compile a LaTeX file to make a PDF.
%
% pdf_file = pdflatex(latex_file, compile_method)
%
% INPUTS:
% latex_file: LaTeX file. A .tex extension is optional.
%
% compile_method: method to use when compiling the file:
% 'pdfla... |
github | mortonne/aperture-master | erp_image_report.m | .m | aperture-master/plotting/reports/erp_image_report.m | 3,726 | utf_8 | 29e75a3778e0baf784502ca32e941e78 | function erp_image_report(exp, pat_name, varargin)
%ERP_IMAGE_REPORT Make a report with ERP images for each subject.
%
% Make a PDF report with one row for each subject. Columns may include
% different channels (useful when plotting voltage) or different
% frequencies.
%
% erp_image_report(exp, pat_name, ...)
%
%... |
github | mortonne/aperture-master | plot_topo.m | .m | aperture-master/plotting/figures/plot_topo.m | 28,313 | utf_8 | 361805b301ffbeec9580229f51794ead | % plottopo() - plot concatenated multichannel data epochs in a topographic
% or
% rectangular array. Uses a channel location file with the same
% format as topoplot(), or else plots data on a rectangular grid.
% If data are all positive, they are assumed to be spectra.
% Usage:
... |
github | mortonne/aperture-master | plot_events.m | .m | aperture-master/plotting/figures/plot_events.m | 3,778 | utf_8 | 474a219bc003c4bfbd28dc1c690300f8 | function h = plot_events(events, opt)
%PLOT_EVENTS Visualize information in an events structure.
% H = PLOT_EVENTS(EVENTS,OPT) makes a plot of the information contained
% in the EVENTS structure, using options specified in the OPT structure.
%
% OPT is a vector structure. Each element of OPT specifies options
... |
github | mortonne/aperture-master | pat_topoplot.m | .m | aperture-master/plotting/figures/pat_topoplot.m | 13,350 | utf_8 | 4628a6c9649c9ab1c3c78ddf0cde46a2 | function pat = pat_topoplot(pat, fig_name, varargin)
%PAT_TOPOPLOT Make topoplots and print them to disk.
%
% Create a topographical plot for each [event X time X frequency] in a
% pattern. Requires EEGLAB for the plotting functions, as well as a
% channel coordinates file that can be read by readlocs. 3D headpl... |
github | mortonne/aperture-master | plot_erp.m | .m | aperture-master/plotting/figures/plot_erp.m | 5,911 | utf_8 | 865630960cebfce5460cd48c80e47851 | function h = plot_erp(data, time, varargin)
%PLOT_ERP Plot an event-related potential.
%
% h = plot_erp(data, time, ...)
%
% INPUTS:
% data: array of voltage values to plot. If data is a matrix, each
% row will be plotted as a separate line.
%
% time: time values corresponding to each column of... |
github | mortonne/aperture-master | thresh_colormap.m | .m | aperture-master/plotting/figures/thresh_colormap.m | 3,080 | utf_8 | ddc1c718108d8fdbaa5e099804f173d9 | function [map, crit] = thresh_colormap(limits, thresh, map_size, colors)
%THRESH_COLORMAP Create a thresholded colormap.
%
% Creates a colormap around zero. If -thresh < limits(1), the map
% will be one-sided positive; if thresh > limits(2), the map will
% be one-sided negative; otherwise, it will be two-sided.
%
... |
github | mortonne/aperture-master | grid_search.m | .m | aperture-master/utils/grid_search.m | 4,352 | utf_8 | 1818ba77c062f71a25d7a88901f84784 | function fit = grid_search(params, fit_fcn, fit_inputs)
%GRID_SEARCH Run a grid search of parameter space.
%
% fit = grid_search(params, fit_fcn, fit_inputs)
%
% INPUTS:
% params: structure of parameters, where each field
% corresponds to one parameter, and gives a
% cell array of ... |
github | mortonne/aperture-master | disp_images.m | .m | aperture-master/utils/disp_images.m | 11,511 | utf_8 | cb92a3b99519bdc50321a423ff88b4d0 | function varargout = disp_images(varargin)
% DISP_IMAGES MATLAB code for disp_images.fig
% DISP_IMAGES, by itself, creates a new DISP_IMAGES or raises the existing
% singleton*.
%
% H = DISP_IMAGES returns the handle to a new DISP_IMAGES or the handle to
% the existing singleton*.
%
% DISP_IMAG... |
github | mortonne/aperture-master | torque_mem_format.m | .m | aperture-master/utils/distcomp/torque_mem_format.m | 1,730 | utf_8 | 882edd17cbef0344ca1e60bac61edbd9 | function mem_str = torque_mem_format(str_in)
%MEM_REQ_TORQUE Reformat memory requirements from SGE to TORQUE
%
% mem_str = torque_mem_format(str_in)
%
% INPUTS:
% str_in: A string indicating node memory requirements in SGE
% format (e.g. '1.7M').
%
% OUTPUTS:
% mem_str: A string indicating node ... |
github | mortonne/aperture-master | remove_small_samples.m | .m | aperture-master/stats/remove_small_samples.m | 3,120 | utf_8 | f6af3acae15a711d198bb70fa79683f2 | function pat = remove_small_samples(pat, id_bin, event_bins, min_n, varargin)
%REMOVE_SMALL_SAMPLES Remove subjects with too few samples in a bin.
%
% Designed to remove subjects from patterns containing multiple
% subjects before running repeated-measures analysis.
%
% pat = remove_small_samples(pat, id_bin, even... |
github | mortonne/aperture-master | paired_stats.m | .m | aperture-master/stats/paired_stats.m | 572 | utf_8 | 09c99775a8b9f100db6ba6dd98db80a7 | function res = paired_stats(x, y, vars)
%PAIRED_STATS Calculate various statistics on paired variables.
%
% res = paired_stats(x, y, vars)
% describe the individual variables
res.(vars{1}) = summary_stats(x);
res.(vars{2}) = summary_stats(y);
% stats on the differences
res.diff = summary_stats(x - y);
% test for ... |
github | mortonne/aperture-master | bootstrap_all_subj.m | .m | aperture-master/stats/bootstrap_all_subj.m | 5,311 | utf_8 | 36359ed5cc5d9c2ff4d7056860ee1cea | function exp = bootstrap_all_subj(exp, pat_name, n_perm, reg_defs, ...
stat_name, wilcox, event_bin_labels, ...
save_as)
%BOOTSTRAP_ALL_SUBJ Performs a bootstrap analysis across subjects.
%
% exp = bootstrap_all_subj(exp, pat_name, n_perm, reg_defs,... |
github | mortonne/aperture-master | dprime.m | .m | aperture-master/stats/dprime.m | 22,039 | utf_8 | c1a363122d08d0f47c7a2d859b04fa06 | % DPRIME -- Signal-detection theory sensitivity measure.
%
% d = dprime(pHit,pFA)
% [d,beta] = dprime(pHit,pFA)
%
% PHIT and PFA are numerical arrays of the same shape.
% PHIT is the proportion of "Hits": P(Yes|Signal)
% PFA is the proportion of "False Alarms": P(Yes|Noise)
% All numbers involved must be... |
github | mortonne/aperture-master | align_subj.m | .m | aperture-master/basic/align_subj.m | 8,389 | utf_8 | 2c48f516f5017b859cf142e15c4df744 | function subj = align_subj(subj, varargin)
%ALIGN_SUBJ Align a subject's events to EEG data.
%
% subj = align_subj(subj, ...)
%
% INPUTS:
% subj: a subject structure, where each sess subfield has the
% following fields:
% dir - directory where behavioral data is stored
% eegf... |
github | mortonne/aperture-master | print_obj.m | .m | aperture-master/basic/print_obj.m | 4,838 | utf_8 | e83291bc7cd291869232056d88a97a7c | function s = print_obj(subobj, obj_type, long_name, dim_labels, obj_names, ...
varargin)
%PRINT_OBJ Print a string description of an object.
%
% s = print_obj(subobj, obj_type, long_name, dim_labels, obj_names, args)
%
% INPUTS:
% subobj: structure with a subfield containing the object(... |
github | mortonne/aperture-master | apply_by_slice.m | .m | aperture-master/basic/apply_by_slice.m | 4,287 | utf_8 | 99494e626a97c2b271f50c4ded307a8a | function x = apply_by_slice(f, matrices, iter_dims, constant_in, varargin)
%APPLY_BY_SLICE Iterate over slices of matrices, applying a function.
%
% x = apply_by_slice(f, matrices, iter_dims, constant_in, varargin)
%
% INPUTS:
% f: handle to a function to apply to each slice. Output
% mu... |
github | mortonne/aperture-master | transfer_obj.m | .m | aperture-master/basic/transfer_obj.m | 2,117 | utf_8 | 4193067eb5c4d8150a80446d8bb02bc0 | function dest = transfer_obj(src, dest, obj_path, varargin)
%TRANSFER_OBJ Transfer an object from one experiment to another.
%
% exp = transfer_obj(src, dest, obj_path, ...)
%
% INPUTS:
% src: source experiment with the object to be transferred.
%
% dest: experiment to move the object to.
%
% obj_path... |
github | mortonne/aperture-master | merge_objs.m | .m | aperture-master/basic/merge_objs.m | 4,243 | utf_8 | b966a0bd9ebe81ef30f3767961063062 | function objs = merge_objs(objs1, objs2)
%MERGE_OBJS Merge two sets of objects.
%
% Will recursively merge two sets of objects. That is, all
% sub-structures of overlapping objects will be merged. Save your
% inputs until after checking the merge; this is a complex operation
% that may still have bugs.
%
% Fields... |
github | mortonne/aperture-master | apply_to_subj.m | .m | aperture-master/basic/apply_to_subj.m | 7,375 | utf_8 | 272ccdcae2156f7c77f963a1b3421e1c | function subj = apply_to_subj(subj, fcn_handle, fcn_inputs, dist, varargin)
%APPLY_TO_SUBJ Apply a function to all subjects.
%
% subj = apply_to_subj(subj, fcn_handle, fcn_inputs, dist, ...)
%
% Apply a function to each element of a subjects vector.
%
% INPUTS:
% subj: a subject object or vector of subject... |
github | mortonne/aperture-master | create_intrusions.m | .m | aperture-master/external/beh_toolbox/fr/create_intrusions.m | 6,349 | utf_8 | 2d76e2fa0358bbc22ae205ef8f741622 | function intrusions = create_intrusions(rec_itemnos, pres_itemnos, ...
subjects, sessions, trials, ...
pres_subj, pres_sess, pres_trial)
%CREATE_INTRUSIONS Create a standard intrusions matrix.
%
% USAGE:
% create_intrusions(rec_itemnos, p... |
github | mortonne/aperture-master | spc_core.m | .m | aperture-master/external/beh_toolbox/fr/spc_core.m | 3,097 | utf_8 | 775df6f60019fb70ca2ffdbeab74bde6 | function [p_recalls] = spc_core(recalls_matrix, subjects, list_length, ...
rec_mask, pres_mask)
%SPC_CORE Serial position curve (recall probability by serial position).
%
% Computes probability of recall for each serial position.
% Unlike SPC, this function does not have any error-checking. It
% assumes all ... |
github | mortonne/aperture-master | FRdata.m | .m | aperture-master/external/beh_toolbox/fr/FRdata.m | 5,974 | utf_8 | fc8af93c06cf8f38756c6159a9ccc738 | function data = FRdata(events, trial_field)
%FRDATA Create a free recall data struct from an events struct.
%
% data = FRdata(events, trial_field)
%
% ASSUMPTIONS
% * 'subject' field with subject id string
% * List length is the same for all trials (because analysis
% scripts assume this, and data.li... |
github | mortonne/aperture-master | union_structs.m | .m | aperture-master/external/beh_toolbox/utils/union_structs.m | 2,618 | utf_8 | 362e131040c56663aa26b2d998d94792 | function s = union_structs(s1, s2, id_fields)
%UNION_STRUCT Structure union.
%
% Return the union of two array structures. Matches are determined by
% identifier fields of the two structures; elements are considered to
% match if their values on each of the identifier fields are equal.
%
% For matching elements, ... |
github | mortonne/aperture-master | events2data.m | .m | aperture-master/external/beh_toolbox/utils/events2data.m | 4,178 | utf_8 | 14cbd5e8b806e809d86d4dba9f9c53ce | function [data] = events2data(events,index,index_rows)
%EVENTS2DATA Convert an events struct to matrix format.
%
% data = events2data(events, index, index_rows)
%
% INPUTS:
% events: a vector structure. Each field in events will be converted
% to matrix format and placed in the data structure. E... |
github | mortonne/aperture-master | cat_data.m | .m | aperture-master/external/beh_toolbox/utils/cat_data.m | 3,864 | utf_8 | 7f43fa322b0760d80cd5bdb3642eef4f | function data = cat_data(data1, data2, scalars, include)
%CAT_DATA Concatenate all fields of two data structures.
% DATA = CAT_DATA(DATA1, DATA2) creates DATA by concatenating
% each field in DATA1 with the corresponding field in DATA2. If
% a field in DATA1 is not in DATA2, an error is raised.
%
% data = ca... |
github | mortonne/aperture-master | update_struct.m | .m | aperture-master/external/beh_toolbox/utils/update_struct.m | 2,601 | utf_8 | 7a5b420b0decdef3409e1d11c6f787d7 | function s = update_struct(old, new, id_fields)
%UPDATE_STRUCT Update elements of a structure with new data.
%
% Find elements of old that match elements of new, and update
% them. Matches are determined by identifier fields of the two
% structures; elements are considered to match if their values on
% each of th... |
github | mortonne/aperture-master | ranksum_ci.m | .m | aperture-master/external/eeg_toolbox/stats/ranksum_ci.m | 5,177 | utf_8 | 33a48ef06fd5ab5955c0f3f6502fe697 | function [p, h, stats, Ws] = ranksum_ci(x,y,alpha,tail)
%RANKSUM_CI - Wilcoxon rank sum test that two populations are identical.
%
% Modified the matlab version to return 95% confidence
% interval. (PBS)
%
% Modified to make it faster in the case of many ties between x
% and y (JJ, 12/07).
%
% Modified to com... |
github | mortonne/aperture-master | load_chan.m | .m | aperture-master/external/eeg_toolbox/io/load_chan.m | 1,501 | utf_8 | 758a97173d4fb049dfd8e4cac9e624d2 | function [eeg, time] = load_chan(fileroot, channel)
%LOAD_CHAN Load raw EEG data from one channel.
%
% [eeg, time] = load_chan(fileroot, channel)
%
% INPUTS:
% fileroot: path to an EEG file. Do not include the prefix that indicates
% the channel.
%
% channel: number of the channel to load. If an a... |
github | mortonne/aperture-master | GetRateAndFormat.m | .m | aperture-master/external/eeg_toolbox/io/GetRateAndFormat.m | 2,151 | utf_8 | 47f754ad9bdb6b9aa7bcde60017de6df | function [samplerate,nBytes,dataformat,gain] = GetRateAndFormat(event)
%GETRATEANDFORMAT - Get the samplerate, gain, and format of eeg data.
%
% function [samplerate,nBytes,dataformat,gain] = GetRateAndFormat(event)
%
if ischar(event)
paramdir = event;
else
paramdir = fileparts(event.eegfile);
end
paramfile = full... |
github | mortonne/aperture-master | pulsealign.m | .m | aperture-master/external/eeg_toolbox/align/pulsealign.m | 4,809 | utf_8 | a6873caddf1c5cfa1948548bb70d11b2 | function [beh_ms,eeg_offset] = pulsealign(beh_ms,pulses,samplerate,threshMS,window,doplot,pulseIsMS)
%PULSEALIGN - Pick matching behavioral and eeg pulses.
%
% This method picks matching behavioral and eeg pulses from the
% beginning and end of the behavioral period for use with the
% logalign function to align behavio... |
github | mortonne/aperture-master | pulsealign2.m | .m | aperture-master/external/eeg_toolbox/align/pulsealign2.m | 3,264 | utf_8 | bddc4a7619bb48e828c0a80904396de1 | function [good_beh_ms,eeg_offset] = pulsealign2(beh_ms,pulses)
%PULSEALIGN - Pick matching behavioral and eeg pulses.
%
% This method picks matching behavioral and eeg pulses from the
% beginning and end of the behavioral period for use with the
% logalign function to align behavioral and eeg data.
%
% This is josh's p... |
github | mortonne/aperture-master | find_pulse_periods.m | .m | aperture-master/external/eeg_toolbox/align/find_pulse_periods.m | 5,409 | utf_8 | 60adbb1168d5a07f2d229eda90b97aa5 | function pulses = find_pulse_periods(dat, samplerate, varargin)
%FIND_PULSE_PERIODS Find sync pulses in EEG data.
%
% pulses = find_pulse_periods(dat, samplerate, ...)
%
% INPUTS:
% dat: vector of EEG data.
%
% samplerate: rate at which the data were sampled, in Hz.
%
% OUTPUTS:
% pulses: time (in... |
github | mortonne/aperture-master | envelope.m | .m | aperture-master/external/eeg_toolbox/align/envelope.m | 986 | utf_8 | a49d0066ce202b85d644a61a851f70d9 | % Find upper and lower envelopes of a given signal
% The idea is from Envelope1.1 by Lei Wang, but here it works well when the signal contains
% successive equal samples and also includes first and last samples of the signal in the envelopes.
% inputs:
% sig: vector of input signal
% method: method of interpol... |
github | mortonne/aperture-master | mark_sync_pulses.m | .m | aperture-master/external/eeg_toolbox/align/mark_sync_pulses.m | 5,310 | utf_8 | ef5060a0808ed465613d7d98c3ba068b | function [pulse_file, pulses] = ...
mark_sync_pulses(fileroot, channel, flip, stat, thresh, doplot)
%MARK_SYNC_PULSES Automatically find and record sync pulses.
%
% pulse_file = mark_sync_pulses(fileroot, channel, flip, doplot)
%
% INPUTS:
% fileroot: root of the EEG file(s) containing sync pulses.
%
% ch... |
github | mortonne/aperture-master | prep_egi_data.m | .m | aperture-master/external/eeg_toolbox/preproc/prep_egi_data.m | 14,058 | utf_8 | f8c1477e687f2dbb116699fc31510a92 | function prep_egi_data(subject, sess_dir, varargin)
%PREP_EGI_DATA Process one session of a pyEPL experiment with EGI recordings.
%
% Makes some assumptions based on standard PyEPL directory structure,
% but many of the default directories may be modified using params (see
% below). If steps_to_run is set, only sp... |
github | mortonne/aperture-master | addArtifacts.m | .m | aperture-master/external/eeg_toolbox/preproc/addArtifacts.m | 6,309 | utf_8 | d73d50776e6c571b8a8befe922834c0c | function events = addArtifacts(eventfile,channels,thresh,plotit,replace_eegfile)
%ADDARTIFACTS - Add artifact info to events structure.
%
% This function loads an events structure, loops over the unique
% files in the events structure, loads the EEG data from each
% channel in each file, makes the channel data bipolar ... |
github | mortonne/aperture-master | find_eog_artifacts.m | .m | aperture-master/external/eeg_toolbox/preproc/find_eog_artifacts.m | 4,482 | utf_8 | 95b7fd16e8d55a8f31fbaf4ef1bb1c32 | function [artifacts,eeg] = find_eog_artifacts(events,channels,offsetMS,durationMS,params)
%FIND_EOG_ARTIFACTS Mark eye artifacts.
%
% [artifacts,eeg] = find_eog_artifacts(events,channels,offsetMS,durationMS,params)
%
% Run findBlinks on segmented data. A buffer can be added around each
% epoch before blink detecti... |
github | mortonne/aperture-master | gete_ms.m | .m | aperture-master/external/eeg_toolbox/core/gete_ms.m | 6,896 | utf_8 | db34e842f7d1118541f82cd6695943fa | function EEG = gete_ms(channel, events, DurationMS, OffsetMS, varargin)
%GETE_MS - Get EEG event data based on MSec ranges instead of samples.
%
% Returns data from an eeg file. User specifies the channel,
% duration, and offset along with an event. The event struct MUST
% contain both 'eegfile' and 'eegoffset' memb... |
github | mortonne/aperture-master | getleads.m | .m | aperture-master/external/eeg_toolbox/tal/getleads.m | 413 | utf_8 | 38772ca3f691eaeba32327a1d3af2499 | % function leads=getleads(fname)
% This function reads the files fname and returns the corresponding
% list of leads. If it can't open the file, it returns an empty
% vector.
function leads=getleads(fname)
in=fopen(fname,'r');
leads=[];
if(in~=-1)
leads=fscanf(in,'%i',[1,inf]);
fclose(in);
end
if(isempty(leads)... |
github | mortonne/aperture-master | getleadcoords.m | .m | aperture-master/external/eeg_toolbox/tal/getleadcoords.m | 1,002 | utf_8 | 99b6f05a47816471e13817af2dae365f | % function [leads,X,Y,plotwidth,legendlead]=getleadcoords(fname)
% This function reads the files fname, properly formatted as a lead coordinates
% files, and returns the plotwidth (from the first line), the lead # at which
% to put the legend (legendlead, also from the first line),the lead # in the
% variable, leads, ... |
github | dannyneil/spiking_relu_conversion-master | myOctaveVersion.m | .m | spiking_relu_conversion-master/dlt_cnn_map_dropout_nobiasnn/util/myOctaveVersion.m | 169 | utf_8 | d4603482a968c496b66a4ed4e7c72471 | % return OCTAVE_VERSION or 'undefined' as a string
function result = myOctaveVersion()
if isOctave()
result = OCTAVE_VERSION;
else
result = 'undefined';
end
|
github | dannyneil/spiking_relu_conversion-master | isOctave.m | .m | spiking_relu_conversion-master/dlt_cnn_map_dropout_nobiasnn/util/isOctave.m | 108 | utf_8 | 4695e8d7c4478e1e67733cca9903f9ef | %detects if we're running Octave
function result = isOctave()
result = exist('OCTAVE_VERSION') ~= 0;
end |
github | dannyneil/spiking_relu_conversion-master | makeLMfilters.m | .m | spiking_relu_conversion-master/dlt_cnn_map_dropout_nobiasnn/util/makeLMfilters.m | 1,895 | utf_8 | 21950924882d8a0c49ab03ef0681b618 | function F=makeLMfilters
% Returns the LML filter bank of size 49x49x48 in F. To convolve an
% image I with the filter bank you can either use the matlab function
% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the
% Fourier transform.
SUP=49; % Support of the largest filter (must be... |
github | nh295/MOHEA-master | computeR3.m | .m | MOHEA-master/m/computeR3.m | 761 | utf_8 | 4cc189ec374c7e75174ceb57bc677f32 | function R3 = computeR3(setA,setB,refpoint,vectors)
R3 = 0;
for i=1:length(vectors)
Butil = popUtility(setB,refpoint,vectors(i,:));
R3 = R3 + (Butil- popUtility(setA,refpoint,vectors(i,:)))/Butil;
%disp(R2)
end
R3 = R3 / length(vectors);
figure(1)
scatter(setA(:,1),setA(:,2),'o')
hold on
scatter(setB(:,1)... |
github | nh295/MOHEA-master | getTime.m | .m | MOHEA-master/m/getTime.m | 859 | utf_8 | 4bca8ace99b103b20bba72a285620441 | function [ET] = getTime(path,selector,creditDef,problemName)
%reads the csv values starting from the 2nd column
%filename must include path and extension
origin = cd(path);
files = dir(strcat(problemName,'*',selector,'*',creditDef,'*.res'));
cd(origin)
nfiles = length(files);
npts = 1;
ET = zeros(nfiles,npts);
for i=1... |
github | nh295/MOHEA-master | plotAOSConvergence.m | .m | MOHEA-master/m/plotAOSConvergence.m | 4,635 | utf_8 | ceb1a6a8944c18b17a9a045ffa125b8e | function plotAOSConvergence
%plots the time history of the metrics of the AOS
problemName = {'UF1_','UF2','UF3','UF4','UF5','UF6','UF7','UF8','UF9','UF10'};
% problemName = {'UF1_','UF2','UF3','UF4','UF5','UF6','UF7','UF10'};
% problemName = {'DTLZ1_','DTLZ2_','DTLZ3_','DTLZ4_','DTLZ7_',};
selectors = {'Probability','... |
github | nh295/MOHEA-master | jmetalAnalyze.m | .m | MOHEA-master/m/jmetalAnalyze.m | 7,057 | utf_8 | 10edd0f1d670eaa9f72d1d0bbe556096 | function jmetalAnalyze
javaaddpath(strcat('dist',filesep,'MOHEA.jar'));
origin = cd;
%
% h1 = figure(1);
% h2 = figure(2);
probs = 1:10;
for i=1:length(probs)
cd(origin)
probfactory = org.moeaframework.core.spi.ProblemFactory.getInstance();
problem = strcat('UF',num2str(probs(i)));
prob =... |
github | nh295/MOHEA-master | getMOEAIndicators.m | .m | MOHEA-master/m/getMOEAIndicators.m | 1,575 | utf_8 | fa0a69a9fa3aea7014d639aca390a48b | function [fHV,IGD,finalHV,finalIGD,ET] = getMOEAIndicators(filename,npts)
%reads the csv values starting from the 2nd column
%filename must include path and extension
%npts is the number of first readings desired
%EI is the epsilon indicator
%GD is the generational distnace
%HV is the hypervolume
%IGD is the inverted g... |
github | nh295/MOHEA-master | computeR2.m | .m | MOHEA-master/m/computeR2.m | 735 | utf_8 | 201a80e71c3ebd2d3d0e435f1d30a055 | function R2 = computeR2(setA,setB,refpoint,vectors)
R2 = 0;
for i=1:length(vectors)
R2 = R2 + (popUtility(setA,refpoint,vectors(i,:))-popUtility(setB,refpoint,vectors(i,:)));
%disp(R2)
end
R2 = R2 / length(vectors);
figure(1)
scatter(setA(:,1),setA(:,2),'o')
hold on
scatter(setB(:,1),setB(:,2),'s','filled')
h... |
github | nh295/MOHEA-master | plot1Opresults.m | .m | MOHEA-master/m/plot1Opresults.m | 7,525 | utf_8 | 172fc3bb411fd64cdfb1556d4f8f2c13 | function plot1Opresults
%plots the boxplots of each UF1-10 problem and the IGD, fast hypervolume
%(jmetal) and the additive epsilon values for each algorithm
%
problemName = {'DTLZ7'};
% problemName = {'UF1','UF2','UF3','UF4','UF5','UF6','UF7','UF8','UF9','UF10'};
% problemName = { 'DTLZ1','DTLZ2','DTLZ3','DTLZ4','DT... |
github | jackonelli/AgentBasedModelling-master | combn.m | .m | AgentBasedModelling-master/combn.m | 4,251 | utf_8 | f015dc1593341aed310c62d20fb6da35 | function [M,IND] = combn(V,N)
% COMBN - all combinations of elements
% M = COMBN(V,N) returns all combinations of N elements of the elements in
% vector V. M has the size (length(V).^N)-by-N.
%
% [M,I] = COMBN(V,N) also returns the index matrix I so that M = V(I).
%
% V can be an array of numbers, cells ... |
github | mojombo/linguist-master | convert_variable.m | .m | linguist-master/samples/Matlab/convert_variable.m | 2,186 | utf_8 | 3d73feb0b3feaa01d8b434d83f275241 | function [name, order] = convert_variable(variable, output)
% Returns the name and order of the given variable in the output type.
%
% Parameters
% ----------
% variable : string
% A variable name.
% output : string.
% Either `moore`, `meijaard`, `data`.
%
% Returns
% -------
% name : string
% The variable name i... |
github | mojombo/linguist-master | create_ieee_paper_plots.m | .m | linguist-master/samples/Matlab/create_ieee_paper_plots.m | 34,238 | utf_8 | 3cf9c020f3fbd215ddc5182743c38bc8 | function create_ieee_paper_plots(data, rollData)
% Creates all of the figures for the IEEE paper.
%
% Parameters
% ----------
% data : structure
% A structure contating the data from generate_data.m for all of the bicycles
% and speeds for the IEEE paper.
% rollData : structure
% The data for a single bicycle at ... |
github | mojombo/linguist-master | plant.m | .m | linguist-master/samples/Matlab/plant.m | 2,087 | utf_8 | daf74d53d9253d37bd69d59c76021156 | function Yc = plant(varargin)
% function Yc = plant(varargin)
%
% Returns the system plant given a number.
%
% Parameters
% ----------
% varargin : variable
% Either supply a single argument {num} or three arguments {num1, num2,
% ratio}. If a single argument is supplied, then one of the six transfer
% functions ... |
github | RuiLiDMML/SubgroupDiscovery_optimization-master | SDexhauSA.m | .m | SubgroupDiscovery_optimization-master/SDexhauSA.m | 6,855 | utf_8 | ecd483c513e1289f9ed55db9cab63056 | function [SDrule targetIdx] = SDexhauSA(data, label, target, thres, weight)
%%% subgroup discovery by exhaustive search on a single target
%%% evalution quality function: gF^a*(pF-p0F), where a = 1
% a is adaptively computed from the svm weights and each feature has an 'a'
if nargin < 4
thres = 0.1;
end
method = ... |
github | RuiLiDMML/SubgroupDiscovery_optimization-master | entroDis.m | .m | SubgroupDiscovery_optimization-master/entroDis.m | 6,919 | utf_8 | 3e67bdb60a6982eb37b944763ce72f50 | function [feature cuts] = entroDis(data, label, type)
%%% entropy discretization
%%% reference: Multi-interval discretization of continuousvalued attributes
%%% for classification learning, Usama M. Fayyad and Keki B. Irani, 1993
%%% usage: load Pima; entroDis(data, label)
data = roundn(data, -3); % round off data, th... |
github | RuiLiDMML/SubgroupDiscovery_optimization-master | sdBeam.m | .m | SubgroupDiscovery_optimization-master/sdBeam.m | 9,775 | utf_8 | 5d0ec40b44267fed2fe6a77b2003e7f8 | function SDrules = sdBeam(data, label, thres, beam)
%%% subgroup discovery by beam search
if nargin < 3
thres = 0.05;
end
if nargin < 4
beam = 15;
end
[n dim] = size(data);
dimension = size(data, 2);
uniLabel = unique(label);
nPos = length(find(label == uniLabel(1)));
nNeg = length(find(label == uniLabel(2)... |
github | RuiLiDMML/SubgroupDiscovery_optimization-master | tOptiEst.m | .m | SubgroupDiscovery_optimization-master/tOptiEst.m | 11,250 | utf_8 | ac3e939393e6151aeea26db7e9741ae8 | function SDrule = tOptiEst(data, label, thres)
%%% subgroup discovery by optimistic estimate
SDrule = [];
if nargin < 3
thres = 0.1;
end
[n dim] = size(data);
uniLabel = unique(label);
nPos = length(find(label == uniLabel(1)));
nNeg = length(find(label == uniLabel(2)));
maxDepth = 4; % maximal feature number for co... |
github | Avnet/hdl-master | read_vectored_ILA_capture.m | .m | hdl-master/IP/Avnet_Inc_SysGen_pdm_filt_v1_0/Simulink/read_vectored_ILA_capture.m | 441 | utf_8 | acf1ec3e261e22e20d4a0f3c8bddd87b |
function [ ila_data] = read_vectored_ILA_capture(ila_data_csv_file)
[ndata, text, alldata] = xlsread(ila_data_csv_file);
ila_data = zeros(length(alldata)-2,length(char(alldata(3,5)))); % import binary data starting row 3 of saved .CSV file from ILA export
for row = 3 : length(alldata)
chars = char(... |
github | Avnet/hdl-master | read_vectored_ILA_PDM_capture.m | .m | hdl-master/IP/Avnet_Inc_SysGen_pdm_filt_v1_0/Simulink/read_vectored_ILA_PDM_capture.m | 606 | utf_8 | 2a36df7530fe4d61d9c433d3ef438caa |
function [ila_PDM_data] = read_vectored_ILA_PDM_capture(ila_data_csv_file)
% Read PDM data captured in ILA as 'vectorized' rows of 1024 bits
% Allows much longer bursts of PDM data to be captured than single bit into ILA
[ndata, text, alldata] = xlsread(ila_data_csv_file);
ila_PDM_data = zeros(length(alldat... |
github | Avnet/hdl-master | read_vectored_ILA_PDM_capture.m | .m | hdl-master/IP/microphone_PDM/Simulink/read_vectored_ILA_PDM_capture.m | 606 | utf_8 | 2a36df7530fe4d61d9c433d3ef438caa |
function [ila_PDM_data] = read_vectored_ILA_PDM_capture(ila_data_csv_file)
% Read PDM data captured in ILA as 'vectorized' rows of 1024 bits
% Allows much longer bursts of PDM data to be captured than single bit into ILA
[ndata, text, alldata] = xlsread(ila_data_csv_file);
ila_PDM_data = zeros(length(alldat... |
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