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github | pylance/BCI-GEM-Pipeline-master | dipfit_gridsearch.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/dipfit_gridsearch.m | 4,519 | utf_8 | cc77806c9d0a7de350e540a72dc1c033 | % dipfit_gridsearch() - do initial batch-like dipole scan and fit to all
% data components and return a dipole model with a
% single dipole for each component.
%
% Usage:
% >> EEGOUT = dipfit_gridsearch( EEGIN, varargin)
%
% Inputs:
% ...
%
% Optional inputs:
% 'com... |
github | pylance/BCI-GEM-Pipeline-master | eegplugin_dipfit.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/eegplugin_dipfit.m | 4,444 | utf_8 | 2bcef6898d8184014480e6ed4f2e170b | % eegplugin_dipfit() - DIPFIT plugin version 2.0 for EEGLAB menu.
% DIPFIT is the dipole fitting Matlab Toolbox of
% Robert Oostenveld (in collaboration with A. Delorme).
%
% Usage:
% >> eegplugin_dipfit(fig, trystrs, catchstrs);
%
% Inputs:
% fig - [integer] eegla... |
github | pylance/BCI-GEM-Pipeline-master | pop_multifit.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/pop_multifit.m | 10,370 | utf_8 | dd98129d0df98fcdfc6532ff87983696 | % pop_multifit() - fit multiple component dipoles using DIPFIT
%
% Usage:
% >> EEG = pop_multifit(EEG); % pop-up graphical interface
% >> EEG = pop_multifit(EEG, comps, 'key', 'val', ...);
%
% Inputs:
% EEG - input EEGLAB dataset.
% comps - indices component to fit. Empty is all components.
%... |
github | pylance/BCI-GEM-Pipeline-master | pop_dipfit_gridsearch.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/pop_dipfit_gridsearch.m | 4,833 | utf_8 | 39566131e1f04827a5eaf4dd7994f07f | % pop_dipfit_gridsearch() - scan all ICA components with a single dipole
% on a regular grid spanning the whole brain. Any dipoles that explains
% a component with a too large relative residual variance is removed.
%
% Usage:
% >> EEGOUT = pop_dipfit_gridsearch( EEGIN ); % pop up interactive window
% >> EEGO... |
github | pylance/BCI-GEM-Pipeline-master | dipfit_erpeeg.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/dipfit_erpeeg.m | 3,634 | utf_8 | c387b5b84e9f4ee03b832f289837c1a2 | % dipfit_erpeeg - fit multiple component dipoles using DIPFIT
%
% Usage:
% >> [ dipole model EEG] = dipfit_erpeeg(data, chanlocs, 'key', 'val', ...);
%
% Inputs:
% data - input data [channel x point]. One dipole per point is
% returned.
% chanlocs - channel location structure (returned by ... |
github | pylance/BCI-GEM-Pipeline-master | pop_dipfit_batch.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/pop_dipfit_batch.m | 2,342 | utf_8 | 349fbd140a3ba8c11fce24b8db9fa20c | % pop_dipfit_batch() - interactively do batch scan of all ICA components
% with a single dipole
% Function deprecated. Use pop_dipfit_gridsearch()
% instead
%
% Usage:
% >> OUTEEG = pop_dipfit_batch( INEEG ); % pop up interactive window
% >> OUTEEG = pop... |
github | pylance/BCI-GEM-Pipeline-master | dipfit_nonlinear.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/dipfit_nonlinear.m | 4,979 | utf_8 | 74c08245e5fcd0c004b5c7b3357238cf | % dipfit_nonlinear() - perform nonlinear dipole fit on one of the components
% to improve the initial dipole model. Only selected dipoles
% will be fitted.
%
% Usage:
% >> EEGOUT = dipfit_nonlinear( EEGIN, optarg)
%
% Inputs:
% ...
%
% Optional inputs are specified in key/value ... |
github | pylance/BCI-GEM-Pipeline-master | adjustcylinder2.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/adjustcylinder2.m | 2,197 | utf_8 | 34ff5cb12c3fc11a2456e7d82aedd980 | % adjustcylinder() - Adjust 3d object coordinates to match a pair of points
%
% Usage:
% >> [x y z] = adjustcylinder( x, y, z, pos1, pos2);
%
% Inputs:
% x,y,z - 3-D point coordinates
% pos1 - position of first point [x y z]
% pos2 - position of second point [x y z]
%
% Outputs:
% x,y,z - up... |
github | pylance/BCI-GEM-Pipeline-master | pop_dipfit_settings.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/dipfit2.3/pop_dipfit_settings.m | 20,001 | utf_8 | 7ef22305183621c09beb2691d0250fd9 | % pop_dipfit_settings() - select global settings for dipole fitting through a pop up window
%
% Usage:
% >> OUTEEG = pop_dipfit_settings ( INEEG ); % pop up window
% >> OUTEEG = pop_dipfit_settings ( INEEG, 'key1', 'val1', 'key2', 'val2' ... )
%
% Inputs:
% INEEG input dataset
%
% Optional inputs:
% 'hdmfile' ... |
github | pylance/BCI-GEM-Pipeline-master | firfiltdcpadded.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/firfiltdcpadded.m | 2,137 | utf_8 | b21b4207bf032e32cc6f0597db3cb4fe | % firfiltdcpadded() - Pad data with DC constant and filter
%
% Usage:
% >> data = firfiltdcpadded(data, b, causal);
%
% Inputs:
% data - raw data
% b - vector of filter coefficients
% causal - boolean perform causal filtering {default 0}
%
% Outputs:
% data - smoothed data
%
% Note:
% f... |
github | pylance/BCI-GEM-Pipeline-master | minphaserceps.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/minphaserceps.m | 1,275 | utf_8 | 7b751637e7eed71e29f91a4ef6b586e6 | % rcepsminphase() - Convert FIR filter coefficient to minimum phase
%
% Usage:
% >> b = minphaserceps(b);
%
% Inputs:
% b - FIR filter coefficients
%
% Outputs:
% bMinPhase - minimum phase FIR filter coefficients
%
% Author: Andreas Widmann, University of Leipzig, 2013
%
% References:
% [1] Smith III, O. J. (20... |
github | pylance/BCI-GEM-Pipeline-master | firws.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/firws.m | 3,219 | utf_8 | 0ab4c517238d31712ba1d97ab2497f45 | %firws() - Designs windowed sinc type I linear phase FIR filter
%
% Usage:
% >> b = firws(m, f);
% >> b = firws(m, f, w);
% >> b = firws(m, f, t);
% >> b = firws(m, f, t, w);
%
% Inputs:
% m - filter order (mandatory even)
% f - vector or scalar of cutoff frequency/ies (-6 dB;
% pi rad / sample)
%
% O... |
github | pylance/BCI-GEM-Pipeline-master | pop_firma.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_firma.m | 3,356 | utf_8 | e6d49147b8406a5ac3fed31ab0809194 | % pop_firma() - Filter data using moving average FIR filter
%
% Usage:
% >> [EEG, com] = pop_firma(EEG); % pop-up window mode
% >> [EEG, com] = pop_firma(EEG, 'forder', order);
%
% Inputs:
% EEG - EEGLAB EEG structure
% 'forder' - scalar filter order. Mandatory even
%
% Outputs:
% EEG - filtered ... |
github | pylance/BCI-GEM-Pipeline-master | pop_firpm.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_firpm.m | 7,851 | utf_8 | 7c3dd000ac470b65949e9914d8dc07d5 | % pop_firpm() - Filter data using Parks-McClellan FIR filter
%
% Usage:
% >> [EEG, com, b] = pop_firpm(EEG); % pop-up window mode
% >> [EEG, com, b] = pop_firpm(EEG, 'key1', value1, 'key2', ...
% value2, 'keyn', valuen);
%
% Inputs:
% EEG - EEGLAB EEG structure
% 'fcutoff' -... |
github | pylance/BCI-GEM-Pipeline-master | pop_eegfiltnew.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_eegfiltnew.m | 8,518 | utf_8 | 2643866f3c2d0e4035854d1705dce948 | % pop_eegfiltnew() - Filter data using Hamming windowed sinc FIR filter
%
% Usage:
% >> [EEG, com, b] = pop_eegfiltnew(EEG); % pop-up window mode
% >> [EEG, com, b] = pop_eegfiltnew(EEG, locutoff, hicutoff, filtorder,
% revfilt, usefft, plotfreqz, minphase);
%
% Inputs:
% EEG ... |
github | pylance/BCI-GEM-Pipeline-master | pop_xfirws.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_xfirws.m | 10,425 | utf_8 | d0777a1329eeb3b766e505a0b61242c9 | % pop_xfirws() - Design and export xfir compatible windowed sinc FIR filter
%
% Usage:
% >> pop_xfirws; % pop-up window mode
% >> [b, a] = pop_xfirws; % pop-up window mode
% >> pop_xfirws('key1', value1, 'key2', value2, 'keyn', valuen);
% >> [b, a] = pop_xfirws('key1', value1, 'key2', value2, 'keyn', valuen);
%... |
github | pylance/BCI-GEM-Pipeline-master | firfiltsplit.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/firfiltsplit.m | 2,363 | utf_8 | 8e58b4fa2694a8b1d55b8fcddcf94b4f | % firfiltsplit() - Split data at discontinuities and forward to dc padded
% filter function
%
% Usage:
% >> EEG = firfiltsplit(EEG, b);
%
% Inputs:
% EEG - EEGLAB EEG structure
% b - vector of filter coefficients
% causal - scalar boolean perform causal filtering {d... |
github | pylance/BCI-GEM-Pipeline-master | eegplugin_firfilt.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/eegplugin_firfilt.m | 2,667 | utf_8 | 681ba5e0933cafd6bb95672f910816cd | % eegplugin_firfilt() - EEGLAB plugin for filtering data using linear-
% phase FIR filters
%
% Usage:
% >> eegplugin_firfilt(fig, trystrs, catchstrs);
%
% Inputs:
% fig - [integer] EEGLAB figure
% trystrs - [struct] "try" strings for menu callbacks.
% catchstrs - [struct] "catc... |
github | pylance/BCI-GEM-Pipeline-master | windows.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/windows.m | 2,876 | utf_8 | 581c2f660f641935667a234f9b024f3a | % windows() - Returns handle to window function or window
%
% Usage:
% >> h = windows(t);
% >> h = windows(t, m);
% >> h = windows(t, m, a);
%
% Inputs:
% t - char array 'rectangular', 'bartlett', 'hann', 'hamming',
% 'blackman', 'blackmanharris', or 'kaiser'
%
% Optional inputs:
% m - scalar window len... |
github | pylance/BCI-GEM-Pipeline-master | pop_firpmord.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_firpmord.m | 3,145 | utf_8 | f4dae3b8e73f8ab3d73c2d207506ddd8 | % pop_firpmord() - Estimate Parks-McClellan filter order and weights
%
% Usage:
% >> [m, wtpass, wtstop] = pop_firpmord(f, a); % pop-up window mode
% >> [m, wtpass, wtstop] = pop_firpmord(f, a, dev);
% >> [m, wtpass, wtstop] = pop_firpmord(f, a, dev, fs);
%
% Inputs:
% f - vector frequency band edges
% ... |
github | pylance/BCI-GEM-Pipeline-master | firfilt.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/firfilt.m | 4,262 | utf_8 | d5703bbd52180bfb0661e6db5e649967 | % firfilt() - Pad data with DC constant, filter data with FIR filter,
% and shift data by the filter's group delay
%
% Usage:
% >> EEG = firfilt(EEG, b, nFrames);
%
% Inputs:
% EEG - EEGLAB EEG structure
% b - vector of filter coefficients
%
% Optional inputs:
% nFrames -... |
github | pylance/BCI-GEM-Pipeline-master | pop_firws.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_firws.m | 10,634 | utf_8 | 830347cf85c318140462a11e9257b53b | % pop_firws() - Filter data using windowed sinc FIR filter
%
% Usage:
% >> [EEG, com, b] = pop_firws(EEG); % pop-up window mode
% >> [EEG, com, b] = pop_firws(EEG, 'key1', value1, 'key2', ...
% value2, 'keyn', valuen);
%
% Inputs:
% EEG - EEGLAB EEG structure
% 'fcutoff' - v... |
github | pylance/BCI-GEM-Pipeline-master | plotfresp.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/plotfresp.m | 3,898 | utf_8 | 71a74a912328b37353e1523b662840fa | % plotfresp() - Plot FIR filter's impulse, step, frequency, magnitude,
% and phase response
%
% Usage:
% >> plotfresp(b, a, n, fs);
%
% Inputs:
% b - vector filter coefficients
%
% Optional inputs:
% a - currently unused, reserved for future compatibility with IIR
% filters {defaul... |
github | pylance/BCI-GEM-Pipeline-master | pop_firwsord.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_firwsord.m | 5,354 | utf_8 | 5150d668b377feb0d9e95b49486978ca | % pop_firwsord() - Estimate windowed sinc filter order depending on
% window type and requested transition band width
%
% Usage:
% >> [m, dev] = pop_firwsord; % pop-up window mode
% >> m = pop_firwsord(wtype, fs, df);
% >> m = pop_firwsord('kaiser', fs, df, dev);
%
% Inputs:
% wtype - char arra... |
github | pylance/BCI-GEM-Pipeline-master | pop_kaiserbeta.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/pop_kaiserbeta.m | 2,315 | utf_8 | 9a8a6636653865493068a0e901deeda6 | % pop_kaiserbeta() - Estimate Kaiser window beta
%
% Usage:
% >> [beta, dev] = pop_kaiserbeta; % pop-up window mode
% >> beta = pop_kaiserbeta(dev);
%
% Inputs:
% dev - scalar maximum passband deviation/ripple
%
% Output:
% beta - scalar Kaiser window beta
% dev - scalar maximum passband devi... |
github | pylance/BCI-GEM-Pipeline-master | findboundaries.m | .m | BCI-GEM-Pipeline-master/scripts/eeglab13_4_4b/plugins/firfilt1.6.1/findboundaries.m | 1,867 | utf_8 | b4b28dadb5f28c802c41266f791d942c | % findboundaries() - Find boundaries (data discontinuities) in event
% structure of continuous EEG dataset
%
% Usage:
% >> boundaries = findboundaries(EEG.event);
%
% Inputs:
% EEG.event - EEGLAB EEG event structure
%
% Outputs:
% boundaries - scalar or vector of boundary event latencies... |
github | pylance/BCI-GEM-Pipeline-master | rocarea.m | .m | BCI-GEM-Pipeline-master/scripts/RSVP/Hybrid_HDCA/rocarea.m | 2,035 | utf_8 | 5acce2b6025959f1be221423f5808bb4 | % rocarea() - computes the area under the ROC curve
% If no output arguments are specified
% it will display an ROC curve with the
% Az and approximate fraction correct.
%
% Usage:
% >> [Az,tp,fp,fc]=rocarea(p,label);
%
% Inputs:
% p - classification output
% label - truth labels {0,1}
%
% Outputs:
% Az - ... |
github | pylance/BCI-GEM-Pipeline-master | logist.m | .m | BCI-GEM-Pipeline-master/scripts/RSVP/Hybrid_HDCA/logist.m | 9,395 | utf_8 | 05b0ea3eef5e0f79eb344ab53543ba96 | % logist() - Iterative recursive least squares algorithm for linear
% logistic model
%
% Usage:
% >> [v] = logist(x,y,vinit,show,regularize,lambda,lambdasearch,eigvalratio);
%
% Inputs:
% x - N input samples [N,D]
% y - N binary labels [N,1] {0,1}
%
% Optional parameters:
% vinit - initialization f... |
github | pylance/BCI-GEM-Pipeline-master | bernoull.m | .m | BCI-GEM-Pipeline-master/scripts/RSVP/Hybrid_HDCA/bernoull.m | 1,578 | utf_8 | 490390cfd4acb3d36100dde7180998e7 | % bernoull() - Computes Bernoulli distribution of x for
% "natural parameter" eta. The mean m of a
% Bernoulli distributions relates to eta as,
% m = exp(eta)/(1+exp(eta));
%
% Usage:
% >> [p]=bernoull(x,eta);
%
% Inputs:
% x - data
% eta - distribution parameter
%
% Outputs:
% p - probability
%
% Aut... |
github | pylance/BCI-GEM-Pipeline-master | view_menu_analysis.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Menus/view_menu_analysis.m | 4,973 | utf_8 | f875e060e0872ad3bb23254d481bdfec | function varargout = view_menu_analysis(varargin)
% VIEW_MENU_ANALYSIS MATLAB code for view_menu_analysis.fig
% VIEW_MENU_ANALYSIS, by itself, creates a new VIEW_MENU_ANALYSIS or raises the existing
% singleton*.
%
% H = VIEW_MENU_ANALYSIS returns the handle to a new VIEW_MENU_ANALYSIS or the handle to
%... |
github | pylance/BCI-GEM-Pipeline-master | test2.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Menus/test2.m | 2,439 | utf_8 | 70bf50b612c261a4347c7dc191c77e1c | function [] = bci_gem_pipeline()
% Demonstrate how to use toggle buttons to mimic tabbed panels.
% Creates a GUI with three toggle buttons which act as tabs. One of the
% tabs is not selectable until the user plots a random quartic by
% pressing the pushbutton at the bottom of the screen. The middle tab
% shows the r... |
github | pylance/BCI-GEM-Pipeline-master | test1.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Menus/test1.m | 5,182 | utf_8 | e27dd1c52753e9c8a1714d141c9d53d1 | function varargout = pipeline_menu(varargin)
% MAIN MATLAB code for main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) ca... |
github | pylance/BCI-GEM-Pipeline-master | menu_analysis.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Menus/menu_analysis.m | 4,943 | utf_8 | 00035e23ccab1ab6f225e5f1403c1d26 | function varargout = menu_analysis(varargin)
% MENU_ANALYSIS MATLAB code for menu_analysis.fig
% MENU_ANALYSIS, by itself, creates a new MENU_ANALYSIS or raises the existing
% singleton*.
%
% H = MENU_ANALYSIS returns the handle to a new MENU_ANALYSIS or the handle to
% the existing singleton*.
%
% ... |
github | pylance/BCI-GEM-Pipeline-master | context_rsvp.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Contexts/Analysis/RSVP/context_rsvp.m | 17,439 | utf_8 | f5ec0e26cc690e4b1d4a8d60c85a2940 | function varargout = context_rsvp(varargin)
% CONTEXT_RSVP MATLAB code for context_rsvp.fig
% CONTEXT_RSVP, by itself, creates a new CONTEXT_RSVP or raises the existing
% singleton*.
%
% H = CONTEXT_RSVP returns the handle to a new CONTEXT_RSVP or the handle to
% the existing singleton*.
%
% CO... |
github | pylance/BCI-GEM-Pipeline-master | view_menu_analysis.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Contexts/Analysis/RSVP/view_menu_analysis.m | 4,972 | utf_8 | 50733a120f3340f6c6ec3e49e6c0dfea | function varargout = view_menu_analysis(varargin)
% VIEW_MENU_ANALYSIS MATLAB code for view_menu_analysis.fig
% VIEW_MENU_ANALYSIS, by itself, creates a new VIEW_MENU_ANALYSIS or raises the existing
% singleton*.
%
% H = VIEW_MENU_ANALYSIS returns the handle to a new VIEW_MENU_ANALYSIS or the handle to
%... |
github | pylance/BCI-GEM-Pipeline-master | context_pipeline_settings.m | .m | BCI-GEM-Pipeline-master/GUI Figures/Contexts/Settings/context_pipeline_settings.m | 17,295 | utf_8 | c55b599bce2c8367a7fc54ee105ba280 | function varargout = context_pipeline_settings(varargin)
% CONTEXT_PIPELINE_SETTINGS MATLAB code for context_pipeline_settings.fig
% CONTEXT_PIPELINE_SETTINGS, by itself, creates a new CONTEXT_PIPELINE_SETTINGS or raises the existing
% singleton*.
%
% H = CONTEXT_PIPELINE_SETTINGS returns the handle to a... |
github | sebdi/ellipse-fitting-master | fitWithLSO.m | .m | ellipse-fitting-master/fitWithLSO.m | 1,760 | utf_8 | 111b8e6a245fd709ee04148a131849f9 | function fit = fitWithLSO( Xi, Yi)
%FITWITHLSO Ellipse fitting with Least-Squares based on orthogonal distance
%
% [1] Sung Joon Ahn, W. Rauh, and M. Recknagel, "Ellipse fitting and
% parameter assessment of circular object targets for robot vision",
% Intelligent Robots and Systems, 1999.
%
% AUTHOR Sebastian D... |
github | sebdi/ellipse-fitting-master | main.m | .m | ellipse-fitting-master/main.m | 1,007 | utf_8 | 53772432f439b63566ed1575a5a666f2 | % Simulation of different ellipse fitting algorithms
%
% Input:
% x_c - x center of ellipse
% y_c - y center of ellipse
% a - major axis a
% b - minor axis b
% alpha - angle of ellipse
% var - variance of measurement noise
% sampleInterval - i... |
github | feuerchop/IndicativeSVC-master | FindLocal.m | .m | IndicativeSVC-master/Matlab/FindLocal.m | 1,518 | utf_8 | f558b80cbcd7276a1120bffed38beb3e | %==========================================================================
%
% Function to find local minimum of trained kernel radius function
%
% Return Values:
% N_locals: local min corresponding to each sample
% local: unique local mins
%
%
%=================================================... |
github | feuerchop/IndicativeSVC-master | moveit2.m | .m | IndicativeSVC-master/Matlab/moveit2.m | 1,900 | utf_8 | 7319254f8a4023c017ea2a46ca2719d2 | function moveit2(h);
%MOVEIT Move a graphical object in 2-D.
% Move an object in 2-D. Modify this function to add more functionality
% when e.g. the object is dropped. It is not perfect but could perhaps
% inspire some people to do better stuff.
%
% % Example:
% t = 0:2*pi/20:2*pi;
% X = 3 + sin(t); Y = 2... |
github | feuerchop/IndicativeSVC-master | dbscan.m | .m | IndicativeSVC-master/Matlab/dbscan.m | 4,433 | utf_8 | 955f026d8b370660b8c2b6b6703da507 |
% -------------------------------------------------------------------------
% Function: [class,type]=dbscan(x,k,Eps)
% -------------------------------------------------------------------------
% Aim:
% Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)
% ----------------------------------... |
github | SmashMouthFanClub/ml-project-2015-master | svdReduce.m | .m | ml-project-2015-master/svdReduce.m | 2,073 | utf_8 | eed654162adc75507f5c3f41c460a77d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% svdReduce.m
% This file will use SVD to reduce the dimensionality of the movie
% recommendation matrix.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | SmashMouthFanClub/ml-project-2015-master | loadMovieIDNameMap.m | .m | ml-project-2015-master/loadMovieIDNameMap.m | 896 | utf_8 | aa3ddb2be6e635b9ef75a4d51022522b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% Main Driver
% Loads the text file that maps movie IDs to their respective titles
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function... |
github | SmashMouthFanClub/ml-project-2015-master | plush.m | .m | ml-project-2015-master/plush.m | 413 | utf_8 | 5aa0c0bc909ad512ab41bf6f494a0110 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% plush.m
% This function, short for "print flush," will fprintf the string passed
% as an argument and flush stdout immediately
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | SmashMouthFanClub/ml-project-2015-master | meanNormData.m | .m | ml-project-2015-master/meanNormData.m | 696 | utf_8 | 2f6453c366136237c717bbfe48acc9b1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% meanNormData.m
% Normalize the data by subtracting the mean from each movie rating
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
functi... |
github | SmashMouthFanClub/ml-project-2015-master | svdReconstruct.m | .m | ml-project-2015-master/svdReconstruct.m | 475 | utf_8 | 0be4ddbf6f56990869ac9b08eae4a9c5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% svdReconstruct.m
% This file will reconstruct an approximate movie recommendation matrix
% from the SVD reduced movie recommendation matrix
%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | SmashMouthFanClub/ml-project-2015-master | collabFilter.m | .m | ml-project-2015-master/collabFilter.m | 1,868 | utf_8 | f79086006c9ef01716bb58ccc2872290 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% collabFilter.m
% This file performs the collaborative filtering cost function, and
% returns the cost and gradient. Both X and Theta are updated
% simultaneously in... |
github | SmashMouthFanClub/ml-project-2015-master | rootMeanSqErr.m | .m | ml-project-2015-master/rootMeanSqErr.m | 705 | utf_8 | ed31cb7fbcaff9a58f30f5ecb85514d8 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% rootMeanSqErr.m
% This function calculates the root mean squared error of the
% recommender system.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | SmashMouthFanClub/ml-project-2015-master | genTestSet.m | .m | ml-project-2015-master/genTestSet.m | 1,055 | utf_8 | 4929ffc6bc1e82a960189b3c4e73aa3a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% genTestSet.m
% Split the data into training and test set, applying p percent of the
% data to the test set. Then extract away q percent of the test values.
%%%%%%%%%... |
github | SmashMouthFanClub/ml-project-2015-master | plotCost.m | .m | ml-project-2015-master/plotCost.m | 733 | utf_8 | 3adab43061f91556ef15884dc98aec53 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 91.427/545 Machine Learning
% Mike Stowell, Anthony Salani, Misael Moscat
%
% plotCost.m
% This function plots the cost J after collaborative filtering learning,
% setting the number of features, lambda, root mean squared error, and
% learning... |
github | a380406228/actRec-master | WolfeLineSearch.m | .m | actRec-master/actRec/minFunc/WolfeLineSearch.m | 11,106 | utf_8 | f97d9ca0bf8aab87df9aa65e74f98589 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting lo... |
github | a380406228/actRec-master | minFunc_processInputOptions.m | .m | actRec-master/actRec/minFunc/minFunc_processInputOptions.m | 3,551 | utf_8 | ea7fbcf303b9cafeca4045921adad934 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCu... |
github | YuanhaoGong/CurvatureFilter-master | CF.m | .m | CurvatureFilter-master/Matlab/CF.m | 8,407 | utf_8 | 410b4dcefc3e26f5d0946fe8cf338395 | function [result, Energy] = CF(im, FilterType, ItNum, stepsize)
% =========================================================================
%
% Curvature Filter
%
% *************************************************************************
%
% @phdthesis{gong:phd,
% titl... |
github | YuanhaoGong/CurvatureFilter-master | Solver.m | .m | CurvatureFilter-master/Matlab/Solver.m | 6,869 | utf_8 | dfe2b20b1992ec815626ed23affc624d | function [result, Energy] = Solver(im, FilterType, DataFitOrder, Lambda, MaxItNum)
% =========================================================================
%
% Curvature Filter
%
% *************************************************************************
%
% @phdthesis{gong:ph... |
github | YuanhaoGong/CurvatureFilter-master | GUI.m | .m | CurvatureFilter-master/Matlab/GUI.m | 16,428 | utf_8 | bfdef254adc8b2a4c6ea1f702f8654b6 | function varargout = GUI(varargin)
% GUI MATLAB code for GUI.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | yudhapane/Master-Thesis-master | animateRobot.m | .m | Master-Thesis-master/1 DoF simulation/animateRobot.m | 1,424 | utf_8 | c79305144ea87fe920df69ad08783194 | function animateRobot(t, x)
% function animateRobot(t, x, figHandle)
par.l = 4.2e-2; % link's length [m]
if (t == 0)
N = size(x,2);
else
N = length(t);
end
colors = [0.7 0.5 0.2];
figHandle = figure;
axesHandle = axes('Parent', figHandle, 'Position', [0 0 1 1]);
link ... |
github | yudhapane/Master-Thesis-master | animateRobotRL.m | .m | Master-Thesis-master/1 DoF simulation/animateRobotRL.m | 2,631 | utf_8 | 1164d198b6e8da6114740abd0c9d11e5 | function animateRobotRL(t, x, iterIndex, indexSelector, params)
Ntrials = params.Ntrial;
trialsIdx = find(iterIndex==1);
iterCount = 1;
par.l = 4.2e-2; % link's length [m]
if (t == 0)
N = size(x,2);
else
N = length(t);
end
colors = [0.7 0.5 0.2];
figHandle ... |
github | tholden/particles-master | DSMH_sampler.m | .m | particles-master/src/DSMH_sampler.m | 12,656 | utf_8 | 7995250f177ced471972a8edefcf53b8 | function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% function DSMH_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Dynamic Striated Metropolis-Hastings algorithm.
%
% INPUTS
%... |
github | ayjavaid/OMNET_OS3_UAVSim-master | estimate_mean.m | .m | OMNET_OS3_UAVSim-master/contrib/octave/estimate_mean.m | 1,500 | utf_8 | 7d22f26bea1c5e10f05f6820746cd94d | ##
## Name:
##
## estimate_mean
##
## Description:
##
## Given a vector of samples, this function estimates the mean
## and confidence interval. It is assumed that the samples
## are taken from a approximately bell-shaped population.
## The function is optimised for sample set sizes of less
## than 30.
##
#... |
github | ayjavaid/OMNET_OS3_UAVSim-master | run_sim.m | .m | OMNET_OS3_UAVSim-master/contrib/octave/run_sim.m | 1,527 | utf_8 | a7d7afcd40c2a4dbd82289ad7f930ca1 | ##
## Name:
##
## run_sim
##
## Description:
##
## This function calls a simulation and displays the output immediately
## on the console. The function only returns when the simulation
## is completed. Apart from the simulation file, the configuration file
## may also be optionally specified.
##
## Parameters... |
github | ayjavaid/OMNET_OS3_UAVSim-master | create_seeds.m | .m | OMNET_OS3_UAVSim-master/contrib/octave/create_seeds.m | 1,338 | utf_8 | 7604c484e31e57c58d0a6c2465d5383a | ##
## Name:
##
## create_seeds
##
## Description:
##
## This function generates a vector of orthogonal seeds suitable for
## simulation replications. The seeds are generated by the omnetpp
## seedtool utility and piped into a vector, which is returned as the
## result to the caller.
##
## Parameters:
##
## ... |
github | flavioluiz/port-hamiltonian-master | exact_slosh.m | .m | port-hamiltonian-master/LHMNLC2015/exact_slosh.m | 1,350 | utf_8 | 5f582dc510f3e439d62abbb03708acd2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | exact_tb.m | .m | port-hamiltonian-master/LHMNLC2015/exact_tb.m | 1,754 | utf_8 | 3b72c2cb4a42d50f9221b2a3897fff6d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | dataexperiment.m | .m | port-hamiltonian-master/LHMNLC2015/dataexperiment.m | 8,557 | utf_8 | a22bc7ed18d4e4a0b22085835bb85dc3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | exact_eb.m | .m | port-hamiltonian-master/LHMNLC2015/exact_eb.m | 2,174 | utf_8 | 44af7084fb2bfb4c58053c65da2ed4aa | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | massspring.m | .m | port-hamiltonian-master/LHMNLC2015/main/massspring.m | 969 | utf_8 | 76149a8c68fa4fc4fc1144864dcaaa2d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | phcat.m | .m | port-hamiltonian-master/LHMNLC2015/main/phcat.m | 898 | utf_8 | ebe911ace1b7e879a1ce65175cafeef3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | plotfull.m | .m | port-hamiltonian-master/LHMNLC2015/main/plotfull.m | 3,205 | utf_8 | 67eac8c76137ee4c6de5b1789b7715e0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | rb.m | .m | port-hamiltonian-master/LHMNLC2015/main/rb.m | 952 | utf_8 | 7fd5742a1eb724269d5e8edcba034efb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | rotsaintvenant.m | .m | port-hamiltonian-master/LHMNLC2015/main/rotsaintvenant.m | 3,107 | utf_8 | 0c66dd9e0ee04614641118b076fe6bd0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | eulerbernoulli.m | .m | port-hamiltonian-master/LHMNLC2015/main/eulerbernoulli.m | 2,529 | utf_8 | 04e82926a4d9396820cfaf337a459e9d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | couplefullsystem.m | .m | port-hamiltonian-master/LHMNLC2015/main/couplefullsystem.m | 2,712 | utf_8 | 61b29dc367a00e999272431e6f3f2ed4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | flavioluiz/port-hamiltonian-master | torsion.m | .m | port-hamiltonian-master/LHMNLC2015/main/torsion.m | 2,654 | utf_8 | fc65047c6c169e016b12296ef829f961 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Flavio Luiz Cardoso-Ribeiro: http://github.com/flavioluiz/ %
% ISAE-Supaero / Instituto Tecnologico de Aeronautica %
% CNPq - Bra... |
github | wuyou33/uclathesis-master | project.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/project.m | 6,041 | utf_8 | 41b78b0b0b47b192efcf34ef590a5a17 | %
% Deterministic subspace identification (Projection)
%
% [A,B,C,D] = project(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estimat... |
github | wuyou33/uclathesis-master | kr2gl.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/kr2gl.m | 571 | utf_8 | 2084f15313cf56728741f811fa774d6d | %
% [G,L0] = kr2gl(A,K,C,R)
%
% Description:
% Find the covariance sequence from
% the Kalman gain (K) and the innovation covariance (R)
%
% References:
% None
%
% Copyright:
% Peter Van Overschee, December 1995
% peter.vanoverschee@esat.kuleuven.ac.b... |
github | wuyou33/uclathesis-master | com_alt.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/com_alt.m | 9,362 | utf_8 | 34fb5493c29fb2b1630af33c4a2a3057 | %
% Combined subspace identification (Algorithm 1)
%
% [A,B,C,D,K,R] = com_alt(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estimat... |
github | wuyou33/uclathesis-master | det_stat.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/det_stat.m | 7,386 | utf_8 | 6452d5d8450e8721a1d287068273d741 | %
% Deterministic subspace identification (Algorithm 1)
%
% [A,B,C,D] = det_stat(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be esti... |
github | wuyou33/uclathesis-master | blkhank.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/blkhank.m | 691 | utf_8 | f20078949b4fb96b573a99f74370e9ae | %
% H = blkhank(y,i,j)
%
% Description:
% Make a block Hankel matrix with the data y
% containing i block-rows and j columns
%
% References:
% None
%
% Copyright:
% Peter Van Overschee, December 1995
% peter.vanoverschee@esat.kuleuven.ac.be
%
%
function H = bl... |
github | wuyou33/uclathesis-master | intersec.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/intersec.m | 5,400 | utf_8 | 6a787ab1ca1de51227768ae2796884e9 | %
% Deterministic subspace identification (Intersection)
%
% [A,B,C,D] = intersec(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be esti... |
github | wuyou33/uclathesis-master | sto_pos.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/sto_pos.m | 7,527 | utf_8 | d6433b53cbd94183b747d688ea6a934e | %
% Stochastic subspace identification (Algorithm 3)
%
% [A,K,C,R] = sto_pos(y,i);
%
% Inputs:
% y: matrix of measured outputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estimated
% Typically: i = 2 * (max o... |
github | wuyou33/uclathesis-master | solvric.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/solvric.m | 1,314 | utf_8 | b1e4e7ca9b4859caa9ad4beb76f5c2e8 | %
% [P,flag] = solvric(A,G,C,L0)
%
% Description:
% Solves the Forward Riccati equation:
%
% P = A P A' + (G - A P C') (L0 - C P C')^{-1} (G - A P C')'
%
% Using the generalized eigenvalue decomposition of page 62
% flag = 1 when the covariance sequence is not positive... |
github | wuyou33/uclathesis-master | sto_alt.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/sto_alt.m | 6,994 | utf_8 | 4ba24ac2974d9ed06e8f41ad957ccbdf | %
% Stochastic subspace identification (Algorithm 2)
%
% [A,K,C,R] = sto_alt(y,i);
%
% Inputs:
% y: matrix of measured outputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estimated
% Typically: i = 2 * (max o... |
github | wuyou33/uclathesis-master | simul.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/simul.m | 2,413 | utf_8 | 473392756ee469fa1bcf3f5e8ebf7d1e | %
% [ys,ers] = simul(y,u,A,B,C,D,ax)
%
% Description:
% Simulation of a state space model A,B,C,D
%
% xs_{k+1} = A xs_k + B u_k
% ys_k = C xs_k + D u_k
%
% The initial state xs_0 is estimated from the first N points
% Where N is equal to 3 times the ord... |
github | wuyou33/uclathesis-master | allord.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/allord.m | 1,213 | utf_8 | 9f22166ab790c7220caa77a4647dda74 | %
% [ersa,erpa,AUX] = allord(y,u,i,nall,AUX,W)
%
% Does subspace identification subid for all orders in the
% vector nall and plots the simulation (ersa) and prediction (ersp)
% errors.
%
% Copyright:
% Peter Van Overschee, December 1995
% peter.vanoverschee@esat.kuleuven.ac.be
%
... |
github | wuyou33/uclathesis-master | show_res.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/show_res.m | 2,796 | utf_8 | feba234159f51e2158f9beffcc0deaa2 | % Function that shows the results of an application
function show_res(tit,m,l,n_id,n_val,...
er1,er2,er3,er4,er5,er6,er7,er8,er9,er10,...
er11,er12,er13,er14,er15,er16,er17,er18,er19,er20,tt1,tt2,n)
if (mean(er1) > 1000);er1 = Inf;end
if (mean(er2) > 1000);er2 = Inf;end
if (mean(er3) > 1000);er3 = Inf;end
if (... |
github | wuyou33/uclathesis-master | subid.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/subid.m | 11,545 | utf_8 | d2de26667f57441bb0e16201f843a70e | %
% General subspace identification
% -------------------------------
%
% The algorithm 'subid' identifies deterministic, stochastic
% as well as combined state space systems from IO data.
%
% [A,B,C,D,K,R] = subid(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: m... |
github | wuyou33/uclathesis-master | chkaux.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/chkaux.m | 1,354 | utf_8 | 099cabce235732fd6ccba21a60f8a14e | %
% [AUX,Wflag] = chkaux(AUXin,i,u(1,1),y(1,1),flag,W,sil)
%
% Description:
% Compatibility of AUXin check
% AUX = [] when nothing is useful
% Wflag = 1 when R information is OK, but weight information not
%
% References:
% None
%
% Copyright:
% Peter Van O... |
github | wuyou33/uclathesis-master | predic.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/predic.m | 3,125 | utf_8 | 51bad113e1d7e53e76c9179ca036e887 | %
% [yp,erp] = predic(y,u,A,B,C,D,K,ax)
%
% Description:
% Prediction with the state space model A,B,C,D,K (one step ahead)
%
% xp_{k+1} = A xp_k + B u_k + K (yp_k - C xp_k - D u_k)
% yp_k = C xp_k + D u_k
%
% The initial state xp_0 is estimated from the firs... |
github | wuyou33/uclathesis-master | gl2kr.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/gl2kr.m | 797 | utf_8 | 25cb5b5c69088b1c1563e8b0af09dbef | %
% [K,R] = gl2kr(A,G,C,L0)
%
% Description:
% Solve for the Kalman gain (K) and the innovation covariance (R)
% The resulting model is of the form:
%
% x_{k+1} = A x_k + K e_k
% y_k = C x_k + e_k
% cov(e_k) = R
%
% Copyri... |
github | wuyou33/uclathesis-master | com_stat.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/com_stat.m | 9,233 | utf_8 | ed568cd18fe6af7f178852483e35ec6c | %
% Combined subspace identification (Algorithm 1)
%
% [A,B,C,D,K,R] = com_stat(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estima... |
github | wuyou33/uclathesis-master | myss2th.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/myss2th.m | 1,164 | utf_8 | 0695a99464202c8e39e1dcc475be05be | %
% th = myss2th(A,B,C,D,K,flag)
%
% Description:
% Converts a state space model to a theta model
%
% The state space model is first converted to observability
% canonical form. The parameters of this model are then
% used in the theta format.
%
% If flag = 'oe', and outp... |
github | wuyou33/uclathesis-master | sto_stat.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/sto_stat.m | 7,430 | utf_8 | 1ab52befd4bdf0272e7cba73adad34f9 | %
% Stochastic subspace identification (Algorithm 1)
%
% [A,K,C,R] = sto_stat(y,i);
%
% Inputs:
% y: matrix of measured outputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estimated
% Typically: i = 2 * (max ... |
github | wuyou33/uclathesis-master | det_alt.m | .m | uclathesis-master/scripts/zArchive/vanoverschee/SUBFUN/det_alt.m | 7,142 | utf_8 | 2f2c102534a0b6845afc52eaaf9c68b5 | %
% Deterministic subspace identification (Algorithm 2)
%
% [A,B,C,D] = det_alt(y,u,i);
%
% Inputs:
% y: matrix of measured outputs
% u: matrix of measured inputs
% i: number of block rows in Hankel matrices
% (i * #outputs) is the max. order that can be estima... |
github | wuyou33/uclathesis-master | find_position.m | .m | uclathesis-master/scripts/zArchive/IMU_calculator/find_position.m | 2,078 | utf_8 | 5481de0ff0ec05a6ef0a34590f6f64f2 | %This function takes the IMU data as the input and returns the plot of x,y,z,theta,phi,psi,u,v,w.
%The data is a 7 coloumn matrix with each row representing a reading of the
%IMU.
%the coloumn should contain the data in the following format:
%col data
%1 time
%2 Ax
%3 Ay
%4 Az
%5 p(angula... |
github | wuyou33/uclathesis-master | CubeHelix.m | .m | uclathesis-master/scripts/plots/CubeHelix.m | 1,572 | utf_8 | b1a4887092160940a7b47eb1c8936ad6 | % Usage:
% colormap(CubeHelix(...))
%
%==========================================================
% Calculates a "cube helix" colour map for MATLAB. The
% colours are a tapered helix around the diagonal of the
% RGB colour cube, from black [0,0,0] to white [1,1,1].
% Deviations away from the diagonal vary quadr... |
github | wuyou33/uclathesis-master | find_position.m | .m | uclathesis-master/scripts/plots/trajectory/find_position.m | 2,078 | utf_8 | 5481de0ff0ec05a6ef0a34590f6f64f2 | %This function takes the IMU data as the input and returns the plot of x,y,z,theta,phi,psi,u,v,w.
%The data is a 7 coloumn matrix with each row representing a reading of the
%IMU.
%the coloumn should contain the data in the following format:
%col data
%1 time
%2 Ax
%3 Ay
%4 Az
%5 p(angula... |
github | CPernet/SPMPC-master | spm_pc.m | .m | SPMPC-master/spm_pc.m | 6,286 | utf_8 | 47bdeca97eba8fb3b8cec2985fb67037 | function varargout = spm_pc(varargin)
% SPM_PC M-file for spm_pc.fig
% the Patient Classification toolbox is designed
% to classify one or several subjects in comparison
% with a control group - bootstrapped CI are computed
% for the control group and subjects are classified
%
% the toolbox requires SPM5 and the Matla... |
github | CPernet/SPMPC-master | spmpc_K_fold.m | .m | SPMPC-master/spmpc_K_fold.m | 11,159 | utf_8 | 001fa20eae1372f05786b0729fc86834 | function spmpc_K_fold(path,P_controls,P_patients,alpha_value,Nboot)
% this function is to compute a 3 fold cross validation
% control data are split in 3, 2/3 are used in turn to compute the CI
% then patients are classified - after the 3 rounds, results are averaged
%
% cyril pernet 05/11/2008 v4
%% --------------
%... |
github | tp6vul3wj/PatchMatch-filter-master | computeColor.m | .m | PatchMatch-filter-master/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | tp6vul3wj/PatchMatch-filter-master | DescriptorsNN.m | .m | PatchMatch-filter-master/DescriptorsNN.m | 3,580 | utf_8 | dcdb8ba6033b37175ec4401fca9e4d5a | function [ ratios NN1s NN1s_dist ] = DescriptorsNN( descps1,descps2, K, descp_type )
%DESCRIPTORSNN Summary of this function goes here
% Detailed explanation goes here
% [height,width,dim]=size(descps1.SIFT);
for i=1:length(descp_type)
tic;
[ratios.(descp_type{i}), NN1s.(descp_type{i}), NN1s_dist.(descp_typ... |
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