sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
41fd5fd6d8218a3a18e1e99cf8019dcb90c60302562662e9a5659a0c01625b88 | MATLAB | 3,487 | 100 | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
e745568617b0646b7d29a30f03d3fc6bfb65f0e706c9001e9ce7af4303dded94 | MATLAB | 3,487 | 78 | function [sz_str, mess, IO] = CheckGlobals(IO, act, na_str, mess)
if ~exist('mess','var') , mess=[]; end
sz_str = na_str;
if strcmp(IO.globvar_edit,na_str), return; end
nP = size(IO.PP,1);
flg=false;
try
switch act
case 'check_globvar'
globvar_str = sprintf('Var: ''%s''',IO.globvar... |
e15bf9df46bee9ef3bcf4143be16219c0c0507e1f29f4243ff258b7535e4652f | MATLAB | 3,495 | 91 | function sY = nk_PerfSpatFilt( Y, CURACT, Param )
if isfield(CURACT,'SPATIAL') && CURACT.SPATIAL.cubetype>1
% If data fusion has happened before the smoothing
% extract the modality, smooth it and fuse it again
S = CURACT.SPATIAL;
dimvecx = Param.dimvecx;
if iscell(Param.brainmask), nM = numel(Par... |
db5e0ab70bff9a8ed51a97437ff7ab37d39a0a8cca76d638336aaed865a41f7f | MATLAB | 3,501 | 124 | function result=mloclogist(x,varargin)
%MLOCLOGIST is an M-estimator of location with psi-function equal to
% (exp(x)-1)/(exp(x)+1) and with an auxiliary scale estimate.
% It is iteratively computed. It can resist 50% outliers.
% If x is a matrix, the location estimate is computed on the columns of x. The
% result is... |
2f8338f3037338981271d779652b3c4dc9eaf57280527666da510403f15bdf64 | MATLAB | 3,502 | 140 | function [Eres,prob_SPL] = resource_efficiency_bin(adj,lambda,SPL,M)
% RESOURCE_EFFICIENCY_BIN Resource efficiency and shortest-path probability
%
% [Eres,prob_SPL] = resource_efficiency_bin(adj,lambda,SPL,M)
%
% The resource efficiency between nodes i and j is inversly proportional
% to the amount of resou... |
34f4039e7d02559b8b441658158b59bc1e650d2e7220f9ff802df56fff077e10 | MATLAB | 3,502 | 73 | function [weights, score] = nk_SimbaMain(X_train, Y_train, extra_param, gpu)
% alphas = simba(X_train, Y_train, extra_param);
%
% input: X_train(i,j) is the value of feature j in training instance i.
% Y_train(i) is the label of training instance i.
% extra_param is a struct that may cont... |
96d68bdd7c557dae965353bba7031078acbd46faa2f1f4df4975f1adff278503 | MATLAB | 3,502 | 87 | <<<<<<< HEAD
% GIfTI Class for MATLAB
%
% Geometry format under the Neuroimaging Informatics Technology Initiative
% (NIfTI):
% http://www.nitrc.org/projects/gifti/
% http://nifti.nimh.nih.gov/
%
% This MATLAB class is part of SPM:
% https://www.fil.ion.ucl.... |
8cc2dbc82f741ffc2d811a2f3ac6be2f984a5a17f7906325d08aefa46e1d3b0a | MATLAB | 3,517 | 100 | function [k,rho,kSuggested]=chooseBestk(X,option)
% Model selection. Search the best number of clusters based on dispersion Coefficients from k=1 to sqrt(#column).
% X: matrix, as input of a NMF, each column is a data point
% option: struct,
% option.rerun: scalar, the time of reruning NMF for a k, the default is 3;
% ... |
ea544c9a10efca7e828aec129c1ad579e9ba58587d2a33b18673771608242f7c | MATLAB | 3,520 | 125 | function [ W, info, Thstar ] = MTFLCd_DSolver( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration
% diagnoal version for faster computation on small sample size.
% dual projected gradient -- using solver.
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 |... |
71293f35143c1521567b79535f80e4f345ede33891fbccce9fa4ae3eb13d0a80 | MATLAB | 3,527 | 105 | function [ fY, C ]= nk_SpatFilt(Y, SPATIAL)
if isfield(SPATIAL,'PX') && numel(SPATIAL.PX.opt)>1
n_opt = numel(SPATIAL.PX.opt);
else
n_opt=1;
end
fY = cell(n_opt,1);
if ~isempty(SPATIAL.badcoords);
tY = zeros(size(Y,1),size(SPATIAL.badcoords,2));
tY(:,~SPATIAL.badcoords) = Y;
else
tY = Y;
end
for... |
75ec05b06592b69addd3b20aa62c749fd4cf34559ebfdcd7d8cadecb26292106 | MATLAB | 3,529 | 82 | %% ================================================================
% Script: generate_individual_networks.m
% Purpose:
% Generate individualized functional networks for each subject
% using the template-matching (TM) approach.
%
% Description:
% - Applies MSC-derived group templates to each subject (MSC/... |
51ea0a9ca6e5a56d44eb4fc0b4b319e44e18aa8c953e3817170a596e001a3e96 | MATLAB | 3,532 | 103 | function h = bootstatFDR_clustercorr(zmap_p,mask,ff,rectime,rectype,act_chan,varargin)
% BOOTSTATFDR_CLUSTERCORR Cluster-based correction of stat. result obtained
% by testing significant change in time-frequency data relative to baseline.
% bootstatFDR_clustercorr(zmap_p,mask,ff,rectime,rectype,act_chan,...)
% ... |
99a7ef47e31894bab1033177465fdf5998ee3a5c11fd665265d83b6ee6cdeae7 | MATLAB | 3,532 | 88 | %==========================================================================
%FORMAT OUT = MultiDichoFoldPerm(IN, OUT, F)
%==========================================================================
%MULTI-GROUP CLASSIFICATION PERFORMANCE EVALUATION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
b7f83d939a1a8b99aa8031a06caba5e6cd96fa34b519b19eff68f8d4c87c1976 | MATLAB | 3,535 | 84 | function CI = polyparci(PolyPrms,PolyS,alpha)
% POLYPARCI takes PolyPrms, the 1xN vector of parameter estimates from polyfit,
% PolyS, the structure returned by polyfit, and alpha, and returns the
% alpha confidence intervals for the parameters in CI. The default
% for alpha is 0.95.
%
% RETURNS: CI (2xN ma... |
c1f208034dd1d8ab8951672d9473db78914b855bf55c739e434e1d27f5037735 | MATLAB | 3,557 | 108 | function [meanInner,medianInner,ARI,sortedBasisNum] = evaluateReproducibility(pathDir1,pathDir2)
% function that evaluates the reproducibility between the results obtained
% for two different splits
% The scripts assumes that experiments using the same range of components
% have been performed for both splits
% outpu... |
6c83037fe578bbb7ad2f3a21df3cc69b37cd1360eb094addbdfe7a4c39f5a80b | MATLAB | 3,559 | 128 | % ========================================================================
classdef joystickManager < optickaCore
%> @class joystickManager
%> @brief Manages the Simia Joystick
%>
%> Copyright ©2014-2023 Ian Max Andolina — released: LGPL3, see LIv12345c12345CENCE.md
% ===================================================... |
8112c706eb2c0efdaf2d87b7c39bc6ad103a2f4bf8f15c9db3cf0d22036476d2 | MATLAB | 3,563 | 102 | function preop_postop_RT(grnm,conditions)
%PREOP_POSTOP_RT RT change relative to preop condition
% PREOP_POSTOP_RT(grnm,conditions) calculates RT difference between preop
% and all other task conditions for the specified patient groups.
%
% Required inputs:
% GRNM cell array of patient group la... |
2ef21609f1f52f03bc8bf53e7c7a6f609d95f4658a57dbb82a640e571e81a59c | MATLAB | 3,568 | 96 | clear
load('ProcessedData\Footprints_MasterTable.mat')
load("ProcessedData\OutputTable.mat")
load("ProcessedData\PreProcessed.mat")
load('ProcessedData\tables_compiled.mat');
% load('animal_Names_Responses.mat')
%% set these things first
% scaling value for cell size; bigger number makes the cells smaller
... |
61d37271f96ceb8a4ed995e343c81a96573bc200065cf0da40a5cf81bb30fc5b | MATLAB | 3,570 | 114 | function y = randsample(n, k, replace, w)
%RANDSAMPLE Random sample, with or without replacement.
% Y = RANDSAMPLE(N,K) returns Y as a 1-by-K vector of values sampled
% uniformly at random, without replacement, from the integers 1:N.
%
% Y = RANDSAMPLE(POPULATION,K) returns K values sampled uniformly at
% rando... |
f06827a3e29c217481df74c85fa57266743d06cc1518e8503dde2f505e8217ac | MATLAB | 3,575 | 98 | function [allDiffLAT_P1Go,allDiffLAT_N1Go,allDiffLAT_P1MovOn,allDiffLAT_N1MovOn,allRT1,allRT2,allBestLag_rVal]=f_CalcDifferenceLatencies_Solo_or_JaSim_Sh(LAT,contrast,fs,saveFig,t_start,t_end)
% contrast=1: solo/solo (randomly paired solo trials)
% contrast=4: jaSim (randomly paired ja trials)
allDiffLAT_P1Go=[]... |
42e41f59c07b5181bdf16cd939d924878d3c53cf7f9581941ae64f57f5723629 | MATLAB | 3,579 | 89 | function PX = nk_ReturnParamChain(PO, optimonly)
if ~exist('optimonly','var') || isempty(optimonly), optimonly = false; end
nP = 1; if iscell(PO), nP = numel(PO); end
PX = struct('Params',[],'Params_desc',[],'Typ',[],'cmd',[],'steps',[],'stepparams',[],'opt',[]);
for n=1:nP
Params = []; Params_desc = []; Typ... |
02c1eca20c926254e62b345f7b747279cd37a8ba8e8a48a8151672f3604dcc53 | MATLAB | 3,590 | 108 | function [allDiffLAT_P1Go,allDiffLAT_N1Go,allDiffLAT_P1MovOn,allDiffLAT_N1MovOn,allRT1,allRT2,allDiff_RT,allBestLag_rVal,allICD_lat]=f_CalcDifferenceLatencies_SoloObs_or_JA_perChDir_vSh(LAT,contrast,fs,saveFig,t_start,t_end)
% contrast=2: solo/obs
% contrast=3: ja/ja
tsBeforeMtOn=round(t_start*fs);
allDiffLAT... |
a3c6a0ccb17aaab401857eb15be57e14a8176de14b6abad65daf5a98ba63dba3 | MATLAB | 3,593 | 131 | function [AtA,Y,numIter,tElapsed,finalResidual]=superkernelseminmfnnls(X,L,k,option)
% Supervised Kernel semi-NMF based on NNLS: phi(X)=A_phiY, s.t. Y>0, diag(AtA)=I.
% solve the problem:
% min_{A,Y,xi} 1/2|X-AY|_F^2 + 1/2|w|_2^2 + C'xi
% s.t. Z'w + xi <= L
% Y>=0
% xi>=0
% where Z=[1;Y];
% to be finished...... |
d81aff6177889274ac5d3f7e18d66ddb686dd1be4fb32f3ebe3bb68989ad7016 | MATLAB | 3,597 | 104 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
ec92adc43e53d05525ee459bc076279adaff7fd53276e1b12705dd8523956499 | MATLAB | 3,599 | 100 | function T = printvis_ranked_cross_binary(analysis, fld, reorder, features, nfeat, act)
% T = printvis_ranked_cross_binary(analysis, fld, reorder, features, nfeat, act)
% ==============================================================================
% The algorithm computes the mean of fld across available binary
% cla... |
52a4093aa43607b0062859d390e3f9d4b33d84a1c37f1cfad2152e4674c61ee1 | MATLAB | 3,600 | 74 | function mapYi = nk_MLOptimizer_ExtractDimMat(mapY, dim_index, cPs)
global PREPROC STACKING MULTILABEL RAND
[m,n,o] = size(mapY.Tr);
mapYi = mapY;
mapYi.Tr = cell(m,n,o);
mapYi.CV = cell(m,n,o);
mapYi.Ts = cell(m,n,o);
IMPUTE.flag = 0;
if iscell(PREPROC), iPREPROC = PREPROC{1}; else, iPREPROC = PREPROC; end
BINMOD... |
d713bf159365cbbe51b966607d7a481248b41b599e6fa9161640ebb96b069922 | MATLAB | 3,602 | 105 | function [A,Y,numIter,tElapsed,finalResidual]=convexnmfrule(X,k,option)
% Convex-NMF based on multiplicative update rules: X=XAY, s.t. A,Y>0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=convexnmfrule(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=convexnmfrule(X,k,option)
% X: matrix of mixed signs, datas... |
b0535affa0b275b23a94b3ce839074b63abc70b6c6d06978e73e7b273bceac1a | MATLAB | 3,603 | 122 | function varargout = blogreg(varargin)
%
% BLOGREG - logistic regression with Bayesian regularisation of a Laplace prior
%
% ALPHA = BLOG(X, T) computes the model parameters of a sparse logistic
% regression model, ALPHA. X is the matrix of input features, where each
% row represents a training pattern and ... |
0b5ca18714e0b20941c959fff8b542d63ae786d3779e22398e61b8f335f3cf0a | MATLAB | 3,610 | 117 | % =========================================================================
% function [P, Perf, d, crit] = nk_MultiDecideHierarchOneVsOne(H, Y,
% Classes, maxfunc)
% =========================================================================
%
% INPUTS:
% -------
% H : ... |
c806630a01a133ea6b45dc12d7aa74afc555fa987af5576f17c91d8b7297b5c8 | MATLAB | 3,621 | 100 | %% msRun2018
% Version 1.0 GE
% Updated version of the msRun script originally proposed by Daniel B
% Aharoni to analyse miniscope 1p calcium imaging data.
% This version is build on top of the original package to maximize compatibility.
% It includes NormCorre for image registration, CNMF-E for source extraction... |
526cab86c9bb403e7a85ad5ac06f5b6b0609c97d438aa4df8bba023f8c3b9a68 | MATLAB | 3,625 | 101 | function [sumNSV, meanNSV, stdNSV, rSV] = nk_ModelComplexityParams(M, S)
% -------------------------------------------------------------------------
% function [sumNSV, meanNSV, stdNSV] = nk_ModelComplexityParams(M)
% -------------------------------------------------------------------------
% Compute model complexity d... |
c0e36b81c63f07ee0668ede68efa620b67dbf2cf4c485b566ba4656f7d20e779 | MATLAB | 3,631 | 99 | classdef reduced_emgf < bt.model.reduced
% This model tests fitting with the EMG frequency allowed to vary
properties
end
methods
function self = reduced_emgf() % Constructor
self.name = 'model_reduced_emgf';
self.n_params = 8;
self.param_names = {'X','Y','Z','Alpha','Beta','t0','EMGa','EMGf'};
... |
185c26f57aef14bdf0716c39b1f80c8728867608ad7f249feb62592fcd193bac | MATLAB | 3,639 | 128 | function result=mlochuber(x,varargin)
%MLOCHUBER is an M-estimator of location with psi-function equal to
% min(1.5,max(-1.5,x)) and with an auxiliary scale estimate.
% It is iteratively computed. It can resist 50% outliers.
% If x is a matrix, the location estimate is computed on the columns of x. The
% result is th... |
91a96fe22f92ff91e3a019bd3e34a1eb43aeac989ab10d73f01cb2c7a0892369 | MATLAB | 3,640 | 92 | clear
%% load PreProcessed file
load('PreProcessed.mat');
% need the one from main Generate_TablesCompiled code to match; NaN
% indicates lack of response, so this is needed instead of the prior
% code to drop missing rows at the end
load('trial_drop_logical.mat');
%% Re-org deltas and drop lost trials so dataset has ... |
b954c4a3f22ab3da2f50abac05e5ffa7539bb4abe15b29109b75b4b87820d1b8 | MATLAB | 3,641 | 121 | function D = dijk(A,s,t)
%DIJK Shortest paths from nodes 's' to nodes 't' using Dijkstra algorithm.
% D = dijk(A,s,t)
% A = n x n node-node weighted adjacency matrix of arc lengths
% (Note: A(i,j) = 0 => Arc (i,j) does not exist;
% A(i,j) = NaN => Arc (i,j) exists with 0 weight)
% s = F... |
068b3dca6a2be75d7035cf7c56f03e0b2af897d8a3986c734b2813fee1ed3acf | MATLAB | 3,644 | 113 | function [mappedX, mapping] = laplacian_eigen(X, no_dims, k, sigma, eig_impl)
%LAPLACIAN_EIGEN Performs non-linear dimensionality reduction using Laplacian Eigenmaps
%
% [mappedX, mapping] = laplacian_eigen(X, no_dims, k, sigma, eig_impl)
%
% Performs non-linear dimensionality reduction using Laplacian Eigenmaps.
% T... |
52566ce4a6bede193129b178a18cfed17bc7a9706e5592e10185713221e46369 | MATLAB | 3,647 | 112 | function OK = WriteTfiles(self)
% Write T files (MClustData)
% ADR 2014-12-17
% now calls EraseTfiles to erase T files (so includes WV and CQ as well)
% now asks if want to erase if save with no clusters and there are previous
% .t or ._t files.
MCS = MClust.GetSettings();
MCD = self;
% ADR 2008 - delete .t set if ... |
ae657e1f05c8ae23968497d5535f366fe0b81e88a3c25a3b7e9c3e937251ae56 | MATLAB | 3,649 | 95 | function [EEGevs] = behav_events(EEG,EventTypes,SubEventTypes,currsess,tag)
%BEHAV_EVENTS Saves event timestamps to EEG (eeglab) data structure
% [EEGevs] = BEHAV_EVENTS(EEG,EventTypes,SubEventTypes,currsess,tag)
% loads TrialEvents.mat (contains timestamps already synchronized
% between EEG system ... |
84ffa0272587b85b76ff964117457bc073564c7806153acff1977d882314a053 | MATLAB | 3,656 | 100 | function [infos, f_val, optgap] = store_nmf_infos(V, W, H, R, options, infos, epoch, grad_calc_count, elapsed_time, metric, metric_param)
% Function to store statistic information
%
% Inputs:
% V (m x n) non-negative matrix to factorize
% W (m x r) non-negative factor matrix (sol... |
29b3a05c253faa39c42b3dc7ad4106c0edecbdc6ebf41f2742d711581e1cec81 | MATLAB | 3,657 | 119 | function [ mappedX, mapping ] = gda(X, Y, no_dims, varargin)
%GDA Perform Generalized Discriminant Analysis
%
% mappedX = gda(X, Y, no_dims)
% mappedX = gda(X, Y, no_dims, kernel)
% mappedX = gda(X, Y, no_dims, kernel, param1)
% mappedX = gda(X, Y, no_dims, kernel, param1, param2)
%
% Perform Generalized Discriminant A... |
41e90f43a496412d6f0c75c8b2fa8110c8f6981112a48db8d392cce44dbf6382 | MATLAB | 3,665 | 112 | function gui_lc (action)
%
% This file is used by FASTICAG
% This file holds the callbacks for load-dialog
% @(#)$Id: gui_lc.m,v 1.4 2003/09/11 12:01:19 jarmo Exp $
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Global variables
% Handle to the window
global hf_FastICA_Load;
% H... |
d4eac7455ace55cee9e402b24687ec5922ecb46bd35df88058ec4c4d1ab247f4 | MATLAB | 3,667 | 134 | function [ U, V, Ss, fv ] = pacifier_iba( X0, rank_max, reg_l1, reg_l2, reg_smooth, opts)
%PERFACT PatiEnt analysis via low-Rank FACtorization.
%
% Input:
% X0: cell of sparse matrices, .
% Omega: cell
%
if nargin<6, opts = []; end
verbose = 1;
fv = [];
maxIter = 10;
tol = 1e-5;
if isfield(opts, 'maxIter'), max... |
3a446435c5b5a3f7a39073df914484671d142bb0b468189e71300a5fda557cf1 | MATLAB | 3,672 | 115 | function result = robstd(x,varargin)
%ROBSTD standardizes a matrix X columnwise by substracting a robust estimate
% of the location and by dividing through a robust estimate of the scale.
%
% Required input arguments:
% x: Data matrix. Rows of x represent observations, and columns represent
% var... |
cf6ba510c2a14be793d9a01026b4b3041eb85145dc433a306f29c213f4c04bab | MATLAB | 3,672 | 105 | % ========================================================================
classdef userFunctions < handle %#ok<*MCFIL>
%> @class userFunctions
%> @brief Customised user functions for a task run.
%>
%> The state machine's job is to run a set of functions when entering and
%> exiting states. Most required functions (me... |
d6c548d42d8fc52f353f7d7edd13fa07b5dd4e202faea272ee7cc96b151b45c2 | MATLAB | 3,672 | 96 | function A = random_graph(N,I,P,type,varargin)
% RANDOM_GRAPH create a random graph
%
% A = RANDOM_GRAPH(N,I,P,T) where N is the number of vertices in
% the graph, I is the proportion of inhibitory vertices (useful for asymmetry or sign problems),
% P is the connection probability, and T is the type of edges tha... |
cc50ca80231709c6e88c450530ceda89f94a46a847fc3cac12f7a60d033f26b3 | MATLAB | 3,678 | 99 | function imData=read_file(path_to_file,sframe,num2read,options,im_info)
% Reads uncompressed multipage .tiff, .hdf5, .avi or .raw files
% Usage: my_data=read_file('path_to_data_file, start frame, num to read);
% INPUTS:
% path_to_file: location of file to be read
% sframe: first frame to read (optiona... |
ce2a019d4c98fdc54827ae6f06f4253fd296a96261511d5d73819328b29a1d6f | MATLAB | 3,690 | 134 | function [x, infos] = onmf(V, rank, in_options)
% Online non-negative matrix factorization (ONMF) algorithm.
%
% Inputs:
% matrix V
% rank rank
% in_options options
% Output:
% w solution of w
% infos information
%
% References:
% S. S. Bucak, B. Gunsel,
... |
02755fbda9a990866f9f008fdbf32d5ee9f5ea8f4b7ecb9f2a54597495dd2ee9 | MATLAB | 3,692 | 127 | clear all
close all
setup_paths;
for monkey=2
if monkey==1
monkey_name='Jupiter';
elseif monkey==2
monkey_name='Mercury';
end
[num,txt,session_name]=xlsread('Session_name.xlsx',monkey_name);
for session_num=1
session=session_name(session_num,1);
session=char(session);... |
d8b794f52fc0afd02c0cf59677eb9282b6bc76b04afcb2f8e672da31ed75a3b8 | MATLAB | 3,696 | 98 | function CM = cv_graph_constructionKLS(A, method, brainmask, atlas)
% KL divergence method following Kong et al. 2014
%
% INPUT: nii images (already smoothed, it's another preprocessing step of
% course which will always be done first)
%
% Steps:
% 1. nk_WriteVol --> save image to disk (.nii); compute network; dele... |
03481487455516afca1ab7780677b85b4e646660c2aaaa31f5de26a124deec9f | MATLAB | 3,698 | 110 | function [A,Y,numIter,tElapsed,finalResidual]=wnmfrule(X,k,option)
% Weighted NMF based on multiple update rules for missing values: X=AY, s.t. A,Y>=0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=wnmfrule(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=wnmfrule(X,k,option)
% X: non-negative matrix, dataset... |
214c8e438754af895a38423a5d9fda38cbe328a6069e98a15a94db75c6bc28a3 | MATLAB | 3,708 | 102 | function [imgfile, imgvol, mess] = nk_FileSelector(nimg, imgtype, titlestr, fltstr, imgfile, startdir, mess)
% =========================================================================================
% [imgfile, imgvol, mess] = nk_FileSelector(nimg, imgtype, titlestr, fltstr, imgfile, mess)
% =========================... |
974bc58fcfe5a8ea1109f0a47993684a32f20cc3e3ecb8b12085fbcded2b93a7 | MATLAB | 3,714 | 131 | % SB2_DIAGNOSTIC Helper function to output diagnostics in SPARSEBAYES
%
% USAGE (1):
%
% OPTIONS = SB2_DIAGNOSTIC(OPTIONS, ACTION)
%
% OUTPUT ARGUMENTS:
%
% OPTIONS Returned OPTIONS settings, possibly updated
% (e.g. with file handle after 'open' action)
%
% INPUT ARGUMENTS:
%
% OPTIONS Options settings (SB2_US... |
3682d21dda1fc0a1327d0e863c89d92ca92d99aba9e77d5d3af02bf659c2b860 | MATLAB | 3,715 | 102 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
1e41493c4688c858a7a8e4d6040adff095cc58b39cb18a2c034d8fb5c28544bf | MATLAB | 3,719 | 97 | function Y = spe(X, no_dims, varargin)
%SPE Perform the Stochastic Proximity Embedding algorithm
%
% Y = spe(X, no_dims, 'Global')
% Y = spe(X, no_dims, 'Local', k)
%
% Perform the Stochastic Proximity Embedding algorithm. The SPE algorithm
% can be compared to the MDS algorithm, although it is much more efficient.... |
38769639f2a7ceb70e4e41034b6578ac094fecfc0f6cffcecd7076f13a4a26e7 | MATLAB | 3,720 | 122 | function nk_ThreshWriteImg(brainmask, P, resl)
if ~exist('P','var') || ~exist(deblank(P(1,:)),'file')
P = spm_select(Inf,'image','Select images for thresholding');
end
if ~exist('brainmask','var') || ~exist(brainmask,'file')
brainmask = spm_select(1,'image','Brainmask image');
brainmask = regexprep(brainma... |
e1e5e37c914ef1a0d003a965d948c6735be723c344a5f553052c77255c7fdf1f | MATLAB | 3,722 | 88 | function [f,F]=motif4funct_bin(A)
%MOTIF4FUNCT_BIN Frequency of functional class-4 motifs
%
% [f,F] = motif4funct_bin(A);
%
% *Structural motifs* are patterns of local connectivity in complex
% networks. In contrast, *functional motifs* are all possible subsets of
% patterns of local connectivity embedded... |
0d9409d122ec342b285014c04d821287d9a43afb626113b9811266ebde807a70 | MATLAB | 3,729 | 80 | function [ERR, TBL] = export_performance(handles, batchmode)
warning off
switch handles.modeflag
case 'classification'
sheetstr = 'Classifier';
case 'regression'
sheetstr = 'Regressor';
end
filename = sprintf('%s_A%g_PredictionMetrics', ...
handles.params.TrainParam.SAV.matname, h... |
10b4d36531b75bbcbc86f6ddbab25c07b3c186d92a2f4009b6c12ae0cccdfb28 | MATLAB | 3,731 | 111 | function [allDiffLAT_P1Go,allDiffLAT_N1Go,allDiffLAT_P1MovOn,allDiffLAT_N1MovOn,allRT1,allRT2,allDiff_RT,allBestLag_rVal,allICD_lat]=f_CalcDifferenceLatencies_SoloObs_or_JA_vSh(LAT,contrast,fs,saveFig,t_start,t_end)
% contrast=2: solo/obs
% contrast=3: ja/ja
allDiffLAT_P1Go=[];
allDiffLAT_N1Go=[];
allDiffLAT_P... |
7d3a95a102afedcd60b91342f63c2a03f025093100e3717a7aee17b404893940 | MATLAB | 3,736 | 114 | function [featmat, emptfl, skiparr, missarr] = nk_GenVISdataMaster(datid, cv, mastermat, tdir, basename, savefl)
global SAV
featmat = []; skiparr=[]; missarr=[];
saveflx = false; if exist('savefl','var') && savefl, saveflx = true; end
if (~exist('mastermat','var') || ~exist(mastermat,'file')),
masterflx = false;
... |
05724381ab41c486e3c0878c6bbb6ec1936cc2062199efd2e18bdd5ca35ebad8 | MATLAB | 3,737 | 143 | function mp2rage_run_interactive_synthetic(isynthetic)
%MP2RAGE_RUN_INTERACTIVE_SYNTHETIC will use a T1map to synthetise an image using the provided TI.
% This is done interactivly in a GUI
%% Initialazation : get or set temporary file name
persistent tmpvolname
if isempty(tmpvolname) % First call, create a tempora... |
9ba9ce3aa1b61ea9344a8c445cae985b31f21212276f46849e61b122aac08fdb | MATLAB | 3,744 | 103 | %Loading the data necessary to create this default input_struct:
matpath = cd;
load([matpath filesep 'brainframe_defaultHuman_datinput.mat']);
%Creating the default mouse input struct
%Specific fields are filled one at a time in the below script
%Basic fields required for any element of brainframe to run:
%Voxel or... |
dd5d404aba1d98b3ab8fbbdc931e2b0f8c03764ba88cef2abb6348d439a7ab17 | MATLAB | 3,745 | 113 | function [featmat, emptfl, skiparr, missarr] = nk_GenDataMaster(datid, datadesc, cv, mastermat, tdir, basename, savefl)
global SAV
featmat = []; skiparr=[]; missarr=[];
saveflx = false; if exist('savefl','var') && savefl, saveflx = true; end
if (~exist('mastermat','var') || ~exist(mastermat,'file')),
masterflx = ... |
f19d5ecd91c3f3e8350d773244b604194a41face81bd5c313ddfd580fc6c40ed | MATLAB | 3,750 | 119 | <<<<<<< HEAD
function s = export(this,target)
% Export a GIfTI object into specific MATLAB struct
% FORMAT s = export(this,target)
% this - GIfTI object
% target - string describing target output [default: MATLAB]
% s - a structure containing public fields of the object
%___________________________________... |
5addf31c5ab1c60ca23061655adaf449ffd2e250f162a8a982ea915f195a75b5 | MATLAB | 3,754 | 117 | %% 0. Setup
% Initialization
clear all; close all; clc;
% Add folder with raw data to path
addpath([pwd '/raw_data_mouse'])
% Add library with eNDM functions to path
addpath([pwd '/lib_eNDM_numeric'])
addpath([pwd '/lib_eNDM_analytic'])
% Load dataset of interest
load Tasic_CTMaps.mat %sw... |
e30c4d687e83a2aebba61bb1d044bb7ccb43c396f254508cd39ecdf9aa82f8af | MATLAB | 3,754 | 103 | function nk_smooth(Vm, P,Q,s,dtype)
% 3 dimensional convolution of an image
% FORMAT spm_smooth(P,Q,s,dtype)
% P - image(s) to be smoothed (or 3D array)
% Q - filename for smoothed image (or 3D array)
% s - [sx sy sz] Gaussian filter width {FWHM} in mm (or edges)
% dtype - datatype [Default: 0 == same datat... |
b8712e78626ca85c3553bbaf95e44746de83a725a915a9c17a3edd291302e4d1 | MATLAB | 3,755 | 122 | %% file LeastmsmtflExp.m
% this file shows the usage of Least_msmtfl_capL1.m function
% and study how to detect outlier tasks.
%
%% OBJECTIVE
% min_W ||XW - Y||_F^2 + lambda*\sum_i \min{||W^i||_1,theta}
%
% It's a nonconvex optimization problem, which can be relaxed into a Multi-Stage Convex optimization:
% min... |
86e71109308e41b1b5f65717ff9d3d9577e14f4477b58f4341b3b12a6bea17b4 | MATLAB | 3,762 | 108 | %% corr combined, 5 days, non PreHab, by ID
clear
load('ProcessedData\tables_compiled_shifterMag.mat');
%% Corr to D1 no best sorting
figure,
t = tiledlayout(1,5,'TileSpacing','Compact');
D_template = tables_compiled{1,1}.Deltas;
D_template = D_template.';
trial_cutoff = 8;
within = cell(1,5);
across = cell(... |
85c733626d915d154356f10da30583d8c7d2cbdb93fdabcd70c71642bd721e84 | MATLAB | 3,768 | 93 | function nk_VisModels_batch(paramfile)
global NM
% Read parameter file
% ===========================
if ~exist(paramfile,'file')
error([paramfile 'not found. Abort job!']);
end
fid = fopen(paramfile);
params = textscan(fid, '%s');
fclose(fid);
% Parse parameters in params
% =====================================... |
27a20b02c079873b3f9dd8bca825298acfbb13bc0dd4126b29e3ef621d4a2441 | MATLAB | 3,771 | 93 | function param = nk_MKLRVM_config(param,setupfl,defaultsfl)
% =========================================================================
% FORMAT param = nk_MKLRVM_config(param, setupfl, defaultsfl)
% =========================================================================
%
% Setup parameters for RVM algorithm by Psor... |
73cd7f1b6e955bedac6412210b231c328c735f6229f3afc50263b076f30c5b8d | MATLAB | 3,773 | 100 | clear
%% load PreProcessed file
load('PreProcessed.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
% pull table values, drop Na... |
3e96eab27bdcfdd025773c32521d2baac3aa09d15a6abf398309cb726fcaf013 | MATLAB | 3,778 | 119 | %% 0. Setup
% Initialization
clear all; close all; clc;
% Add folder with raw data to path
addpath([pwd '/raw_data_mouse'])
% Add library with eNDM functions to path
addpath([pwd '/lib_eNDM_numeric'])
addpath([pwd '/lib_eNDM_analytic'])
% Load dataset of interest
load Tasic_CTMaps.mat %sw... |
48c0763d77e97d77d8df55bf3ddf82e87ea48485d45be8e71c730c0dc1ec23bc | MATLAB | 3,779 | 135 | function [eigvector, eigvalue, sampleMean, ener, elapse] = PCA(data, options)
%PCA Principal Component Analysis
%
% Usage:
% [eigvector, eigvalue] = PCA(data, options)
% [eigvector, eigvalue] = PCA(data)
%
% Input:
% data - Data matrix. Each row vector of fea is a data point... |
fb9f58e25139792c1b8037c04b7e4bb7d759cac968fc6b401ca28c7dee62922b | MATLAB | 3,782 | 93 | function dat = nk_PermPrep(dat, analind)
global CV CLUST
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PREPARATIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
analysis = dat.analysis{analind};
spm5ver = nk_CheckSPMver;
% ************************ LOAD PREPROCESSED DATA *************************
[preprocmat, dims] = nk_GenPreprocMaster(spm5ver... |
68e0cb47c1a2dc811f6a5486ae10511f1697a8936b7a39422add6ee7516bc2aa | MATLAB | 3,784 | 115 | function imrelief = nk_IMRelief_config(imrelief, classhdr, setupfl, defaultsfl)
% function param = nk_IMRelief_config(param, setupfl)
%
% This function sets up the parameters for the IMRelief feature
% selection algorithm presented by Yijun Sun and colleagues:
%
% IMRelief_Sigmoid_FastImple: fast implemetation of IMRe... |
f1628b196d82c005707d3e53f68cdc7db00bacfad72d56494d67ee43cb68bb98 | MATLAB | 3,785 | 103 | %Loading the data necessary to create this default input_struct:
matpath = cd;
load([matpath filesep 'brainframe_defaultMouse_datinput.mat']);
%Creating the default mouse input struct
%Specific fields are filled one at a time in the below script
%Basic fields required for any element of brainframe to run:
%Voxel or... |
80c40dfbb09414f710d9187e7020a25441f82ba97a7a4b0586d63adc1aa22c39 | MATLAB | 3,792 | 78 | % Demo code for mapping cortical thickness difference taking into account covariate of sex and age, i.e., ANCOVA, using MBM.
%
% This demo uses 95 empirical thickness maps from T1 maps in an open dataset.
% All maps are combined in inputMaps_ANCOVA_twosample.mat where each row is a map (or inputMaps_ANCOVA_twosample.mg... |
b5103e00bcf8dc380edd2de9dcd845f77be157351fa94b8e3d1b7fa8124ac42b | MATLAB | 3,795 | 101 | clear
%% load PreProcessed file
load('PreProcessed_peakMag.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
% pull table values,... |
7159cd8a9f2951d9d62a21ca6a542e6422b0312b4663780346a807fcb15b1ed1 | MATLAB | 3,798 | 112 | function [U, V, eigvalue_U, eigvalue_V, posIdx, Y] = TensorLPP(X, W, options)
% TensorLPP: Tensor Locality Preserving Projections
%
% [U, V, eigvalue_U, eigvalue_V, posIdx, Y] = TensorLPP(X, W, options)
%
% Input:
% X - 3-d data matrix. X(:,:,i) is the i-th data
% ... |
fc9ae869c8fc14e53f67efdabc16c8665fed026d6ffa43f4c352c192d1d9330d | MATLAB | 3,800 | 106 | function fdata = fit_cluster(model,dataset,subject_idx,npts_per_fit,suffix)
% if matlabpool('size') == 0
% matlabpool_cluster(12)
% end
debugmode = false;
%npts_per_fit = 12e5;
output_dir = sprintf('%s_%i_%s',dataset,subject_idx,suffix);
if exist(sprintf('%s/fit_output.mat',output_dir)) % Resume an interrupt... |
08d221c1fb30d08df3e32fd0ee34ebc5b5788471535f16898e47b3ef7613d136 | MATLAB | 3,801 | 88 | %% 步骤 1: 计算所有通道的功率谱密度(PSD)
% -------------------------------------------------------------------------
lfp_data = LFP_NoNeedle_segment;
% --- pwelch 参数定义 ---
win_duration_sec = 2; % 窗口长度 (s), 2秒的窗口可以提供较好的频率分辨率
win_len_points = win_duration_sec * fs; % 窗口长度 (采样点)
window = hamming(win_len_poi... |
045ad60874daf96bf3ce7cc467ed7360a11e8da35b6231681d2074c8a9416613 | MATLAB | 3,808 | 102 | function [ERR, TBL] = export_scores(handles, batchmode)
warning off
if ~exist('batchmode','var') || isempty(batchmode)
batchmode = false;
end
switch handles.modeflag
case 'classification'
sheetstr = 'Cl_';
case 'regression'
sheetstr = 'R_';
end
% Do we print the results of the bagged ... |
7a9111fa72d781b5032238093c1596bfe9e2b2ce761a4684d1aebe1b063976c9 | MATLAB | 3,810 | 115 | function [ GRAPHMETRICS, PX, act ] = cv_graphMetrics_config(GRAPHMETRICS, PX, parentstr, defaultsfl)
MetricsYes = 1;
MetricsList = [];
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
if ~defaultsfl
if isfield(GRAPHMETRICS,'metrics'), MetricsYes = GRAPHMETRICS.metrics; end
... |
8da12a2d3d9b08236c655433cdde5db84b472524a44c770abad3666e5a51f774 | MATLAB | 3,813 | 112 | function animate_fit_old
% This version used the old fitting based on the states etc.
load steve
load pp_master db_data
[time_bar,state_txt,tent_point,tent_trail,spec_axis,tent_axis,exp_plot,fit_plot] = init_plot(t,f,s);
xyz_trail = zeros(length(t),3);
% OK so let's first get average of the first few points
i... |
cc1c58c2e5925c69f0e992741450c199a8f15e2b1ff194f01f973e74b47cd905 | MATLAB | 3,815 | 97 | %Biases = rand(20,2*3*2); % subjects, Hemi*Mask*Fam
load('N250_left.mat') % left
A = erpval;
load('N250_right.mat') % right
B = erpval;
erpval = horzcat(A,B);
[n,c2] = size(erpval);
varNames = cell(2*3*2,1);
for i = 1 : 2*3*2
v = strcat('V',num2str(i));
varNames{i,1} = v;
end
% Create a table storing th... |
2943b112b8c0e9bc157dd81aea313f2785df99eccc162dc92cceb90e948ff3be | MATLAB | 3,817 | 1 | function CheckCluster(self)
%
% CheckCluster(@Cluster)
%
% INPUTS
%
% OUTPUTS : none
%
% Shows key aspects to cluster for judging quality
%
% ADR 1998
% version L1.0
% RELEASED as part of MClust 2.0
% See standard disclaimer in Contents.m
% cowen
%
% Status: PROMOTED (Release version)
% See documentation for copyright... |
ce261383fe9281fc9fe2c9615d3d28e907aab5d2479294045a03fe794d909fe6 | MATLAB | 3,824 | 134 | function [h,p,stats] = nk_CompareClassResample(Cl1, Cl2, L, scoretype, alternative, Test, alpha)
modes = {'5x2t', '5x2F', 'rkCV', 'rank', 'rank_cardillo'};
if ~isequal(size(Cl1), size(Cl2))
error('Score grid of the two models must have same dimensions')
end
if ~exist('alternative','var') || isempty(alternative),... |
27875850c2fd5f4741d069d1361165e1a97aa48df03280a22a46d88049479e26 | MATLAB | 3,826 | 128 | function [PREPROC, ...
RFE, ...
GRD, ...
SVM, ...
LIBSVMTRAIN, ...
LIBSVMPREDICT, ...
RVM, ...
MKLRVM, ...
CMDSTR, ...
MULTI, ...
VIS, paramstr] = nk_CompatParams2(TrainParam, varind, paramstr)
globa... |
e5eadbe798342ee233bde493d41e2650f828d5c45d2366ed3c0098c1aef62a9e | MATLAB | 3,827 | 101 | clear
%% load PreProcessed file
load('PreProcessed_behaviorShifter_smoothed.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
% pu... |
cc733c7078aea09c89c020704a7302d958ecb05383c33c9122f3471b52c88715 | MATLAB | 3,837 | 138 | clear; clc;
% Load File %%
load ('...\LFPdata_Fig4B.mat')
%%
% An example of Time-Frequency Plot: Session 9, ch 8
% frequency parameters
min_freq = 1; % in Hz
max_freq = 100;
num_freq = 100;
sRate=units.FSnb;
t1CHT=200;
t2CHT=900;
num_cycles=6;
lowPowerS=[];
lowPowerO=[];
lowPowerJA=[];
figur... |
e5f82252dfa9148c7be03d8e84e8173774279b696bf5342275a6c7f9096b1eb1 | MATLAB | 3,841 | 113 | function [en,tn,en25,en75,significant]=significantAcc(D,classes,numTrain,classifier,optCl,option)
% determine if the accuracy is significant
% D: matrix, whole data set, feature X sample.
% classes: vector, the class labels: 0,1,2,...
% numTrain: number of training samples to be seperated from D
% classifier: string, ... |
7271aeacaa2681d871d68ba064e0cae9eb77a0dbb39c33793bb40411a077bdf0 | MATLAB | 3,843 | 131 | function ClusterFunc_LimitSpikesByWaveforms(self)
% ClusterFunc_LimitSpikesByWaveforms (SpikelistCluster)
%
% Plots four subplots with waveforms, allows limits
%==========================================================
% PARAMETERS
%===========================================================
useSelfColor = false;
... |
7bbf227d3ef9cd8b41282ca4b60a6f8cde0c43030b96d11aae65e5dac889182e | MATLAB | 3,844 | 109 | function [A,Y,numIter,tElapsed,finalResidual]=sparsenmf2rule(X,k,option)
% Sparse NMF, X=AY, using multiplicative update rules. Both basis matrix and coefficient matrix are constrained by
% l_2 and l_1 norm.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=sparsenmf2rule(X,k)
% [A,Y,numIter,tElapsed,finalRe... |
a27ace41d9c3b6c512754e3a89ed79494d73d9c7651626758ef18c61a8904902 | MATLAB | 3,856 | 127 | function RunClustBatch(varargin)
% RunClustBatch
%
% rebuilt completely for MClust 4.0
%
% No longer uses batch files. Now is a function that takes parameters.
% parameters are passed as string-set pairs. For example:
% RunClustBatch('minClusters', 10, 'maxClusters', 25);
% RunClustBatch();
% RunClustBatch('fcTT',... |
8751949cd6f458f6bad8e76b780b754798ac8aa80b394cef793ae435cd2c9b3b | MATLAB | 3,857 | 109 | function [sY, sX, sYX, IN] = nk_PLS(Y, X, IN)
% Computes PLS model from experimental predictors X and predictands Y
% _________________________________________________________________________
% (c) Nikolaos Koutsouleris, 10/2023
if ~isfield(IN,'trained'),IN.trained=0;end
if ~isfield(IN,'algostr'),IN.algostr='pls';end
... |
d3158bde40afd72687d62166d8c78b3b5dad7e8206bf4737e1c0df35ab0c732a | MATLAB | 3,857 | 77 | % Demo code for mapping cortical thickness difference, i.e., two sample t-test, using MBM.
%
% This demo uses the thickness maps generated by the framework described in
% the MBM paper. In particular, 25 maps were generated from each of the two
% ground truths. The list of the paths of the maps stored in 'thickness'
% ... |
4a8c90247bca847f033952e86dcba6923cf84aec87ac9096bf19749cad3a6813 | MATLAB | 3,862 | 114 | %% corr combined, 5 days, non PreHab, by ID
clear
load('ProcessedData\tables_compiled_shifterMag.mat');
%%
figure,
tiledlayout(1,5,'TileSpacing','Compact');
trial_cutoff = 8;
within = cell(1,5);
across = cell(1,5);
for i = 1:5
day_now = tables_compiled{i,1}.Deltas;
day_now = day_now.';
% do the ... |
d4f1ecfa5bf8c201558dfa7a39d26d3fc9a516baf59341d5937ad76ce550f564 | MATLAB | 3,863 | 71 | function ProcessedData = removeBadSignals(plxFileName,a,ProcessedData,sw)
%This function allows the user to exclude specific signals from analysis if
%desired. It will run through a series of prompts for the user to list the
%signal(s) to remove.
if sw.LabelBadSignals == 1 || ~exist([pwd '\Output Files\' plxFile... |
bce425b29beacf76af37eacac11c933231a71249e4cb31e354dcd39b5d0653ef | MATLAB | 3,867 | 130 | % ========================================================================
%> @brief magstimManager - controls a MagStim Rapid TMS system using a LabJack
%>
%> Connects to and manages a TMS stimulator using a LabJack U3 / U6.
%>
%> >> mM = magstimManager() %create magstim manager object
%> >> mM.open %connect via a La... |
ebcde164c74b4bded592a0bc02112b479bb7a0c783417160a2361820c69a2909 | MATLAB | 3,867 | 162 | function [W, funcVal] = Least_FGLasso(X, Y, rho1, rho2, opts)
% Fused Group Lasso with Least Squares Loss.
% By Jiayu Zhou (jiayu.zhou@asu.edu) Jan. 2011
% Objective:
% argmin_W { \sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + \sum_{i=1}^d rho1{i} * \|W(i, :)\|_1
% + \sum_{i=1}^d rho2{... |
c88e9151f63f0e53f2a30d36a36f3f56d2e55c33f96108b2685564418c0239f6 | MATLAB | 3,868 | 104 | function [mask,featNames,scores,A]=featSelNMF(D,featNames,opt)
% feature selection using VSMF
% Usage:
% [mask,featNames,A,scores]=featureFilterNMF(D)
% [mask,featNames,A,scores]=featureFilterNMF(D,featNames)
% [mask,featNames,A,scores]=featureFilterNMF(D,featNames,opt)
% [mask,featNames,A,scores]=featureFilterNMF(D,[]... |
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