sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
8b700771de620533bb1e21b49e67fade3e5224d0a827536cf187ddcb3916036e | MATLAB | 4,894 | 129 | % M. H. Van Benthem and M. R. Keenan, J. Chemometrics 2004; 18: 441-450
%
% Given A and C this algorithm solves for the optimal
% K in a least squares sense, using that
% A = C*K
% in the problem
% min ||A-C*K||, s.t. K>=0, for given A and C.
%
function [K, Pset] = fcnnls(C, A)
% NNLS using normal equations... |
5852c31d00acff274c62ec44d0d525b2169e59b895f9f078290c261dc3cabf6d | MATLAB | 4,896 | 118 | function ydata = tsne_p(P, labels, no_dims)
%TSNE_P Performs symmetric t-SNE on affinity matrix P
%
% mappedX = tsne_p(P, labels, no_dims)
%
% The function performs symmetric t-SNE on pairwise similarity matrix P
% to create a low-dimensional map of no_dims dimensions (default = 2).
% The matrix P is assumed to be s... |
8398bbc784cfa16f511f7833279dcd35958ed7aa254140ef36797b9622236a16 | MATLAB | 4,900 | 126 | function GD = nk_CompPlattProbOptimum(GD, Ps, mapY)
global CV SVM MULTI RFE
%%%% PREPARATIONS %%%%
[CV1perms, CV1folds] = size(mapY.Tr);
nclass = length(CV.class{1,1});
F = cell(CV1perms, CV1folds, nclass); W = F;
SlackParam = cell(nclass,1); KernParam = cell(nclass,1);
%%%% Loop through dichotomizers %%%%
for curcl... |
ac2777bd6e6b2c8a7064d202bd008249fa157a5ab3e7302f4cdb96c670a454b8 | MATLAB | 4,901 | 120 | function testClassPredicted=bootstrapnnlsClassifier(trainSet,trainClass,testSet,testClass,option)
% Bootstrap NNLS Classifier: testSet=trainSet*Y, s.t. Y>=0.
% Usage:
% [testClassPredicted,sparsity]=bootstrapnnlsClassifier(trainSet,trainClass,[],testClass)
% [testClassPredicted,sparsity]=bootstrapnnlsClassifier(trainSe... |
6b08398903bff25f7f5240b787a8079c644ee39cce013c0de7a4c0b61030a8b0 | MATLAB | 4,902 | 133 | function [NM, act] = nk_DataIO_config( NM, parentstr, act, varind, IO )
mn_str=[];
nk_PrintLogo
if isfield(NM,'Y') && ~isempty(NM.Y), Yfl = 1; else, Yfl = 0; end
if isfield(NM,'covars') && ~isempty(NM.covars), covfl = 1; else, covfl = 0; end
mestr = 'NM data workspace manager'; navistr = [parentstr ' >>> ' mestr];
i... |
5a3c954d5b725286c12672cb34f6cf8829c5cd69efb7909eb414c338653aeb26 | MATLAB | 4,903 | 151 | %% FUNCTION init_opts
% initialization options for multi-task learning library
%
% If one of the ncessary opts are empty then it will be set to default
% values as specified in this file.
%
% Table of Options. * * indicates default value.
% FIELD DESCRIPTION
%% Optimization options
%
% .max_iter ... |
706abae16cf83984173356da1ea426403d3dc13e2428bf28495681b6573d7073 | MATLAB | 4,907 | 162 | function [opt_hE, opt_E, opt_F, opt_Fcat, opt_D, opt_Pred] = nk_MultiDiversityMax(E, L, EnsStrat, C, G)
global BATCH
ind0 = L~=0;
E = E(ind0,:);
L = L(ind0);
nclass = max(C);
if EnsStrat.Metric == 2, T = sign(E); else T = E; end
% Compute initial ensemble performance
[opt_hE, opt_Pred] = nk_MultiE... |
6a9430f394e29a9e3d474edfcdcfee6071d0f9e27488e3287c52422aa93b92b8 | MATLAB | 4,909 | 124 | function [LX, MX, PX] = mppca(X, no_dims, no_analyzers, tol, maxiter, minstd)
%MPPCA Runs EM algorithm and computes local factor analyzers
%
% [LX, MX, PX] = mppca(X, no_dims, no_analyzers, tol, maxiter, minstd)
%
% Runs EM algorithm to determine coordinates of factor analyzers. The data
% is given in the DxN matrix ... |
c9cddd8027d92afed68f66d9bbeef04dd7864c32fc28eb8555e3126af6c51ffc | MATLAB | 4,912 | 133 | function [sY, IN] = nk_PerfRemMeanDiffObj( Y, IN )
% =========================================================================
% FORMAT [sY, IN] = nk_PerfRemMeanDiffObj( Y, IN )
% =========================================================================
% Normalizes given data (specified ind dIND) to global mean of giv... |
6f6473d7ff17a1f726c45735c3ef576d52449f084e0bb5a89903b67115fc59e5 | MATLAB | 4,926 | 153 | classdef tittaAdvMovieStimulus < handle
properties (Access=private, Constant)
calStateEnum = struct('undefined',0, 'showing',1, 'blinking',2)
end
properties (SetAccess=private)
calState
pointStartT
blinkStartT
end
properties (Dependent, SetAccess=private)
pos % can't set position here, you set it thr... |
b635881fa51b868617f9e8b6196b2f1d25652154730e15784e11253bf6adcaa1 | MATLAB | 4,933 | 119 | function [meanMeanPerformsAllRun,stdSTDAllRun,meanconMatAllRun,meantElapsedsAllRun]=cvExperiment(D,classes,kfold,rerun,fsMethod,fsOption,feMethod,feOption,classMethods,options)
% Conduct experiment of k-fold cross-validation on a dataset
% D: matrix, each row corresponds to a feature, each column a sample or
% data poi... |
e7ba4f7a8393d3d16bc5917f70e9f62639e6676c624ef901768b64f0bf0045fd | MATLAB | 4,937 | 159 | function [Objective, Weight, History] = iDetect(patterns, Para)
% iDetect: feature selection for clustering and manifold learning with
% norm-2 and norm-1 regularization
%INPUT:
% patterns: D-by-N input data
% D is # of features and N is # of samples
% Para: parameter s... |
e49acb1479feb6265373bdc06edcad23a3bb585fca06c650dca5be922f7d3906 | MATLAB | 4,950 | 145 | % ========================================================================
classdef eyetrackerSmooth < handle
%> @class eyetrackerSmooth
%> @brief Smoothes incoming eye sample data
%>
%> Copyright ©2014-2023 Ian Max Andolina — released: LGPL3, see LICENCE.md
% ===========================================================... |
a140bfbdcf1c72f57e34c7ebb83666ebc0918825dd94621f617b09c41b3d105d | MATLAB | 4,956 | 143 | function result = ols(x,y,varargin)
%OLS is the classical least squares estimator for multiple
% linear regression. It can handle both one or several predictor variables,
% and one response variable.
% If there are several response variables, the function mlr.m should be used.
%
% Required input arguments:
% ... |
1a4f324f143283637698d89c318db72064c23148dd9b03d9d0e2c22ca16d3a94 | MATLAB | 4,958 | 169 | classdef touchData < optickaCore
properties
subject
data struct
info struct
verbose = true
end
properties(Hidden)
% for plotting recent correct rate
lastN = 10
end
properties (GetAccess = public, SetAccess = protected)
nData
nSessions
end
properties (Access = protected)
dataTemplate = struc... |
3082dc16caf875326a81e0330ddcfd29adabd33bc803b68688c6a8cd50fcbaa6 | MATLAB | 4,960 | 99 | function [status, analysis] = nk_NMLogFileManager(act, dat, analysis, caller, mess)
myname = 'NM Logfile Manager';
if ~exist('caller','var'), caller = []; end
if ~exist('mess','var'), mess = []; end
if strcmp(act,'add_entry') && ( isempty(caller) || isempty(mess) )
status.error.message = 'Logfile manager cannot a... |
b1489683469d3db70129af8f743a8d7c69888bf8e3462839ee6e1b1b6017d21a | MATLAB | 4,965 | 131 | % Produce Figure 4C
% (explore a circuit of Kuramoto oscillators with a given external input)
colormap default;
Nneurons = 5; % group size.
% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%
% if we want to do a paramater space exploration:
ScaleInputs = linspace(0, 10, 6); % this is how much the input matter more or... |
a3f2249bdc29992f7cf902eb7e9f05c29c2db3238aa462ef5a1a4c7daf2bff98 | MATLAB | 4,967 | 185 | function hdr = load_nifti(niftifile,hdronly)
% hdr = load_nifti(niftifile,hdronly)
%
% Loads nifti header and volume. The volume is stored
% in hdr.vol. Columns and rows are not swapped.
%
% Handles compressed nifti (nii.gz) by issuing a unix command to
% uncompress the file to a temporary file, which is then deleted.
... |
74d373d2684820b99881ef53ddd5628678b80b5c2687f07df5aad423485d069b | MATLAB | 4,970 | 139 | function [AtA,Y,numIter,tElapsed,finalResidual]=kernelseminmfrule(X,k,option)
% Kernel semi-NMF based on update rule: phi(X)=A_phiY, s.t. Y>0.
% Definition:
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k)
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k,option)
% X: matrix of mixed sign... |
bc12753f4817c53dd2fbb148be1352cf99e4ad87be6ca7b2b6eb0cdbf956484e | MATLAB | 4,972 | 145 | function [ IN, OUT ] = SelectFeaturesAcrossCV1( IN, OUT, Param, minmaxfl, OptMode )
global VERBOSE
nc = size(OUT.F,3);
ns = size(IN.F,3);
nP = numel(Param.PFE.Perc);
funcomp = {@max, @min};
if check_fsizes(IN)
fprintf('\nFeature masks have unequal dimensionalities due to feature preprocessing setup. Skipping Cro... |
1cf6c00c3fa503fb3bef9c2585db6ef05ccf6d36d620d0967006221bc83f425e | MATLAB | 4,976 | 157 | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much... |
5498c7cd007c045cf8bc0b19b138a8e90842eb7bb0848c5c41b171c14fc84056 | MATLAB | 4,978 | 169 | function simcaplot(result);
%SIMCAPLOT plots a scatter plot with the boundaries defined by the SIMCA method.
% It is based on the results from a simca analysis (see rsimca.m or csimca.m).
%
% For technical reasons, 6 different groups can be plotted (with different symbols).
% In case there are more groups, please a... |
a4ed5b21faa8feeac0a027b0eeb50d66712ae46b8ab2c0211f34ab09f11f6c4e | MATLAB | 4,980 | 144 | function Prediction = SVR_NFolds_RandomCV(Subjects_Data, Subjects_Scores, FoldQuantity, Pre_Method, C_Parameter, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous variable to be predict... |
c6a89d4e87a9af3642662df2688057322581b41987201738bf00b29499ab6208 | MATLAB | 4,998 | 181 | function result=mona(x,plots)
%MONA returns clusters according to mona (monothetic
% clustering)
%
% The algorithm is fully described in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cluster analysis",
% Wiley-Interscience: New York (Series in Applied Probability and
% ... |
ad06c295674f0755ef5324745255271c862df951dffde10a4573d44072dc79f6 | MATLAB | 5,001 | 172 | function [x, infos] = onmf_acc(V, rank, in_options)
% Online non-negative matrix factorization (ONMF) algorithm.
%
% Inputs:
% matrix V
% rank rank
% options options
% Output:
% w solution of w
% infos information
%
% References:
% S. S. Bucak, B. Guns... |
15e4dd47ba125ec0765949cd72a941f2cd3ea8e87cb82d92ad018f79336fe4fd | MATLAB | 5,008 | 105 | function [ProcessedData, RecordingMetrics] = calculateRasterMetrics(ProcessedData,plotProps)
%This function calculates and plots recording statistics (raster, firing
%count histogram, firing interval histogram, and number of raster
%events/min) independent of correlograms
%Display general information about the ... |
b8def38a2b7ad73504f098ad1e5019aa8a4458aa31832ff876d41792dd92afc0 | MATLAB | 5,017 | 177 | function gui_s (x, y)
%
% This file is used by FASTICAG
% The save dialog for saving the results
% @(#)$Id: gui_s.m,v 1.4 2004/07/27 13:09:26 jarmo Exp $
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Global variables
% Handle to the window
global hf_FastICA_Save;
% Handles to s... |
241dff260d80245f97bfecb7f93628fa3267810b5b0ae9fdfce7598a91ae6836 | MATLAB | 5,033 | 140 | function str = nk_GetAnalysisInfo(dat, analysis)
if iscell(analysis.params.datadescriptor), nvar = length(analysis.params.datadescriptor); else nvar = 1; end
showmodalmax = Inf;
showmodalvec = 1:nvar;
params = analysis.params;
FUSION = params.TrainParam.FUSION;
if isempty(FUSION), FUSION.flag = 0; end
nF=1; str=[];
if... |
89fa73d9a3b0b21dff86840feeaa21710a58149c24bde038fa2cb01fd3c25fff | MATLAB | 5,035 | 144 | classdef exampleStimulus < baseStimulus
%> This is a example of how to make a minimal stimulus for Opticka.
%> All it does is draw a filled circle. All children
%> of baseStimulus need to implement setup(), update(), draw(),
%> animate() and reset(). These are used when running an
%> experiment, so each type of s... |
830598e272d51c2e279310cd24ac978637a2106c48c426067ff563281be4359f | MATLAB | 5,049 | 164 | function [x, infos] = nmf_orth_mu(V, rank, in_options)
% Orthogonal multiplicative upates (MU) for non-negative matrix factorization (Orth-NMF).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2,
% where
% {V, W, H} >= 0, and W or H is orthogonal.
%
% Given a non-negative... |
8dffe09657d2f12248a55562848a48c08bf608c757c14556ac924c382e89ce89 | MATLAB | 5,086 | 114 | %% ================================================================
% Script: hierarchical_clustering_and_modular_summary.m
% Purpose:
% Quantify hierarchical and modular organization of internetwork
% topographic covariance (FOCA matrix) in the MSC dataset.
%
% Description:
% - Compute pairwise correlati... |
1190acc7eac3313c12614d2ce1c472a89d65522b25a3c4f3977c3d414d3672c2 | MATLAB | 5,087 | 166 | function [strtype, suff] = nk_FeatSel_StrCfg(FEATSEL, varind)
suffx='';
% Build output data strings
if isfield(FEATSEL,'ACTPARAM')
for i=1:numel(FEATSEL.ACTPARAM)
if ~isempty(FEATSEL.ACTPARAM{i})
switch FEATSEL.ACTPARAM{i}.cmd
case 'sc... |
5275ff285bf1a275469fc77fadfecb624d98cb285634f72357fd04a16c1524f0 | MATLAB | 5,114 | 142 | function [K, Pset] = l1NNQPActiveSet(CtC, CtA,lambda)
% kernel l1 Regularized NNQP solving min K^t CtC K + CtAK + lambda [||K_1||_1,...], s.t. K>=0, for given A and C.
% CtC: kernel matrix, k(C,C), Hessian.
% CtA: kernel matrix, k(C,A), Gradient.
% lambda: lambda.
% References:
% [1] \bibitem{NNLS2004}
% M.H. Van Benth... |
9c52e136e51e1d3a66916dc5833c82749b3b74c4eb68e54527bb80b0798b19b9 | MATLAB | 5,116 | 144 | function [dPTE, PTE] = PhaseTE_MF(data, delay, binsize);
%% function [dPTE, PTE] = PhaseTE_MF(data, delay, binsize);
%%
%% Input:
%% data: time x channel filtered signals
%% delay: prediction delay in samples; leave empty if you want the delay to be based on the frequency content of the data
%% binsize: binsize for the... |
4eba5892c77f5e727b29ace7833fbba4cedbaf04366b2c65103a94d141865bb5 | MATLAB | 5,127 | 175 | function [ W, info, Th ] = MTFLCd_DPG( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration
% diagnoal version for faster computation on small sample size.
% dual projected gradient.
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
%
% We solve... |
e8d35059474cd3ffdac2e30c9ad2156055b8cc1b672c245a118addd6a5b44ae8 | MATLAB | 5,129 | 180 | function gui_l (x, y)
%
% This file is needed by FASTICAG
% The load dialog for loading new data
% and new initial guess.
% @(#)$Id: gui_l.m,v 1.4 2004/07/27 13:09:26 jarmo Exp $
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Global variables
% Handle to the window
global hf_Fast... |
0daa78784e68dae025b1cba1f9d7881717c6ed300e58983e146a76b8640ed7ad | MATLAB | 5,130 | 174 | function [ W, info, Th ] = MTFLCd_DFISTA( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration
% diagnoal version for faster computation on small sample size.
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
%
% We solve this by the dual form
% ... |
d46816f1163cd5b7a47608daca94084f96a77ed3ca33cf224562e930915b1ea3 | MATLAB | 5,132 | 207 | %% FUNCTION Least_RMTL
% Robust Multi-Task Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * \|L\|_* + rho2 * \|S\|_{1, 2}}
% where W = L + S
% \|S\|_{1, 2} = sum( sum(S.^2) .^ 0.5 )
% \|L\|_* = sum( svd(L, 0) )
%
%... |
7019ba5692681bf655eb6ee51a03c1376369a312c43b6b678b5046b4cfdea961 | MATLAB | 5,133 | 112 | function [statMapNull, output1, output2] = mbm_perm_test_map(inputMap, stat, observedMap)
% Permutation tests on the statitical map
%
%% Inputs:
% inputMap - Matrix of rows of anatomical maps.
%
% stat - structure having the input fields:
% stat.test - Statistical test to be used:
% ... |
e56790257ac2e39eebf14ea7a11aba3894c29cb50cd2a106a982a0ae41bffecf | MATLAB | 5,138 | 180 | function h = projectMasks(h)
%%
ncomps = size(h.uMotMask{1},2);
%
npix = h.npix;
tpix = h.tpix;
nframes = h.nframes;
np = cumsum([0 h.npix]);
tp = cumsum([0 h.tpix]);
imendA = zeros(sum(npix),1,'single');
motSVD{1} = zeros(sum(nframes),ncomps,'single');
zf = find(h.plotROIs(2:end-4)) + 1;
for z = zf(:)'
imend{z}... |
c020a0272cdcb59083aac760e1a53740f7ab6f4377941a4648409e0b5691e219 | MATLAB | 5,141 | 144 | function interactive_fit(self)
if self.latest == 0
return
elseif self.latest > 1
fit = self.subrange(self.latest);
fprintf(2,'Multiple fits provided - displaying the last one\n')
else
fit = self;
end
h.m = fit.model;
h.fit_data = fit.fit_data;
h.pars_orig = fit.fitted_params;
h.f = linspace(1,45,1000);... |
9e76d3839612b3af4fcff01dc9dcd28b2367cdacf3f95af1a10947a3c41087b2 | MATLAB | 5,146 | 98 | % =========================================================================
% = CONTINGENCY MATRIX INFO =
% =========================================================================
function handles = display_contigmat(handles, contigmat)
contig=[];
GraphType = get(handle... |
835c212d2a5128fbef2c9f4a17e3dbfb6dc417f2590f7a36fef3d2832c892da7 | MATLAB | 5,151 | 148 | function regresdiagplot(xdist,ydist,cutoffx,cutoffy,k,multi,attrib,labsd,labresd)
%REGRESDIAGPLOT makes a regression outlier map.
% The score distances (SD)/mahalanobis distances (MD)/robust distances (RD) and
% residual distances (ResD) calculated in a PCR or PLS analysis, or in a
% univariate or multivariate regre... |
24307c7055cc0ae308bdd4b3cfcaee221f3e96089245fd3d3f14499c13e9adae | MATLAB | 5,154 | 195 | clear; clc;
matdir = '/Users/justintorok/Documents/MATLAB/Nexis_Project/Nexis/raw_data_mouse';
load([matdir filesep 'eNDM_mousedata.mat'],'Networks');
load([matdir filesep 'Zeisel_CTMaps.mat'],'Zeisel_ng1360');
load([matdir filesep 'classkey_zeisel.mat'],'classkey_zeisel');
load([matdir filesep 'mouse_aSynData_426.mat'... |
acf95c73348f420a0bce2a61ced59c35cf586d236abddd424f21572e5dc6fe8d | MATLAB | 5,161 | 122 | function [Ci,Q]=modularity_und(A,gamma)
%MODULARITY_UND Optimal community structure and modularity
%
% Ci = modularity_und(W);
% [Ci Q] = modularity_und(W,gamma);
%
% The optimal community structure is a subdivision of the network into
% nonoverlapping groups of nodes in a way that maximizes the number of
%... |
00461a0628afba026ad346ea2c2463acad97d2061b9b5358dbf91d7b369ac9c1 | MATLAB | 5,166 | 136 | function [alphas, margin_list] = nk_SimbaLinearSigmoid_gpu(X_train, Y_train, extra_param)
% alphas = simbaOneTime(X_train, Y_train, extra_param);
%
% This function runs simba with linear utility function one time (i.e. with one starting point).
%
% input: X_train(i,j) is the value of feature j in training instance i... |
2660f293ef81c2cb9292aa6efe4c63539affe35f44b00b44843d816dc599ebbf | MATLAB | 5,169 | 188 | % use the 'Electrode LUT.pdf' look-up table to correctly cluster the electrodes
% clustering PO3-PO7 and PO4-PO8
figure()
% === PO8-PO4 ===
f1 = strcat('unmasked_familiar_all_only_epochrej_new.mat');
A = load(f1);
%data1 = A.Data_4;
data1 = A.UM_Fa_all;
data1a = data1(... |
f1e9bc9aa3eed15213fe6e11ba6c2b633e78241fcc00cd52b9453c86446f68c2 | MATLAB | 5,172 | 133 | function [X, labels, t] = generate_data(dataname, n, noise)
%GENERATE_DATA Generates an artificial dataset (manifold)
%
% [X, labels, t] = generate_data(dataname, n, noise)
%
% Generates an artificial dataset. Possible datasets are: 'swiss' for the Swiss roll
% dataset, 'helix' for the helix dataset, 'twinpeaks' for th... |
10499179d0fb5ddf83c21ef1b62739bd8e2071139554911832193073257cc7d3 | MATLAB | 5,192 | 124 | function [Ci,Q]=modularity_dir(A,gamma)
%MODULARITY_DIR Optimal community structure and modularity
%
% Ci = modularity_dir(W);
% [Ci Q] = modularity_dir(W);
%
% The optimal community structure is a subdivision of the network into
% nonoverlapping groups of nodes in a way that maximizes the number of
% wit... |
454d6a5c0388c8fac29339775f9dcfb79a2ff7db25338f0ed0aba34baadc6e9c | MATLAB | 5,198 | 162 | function [x, infos] = nmf_anls(V, rank, in_options)
% Alternative non-negative least squares (ANLS) for non-negative matrix factorization (NMF).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2,
% where
% {V, W, H} > 0.
%
% Given a non-negative matrix V, factorized non-n... |
4b33045c1259e2e80d77784362159595d634f5fd3c8d48c8c78ffece0ebd7e0d | MATLAB | 5,198 | 136 | function strout = nk_Preprocess_StrCfg(ix, jx)
global PREPROC
if isfield(PREPROC,'FEATSEL') && ...
isfield(PREPROC.FEATSEL,'BINMOD') && ...
PREPROC.FEATSEL.BINMOD ==1 && ...
PREPROC.BINMOD == 0
suffx = '_Concat';
else
suffx='';
end
if isfield(PREPROC,'SPATIAL')
switch PREPROC.SPAT... |
305b37bdffa65603f3dc79d2f7e306e95b15744cec903aa014691a9c94e82529 | MATLAB | 5,199 | 193 | function comp_nmf_robust_algorithms()
% Test file for NMF with outlier algorithm.
%
% Created by H.Kasai on May 21, 2019
clc;
clear;
close all;
mu_nmf_flag = 1;
svrmu_nmf_flag = 1;
rnmf_flag = 1;
ronmf_flag = 1;
rsvrmu_nmf_flag = 1;
%% generate/load data
% d=1: synthetic da... |
ed8858b59eb2bd8ca73e89d88fd7389c7a488e4800277ef7f75e305a127741f3 | MATLAB | 5,205 | 171 | function avi_filename = show_demixed_video(obj,save_avi, kt, frame_range, amp_ac, range_ac, range_Y, multi_factor, use_craw)
%% save the final results of source extraction.
%% inputs:
% kt: scalar, the number of frames to be skipped
%% author: Pengcheng Zhou, Columbia University 2017
% zhoupc1988@gmail.com
if ~exis... |
de55515b9d1e606b1d3e95544022b9121cd4f2170aba1931eecce199018c6e54 | MATLAB | 5,209 | 142 | %% ================================================================
% Script: FOCA_vs_FC_stability_comparison.m
% Purpose:
% Compare stability of FOCA (Functional Topographic Covariance Analysis)
% with conventional FC (Functional Connectivity) under GSR vs. non-GSR.
%
% Description:
% - Compute mean-norma... |
ce8272a313e817103f3d25a33b8a2f88b5f0752cf10082857f9857ed52ab7510 | MATLAB | 5,223 | 138 | % Compute h-step Weisfeiler-Lehman kernel for a set of graphs
% Author: Nino Shervashidze - nino.shervashidze@tuebingen.mpg.de
% Copyright 2009 Nino Shervashidze
% Input: Graphs - a 1xN array of graphs
% Graphs(i).am is the adjacency matrix of the i'th graph,
% Graphs(i).al is the adjacency list of the i'th gr... |
4e9ce5e4df516f85518871e2b3d716314efe81f06b8ceff83d926ca3b655fc04 | MATLAB | 5,228 | 133 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by multimo... |
c4c47acafb32f0773cb003dabda38f6a2bf035ef1e8d8187949e3728ad89389a | MATLAB | 5,229 | 222 | function [eigvector, eigvalue, elapse] = SDA(gnd,fea,semiSplit,options)
% SDA: Semi-supervised Discriminant Analysis
%
% [eigvector, eigvalue, elapse] = SDA(gnd,feaLabel,feaUnlabel,options)
%
% Input:
% gnd - Label vector.
% fea - data matrix. Each row vector of ... |
508882208c97a10d0638366f05f2326e60b6f71cfbb29e415ce3933c068e701a | MATLAB | 5,233 | 170 | function [W H] = opnmf(X, K, w0, initMeth, max_iter, tol, ...
iter0, save_step, outputdir)
%
% compute OPNMF based on Euclidean distance
% Orthogonal Projective NonNegative Matrix Factorization
% input:
% X nonnegative data input (D times N)
% K number of components
% w0 given initi... |
db48db81adf9254cb1387b0d7eeea8146d039c7f2d2a532d96c7394a089cd566 | MATLAB | 5,233 | 111 | function [I,Q,F]=motif4funct_wei(W)
%MOTIF4FUNCT_WEI Intensity and coherence of functional class-4 motifs
%
% [I,Q,F] = motif4funct_wei(W);
%
% *Structural motifs* are patterns of local connectivity in complex
% networks. In contrast, *functional motifs* are all possible subsets of
% patterns of local con... |
60cd388e370bfe704672a69594060e40c7578fd4d33347e4e082f49a9524c3a0 | MATLAB | 5,235 | 173 | function [x, infos] = inmf(V, rank, in_options)
% Incremental non-negative matrix factorization (NMF) with outliers (INCNMF) algorithm.
%
% Inputs:
% matrix V
% rank rank
% in_options options
% Output:
% w solution of w
% infos information
%
% References:
% ... |
0166fd4ef7f15702590ba82a05e8c600d2743f928880b6f99533c55bceaeb414 | MATLAB | 5,241 | 213 | %% FUNCTION Least_Lasso
% Sparse Structure-Regularized Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * \|W\|_1 + opts.rho_L2 * \|W\|_F^2}
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% rho1: sprasity... |
9fd59581cf47971a2e379d4145d6962d3415d01f4a0b1d1eba21d5e2812350ea | MATLAB | 5,245 | 172 | function [W, info] = MTFLCd_ADMM(X, y, lambda1, lambda2, opts)
%
% Multi-Task Feature Learning with Calibration - ADMM
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
%
% We use ADMM to solve the problem.
%
% INPUT
% X - cell array of {n_i by d matrices} by m
% y - cell... |
88182d21166c1d2f94e53ba828a18629fc44d7f21e19717533b9d0d74793d36b | MATLAB | 5,246 | 132 | function Status = nk_GetAnalysisStatus(NM, analdim)
Status.completed_analyses = [];
Status.isequal_cv = [];
Status.betweenequal_cv = [];
Status.betweenfoldpermequal_cv = [];
Status.nmodal_analyses = [];
Status.analexistflag = false;
Status.analreadyflag ... |
ec0f1086995d9b70c6cff703f1d58f31a72dfd7729971a37fa297e6aa47d824d | MATLAB | 5,247 | 160 | %% Network-level Counts Plot (7×7, 3×3 subplot)
% Plots lower-triangular heatmaps of significant edge counts
% aggregated at the Schaefer 7-network level.
%% ===== USER SETTINGS =====
% csvPath: CSV file with columns [condition, row, col, value]
% - condition: one of 'sweet_sour','sweet_salty','sour_sweet','sour_sal... |
ae252408d73f12101de780fa41c2c6cef8f92a5a044f9419ca424277c1cd3787 | MATLAB | 5,253 | 141 | function R = nk_SVMFeatRank(Y, label, SVM, ModelOnly)
global MODEFL
if ~isfield(SVM,'modeflag')
modeflag = MODEFL;
else
modeflag = SVM.modeflag;
end
CMDSTR = nk_DefineCmdStr(SVM, modeflag);
[~,~,EVALFUNC,~] = nk_GetScaleYAxisLabel(SVM.evalfunc);
if strcmp(SVM.prog,'LIBSVM')
[LIBSVMTRAIN, LIBSVMPREDICT] =... |
5abf8421e8c0d765ace99626fc3093ff4ecd3f3d8ba1b255aebb4e141bee4f2a | MATLAB | 5,255 | 125 | function [gratingid, gratingrect] = CreateProceduralSineSquareGrating(windowPtr, width, height, backgroundColorOffset, radius, contrastPreMultiplicator)
% [gratingid, gratingrect] = CreateProceduralSineGrating(windowPtr, width, height [, backgroundColorOffset =(0,0,0,0)] [, radius=inf][, contrastPreMultiplicator=1])
%
... |
96a326f634286e8cc632d71e52a09449c2b907d040418c7db019d5e117b1f630 | MATLAB | 5,257 | 126 | function statMap = mbm_stat_map(y,stat)
% Calculates a statistical map.
%
%% Inputs:
% y: - measurement matrix (mxn), where n and m are the
% number of independent measurements and number of
% samples in each measurement, respectively.
%
% stat.designMatrix: - Design ... |
876bfc1654d9611fdc07d5c7ed3a64a4c44eadd8cf0b53902baac77214860717 | MATLAB | 5,265 | 148 | function ms = msNormCorreConcat(ms,isnonrigid,ROIflag,plotFlag)
% Performs fast, rigid registration (option for non-rigid also available).
% Relies on NormCorre (Paninski lab). Rigid registration works fine for
% large lens (1-2mm) GRIN lenses, while non-rigid might work better for
% smaller lenses. Ideally you want to... |
b32510242970ef1d11d34f4f9e939ec8c3c3ef5df740505cab6ccadf05833517 | MATLAB | 5,265 | 152 | function [p_exact,dist_rand] = compute_exact_pvalue(D1,D2,data_PET,res,Nperm,options,T1)
% function [p_exact,dist_rand] = compute_exact_pvalue(D1,D2,data_PET,res,Nperm,options,T1)
% the functions allow to compute exact p-values based on the output from
% the compute_DomainGauges function, see help for this function for... |
5008b16ffc81d27f4c1c51cf191aafa2666a109494df127fe0bb2718ff9f60bc | MATLAB | 5,266 | 196 | function varargout=shadedErrorBar(x,y,errBar,varargin)
% generate continuous error bar area around a line plot
%
% function H=shadedErrorBar(x,y,errBar, ...)
%
% Purpose
% Makes a 2-d line plot with a pretty shaded error bar made
% using patch. Error bar color is chosen automatically.
%
%
% Inputs (required)
% x - vec... |
09b98f2df695e1484db6924d042191cc6b6335c3fb86e003b48bb3b50be72067 | MATLAB | 5,274 | 154 | function [mask,featNames,scores,A]=featureFilterNMF(D,featNames,opt)
% feature filtering using NMF
% 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... |
579e492b48517fcf0ae72d41c598336157c534e1db071683f6b250ecee635d0f | MATLAB | 5,279 | 135 | function [wcoh,phlag,f,coi,chnames,TEinx_eeg2] = get_patient_wcoh(patnm,side,curr_resdir_eeg,event,subevent,downsamp);
% GET_PATIENT_WCOH Wavelet coherence of intraop EEG and LFP in one patient
% GET_PATIENT_WCOH(patnm,side,curr_resdir_eeg,event,subevent,downsamp)
% calculates mean squared wavelet coherenc... |
a7547eb3a2fa2d4408937f527cec17b0dea95b17ea19249115cf6138217be8bb | MATLAB | 5,279 | 182 | function [ W, info ] = MTFLCd_SFISTA( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration - Smoothing Proximal
% Gradient (SPG)
%
% diagnoalized
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi||_mu + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
% ||Xi wi - yi||_mu = max <vi, Xi wi - yi... |
efc3a0e0a1a82b66f5bce57f4d5e677770667753a208814da02a30338b8e01ae | MATLAB | 5,283 | 226 | %% FUNCTION Least_SparseTrace
% Incoherent Sparse and Low-Rank Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + gamma * \|P\|_1 }
% subject to: W = P + Q, \|Q\|_* <= tau
% where \|Q\|_* = sum( svd(Q, 0) )
%
%% INPUT
% X: {n * d} ... |
b2f2949186b6e205d875e91d8467aa98cbff1f9f9fc6a78dfabc59df4bebe95f | MATLAB | 5,285 | 132 | function bpX = project2InputSpace(X, PREPROC, prevP)
nPREPROC = PREPROC;
nPX = prevP;
nA = length(nPX);
%%%%%%%%%%% PROJECT DATA BACK TO INPUT SPACE %%%%%%%%%%
% Revert dimensionality reduction if previously used
% Find Dimensionality reduction parameters
reducedimfl = false;
for a = nA:-1:1
% Adjust pnt ac... |
aabed7a1b95687d496c62ead0ecd26698d3bcb7d1beb83b008bc8b3769478f18 | MATLAB | 5,287 | 211 | % % Non-negative Matrix Factorization via Nesterov's Optimal Gradient Method: Improved via Randomized Subspace Iterations NeNMF (RSI)
% Reference
% F. Yahaya, M. Puigt, G. Delmaire, G. Roussel, Faster-than-fast NMF using
% random projections and Nesterov iterations, to appear in the Proceedings
... |
d4774e90bdcb17d2ff281f6a35da42bd88f2028341ea048fdc437d9040ef7daf | MATLAB | 5,287 | 189 | %% FUNCTION Least_rMTFL
% Robust Multi-Task Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W ||X(P+Q) - Y||_F^2 + lambda1*||P||_{1,2} + lambda2*||Q^T||_{1,2}
% s.t. W = P + Q
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% lambda1: regularized parameter
% lambda2: ... |
6ff1e6749d5e52026b8c0df5dc2626e2b27331b252881f10e46ef957ee4ce993 | MATLAB | 5,288 | 148 | function Mx = display_classcomparison_matrix(pvals, ticklabels, mw_all, sw_all, T, G, hlinepos, perftitle)
% =========================================================================
% function Mx = display_classcomparison_matrix(pvals, ticklabels, mw, sw)
% =============================================================... |
3b6f0d2683421d774ef4312b548e92fda167255b2e59091c113a117b1facb099 | MATLAB | 5,295 | 145 | function ClusterFunc_RunKKwikOnCluster( self )
%
% ClusterFunc_RunKKwikOnCluster
% Opens window and runs KlustaKwik on cluster
%================================================
% PARAMETERS
%================================================
minClusters = 2;
maxClusters = 7;
%========================================... |
d2fceacad59a77808943d266763acd81647537b6378acf1bba6f25d41a4af5b6 | MATLAB | 5,296 | 165 | function [x, infos] = nmf_sparse_mu_v(V, rank, in_options)
% Sparse Multiplicative upates (MU) for non-negative matrix factorization (Orth-NMF-V).
%
% The problem of interest is defined as
%
% min D(V||W*H) + lambda*phi(H),
%
% where
% {V, W, H} >= 0, and phi(H) is a sparsity penalty def... |
b232173124bdd9bb7526fb457a8cc804b60d0ec71f6547d82cc50cc6a36a843a | MATLAB | 5,300 | 120 | function errReturn = mbm_check_read_inputs_app(MBM, inputMap)
% Check read inputs are compatable with each other.
%
%% Input:
% MBM - structure having the fields:
% MBM.maps.anatListFile - Character vector.
% - Path to either:
% ... |
c54d00176ae8cec680c6d12a273fa60fc4bdb4be37b4c406a8c9134968cd7110 | MATLAB | 5,312 | 174 | function [eigvector, elapse, Y] = SDA_MR(gnd,fea,semiSplit,options,RegularizerOptions)
% SDA: Semi-supervised Discriminant Analysis (Manifold Regularization Way)
% NOTE: Ref [2][3] shows that LDA can be formulated as a regression
% problem. Thus, manifold regularization [3][4] can naturally
% ... |
59be8f72c001a16de6d6c69386974a45b4df7a8ba7807082c633a9f9f4375c2d | MATLAB | 5,321 | 124 | function [K,runtime] = RGkernel(Graphs,k)
% Compute height k Ramon & Gaertner kernel for a set of graphs
% Copyright 2012 Nino Shervashidze
% For the algorithm see P. Mahe and J.-P. Vert, Graph kernels based
% on tree patterns for molecules, Arxiv.org, 2008, section 4.2
%
% Input: Graphs - a 1xN array of graphs
% ... |
05dd7167b22eacad5d8ac2f00d2804cd205056c1ffe234f8cdf5920ea89be1bf | MATLAB | 5,328 | 85 | % doLogGabor: spatial-frequency and/or orientation band-pass filter an
% image using a logGabor filter.
%
% This is a very flexible/useful spatial filter, since you can specify
% if it cares about orientation or not, and you can specify if it cares
% about spatial frequency or not.
%
% Using it looks like this:
% res =... |
60b84b61a57171daf9f495eab6c68a0539acbd26c581a62b0c1e9fe27f10b4ac | MATLAB | 5,328 | 133 | function [faces, vertices, vertexColors, data] = readMz3(filename, isForceRGBA2RGB, isVerbose)
%function [faces, vertices, vertexColors, data] = readMz3(fileName)
%inputs:
% filename: the nv file to open
% isForceRGBA2RGB: (optional). If true (default) then 4-component RGBA and 5-component RGBA+scalar are read as RGB... |
8dd82b81745fe16dc4e3f708a1ecf7b6356da9257e3c04cd2fc1f2761513151e | MATLAB | 5,331 | 172 | function [Weight, tElapsed] = IMRelief_Sigmoid_FastImple(patterns, targets, distance, sigma, lambda, tmax, plotfigure)
%IMRelief_Sigmoid_FastImple: fast implemetation of IMRelief_Sigmoid
%--------------------------------------------------------------------------
%INPUT:
% patterns: training data: [x1,x2,...xn] Eac... |
b1b7131da23f1ec404d0eb62617d49e021970281b628a441a6105e8928792981 | MATLAB | 5,345 | 177 | %%
clear; clc;
fs = 26000;
addpath('J:/930_project/Release/matnwb-master/');
cd 'J:/930_project/20231031/Monkey_8888_20231031_111128';
%%
nwb_1031 = nwbRead('Monkey_8888_hinsx_20231031_111128_mat.nwb');
data_1031 = nwb_1031.acquisition.get('ElectricalSeries').data.load();
disp('nwb data extraction done');
%%
... |
a0550f83fefae9484af83bab85a3fb0a8363fd04a312af75ba9d43719331f568 | MATLAB | 5,346 | 160 | function [ts, ds, rs, hrs, hds, hrx, hdx, hts_rs, hts_ds] = nk_GetTestParam(Y, nclass, model, features, weights)
%
% function [ts, ds, rs, hrs, hds, hrx, hdx, hts_rs, hts_ds] =
% nk_GetTestParam(Xtest, Ytest, model, features)
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% INPUTS
% -----... |
b013128ea6235593e76b1f3e05b1f2ed13f7ca2b501a0d0895981ecdf14824ff | MATLAB | 5,346 | 149 | function test_toolbox
%TEST_TOOLBOX Tests all functionalities of the dimension reduction toolbox
%
% test_toolbox
%
% Tests all functionalities of the dimension reduction toolbox.
%
%
% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl... |
626b6409a54830a4f36d3fa8c188a8d7e943e95fbf744107b0f358de74598ddb | MATLAB | 5,350 | 165 | function [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelconvexnmf(X,k,option)
% Kernel Convex-NMF based on update rules: k(X)=k(X)AY, s.t. A,Y>0. Ak=kernel(X,X)
% Definition:
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k)
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k,option)
% X: mat... |
e65a66434ee50e4b0f558abd235075801912f1aa3a537f83cf6edad44bcf6ecf | MATLAB | 5,352 | 186 | classdef BestProjectionCutter < MClust.Cutter
% A subcutter for manual cutter.
properties (Constant)
maxUndoRedo = 10;
end
properties
parent; % ManualCutter called from;
PrimaryCluster; % Cluster we are comparing to
SecondaryCluster; % Comparison Cluster that has cur... |
e2c9a9f0c1ee6cfd9d70009e0e78b9f632c640a1f8b8f39aea96195a7aac2c40 | MATLAB | 5,353 | 204 | clear; clc;
matdir = '/Users/justintorok/Documents/MATLAB/Nexis_Project/Nexis/raw_data_mouse';
load([matdir filesep 'eNDM_mousedata.mat'],'Networks');
load([matdir filesep 'Zeisel_CTMaps.mat'],'Zeisel_ng1360');
load([matdir filesep 'classkey_zeisel.mat'],'classkey_zeisel');
load([matdir filesep 'mouse_aSynData_426.mat'... |
ec85f96677b18a03e4e79d4405c00eb497832af6e6a75457d132e44b2c41dccb | MATLAB | 5,369 | 183 | function varargout=subsref(obj,subs)
% SUBSREF Subscripted reference
% An overloaded function...
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: subsref.m 7439 2018-10-10 17:19:31Z john $
if isempty(subs), return; e... |
89549559849c3779222e62a55eabcfc50732a66143c8bd455d98e3e54a642075 | MATLAB | 5,370 | 176 | function RT_comp_cuepair_trialtypes(conditions)
% RT_COMP_CUEPAIR_TRIALTYPES Compares RT values in high and low conflict trials
% RT_comp_cuepair_trialtypes(conditions) draws boxplots of RT values
% associated with high and low conflict trials for each task condition.
% Values are compared with Mann-Whitney... |
3d88e95e8928d851cccd7890bc326d18f7f52775dbb8a4776fbaf62407ed559e | MATLAB | 5,376 | 119 | % Sept 2012 Update: Program is to handle new Zebrabox tracking 192 fish at
% once. I will pull apart the data into two boxes.
% This is a brand new program that simplifies the sleep
% analysis, to 1) be streamlined in one function 2) expand the stats 3)
% batch everything together in one step.
function perl_... |
886e6099962882be40dd8dd283bc5a46dd011a501bd4b263690068dee88a2560 | MATLAB | 5,383 | 168 | function [sl, sv, pcal, pcav, sampleMean, paths] = spca(X, Gram, options)
% SPCA The SPCA algorithm for computing sparse principal components.
% [SL SV PCAL PCAV PATHS] = SPCA(X, Gram, K, LAMBDA, STOP) computes
% sparse principal components of the data in X. X is an n x p matrix
% where n is the number of observations... |
cc7adcc16773c58ac8065b0d837aebcfef01060f0a2822fc7cf4ed1047acb6fb | MATLAB | 5,384 | 152 | function y = rand_pair(a,b,n,varargin)
% RAND_PAIR Uniformly-sampled random pair combinations
%
% RAND_PAIR(A,B,N) is a 2-column matrix of length N, where each row is a unique pair of numbers
% chosen at random from the arrays A and B.
%
% RAND_PAIR(A,B,N,S) starts the pseudo-random number generator using seed S... |
da48b6c9b9aff680a569ae200506618beac7228c38716059a4ed52c17f8d6888 | MATLAB | 5,387 | 176 | function avg_psth_stat(event,partition,resdir,plot_win,parttags,patgr_nm,yL,varargin)
%AVG_PSTH_STAT Compares PSTHs of partitioned trials
% AVG_PSTH_STAT(event,partition,resdir,plot_win) selects units responsive
% to EVENT. Partitions epochs around EVENT based on PARTITION criteria.
% Calculates average PSTHs of p... |
4e252c8f77003c9a1a86e513ccffa3718f8358a5133f4e5e309d2a7ce9cf9046 | MATLAB | 5,392 | 195 | % SB1_ESTIMATE Estimate parameters in a sparse Bayesian model
%
% [W,USED,ML,A,B,G] = SB1_ESTIMATE(PHI,T,A,B,MAXITS,MONITS)
%
% OUTPUT ARGUMENTS:
%
% W Estimated weights (subset of full model)
% USED Indices of relevant basis vectors
% ML Marginal likelihood of final model
% ... |
f50183cb55619385b1ec938ebc3d1f76521158972e7284ee8adaa8bc98463c2d | MATLAB | 5,403 | 152 | function y = kernel_function(v, X, center, kernel, param1, param2, type)
%KERNEL_FUNCTION Computes sum of (K * X) where X is a possible eigenvector
%
% y = kernel_function(v, X, center, kernel, param1, param2)
%
% The function computes the sum of the elements of (K * v), where v is a
% possible eigenvector of K. This... |
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