sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7abff2da6936beb935d64a50e3776e68da1d92c8e0b3a34381739f952b5cc557 | MATLAB | 2,824 | 98 | function msPlotAnalysisSummary(ms,behav)
%MSPLOTANALYSISSUMMARY Plot summar of your results
% This is a quick function to get an overview of your results
% Copyright (C) 2017-2018 by Guillaume Etter
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Publi... |
b2ef71d72935987038b90af1ea820bfc8fb13e81b8afcfe3989540852e1bb201 | MATLAB | 2,829 | 75 | function perftab = cv_extract_performanceMetrics_fromNMstruct(NMstruct, varargin)
if nargin>1
analyses_status = nk_GetAnalysisStatus(NMstruct);
analyses_ind = find(analyses_status.completed_analyses);
if sum(analyses_ind) == 0
warning('no analyses completed');
exit;
end
oocv_fl... |
d08ff3d0671d7c405689a7094e26ef99b8a381e68aa66d14e4fe07355fdeb744 | MATLAB | 2,834 | 82 | function [Y,numIter,tElapsed]=orthnmfruletest(X,outTrain)
% map the test/unknown samples into the orth-NMF feature space
% X: matrix, test/unknown set, each column is a sample, each row is a feature.
% outTrain: struct, related options in the training step.
% outTrain.factors: column vector of cell of length 2, contain... |
f1c33655c950532881dce8266c19877f63056888979d3da8ee02407e06ace94e | MATLAB | 2,834 | 115 | function nk_AdjustData
spm5ver = 0; if strcmp(spm('ver'),'SPM5') || strcmp(spm('ver'),'SPM8') , spm5ver = 1; end
if spm5ver
brainmask = spm_select(1,'image','select brainmask');
else
brainmask = spm_get(1,'IMAGE','select brainmask');
end
Vm = spm_vol(brainmask);
if spm5ver
P = spm_select(Inf,'image','S... |
9da5fd172c67077c994cd9f26d390eca5d06caf853f067d8d96e9dcea9c17e35 | MATLAB | 2,835 | 82 | function [ param, act ] = nk_ROBSVM_config(prog, param, modefl, defaultsfl)
if strcmp(modefl,'classification'), modefl = 'binaryclass'; end
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = true; end
probflag = 0;
cachesize = 500;
shrinkheur = 1;
termcrit = 0.001;
weighting = 0;
act = 0;
if isfield(... |
dfb725e7e6cf02ba943e0f14582cf026472ecb97cea67317dc74b87f9b4226ed | MATLAB | 2,836 | 112 | function [ W, info ] = MTFLCd_Subg( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration
% Subgradient descent.
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
%
%
% INPUT
% X - cell array of {n_i by d matrices} by m
% y - cell array of {n_i b... |
acfd5b445348d2a277c5fac9897e287b80db038b24392b8106bbb1d853e03787 | MATLAB | 2,837 | 90 | function make_motif34lib
%MAKE_MOTIF34LIB Auxiliary motif library function
%
% make_motif34lib;
%
% This function generates the motif34lib.mat library required for all
% other motif computations.
%
%
% Mika Rubinov, UNSW, 2007-2010
%#ok<*ASGLU>
[M3,M3n,ID3,N3]=motif3generate;
[M4,M4n,ID4,N4]=motif4gen... |
95063d7ecd3b31b2cf30635af2907241f6bea10b3afa55ce0a8585563adbe535 | MATLAB | 2,842 | 82 | function [newVectors, whiteningMatrix, dewhiteningMatrix] = whitenv ...
(vectors, E, D, s_verbose);
%WHITENV - Whitenv vectors.
%
% [newVectors, whiteningMatrix, dewhiteningMatrix] = ...
% whitenv(vectors, E, D, verbose);
%
% Whitens the data (row vectors) and reduces dimension. Return... |
3d051ad317da3d31fc24bbc27f03d2c7e699d8527b3cce1bf01583f5d02c402b | MATLAB | 2,844 | 65 | function cParams = nk_PrepMLParams(Params, Params_desc, i)
global SVM GRD CMDSTR
% Pull parameters according to the algorithm selected
switch SVM.prog
case 'matLRN'
cParams.val = Params(i,:);
cParams.desc = Params_desc;
case 'GLMNET'
% Use only the first 5 params
cParams.val = ... |
ef4af6457d4465ec41b0ebcaf2988ff33562c3c838ca34b6651e30875e5094dc | MATLAB | 2,849 | 88 | function varargout = avg_psth_plot(psth_R1,psth_R2, alignevent, colors,varargin)
%AVG_PSTH_PLOT Average peri-event time histogram
% AVG_PSTH_PLOT(psth_R1,psth_R2, alignevent, colors,...) draws averaged
% PSTHs with standard error for data in PSTH_R1 and PSTH_R2.
%
% Required inputs:
% PSTH_R1, PSTH_... |
067bba8b8e753924462471fe625a16215cca6d04627a218fc8996043b5998477 | MATLAB | 2,851 | 109 | % SB1_EXAMPLEREGRESS Example of Sparse Bayes Regression
%
% SB1_EXAMPLEREGRESS(N,NOISE,KERNEL,WIDTH,MAXITS)
%
% INPUT ARGUMENTS:
%
% N Number of training points
% NOISE Noise standard deviation to be added
% KERNEL Kernel function to use (see SB1_KERNELFUNCTION)
% WIDTH Kernel lengt... |
10efa0ff1cb4cbd9d38e290008842c90be6af85cb0063131b796c16457d1ced1 | MATLAB | 2,853 | 76 | function BC=betweenness_wei(G)
%BETWEENNESS_WEI Node betweenness centrality
%
% BC = betweenness_wei(L);
%
% Node betweenness centrality is the fraction of all shortest paths in
% the network that contain a given node. Nodes with high values of
% betweenness centrality participate in a large number of shor... |
0c9c4adcce4699ef21af792752627f252dc96c53efe9a6f8249d8cfa64b977e1 | MATLAB | 2,856 | 79 | function [CUSTOMPREPROC, PX, act ] = cv_customPreproc_config(CUSTOMPREPROC, PX, parentstr, defaultsfl)
funcFile = '';
funcParams = [];
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
if ~defaultsfl
if isfield(CUSTOMPREPROC,'filename') && ~isempty(CUSTOMPREPROC.filename)
... |
0e24fbb863c9914326c6e0dc88821ed70040274c1081fc8fc26b50ec22f40fcc | MATLAB | 2,858 | 97 | function component_PC_RT_correlation
% COMPONENT_PC_RT_CORRELATION Finds clusters using spike-phase-coupling strength (MRL) and reaction time value pairs.
% COMPONENT_PC_RT_CORRELATION fits mix. model of Gaussian distributions
% on delta spike-phase-coupling strength (MRL) and reaction time value
% pairs and ... |
f4ee7ff98103fd8b3099468c88a401d60d5fab3d60e8a0470512d2a4d1282767 | MATLAB | 2,860 | 122 | function [result,mean_accuracy,std_accuracy] = runIndependent(K,lk)
% Copyright 2012 Nino Shervashidze, Karsten Borgwardt
% K = kernel matrix (n*n)
% lk = vector of labels (n*1)
% cv = number of folds in cross-validation
% standard deviation
% independent scheme
% best c
addpath('~/code/libsvm');
% randomly permute k... |
2728dcca38957f8652bccda03ba28fd7f737d9e877123be7de4f20535747703c | MATLAB | 2,862 | 96 | function result=mahalanobis(x,locvct,varargin)
%MAHALANOBIS computes the (squared) distance of each observation in x
% from the location estimate (locvct) of the data,
% relative to the shape of the data.
%
% Required input arguments:
% x : data matrix (n observations in rows, p variables in co... |
a04c015bcdbb8c879626b4a99e4c477b4595640d9202f539073801afa27da034 | MATLAB | 2,864 | 94 | function [act, GridAct] = nk_CVGridSelector(ix, jx, GridAct, proceedfl)
if ~exist('GridAct','var') || isempty(GridAct)
GridAct = false(ix,jx);
end
if ~exist('proceedfl','var') || isempty(proceedfl)
proceedfl = true; end
print_grid(ix, jx, GridAct)
menustr = ['Select all CV2 positions|' ...
'Select single singl... |
5eb14a4584ea1a763a7ad97e847dab17404b0605e88b16f704ca439ba029a4fc | MATLAB | 2,866 | 69 | function Thresh = nk_DataIO3_SpaceDefImage_config(Vm, Lm, datatype)
if ~exist('datatype','var') || isempty(datatype), datatype = 'nifti'; end
switch datatype
case {'nifti','spm'}
% Read-in space-defining image and check number of unique values
if ~exist(Vm.fname,'file')
if isempty(file... |
2a8d775a58f09b6fe467e8c947db21759a486c07a8c51862a3212f9745279cd1 | MATLAB | 2,871 | 80 | clear; clc;
%% Load File %%
data=readtable('...\Monkey_Behavioral_Data\Monkeys_Behavioral_Data_Analysis2',VariableNamingRule='preserve');
%%
allSess=unique([data.Session]);
colorSolo=[10 10 92]/255;
colorJa=[.9 .1 .1];
faceAlpha=0.7;
RT1_Solo=NaN(12,1);
RT1_Ja=NaN(12,1);
RT2_Solo=NaN(12,1);
RT2_Ja=NaN(... |
772bcde095f7010a461b1867932409f7bed1789721831a1138c21932f061d08c | MATLAB | 2,871 | 80 | clear; clc;
%% Load File %%
data=readtable('...\Monkey_Behavioral_Data\Monkeys_Behavioral_Data_Analysis2',VariableNamingRule='preserve');
%%
allSess=unique([data.Session]);
colorSolo=[10 10 92]/255;
colorJa=[.9 .1 .1];
faceAlpha=0.7;
MT1_Solo=NaN(12,1);
MT1_Ja=NaN(12,1);
MT2_Solo=NaN(12,1);
MT2_Ja=NaN(... |
1f6bd66501dcc7f16d984af0937de205cadcf83d69f8cfa11b40f28c15bba4a5 | MATLAB | 2,873 | 96 | function [Ain,Cin,bin,fin] = sparse_NMF_initialization(Y,K,options) %beta,eta,X0,err_thr,max_iter)
T = size(Y,ndims(Y));
if ~ismatrix(Y)
Y = reshape(Y,numel(Y)/T,T);
end
defoptions = CNMFSetParms;
if nargin < 3 || isempty(options); options = defoptions; end
if ~isfield(options,'snmf_max_iter'); options.snmf_max_i... |
39b7b509d0c4e36a118eff83d26942ee10ed228ddd83477d89323cb1fd661702 | MATLAB | 2,876 | 90 | function [varargout] = WeightedMIToolbox(functionName, weightVector, varargin)
%function [varargout] = WeightedMIToolbox(functionName, weightVector, varargin)
%
%Provides access to the functions in WeightedMIToolboxMex
%
%Expects column vectors, will not work with row vectors
%
%Function list
%"entropy" or "h" = H(X)
%... |
e01131de93f4ddbaaa70cf8c6e56c9d3964099b2a4debb38a54831f8f8967412 | MATLAB | 2,880 | 104 | function [M,IND] = combn(V,N)
% COMBN - all combinations of elements
% M = COMBN(V,N) returns all combinations of N elements of the elements in
% vector V. M has the size (length(V).^N)-by-N.
%
% [M,I] = COMBN(V,N) also returns the index matrix I so that M = V(I).
%
% V can be an array of numbers, cells or strings.
%
%... |
877d70e561553da261aada38fbe4487881238a5fc88e6c0b69e03bed7acfd3d6 | MATLAB | 2,882 | 73 | function [tscounts, wfcounts, evcounts, contcounts] = plx_info(filename, fullread)
% plx_info(filename, fullread) -- read and display .plx or .pl2 file info
%
% [tscounts, wfcounts, evcounts, contcounts] = plx_info(filename, fullread)
%
% INPUT:
% filename - if empty string, will use File Open dialog
% fullread - ... |
10c3dd654eb9d8933de50438f6c628d6942edad160a51979909b348317e4b708 | MATLAB | 2,885 | 98 | function [P, Perf, Sim] = nk_MultiDecideDAG(H, Y, Classes, ngroups)
% function [P, Perf, d, crit] = nk_MultiDecideOneVsOne(H, Y, Classes,
% maxfunc, weightflag)
%
% Perform One-Vs-One multi-group classification
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% (c) Nikolaos Koutsouleris, 01/2... |
3def2492ea9b9dfc2fd0ba54fc8d93c6888d648bddb953bfcd9e46afb09e8a71 | MATLAB | 2,885 | 86 | function test_symm_clustering()
%
% demonstration file for NMFLibrary.
%
% This file illustrates how to use this library.
% This demonstrates Symm-ANLS algorithm and Symm-Newton algorithm.
%
% This file is part of NMFLibrary.
%
% Created by H.Kasai on Jun. 26, 2019
clc;
clear;
close all;
%% generate ... |
ed0adc923e432cf236c92c425565d84f3bba6b46b6b00498a6e9e75692429758 | MATLAB | 2,887 | 114 | function check_resp_pvalues(EventTypes)
% CHECK_RESP_PVALUES Q-Q plot of observed and expected p values obtained by testing unit responsiveness.
% check_resp_pvalues(EventTypes) gets p values resulting from testing
% unit responsiveness to events included in cell array of EVENTTYPES,
% plots observed and expe... |
d6ab37c058dbf8e1abdf54aab625ca33c7cdaef6f80c737f08623949b5b0f110 | MATLAB | 2,889 | 104 | %%% Analysis of correlation ICD vs activity %%%
clear; clc;
load('...\LFPdata_Fig5D_S1.mat')
X_name='ICDmean';
Y_name='RMSja';
condPD=3; colore=[.7 0 0]; % Red
dir=NaN(2,2,79);
posLayout=[1,3,4];
for dset=1
iter=0;
figure()
set(gcf,'position',[0 0 825 935])
mkToEval=1:2;
f... |
2d83c4ecb30b93b50c61f8b286b8e3e03fb8f23ade11c8a7e894b29747c9587d | MATLAB | 2,896 | 94 | function [FeatureTimestamps, Features] = CalculateFeatures(featuresToCalc)
% [FeatureTimeStamps,Features] = CalculateFeatures(featuresToCalc)
%
% input string list of featurenames
%
% output
% FeatureTimeStamps = timestamps
% Features = set of features
MCS = MClust.GetSettings();
MCD = MClust.GetData();
nFeat = ... |
ceb000f792539bf912d6475ad2eda6a52919da5f52b72d6966c44765901dc62c | MATLAB | 2,896 | 90 | classdef ZeroCluster < MClust.ClusterTypes.DisplayableCluster
% returns all spikes
properties(SetObservable)
ShowUnaccountedForOnly = false;
end
methods(Static, Access=public)
function bool = Modifiable()
bool=false;
end
end
methods
% --... |
de9d23a441432c3df97cad1da4d0a44672e890bb7be2364625454ee1a873ae03 | MATLAB | 2,900 | 99 | function [CompleteVideo,concatInfo] = ConcatVideos(path,concatInfo,dsFOVflag)
% INPUT:
% Path: String with the path for the videos to be concatenated. The videos
% should be already motion corrected and named in the right order (e.g.:
% "msvideo1.avi", "msvideo2.avi", etc). The Alignment file should also be
% placed ... |
75c5d57d7a6fa665f37c5e54d9d0b5c64da7a8acbff62269b91ed79366130969 | MATLAB | 2,905 | 123 | % SB1_KERNELFUNCTION Compute kernel functions for the RVM model
%
% K = SB1_KERNELFUNCTION(X1,X2,KERNEL,LENGTH)
%
% OUTPUT ARGUMENTS:
%
% K N1 x N2 kernel matrix.
%
% INPUT ARGUMENTS:
%
% X1 N1 x d data matrix
% X2 N2 x d data matrix
% KERNEL Kernel type: currently one... |
71f9b303dec0e5b7c43bc41b9b7fb0bdf8693935e59b9b35e38f5f9fc18510ed | MATLAB | 2,907 | 59 | function [curr_dat_sz, curr_lab_sz] = store2hdf5(filename, data, labels, create, startloc, chunksz)
% *data* is W*H*C*N matrix of images should be normalized (e.g. to lie between 0 and 1) beforehand
% *label* is D*N matrix of labels (D labels per sample)
% *create* [0/1] specifies whether to create file newly ... |
01ffa3e08e16ae075185bdbc0b85a5fe8ee3fe83d4ffcfffe93a6aa88bb88270 | MATLAB | 2,910 | 89 | %% file example_Robust.m
% this file shows the usage of Least_RMTL.m function
% and study how to detect outlier tasks.
%
%% 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 )
% ... |
1a9b6daae14816c015d148b8ad7a30d83ddbd5c2d94242c30a27b27401cbf04b | MATLAB | 2,910 | 78 | classdef spatial_express_t0 < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods
funct... |
1df2ac10d8f831a9c6602761692a1fee90568c1b06238e44218e115af4dd6b8d | MATLAB | 2,911 | 77 | %% ================================================================
% Script: age_effects_neonatal_FOCA_GAM.m
% Purpose:
% Model age-related trajectories of internetwork covariance (FOCA matrix)
% during the early postnatal period using mass univariate GAMs.
%
% Description:
% - Input: precomputed F-statis... |
4072a23151118b15ed262b7c037d24ec032e1905afd8f2ed45ad6f725e1732ae | MATLAB | 2,914 | 71 | function [D,B]=distance_wei(L)
% DISTANCE_WEI Distance matrix (Dijkstra's algorithm)
%
% D = distance_wei(L);
% [D,B] = distance_wei(L);
%
% The distance matrix contains lengths of shortest paths between all
% pairs of nodes. An entry (u,v) represents the length of shortest path
% from node u to node v... |
0df6a3bd8733b6d78693828fd4c7f2da2ef5064c73c5183a56e5e6c08f0fca2d | MATLAB | 2,930 | 94 | function result = daisy(x,vtype,metric)
%DAISY returns a row vector containing all the pairwise dissimilarities
% (distances) between observations in the dataset. The original
% variables may be of mixed types.
%
% The calculation of dissimilarities is explained in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Fin... |
26daac66c2bbd5f30877915e01dd1790a2a2cd43d80962481ce13e6ea6bdeead | MATLAB | 2,930 | 78 | classdef Blob < handle
% Wrapper class of caffe::Blob in matlab
properties (Access = private)
hBlob_self
end
methods
function self = Blob(hBlob_blob)
CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle');
% setup self handle
self.hBlob_self = hBlob_blob;
end
fu... |
bd0d78d83db6536c866a46eb406a44b8af92348601ad2a244bcd65fd0c22d393 | MATLAB | 2,933 | 81 | function [handles, visdata, oocvdata, mlidata] = switch_analysis(handles)
visdata = []; oocvdata = []; mlidata = [];
analind = handles.curranal;
handles.curmodal = 1; if strcmp(handles.selModal.Enable,'on'), handles.curmodal = handles.selModal.Value; end
if handles.multi_modal && strcmp(handles.selModal.String{handle... |
ac68d8df562840c819c53393383b29837dbf073315a0fc9f35a7b0f442496bb6 | MATLAB | 2,935 | 90 | function ms = msNormCorre(ms,isnonrigid)
% 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 compare both on a smal... |
b9ce039fb4a3d51751b05b36d494fd8f95ee91f529c12a1a257b5184a7fde648 | MATLAB | 2,940 | 117 | function [model] = ml_binaryclass_SSVM(X,y,options)
% ml_binaryclass_SSVM(X,y,options)
%
% Description:
% - Fits a smooth support vector machine by minimizing squared hinge loss
%
% Options:
% - addBias: adds a bias variable (default: 0)
% - lambdaL2: strength of L2-regularization parameter (default: 0)
% - k... |
30fe892b997c71e919b877439838c11885bf1146701ca09fb772fe58914b4bcc | MATLAB | 2,945 | 89 | function plotBarError(Acc,std,dataSets,classifiers,xDescription,yDescription,saveFigName)
% Plot bar with STD
%example:
% Acc=zeros(3,9);
% Acc(1,:)=[0.8987,0.8945,0.8912,0.8863,0.8940,0.8893,0.8571,0.8467,0.8008];
% Acc(2,:)=[0.9708,0.9689,0.9689,0.9616,0.9683,0.9675,0.9604,0.9576,0.9155];
% Acc(3,:)=[0.9225,0.9252,0.... |
1312c7fd5e7e00d1a48fb9956a8fc65e27cceb7e2cb65dd49fa029f6fc7a74dc | MATLAB | 2,951 | 106 | function varargout = ds2nfu(varargin)
% DS2NFU Convert data space units into normalized figure units.
%
% [Xf, Yf] = DS2NFU(X, Y) converts X,Y coordinates from
% data space to normalized figure units, using the current axes. This is
% useful as input for ANNOTATION.
%
% POSf = DS2NFU(POS) converts 4-element positi... |
5087b1018f0885aa60fa0a215790277e0e3181fc14090193965d4d4dcaec7c41 | MATLAB | 2,953 | 101 | function newmap = mbm_bluewhitered(ax,m)
%BLUEWHITERED Blue, white, and red color map.
% BLUEWHITERED(M) returns an M-by-3 matrix containing a blue to white
% to red colormap, with white corresponding to the CAXIS value closest
% to zero. This colormap is most useful for images and surface plots
% with ... |
b124c23bf5e10e7572b5e94c046631d50a543dfcb927e0fefc6bac005ef05630 | MATLAB | 2,955 | 93 | function FEAT = nk_PerfCommonFeatExtraction(FEAT, param)
global PREPROC
[iy, jy] = size(FEAT);
n = iy*jy;
[d, nclass] = size(FEAT{1,1}.dat{1}.vol);
switch param.mode
case {1,2,3}
Dx = false(d, nclass);
param.mode = 1;
param.thresh = 75;
case 4
Dx = zeros(d, nclass);
case 5
... |
5818358db60e221072ab94f09528330f140876e23f8604579a44a8257d75f8b5 | MATLAB | 2,956 | 96 | clear; clc;
load('...\LFPdata_Fig4C')
%%
figure()
set(gcf,'Position',[-1151 411 1145 483])
tiledlayout(1,2)
xTickLabels = {'Delta (1-4 Hz)', 'Theta (4-7 Hz)', 'Alpha (8-12 Hz)', 'Beta (13-30 Hz)', 'low-Gamma (31-60 Hz)', 'high-Gamma (61-100 Hz)'};
xTickValues=1:6;
for mkID=1:2
switch mkID
ca... |
4cbbb4f835a618b7164c573fcc2cbf7dd5385c95e087b320aa79ef5fb3e9d5d4 | MATLAB | 2,991 | 43 | %-----------------------------------------------------------------------
% Job saved on 16-Dec-2020 18:48:24 by cfg_util (rev $Rev: 7345 $)
% spm SPM - SPM12 (7771)
% cfg_basicio BasicIO - Unknown
%-----------------------------------------------------------------------
matlabbatch{1}.cfg_basicio.file_dir.file_ops.file_... |
b2f1b85436d667916728a840899bbc7f2986f4e8469c3ccf031ecb12a1595064 | MATLAB | 2,992 | 79 | function [C, dC] = backprop_gradient(v, network, X, targets, lambda)
%BACKPROP Compute the cost gradient for CG optimization of a neural network
%
% [C, dC] = backprop_gradient(v, network, X, targets, lambda)
%
% Compute the value of the cost function, as well as the corresponding
% gradient for conjugate-gradient o... |
5fa1331ac31876801a50be3657f6472f2103926311b06b49b432244c65a522e0 | MATLAB | 2,993 | 65 | clc;clear;close all;clear gca
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Analyses\Data");
%d=load('Jupiter_test_data_TOJ_whole_period.mat');
%spikes=d.data.spikes;
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Data\PSTH_1_to_401_jupiter\PST... |
0149621e08a0e7cb54012f1f50200f50fb717f38c034203988595d610db7629e | MATLAB | 2,995 | 86 | function [metaSample,metaClass]=computeMetaSample(trainSet,trainClass,option)
% Compute metasamples for each class using SVD. This function is used by
% MSRC.
% trainSet: matrix, each column is a training sample
% trainClass: numeric column vector, the class labels of the training samples
% option: struct, with fields:... |
9e43589c9c17e87e06ceab322976e33f58e8a5c97760b981bb17291169942bc6 | MATLAB | 2,996 | 57 | function [act, param] = nk_PSO_config(param, defaultsfl, parentstr)
PSO.N = 10; % Number of particles
PSO.max_Iter = 100; % Number of iterations
PSO.c1 = 1; % Cognitive factor
PSO.c2 = 1; % Social factor
PSO.w = 0.5; % Inertia weight
PSO.lb ... |
49504a301c5361acf28dc9968a879da19c24ac6c1bfb1f199f6482944b21b9d2 | MATLAB | 3,000 | 76 | classdef spatial_express_gains < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods
fu... |
ef2e28f790257edb8ddc32ec73bd5dcf3b8860a36ea61cf6fd1dae77af1d1863 | MATLAB | 3,003 | 79 | function d = nk_mahalanobis(varargin)
%MAHALANOBIS Computes the Mahalanobis distance.
% D = MAHALANOBIS(Y, X) computes the Mahalanobis distance between
% each vector in Y to the mean (centroid) of the vectors in X, and
% outputs the result in vector D, whose length is size(Y, 1). The
% vectors in X and Y ... |
c93994177074715bf3cfa4a07314c5379284ad9bf068f018ba8a29655ebc32c3 | MATLAB | 3,004 | 77 | function [ act, params ] = nk_FEAST_config( params, askFeatNum, parentstr )
NumFeat = 10;
Method= 4;
na_str='?';
if ~exist('askFeatNum','var') || isempty(askFeatNum), askFeatNum = true; end
if isfield(params,'FEAST'),
if isfield(params.FEAST,'Method'), Method = params.FEAST.Method; end
if isfield(params.FE... |
25256dad22db0b1cba6bdf241192e9623d0e626a426f195b65dc6aaf17114015 | MATLAB | 3,009 | 92 | function scores = SKS(X_train, Y_train, extra_param);
% scoress = SKS(X_train, Y_train, extra_param);
%
% Implementation of SKS algorithm, described in
%
% A. Navot, L. Shpigelman, N. Tishby, E. Vaadia. Nearest Neighbor Based Feature Selection for Regression and its
% Application to Neural Activity. Submitted to NIPS ... |
d6d07a9b1060f0536317b231da601c6206c49443206e2b921d8185a14f06893b | MATLAB | 3,014 | 93 | function [catarray, numelems] = nk_cellcat(arr, arr2, dim, F)
[ix,jx] = size(arr);
if ~exist('dim','var') || isempty(dim), dim = 1; end
catarray = [];
mx = 0; my = 0; cx = 0; cy = 0;
sx = zeros(ix*jx,1); sy= zeros(ix*jx,1);
% Determine output sizes of catarray
cnt=1;
for i=1:ix
for j=1:jx
if ~exist('arr2',... |
bf812743cd91b18263044a7ceb5cc09205bbeb227e16e7c2582a95cde851c794 | MATLAB | 3,019 | 96 | function [GridSelectorResults, ipos] = ...
nk_GridOptimumSelector(TR, TS, C, MD, FEAT, Weights, CV2Pred, CV1Pred, P, Pdesc, combcell, act, perc)
global SAV
if ~exist('perc','var'), perc = []; end
%%%%%%%%%%%%%%%%%%%%%%%% MAX SELECTION %%%%%%%%%%%%%%%%%%%%%%%%%%
C(isnan(C)) = 0;
[ipos, ind0] = nk_FindGridOpt(TR,... |
b01e146d469cae4e04e5eb49cc814efa10eed7c59726522f2bff8b6382bc0038 | MATLAB | 3,020 | 81 | classdef spatial_express_t0_noemg < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods
... |
abdc6d4063b3554a91b8cf8a21b118fb805994597c137781f0d2952a50905941 | MATLAB | 3,021 | 85 | clear; clc;
%% Load File %%
data=readtable('...\Monkey_Behavioral_Data\Monkeys_Behavioral_Data_Analysis2',VariableNamingRule='preserve');
%%
allSess=unique([data.Session]);
colorSolo=[10 10 92]/255;
colorJa=[.9 .1 .1];
faceAlpha=0.7;
TD1_Solo=NaN(12,1);
TD1_Ja=NaN(12,1);
TD2_Solo=NaN(12,1);
TD2_Ja=NaN(... |
5d5adcca81f72a9c53cc8587dbe3374a41a73ee8de7433a65a8bcb526ae50644 | MATLAB | 3,025 | 79 | %% ================================================================
% Script: visualize_developmental_difference_FOCA_CohenD.m
% Purpose:
% Visualize developmental differences in individualized FOCA matrices
% between neonates (dHCP) and adults (HCP) using precomputed Cohen’s d.
%
% Description:
% - Load p... |
a51e58bfe2d39ee1f713a216d242f63515a31e58f5b8608f3ed814285bc9fcab | MATLAB | 3,028 | 94 | function animate_fit
load steve
load pp_allstates
[time_bar,tent_point,tent_trail,spec_axis,tent_axis,exp_plot,fit_plot] = init_plot(t,f,s);
xyz_trail = zeros(length(t),3);
constraint_data = [];
for j = 1:length(t)
[f1,P1,idx] = db_fit(f,s(:,j),db_data,[]);
keyboard
title(spec_axis,sprintf('Step %d/%d'... |
6778ce92084fbbdafca6d9ffe5cac16abe91d552635b4a12dc0297c21b9a1246 | MATLAB | 3,030 | 89 | clear
load('ProcessedData\tables_compiled_shifterMag.mat');
%% classifier code; runs on increasingly larger random subsets of cells
% % reorg main table if needed
% re_org = vertcat(tables_compiled{:});
% tables_compiled = cell(1,1);
% tables_compiled{1,1} = re_org;
tic
% run within days by iter; params fo... |
36151db750de4aef38f69a5f508c4d43e821991e3dc5c852628c6b4c40a931ff | MATLAB | 3,033 | 82 |
% this script performs the signal vector decomposition using graph signal
% processing approach (Graph Fourier Transform) given Adjacency matrix
%%%change this:
p_dim = 126; % dimension of probability matrix (nROIs)
%%%%aligned means coupled; liberal means decoupled
%%%change this:
choose_aligned_range = 12; % numbe... |
b93d00fef796c5b325349e976a630d31760332a9a000c8c4a6799968ff9bd2fb | MATLAB | 3,033 | 88 | function [A,Y,numIter,tElapsed,finalResidual]=sparseseminmfnnls(X,k,option)
% Sparse Semi-NMF based on NNLS: X=AY, s.t. Y>0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=seminmfnnls(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=seminmfnnls(X,k,option)
% X: matrix of mixed signs, dataset to factorize, each... |
b971ca1bc2bcd31f011714b24c3078744c0d2732a568c9c815b31a6dd5f7bb62 | MATLAB | 3,033 | 87 |
function [w_Brain, model_All] = W_Calculate_SVR_CSelect(Subjects_Data, Subjects_Scores, Pre_Method, C_Range, 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 predicted,... |
d46194c92e9ed92af45faa2281c81281114f31f6f88207b621d5f026e7d8a8ee | MATLAB | 3,034 | 87 | function read_inomed(varargin)
%READ_INOMED Spike detection for Inomed data.
% READ_INOMED loads Inomed continuous files, performs filtering,
% censoring and spike detection of channels. Files are saved in Ch*.mat
% files for clustering. To support MClust, 'fake tetrode' data is
% generated by multiplyin... |
e57c9de7bc1736385f442401310958ab25a80ff183793371250d31f75de0b683 | MATLAB | 3,034 | 96 | function [varargout] = WeightedMIToolbox(functionName, weightVector, varargin)
%function [varargout] = WeightedMIToolbox(functionName, weightVector, varargin)
%
%Provides access to the functions in WeightedMIToolboxMex
%
%Expects column vectors, will not work with row vectors
%
%Function list
%"entropy" or "h" = H(X)
%... |
1b9234bed3576bd36de112d12f84db6666de051356d79dd4946a00e77c968399 | MATLAB | 3,035 | 97 | function [VIn, MIn] = partition_distance(Cx, Cy)
%PARTITION_DISTANCE Distance or similarity between community partitions
%
% This function quantifies information-theoretic distance (normalized
% variation of information) or similarity (normalized mutual information)
% between community partitions.
%
% VIn ... |
a4ded3ab8e860493c5f39da0144a3f94befd76cc637ca82dd234d948c51d246c | MATLAB | 3,041 | 88 | function varargout = OpenEphysLoadingEngine(fn,varargin )
%MClust Loading engine for reading in tetrode data collected with
%Open Ephys GUI software (http://open-ephys.org/#/gui/). Requires
%pre-processing with Simpleclust (Jacob Voigts,
%http://jvoigts.scripts.mit.edu/blog/simpleclust-manual-spike-sorting-in-matlab/... |
9b24a76364d0bf1dff3801d4eeedc65b0595abc695948b16d22d5a346afe377b | MATLAB | 3,043 | 86 | function getActivity(path)
% Function to project the raw and deconvolved activity of data acquired
% through the CNMF-E algorithm using the ConcatMiniscope pipeline.
%
% INPUT:
% path: path of the folder containing the dataset to be treated. Outputs
% will be saved to the folder as ".mat" files with the name
% "n... |
20b3f5f9c82e17e7ddcf5aa0308c43597334c12320acc91483e2530b501052e6 | MATLAB | 3,044 | 98 | function [D, max_k_val, no_dims] = find_nn_adaptive(X)
%FIND_NN Constructs nearest neighbor graph using adaptive nbhd selection
%
% [D, max_k_val, no_dims] = find_nn_adaptive(X)
%
% Constructs nearest neighbor graph on the data in dataset X using adaptive
% neighborhood selection. In X, rows correspond to the observati... |
5af714f1530450b60763c00183bd5d2b5bb59ef99373b0fc059b9cb43f5dda7b | MATLAB | 3,049 | 90 | function ext = palm_checkprogs
% Test whether some external programs or toolboxes
% are available, namely, FSL, FreeSurfer, SPM and
% Jimmy Shen's NIFTI toolbox.
%
% ext = checkprogs
%
% 'ext' is a struct containing one field for each
% of these applications, each being containing
% a 0 (false) or 1 (true) depending o... |
71c744fba0a610b7c7a1e7a8beff8fc44513f2c0d13c2943419d2841402564de | MATLAB | 3,052 | 115 | function [A, iter, alpha] = nesterov_mnls_general(X, C, B, A_star, alpha, max_iter, func_type)
% Accelerated proximal gradient algorithm for nonnegative constraint
% min_A f(A) := 1/2 | X - C*A*B' |^2_F,
% s.t. A >= 0.
%
% f(A)' = -C'*(X - C*A*B')*B = C'*C*A*B'*B - C'*X*B
%
tol_1 = 1e-2;
tol_2 = 1e-2;
... |
736d8874d58008e74bf6a404389aa69983c3b94578f7d22a71dbf8f5b0eb01f5 | MATLAB | 3,053 | 102 | % SB2_PARAMETERSETTINGS User parameter initialisation for SPARSEBAYES
%
% SETTINGS = SB2_PARAMETERSETTINGS(parameter1, value1, parameter2, value2,...)
%
% OUTPUT ARGUMENTS:
%
% SETTINGS An initialisation structure to pass to SPARSEBAYES
%
% INPUT ARGUMENTS:
%
% Optional number of parameter-value pairs to specify so... |
2ddd4b15a2f0d67d0b53242f5f0063e1fc9d2e79cb07e7ffd4a302ddced3a233 | MATLAB | 3,056 | 89 | function [sY, params] = nk_Scale(Y, cv, i, j, kbin, params, labels)
binmode=1;
TrInd = cv.TrainInd{i,j};
TsInd = cv.TestInd{i,j};
if iscell(Y)
sY=Y;
end
[iy,jy] = size(cv.cvin{i,j}.TrainInd);
for u=1:kbin
sY{i,j,u}.Tr = cell(iy,jy);
sY{i,j,u}.CV = cell(iy,jy);
sY{i,j,u}.Ts = cell(iy,jy);
params... |
6b2a66459acf8f25819f73b898610fc8e510bb2da8fe7649d607c60b1ced21a3 | MATLAB | 3,056 | 103 | function cones(f)
%if ~isnumeric(varargin{1})
%f = varargin{1};
xyz = f.xyz;
fitted = isfinite(chisq);
valid = [fitted(2:end)==1 & fitted(1:end-1)==1]; % This says
% else
% xyz = varargin{1};
% xyzdiff = varargin{2};
% valid = true(size(xyz,1),1);
% end
... |
5f5ce2e31edbb770bbbad119da621b7bfda941e0b89ae32939d3e4f5f4c3e97f | MATLAB | 3,058 | 96 | function [ best_param perform_mat] = CrossValidation1Param...
( X, Y, obj_func_str, obj_func_opts, param_range, cv_fold, eval_func_str, higher_better)
%% Function CROSSVALIDATION1PARAM
% Model selection (cross validation) for 1 parameter
% multi-task learning functions
% For usage see test_script.m
%
%%... |
98b92b3909f696aeba6204435aab172a50ba43cd825914482283bc8a69c8f99f | MATLAB | 3,058 | 116 | classdef MClustData < handle
% Settings used in MClust
%
% ADR 2012/12
%
% contains data used in MClust that does change on a tetrode to tetrode
% cycle
properties(Constant)
maxClusters = 98;
end
properties(Access = public)
% constants
... |
fe09f1e5312b51a2c3ef9f74edac3f6c39e6442411ecba1c5dca6048c5f0d8d0 | MATLAB | 3,061 | 114 | function [G H w] = mnmf(X,r,labl,C,K)
theta = C/K;
str = ['-t', ' ', '0', ' ', '-c', ' ', num2str(C)];
fprintf('K = %f C = %f theta = %f\n',K,C,theta);
MAX_ITER = 30;
[siz_train_dim siz_no_fea] = size(X);
Ginit = rand(siz_train_dim,r);
Hinit = rand(r,siz_no_fea);
[m,n] = size(X);
G = Ginit;
H = Hinit;
% [aa b... |
5e09f4dec7a2eab5317039ea3119f0400a479e35f016bd9866e7ab2fb0979d50 | MATLAB | 3,063 | 103 | % demonstration file for NMFLibrary.
%
% This file illustrates how to use this library.
%
% This file is part of NMFLibrary.
%
% Created by H.Kasai on July 24, 2017
clc;
clear;
close all;
%% generate synthetic data of (mxn) matrix
m = 150;
n = 150;
V = rand(m,n);
%% Initialize of rank to be factorized
rank ... |
050c2ee5dbb8c9e3fbfc523d11f442f6abd9b4f76e6fb5c393e1efce50e3ca9c | MATLAB | 3,065 | 110 | function [ W, info ] = MTFLCd_SSolver( X, y, lambda1, lambda2, opts )
%
% Multi-Task Feature Learning with Calibration
% diagnoal version for faster computation on small sample size.
% smoothing -- using solver.
%
% OBJECTIVE
% min_W { sum_i^m ||Xi wi - yi|| + lambda1 ||W||_{1,2} + lambda2/2 ||W||_F^2 }
%
% INPUT
% ... |
cf9db629b14643a3e0cf80370e9a58394b5e40f9a8676da7f4bf6cc6f4953711 | MATLAB | 3,068 | 106 | function chain_diagnostics(model,out,posterior_out,j_burnin,automode)
% Display diagnostics for the chain (trace plot and posteriors)
% posterior_out is the probability (from model.probability)
% j_burnin controls how much is displayed in red/in first segment
% automode=false will pause with a keyboard prompt
if ... |
9db7101fbb7a06f76acd36577f452aec875c48d7b615e5e46616982624d3fd9e | MATLAB | 3,074 | 68 | % ==========================================================================
function [mn_str, mn_act] = nk_matLearn_BuildMenu_config(param, opt)
if isempty(opt), mn_str = []; mn_act = []; return; end
nO = numel(opt.desc);
desc = []; icnt=1;
for i=1:nO
opt_str = '?'; no_mnu = false;
if isfield(param,'Params') &... |
ca60abb80f3ee0be6ea8ebc3f984b157ee6254854b980de301b79a35f0ffd69f | MATLAB | 3,074 | 89 | clear
load('ProcessedData\tables_compiled_behavior_retuned_021726.mat');
%% classifier code; runs on increasingly larger random subsets of cells
% % reorg main table if needed
% re_org = vertcat(tables_compiled{:});
% tables_compiled = cell(1,1);
% tables_compiled{1,1} = re_org;
tic
% run within days by it... |
eca7ad1922335cb8ab6aa190eb28c72025d7d18fa1b4bab388f862fef6fd4673 | MATLAB | 3,078 | 65 | clc;clear;close all;clear gca
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Analyses\Data");
%d=load('Jupiter_test_data_TOJ_whole_period.mat');
%spikes=d.data.spikes;
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Data\PSTH_1_to_401_jupiter\PST... |
fbba764431bea4aea96f6a6d24dbe874122a96604cf9abc34894ae67e2093007 | MATLAB | 3,085 | 79 | function [testClassPredicted]=repetitivenmfClassifier(trainSet,trainClass,testSet,testClass,option)
% Repetitive NMF classifier.
% Usage:
% [testClassPredicted,sparsity]=repetitivenmfClassifier(trainSet,trainClass,[],testClass)
% [testClassPredicted,sparsity]=repetitivenmfClassifier(trainSet,trainClass,testSet,testClas... |
a7a39bfbc8509a434d8b3d311e690d01e67130ee62d7ddd4a193b614d5f38bfa | MATLAB | 3,086 | 82 | function [EBC,BC]=edge_betweenness_wei(G)
%EDGE_BETWEENNESS_WEI Edge betweenness centrality
%
% EBC = edge_betweenness_wei(L);
% [EBC BC] = edge_betweenness_wei(L);
%
% Edge betweenness centrality is the fraction of all shortest paths in
% the network that contain a given edge. Edges with high values of
% ... |
4a9bc79d5b449af599bdb285a064353b5f50fb93877ae5c0b39a5bf579ab4727 | MATLAB | 3,087 | 84 | function [corrCoeffs,recon,betaCoeffs,fcCorr,fcRecon] = ...
calc_eigenreconstruction(data,eigenvectors,method,modesq,params)
% calc_eigenreconstruction.m
%
% Reconstruct data using eigenvectors and calculate the coefficient of
% contribution of each vector
%
% Inputs: data : data [MxP]
% ... |
dfcc80771fb5e0b007b5c59870b8051bd1af59a967536006c7b8f7539d130d97 | MATLAB | 3,089 | 90 | clear
load('ProcessedData\tables_compiled.mat');
%% classifier code; runs on increasingly larger random subsets of cells
% % reorg main table if needed
% re_org = vertcat(tables_compiled{:});
% tables_compiled = cell(1,1);
% tables_compiled{1,1} = re_org;
tic
% run within days by iter; params for number of... |
7416afcfe3b7e764fb79a5a49d1374b652b413631ca7842aad7414d5451f55db | MATLAB | 3,094 | 139 | <<<<<<< HEAD
function varargout = plot(varargin)
% plot method for GIfTI objects
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Guillaume Flandin
% $Id: plot.m 7513 2019-01-15 15:17:29Z guillaume $
% if ishandle(varargi... |
8f2e836d0087d565c7281784945fe0f5052d19fb5cb85ae60fe2f17f21d01c44 | MATLAB | 3,100 | 110 | function [opt_hE, opt_E, opt_F, opt_D] = nk_EDMin(E, L, EnsStrat)
global BATCH
ind0 = L~=0; E = E(ind0,:); L=L(ind0);
[m,k] = size(E);
% Compute initial ensemble performance
if EnsStrat.Metric == 2, T = sign(E); else T = E; end
opt_hE = nk_EnsPerf(E, L); orig_hE = opt_hE;
opt_D = nk_Lobag(T, L); orig_D = opt_D;
ori... |
d60e5e9765b00c8e47b61a48fd241a20c2f57977b564a6cc5c62880f55cc6cb3 | MATLAB | 3,105 | 90 | clear
load('ProcessedData\tables_compiled_shifterMag.mat');
%% classifier code; runs on increasingly larger random subsets of cells
% % reorg main table if needed
% re_org = vertcat(tables_compiled{:});
% tables_compiled = cell(1,1);
% tables_compiled{1,1} = re_org;
tic
% run within days by iter; params fo... |
e95cf7a9597bae217e0524215ecc70e0842a128067a3af289971a2e9bb582789 | MATLAB | 3,106 | 72 | function runConcatStep1(path,equipment)
%% Pipeline for the proper concatenation of miniscope data across sessions
% Developed by Daniel Almeida Filho Mar/2020 (SilvaLab - UCLA)
% If you have any questions, please send an email to
% almeidafilhodg@ucla.edu
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
aa48b2ef002d751cdccf624ea464f1b1db98846f7afcdca0e425b4e6ea380b69 | MATLAB | 3,107 | 96 | function [gammaOptimal,KOptimal,scoreMax]=modelSelectionGammaK(D,classes,option)
% Grid Search
% usuages:
%[gammaOptimal,KOptimal,accMax]=modelSelectionGammaK(D,classes,option);
%[gammaOptimal,KOptimal,accMax]=modelSelectionGammaK(D,[],option);
%[gammaOptimal,KOptimal,accMax]=modelSelectionGammaK(D);
%%%%
% Copyright (... |
234dd9fe619046157bfa62e830859bc5c6fd619b0adeebd87504e8c2916290e9 | MATLAB | 3,111 | 78 | function [f,F]=motif3funct_bin(A)
%MOTIF3FUNCT_BIN Frequency of functional class-3 motifs
%
% [f,F] = motif3funct_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... |
139b15026b1efcc274bbe783fcc2bc5f8ff3ac3f77efff76c88b124cf3e72222 | MATLAB | 3,114 | 78 | function answer = checkMotionCorrection(BeforeCorrection,AfterCorrection)
global ff
% global answer
ff=figure('units','normalized','outerposition',[0 0 1 1]);
set(ff,'KeyPressFcn',@myfun);
%
colormap gray
prompt1 = {['Frame # (from ' num2str(size(BeforeCorrection,3)) '):']};
dlgtitle1 = 'Start from...';
dims1 = [1 55... |
4d269f163867d366dd4aa11c051c608070902e55a36a9d7a17c51cc844571316 | MATLAB | 3,125 | 73 | function [ SPATIAL, PX ] = nk_Spatial_config(SPATIAL, PX, defaultsfl, parentstr)
% Compute spatial consistency of discriminative effects
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = 0; end;
if ~exist('PX','var'), PX = []; end;
cubetype = 1;
cubefwhm = 8;
cubevoxres = 3;
if ~defaultsfl
... |
9d1bead296e6c4c30399eda5b2a91fa11b2549ec77ffab5ee712aeb6ef4815fd | MATLAB | 3,130 | 109 | function [opt_hE, opt_E, opt_F, opt_D] = nk_EntropMax(E, L, EnsStrat)
global BATCH VERBOSE
ind0 = L~=0; E = E(ind0,:); L=L(ind0);
[m,k] = size(E);
% Compute initial ensemble performance
if EnsStrat.Metric == 2, T = sign(E); else T = E; end
opt_hE = nk_EnsPerf(E, L); orig_hE = opt_hE;
opt_D = nk_Entropy(T,[-1 1], m, ... |
5e8cb9e89c43ef63e7566fc3b8249c7b1d2ec7dd9604dbf8a1bf0b8eb1b8b0ca | MATLAB | 3,132 | 144 | function [sc] = chkconv(filname,rthresh)
load(filname);
for nsc=1:11
if nsc==1
sc = [1 2 3 4];
elseif nsc==2
sc = [1 2 3];
elseif nsc==3
sc = [1 2 4];
elseif nsc==4
sc = [1 3 4];
elseif nsc==5
sc = [2 3 4];
elseif nsc==6
sc = [1 2];
elseif nsc==7
sc = [1 3];
... |
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