sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
05ab6884615d21e8fdfe72c96305deb98690dad49c0cfb50c7148a4e6c9165d2 | MATLAB | 5,412 | 147 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
8b06d3bcba97972655f36ca60c8244329c636a2215f1cc8fa335b55376ce5207 | MATLAB | 5,413 | 82 | function load_selModality(handles)
if strcmp(handles.popupmenu1.String{handles.popupmenu1.Value},'Multi-group classifier')
multiflag = true;
else
multiflag = false;
end
popuplist=[];
if ~multiflag
for i=1:size(handles.visdata,1)
popuplist{i} = sprintf('Modality %g: %s', i, handles.NM.datadescript... |
0666484560e40a401f9a03008a58c6fdb1fb2942a3625b9e04596e4cdbe1f3c8 | MATLAB | 5,416 | 118 | %% EEG Pre-processing Batch (Subject 01–20)
%% ===== USER SETTINGS =====
data_dir = '/path/to/ds004995';
output_dir = fullfile(data_dir, 'derivatives', 'eeglab');
subjects = 1:20;
% ===================================================
eeglab;
for sub_num = subjects
fprintf('\n========== Processing sub-%02i... |
a63a031bfd1f8fec58bac596d78c6620a77bfb99992e2cd5436a69a8c4bfcbc5 | MATLAB | 5,421 | 145 | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
579478cb6ff810252ad9bd56ece7f5329a7bc3d953fc09df9958daa78cc09f4d | MATLAB | 5,425 | 163 | %% corr combined, 5 days, non PreHab, by ID
clear
load('ProcessedData\tables_compiled.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 corrs
... |
d841d5b30e7041161982e232d0aa1ff33a783b506bc80f2aa250b548b0bbf3e9 | MATLAB | 5,425 | 149 | function result = mlr(x,y,varargin)
%MLR is the classical least squares estimator for multivariate multiple
% linear regression. It can handle both one or several ('multiple')
% predictor variables and one or several ('multivariate') response variables.
% If the regression model contains an intercept, the x-matrix ma... |
44090760ae550ac81003e4bae34a501541fae22a63c5b780d69d73a280e7c847 | MATLAB | 5,436 | 169 | function signal = mp2rage_solve_bloch( parameters, T1, B1, invEFF )
%MP2RAGE_SOLVE_BLOCH function solves Bloch equations for the MP2RAGE pulse
%sequence. This process uses the sequence parameters and the T1 of the
%tissue, and compute the signal as output.
% MP2RAGE_SOLVE_BLOCH is used to build a lookuptable to associa... |
7bbbfacaaddcf36bb87ee1c5a54ccbb0ecf008fb680db03a025dd21d661b904b | MATLAB | 5,438 | 191 | %% FUNCTION LSSMTC
% Learning the Shared Subspace for Multi-Task Clustering
%
%% OBJECTIVE
% min lambda*sum_k||X_k-M_k*P_k||^2+lambda*||W'*X_k-M*P_k||^2
% s.t. P_k>=0, W'W=I, W>=0;
%
%% INPUT
% cellX: cell array of dxn Data matrices, n is the number of documents,
% d is the number of features
% ... |
d7612d38917476480c19a90e62be9dbe876834b89e53a84d8e8ad978111fb3e6 | MATLAB | 5,444 | 209 | function [U, V, eigvalue_U, eigvalue_V, posIdx, Y] = TensorLGE(X, W, D, options)
% TensorLGE: Tensor-based Linear Graph Embedding
%
% [U, V, eigvalue_U, eigvalue_V, posIdx] = TensorLGE(X, W)
% [U, V, eigvalue_U, eigvalue_V, posIdx] = TensorLGE(X, W, D)
% [U, V, eigvalue_U, eigvalue_V, posIdx] = Tensor... |
ab168f356dc03d18dbfa924eca703271c78b4ef2134633b4dc283ec927270a05 | MATLAB | 5,453 | 218 | % Randomized Power Iterations NeNMF (RPINeNMF)
% Reference
% N. Guan, D. Tao, Z. Luo, and B. Yuan, "NeNMF: An Optimal Gradient Method
% for Non-negative Matrix Factorization", IEEE Transactions on Signal
% Processing, Vol. 60, No. 6, PP. 2882-2898, Jun. 2012. (DOI:
% 10.1109/TSP.2012.219040... |
09179eda28bfd14b69c7ae5992217c922d47ec13ae583ebeb8a3c2ec5bbf0b74 | MATLAB | 5,465 | 152 | % ========================================================================
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... |
81661fb5b67e2c5f524a3a051a8ba613fe3aff1143ee542e748d95a8df8f6746 | MATLAB | 5,474 | 158 | function Prediction = SVR_NFolds_Sort(Subjects_Data, Subjects_Scores, FoldQuantity, Pre_Method, C_Parameter, Permutation_Flag, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous variable... |
248dbeee4fbb9784af90aac8bba0d6b9832e80a06bf37388dc094b66744598c7 | MATLAB | 5,476 | 174 | % type
%analysis_type = 'calib-none'; % no calibration
%analysis_type = 'calib-lin'; % Ganzetti eye/musle
analysis_type = 'calib-nonlin'; % Ganzetti newer non-linear
%analysis_type = 'calib-nonlin2'; % Cappelle & Sunaert - mimics Ganzetti, but no MNI needed
%analysis_type = 'calib-nonlin3'; % Cappelle & Sunaert - use ... |
358b3ac0dc813ef3369088bba6843b6ca008989117a834dcd99a9d7f58f1cd32 | MATLAB | 5,484 | 145 | 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... |
cd279bca64a818a9a1c6f982d9ac51d8871605171b420f2cadcb014c3a059245 | MATLAB | 5,489 | 156 | function ms = msNormCorreConcat(ms,isnonrigid,ROIflag,plotFlag,checkMotCorr)
% 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. Ideall... |
912a059cc6bdce7e5ee95fbc2da88b4ba076c60a9e37bdf1df7d6d8ca50030e3 | MATLAB | 5,496 | 167 | function result = fanny(x,kclus,vtype,metric,plots)
%FANNY is a fuzzy clustering algorithm. It returns a list representing a fuzzy clustering of the data
% into kclus clusters.
%
% The algorithm is fully described in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cluster a... |
745f5518335ec3eb7d80db1bd045f0b569ff42df771e45a6f94f26b9fa98ab1f | MATLAB | 5,502 | 140 | % MATLAB Code for Correlation Analysis with Normalization
% Analyzing correlation between 3rd column (mean_cv_abserror ÷ 4) and last column (beh_Accuracy)
clear all
% Read the Excel file
filename = 'Jupiter_Mercury.xlsx';
data = readtable(filename);
% Display basic information about the data
fprintf('Data lo... |
fa940c61f78d332143870adc7eec21bd2c87548bc18cba215f34542dd29afbe9 | MATLAB | 5,502 | 164 | function P = palm_quicksave(X,flg,opts,plm,y,m,c,filename)
% An intermediate function to be used many times in
% palm_saveall.m. It chooses an adequate mask, places back
% the data into the points defined by this mask, and save.
%
% Usage:
% P = palm_quicksave(X,flg,opts,plm,y,m,c,filename)
%
% Inputs:
% X : Da... |
72293f404a63d2b9f0827854770b3ad6f6fd7e792e5d7e6ab71ea150385ad145 | MATLAB | 5,514 | 171 | function [p_exact,dist_rand] = compute_exact_spatial_pvalue(D1,data_PET,atlas,res,Nperm,options,filesPET,T1)
% function [p_exact,dist_rand] = compute_exact_spatial_pvalue(D1,D2,data_PET,res,Nperm,options,filesPET,T1)
% the functions allow to compute exact p-values based on the output from
% the compute_DomainGauges fun... |
f8db6c78d655f2af4ce080109b7e803294a327d27ba000b1b917657a6039210a | MATLAB | 5,517 | 176 | function [W H] = opnmf_mem(X, K, w0, initMeth, max_iter, tol, ...
iter0, save_step, outputdir)
%
% compute OPNMF based on Euclidean distance
% Orthogonal Projective NonNegative Matrix Factorization
%
% The update rule is slightly modified to account for the high
% dimensionality of the data
%
% input:
% X ... |
0940f4e31027bb125465806d811ce59f810ef6c52ac09b6dbd7d914d5763eb1f | MATLAB | 5,520 | 160 | clear
% for all of these, just adapted my 5 day code; cropping for D1 in the fig
%% corr combined, 5 days, non PreHab, by ID, shifted window
load('ProcessedData\tables_compiled.mat');
figure,
tiledlayout(1,5,'TileSpacing','Compact');
trial_cutoff = 8;
within = cell(1,5);
across = cell(1,5);
for i = 1:5
... |
aa9696594616bb052d3a243f9694a7a6b071c85237738910eb02f22c055188c2 | MATLAB | 5,520 | 154 | function oimg = loadtiff(path, sFrame, num2read)
% Copyright (c) 2012, YoonOh Tak
% All rights reserved.
% Modified by Eftychios Pnevmatikakis, 01/2017, 02/2018.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
% ... |
b3de336de12f69e3bcfbb6dc3eff2c2048f3293a7dcd440b195ddd4f3bc2290b | MATLAB | 5,524 | 159 | function [RT,Go_RT,FAlarm_RT,Hit,NoStopTrials,StopTrials,perf,stopperf] = calc_RT(sess2analyse,TEfnm,isfig)
%CALC_RT calculates behavioral parameters.
% CALC_RT(sess2analyse,TEfnm,isfig) calculates behavioral parameters for each
% trial of patients specified in SESS2ANALYSE struct (see getdata2analyse.m),
% ... |
bc8044c8663ac946bd1745cd52fc14e2b977db7b6f5ced3e3856f056fd013cfa | MATLAB | 5,528 | 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_... |
891e6c4c29ea11ddfbd7e7242ac05110d118622258c4560126f21e86ecff1063 | MATLAB | 5,532 | 180 | 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... |
e57563e6a80e776206cbb5675226048f6628e0f1ef29b9d0e2bdd43c8232c094 | MATLAB | 5,533 | 203 | % extract_qT1_laminar_profile.m
%
% Extract a laminar qT1 profile from a discrete LAYNII layer map (1..8)
% within an ROI, while preserving the original analysis logic:
% - optional ROI dilation
% - layer-wise extraction using (layer == i)
% - first derivative normalization
% - second derivative computat... |
2ddd9755a3d6b0613704b798be8cf5f6d5019e6e1a146489f6f5ce415450a0d3 | MATLAB | 5,539 | 162 | %% Network Enrichment Visualization
network_names = {'Cont', 'Default', 'DorsAttn', 'Limbic', 'SalVentAttn', 'SomMot', 'Vis'};
network_sizes = [13, 24, 15, 5, 12, 14, 17];
num_networks = numel(network_names);
total_nodes = sum(network_sizes);
conditions = {'Sweet', 'Sour', 'Salty'};
%% ========== INSERT YOUR OBSERVE... |
7497163f5dfabdc76c3a1a81ad98e46d7851deb3afd4e422141bda5efd5003cd | MATLAB | 5,551 | 172 | clear all;
close all;
data=load('JupiterSPIKE distance all session_v2.mat');
winSize = 100;
xx=[];
yy=[];
xx2=[];
yy2=[];
xx3=[];
yy3=[];
xx4=[];
yy4=[];
for j=1:42
if j<4 |j>4
TOJ_begin_correct=data.TOJ_begin_correct_all{j,1};
TOJ_begin_incorrect=data.TOJ_begin_incorrect_all{j,1};
TOJ_end_correct=d... |
092a8dde0aa61ca93029010bae1690454c71ff40b34b2028f6d1dc92447ab1f3 | MATLAB | 5,553 | 163 | function CorrelationPlotter_single(predicted,data,tpts,study,sv_types,...
geneorct,ispca,wdir,color,shape,savenclose)
if nargin < 11
savenclose = 0;
if nargin < 10
shape = 'o';
if nargin < 9
color = [1 0 0];
if nargin < 8
... |
d190b6290bbedfd43c596917660af90ab36918ec71e001f607b68269853eb932 | MATLAB | 5,557 | 191 | function [ W, info ] = MTFLCd_SPG( 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> s... |
626c991012a04a9ae31cd47d8f0391f6b974677442528f4738a433b11289f2f8 | MATLAB | 5,560 | 235 | <<<<<<< HEAD
function [a, b] = isintent(this,intent)
% Correspondance between fieldnames and NIfTI intent codes
% FORMAT ind = isintent(this,intent)
% this - GIfTI object
% intent - fieldnames
% a - indices of found intent(s)
% b - indices of dataarrays of found intent(s)
%___________________... |
f7bff1bfe8a8ab65440c65671ed9f95a09dc085b1cda713e1c06277c4e1299af | MATLAB | 5,564 | 160 | % Produce Figure S1
% (explore a large circuit of Kuramoto oscillators with given external input)
colormap default
Nneurons = 100; % group size.
% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%
% if we want to do a paramater space exploration:
ScaleInputs = linspace(0, 1000, 6); % this is how much the input matters more or ... |
f1985df8f164ad38e94608e32e0902baf692c6f821121f4d899f88b838a013df | MATLAB | 5,569 | 156 | % =========================================================================
% Coded by Youngmin Ha (y.ha.1@research.gla.ac.uk) on 21 Jan 2015
% by referring to Ha, Youngmin. "Fast multivariate relevance vector
% regression," submitted to Annals of Mathematics and Artificial
% Intelligence (2015).
% ============... |
07b1d1a30e921cf1a84ec91712690de3749b69983a98baf04df7ab7d74b5a5c9 | MATLAB | 5,570 | 186 | function err = save_nifti(hdr,niftifile)
% err = save_nifti(hdr,niftifile)
%
% Pixel data should be in hdr.vol
%
% Handles data structures with more than 32k cols by setting
% hdr.dim(2) = -1 and hdr.glmin = ncols. This is FreeSurfer specific,
% for handling surfaces. The exception to this is when the total
% number of... |
2eff198f3d0da6c513ef1d79fa0cdc08701b089b14f04c02c6dfab7fe9b8f9e5 | MATLAB | 5,578 | 135 | function param = nk_Simba_config(param, setupfl, defaultsfl, ngroups)
% function param = nk_Simba_config(param, setupfl)
%
% Setup parameters for Simba algorithm
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% (c) N. Koutsouleris 04/2015
% Defaults
max_iter = 5;
block_size = 1;
st_points ... |
5dc17e366760febb85551babc1917f611ffa38a32812b0a898c57f2a5f6f7844 | MATLAB | 5,584 | 196 | classdef nus_mass < bt.model.template
% This model is set up to fit t0 keeping the prior fixed if the alpha peak is not present
% Note that prepare_for_fit must be used before spectrum() can be used
% Uniform priors and DB initial fit
properties
kmax
k0
k2u
k2_volconduct
stab_gamma_prefactor
spec_gamm... |
9832f8532b17a806fa1fa7f2e799ee5ccbf07a72cacc95a1395f57262f234aa9 | MATLAB | 5,587 | 204 | function [eigvector, eigvalue, elapse] = LSDA(gnd, options, data)
% LSDA: Locality Sensitive Discriminant Analysis
%
% [eigvector, eigvalue] = LSDA(gnd, options, data)
%
% Input:
% data - Data matrix. Each row vector of fea is a data point.
%
% gnd - Label vector... |
d1bc6f5cc7ee79f758c77b3834c801b2ba0caf30dc465ddc063a3836e06ee51b | MATLAB | 5,591 | 139 | function mz3_demo
%creates different variations of the mz3 format
%Object is based on
% https://brainder.org/2011/09/25/braindering-with-ascii-files/
%vertex coordinates (x,y,z)
vert = [1 0 0; 0 1 0; 0 0 1; -1 0 0; 0 -1 0; 0 0 -1];
%definition of faces from vertex index (1st vertex is 1)
face = [1 2 3; 2 4 3; 4 5 3; 5... |
0a13e2e54800bc0051c6416a836ff6d718fad00c3802853f1e4fcaa980cc88ec | MATLAB | 5,595 | 168 | function res = removeObsRobpca(data,i,k,Hsets_min_i,same,factor_ind,csteps)
%REMOVEOBSROBPCA is an auxiliary function to perform cross-validation with ROBPCA,
% RPCR, RSIMPLS, RSIMCA (see cvRobpca.m, cvRpcr.m, cvRsimpls.m).
%
% Input:
% data : the data set
% i : the observation that is removed, index with r... |
0e9bc3d49f13c6b5de335ff931891fa9ac9caa4e07fa12786db38069326259a9 | MATLAB | 5,603 | 117 | function [A,Y,numIter,tElapsed,finalResidual]=nmf(X,k,option)
% nmf using various algorithms
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=nmf(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=nmf(X,k,option)
% X: nonnegative matrix, or matrix of mixed signs, dataset to factorize, each column is a sample, and ... |
e91532ba5f01d81a4031de8e8431cf23846ac2db32c33373cbc6a9a26625cd53 | MATLAB | 5,613 | 135 |
function [Alignment,Correlation]=AlignAcrossSessions(animal)
figure('units','normalized','outerposition',[0 0 1 1])
colormap gray
hLarge = fspecial('average', 40);
hSmall = fspecial('average', 3);
[optimizer,metric] = imregconfig('multimodal');
optimizer.MaximumIterations=300;
optimizer.GrowthFactor=1.01;
optimizer.E... |
b8bf57a84fecf6846faffce5258cad87b181e8407bac77ab6cbcf70cf9a87b8d | MATLAB | 5,615 | 166 | function Prediction = SVR_NFolds_RandomCVBin(Subjects_Data, Subjects_Scores, FoldQuantity, Pre_Method, C_Parameter, Permutation_Flag,ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous va... |
baebcba65c20d473d74f8a2a759742e04cb8ef947d6a8f0ef055c445446cf20e | MATLAB | 5,616 | 157 | % This is an example of how to use the SVM, NNLS, NMF, and RNMF classifiers.
%%%%
% Copyright (C) <2012> <Yifeng Li>
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the L... |
9817a440ca551da749cb92f2218bf7bd9daf5b14f8384cbb26e61a5ec507b396 | MATLAB | 5,639 | 136 | function [C_pos,C_neg,Ctot_pos,Ctot_neg] = clustering_coef_wu_sign(W,coef_type)
%CLUSTERING_COEF_WU_SIGN Multiple generalizations of the clustering coefficient
%
% [C_pos,C_neg,Ctot_pos,Ctot_neg] = clustering_coef_wu_sign(W,coef_type);
%
% The weighted clustering coefficient is the average weight or intensity
... |
a6c01c0828a3721e5325e6c3c395d511186225389f11053e0d4442eadb68b538 | MATLAB | 5,652 | 199 | %% FUNCTION Least_Dirty
% Dirty Multi-Task Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W ||X(P+Q) - Y||_F^2 + lambda1*||P||_{1,inf} + lambda2*||Q||_{1,1}
% s.t. W = P + Q
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% rho1: group sparsity regularization paramete... |
5b845ef944aa54a664428130966d7d67ca668357f3d6679d05d00f6e424d67e7 | MATLAB | 5,655 | 186 | function [out,final_posterior,accept_ratio] = chain(model,initial_values,n_points,debugmode,timelimit,n_burnin)
% Take in a spectrum, prior distribution, and initial parameters
% Return the MCMC chain and corresponding chisq values
% Burn-in method. First get an initial set of points, n_initial. Then take
% 10% ... |
8ce4a73c96368c0f0bda19b03195b6263d534d082d410ec0c4856781ef5951b9 | MATLAB | 5,664 | 221 | %% FUNCTION Least_Trace
% Trace-Norm Regularized Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 \|W\|_*}
% where \|W\|_* = sum(svd(W, 0)) is the trace norm
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output ma... |
addfe8ad7e2c52e2e4016bdaf19c267854173c3ba31066f081fe144084436eaa | MATLAB | 5,672 | 177 | % =========================================================================
% FORMAT [opt_hE, opt_E, opt_F, opt_Fcat, opt_D, opt_Pred] = ...
% nk_MultiEDMin(E, L, EnsStrat, C)
% =========================================================================
%
% Inputs
% ------
% E ... |
ae492a35ece2bb71a7d4e9d16463af5dcf60a3d26cd4a92dd2fdf4e094de2472 | MATLAB | 5,676 | 172 | function [c, s, b, g, active_set] = foopsi_oasisAR1(y, g, lam, smin, optimize_b,...
optimize_g, decimate, maxIter, gmax)
%% Infer the most likely discretized spike train underlying an AR(1) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min 1/2|c-y|^2 + lam |s|_1 subject to s_t = c_t-g... |
007fde3e1fe849985d3460fcc23218f0fdba07a85629958930670e58d9f23c7d | MATLAB | 5,677 | 146 | function [alphas] = nk_SimbaLinearSigmoid(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
% Y_train... |
a3b7dbdd24f932e28f649b2f8029240721cd18c6775ffa4fb2eb01741d3560d7 | MATLAB | 5,677 | 204 | function [b, steps] = larsen(X, y, delta, stop, Gram, storepath, verbose)
%LARSEN The LARS-EN algorithm for estimating Elastic Net solutions.
%
% BETA = LARSEN(X, Y, DELTA, STOP, GRAM, STOREPATH, VERBOSE) evaluates
% the LARS-EN algorithm [1] using the variables in X to approximate the
% response y given regulari... |
ef575bd33800c6361344e6e8dce97bb5438061551e2b88feb890c72ef1f862c4 | MATLAB | 5,678 | 152 | % =========================================================================
% FORMAT Res = OptimCore(IN, OUT, strout, Param, OptMode, Weighting)
% =========================================================================
% The OPTIMCORE function is the central pacemaker of the training module of
% NM. It allows to inv... |
94be01e6c6e25463d09c68b1ec2be0fb628759c7dab4dbb45e74cd289ec009f7 | MATLAB | 5,690 | 151 | function Prediction = SVR_NFolds_Sort_CSelect(Subjects_Data, Subjects_Scores, FoldQuantity, Pre_Method, C_Range, Weight_Flag, Permutation_Flag, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the co... |
ed4fb0239a68920bcfa2a225e0926ed40fb3fe46f45cbe6dfa065472773d102c | MATLAB | 5,691 | 155 | function [testClassPredicted,OtherOutput]=classificationPredict(model,testSet,testClass)
% predict the test samples by the model learned by "classificationTrain"
% Usage:
% [testClassPredicted,OtherOutput]=classificationPredict(model,testSet,testClass)
% testSet: matrix, the test set.
% testClass: column vector of numb... |
161dc940809381d74d720071c9a81371060ff49bc1ba3422e9c90fed56a57381 | MATLAB | 5,707 | 187 | function [ U, V, Ss, fv ] = ...
pacifier_sba( 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 = 2;
fv = [];
maxIter = 100;
tol = 1e-5;
estRank = true;
if isfi... |
85fad4f2b90718028300c327aee2b1f0556b867df1ee27de664b871aeabfdd3c | MATLAB | 5,708 | 189 | function SSRTime_PD(sess2analyse)
%SSRTIME_PD estimates SSDp0.5 and SSRT
% SSRTIME_PD(sess2analyse, ssrt_figdir) calculates the stop signal delay (SSD)
% where the estimated probability of stopping is 50% probability (SSDp0.5)
% by fitting a generalized linear regression model to the SSDs corresponding
% ... |
3a5bf554090008d32a4504ad40fa8eb718fae3176c5729f78989ff4324fff697 | MATLAB | 5,711 | 142 | function step2_beta_estimate(subject)
%% Add GLMsingle in path
root = '/public/home/lishr2022/Project/Cross-modal/beta_estimate';
cd(root);
cd("GLMsingle");
setup
cd(root)
%% Initialization
subjects = {'S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8'};
script_root = '/public... |
bcffb0591010ef21601ac50c3970ad008395411da478fb5905e17dac2271f5e8 | MATLAB | 5,715 | 212 | % SB2_USEROPTIONS User option specification for SPARSEBAYES
%
% OPTIONS = SB2_USEROPTIONS(parameter1, value1, parameter2, value2,...)
%
% OUTPUT ARGUMENTS:
%
% OPTIONS An options structure to pass to SPARSEBAYES
%
% INPUT ARGUMENTS:
%
% Optional number of parameter-value pairs to specify the following:
%
% ITERAT... |
354541885633a0d86d17a61138ee7eb9c94dbe32d4d73f95650c08b60ab8a001 | MATLAB | 5,761 | 159 | function result=cpcr(x,y,varargin)
%CPCR performs a classical principal components regression.
% First, classical PCA is applied to the predictor variables x (see cpca.m) and
% k components are retained. Then a multiple linear regression method (see mlr.m)
% is performed of the response variable y on the k principal ... |
216b69c7e3018ad0dd99fe4e97d593b88de49dce6c2e5c53a4159458447602f2 | MATLAB | 5,770 | 180 | function [x, infos] = symm_halsacc(V, rank, in_options)
% Symmetric non-negative matrix factorization by HALS (SymHALS).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2 + alpha * ||W-H'||_F^2,
% where
% {V, W, H} >= 0, and W is close to H'.
%
% Given a symmetric non-neg... |
14157e9853b183cb60030c113111eae3677ec43743531af349271b77f0e5e665 | MATLAB | 5,773 | 128 | function [ sY, IN ] = nk_PerfWActObj( Y, IN )
% =========================================================================
% FORMAT function [sY, IN] = nk_PerfWActObj(Y, IN)
% =========================================================================
% This function applies precomputed weight vector(s) over the features ... |
784fff6340b6ec733476f725064005b594def376818009ceb123c5752b010756 | MATLAB | 5,783 | 180 | %% 0. Setup
% Initialization
clear; 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
study = 'Tasic';
if... |
093bd14703204f60b9fd58f73306701689eea3dd8edd314f9cc72dce0e9ed080 | MATLAB | 5,786 | 183 | clear; clc;
load('...\LFPdata_Fig5B.mat')
figure()
set(gcf,'position',[-770 446 730 455]);
fs=LFP.fs;
t_start=-0.3;
t_end=0.3;
window=40; % time in ms
step=20; % time in ms
windowSize = round(window/1000*fs); % windows width
stepSize = round(step/1000*fs); % advancing step for sliding window
% Color
... |
5e8949f187bf581ecda7556bc0475c5a79c55b910574158a3170b7103ed99fa7 | MATLAB | 5,803 | 163 | function d = getdict
% Dictionary of NIFTI stuff
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: getdict.m 7147 2017-08-03 14:07:01Z spm $
persistent dict;
if ~isempty(dict)
d = dict;
return;
end
% Datatype... |
d5fd562963414720d6516478fbfa668ff24f9487a0bc75ef3f9f80db3b468a2a | MATLAB | 5,805 | 250 | % @file mvrvm.m
% Author Arasanathan Thayananthan ( at315@cam.ac.uk)
% (c) Copyright University of Cambridge
%
% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public
% License as published by the Free Software Founda... |
81a77d1fca44c224aedef1d706a8074f1b35311e8585b436b31a70aff8ec45ba | MATLAB | 5,812 | 79 | %-----------------------------------------------------------------------
% Job saved on 06-Dec-2021 17:00:40 by cfg_util (rev $Rev: 7345 $)
% spm SPM - SPM12 (7771)
% cfg_basicio BasicIO - Unknown
%-----------------------------------------------------------------------
matlabbatch{1}.cfg_basicio.file_dir.file_ops.file_... |
d22c98776acd68111acdc80ff26473478510ac68c0e4655a2673a48e3adfd40a | MATLAB | 5,816 | 190 | % SB2_FULLSTATISTICS Compute all relevant statistics in full for SPARSEBAYES
%
% [SIGMA,MU,S_IN,Q_IN,S_OUT,Q_OUT,FACTOR,LOGML,GAMMA,...
% BETABASIS_PHI,BETA] = ...
% SB2_FULLSTATISTICS(LIKELIHOOD,BASIS,PHI,TARGETS,USED,ALPHA,BETA,...
% MU,BASIS_PHI,BASIS_TARGETS,OPTIONS)
%
% OUTPUT ARGUMENTS:
%
% SIGMA Poster... |
29ae81409da40bffe6d9acb344ffbca17dc53e985168579926431065c81f0ba5 | MATLAB | 5,819 | 168 | function Prediction = MultipleRegression_NFolds_Sort(Subjects_Data, Subjects_Scores, FoldQuantity, Pre_Method, Permutation_Flag, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous varia... |
b3caafbea5c6c2eae8c4773ecbf15f39e791bdd37c590d6cbc60b8bbf9fe739b | MATLAB | 5,840 | 159 | function nk_PrintWs(dat, oTrainParam)
clc
if iscell(dat.Y), nvar = length(dat.Y); else, nvar = 1; end
showmodalmax = 3;
showmodalvec = 1:nvar;
nF=1;
if isempty(oTrainParam.FUSION), oTrainParam.FUSION.flag = 0; end
if nvar > 1
if oTrainParam.STACKING.flag == 1
fprintf('WORKSPACE VIEWER: ');
fprintf(... |
d1cdace02e6cc7112ebfdf6d8d6fc2aa935014d38080868002d7858d2e07bbc2 | MATLAB | 5,848 | 206 | function result = cvRobpca(data,kmax,resrob,rawres,h,csteps)
%CVROBPCA calculates the robust cross-validated PRESS (predicted residual error sum of squares) curve
% for ROBPCA in a fast way. This curve can be used to make a selection of the optimal number of
% components. The function is used in robpca.m.
%
% Input ... |
f9b5f81e6dc12d6a9f63ce8caf7aa0de116179afe8df6daadc84df1bc1a2feb5 | MATLAB | 5,855 | 148 | function [gratingid, gratingrect] = CreateProceduralSineSmoothedGrating(windowPtr, width, height, backgroundColorOffset, radius, contrastPreMultiplicator, sigma, useAlpha, method)
% [gratingid, gratingrect] = CreateProceduralSineSmoothedGrating(windowPtr, width, height [, backgroundColorOffset =(0,0,0,0)] [, radius=inf... |
1ce1591e5c02f043f5450eab950be5a4377cffaa3befabf0c31739656c404bba | MATLAB | 5,858 | 174 | function [ retval, MTL_time, flipTime ] = fgetl_nonblocking(serialport, flipInx, win)
%FGETL_NONBLOCKING Nonblocking read of serial data, returning entire lines
% Collect characters from serial port, return empty array or a line of
% string if line terminator (CR or LF) was hit
global backup
global backup_buff
... |
4d86e3ab79a2e2b3a3195c3f8c064462ccdbfd942a104c7a9ca060260c6a2f64 | MATLAB | 5,871 | 171 | function [vertices2, faces2, rois2, mask] = trimExcludedRois(vertices, faces, rois, varargin)
%% TRIMEXCLUDEDROIS Remove specified vertices and faces from surface mesh
% Removes rois with a value of 0 and the corresponding vertices and faces.
% Reindexes the remaining vertices and faces.
%
%
%% Syntax
% [verti... |
d9a4cf8ba5af61c477c6cb546b2e8c5b634b3d18259b4a87ea2c0cf4eacfb4f5 | MATLAB | 5,891 | 179 | function [x, infos] = symm_anls(V, rank, in_options)
% Symmetric non-negative matrix factorization by ANLS (Symm ANLS).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2 + alpha * ||W-H'||_F^2,
% where
% {V, W, H} >= 0, and W is close to H'.
%
% Given a symmetric non-nega... |
f6d8340bc77e152ebc0b2c46ead1f53f96e1decae4bc7b9e905fdc6fc2efd42e | MATLAB | 5,891 | 110 | function [SYNTH, act, mess] = nk_Synth_config(SYNTH, mess, parentstr)
global NM
%% Set-up configuration interface
% Define variables
if ~exist('mess','var'), mess = []; end
na_str = '?';
synth_flag = 2;
methodnum = 1;
k = 5;
distanceMea... |
7404c203bce6e81f7dbc3f9a65af49599135a08d904e49d91433e746a6b47d90 | MATLAB | 5,903 | 120 | function [ METAres, IN ] = nk_MLOptimizerMeta( analysis, IN )
global FUSION META MODEFL
GDdims = analysis.GDdims;
aggmode = 1; METAres = [];
% Perform 2nd-level learning
if FUSION.flag == 3 && ~isempty(META)
switch META.flag
case 1 % Bagging
ind0 = find(~cellfun(@isempty,GDdims));
... |
6110d8d3292dac3ff9249bae3842d401087f5721a360c81e86acf5fc31b33b46 | MATLAB | 5,904 | 230 | function [model] = ml_binaryclass_MLP(X,y,options)
% ml_binaryclass_MLP(X,y,options)
%
% Description:
% - Binary classification using a multilayer perceptron with logistic
% loss
%
% Options:
% - nHidden: a vector of the size of each hidden layer,
% including bias variable (default: [3,3,3])
% - stan... |
bd3497716df05db637a167c4fa7d38ce65ff07543f88173e4c178e326699a987 | MATLAB | 5,922 | 130 | function [sY, sYocv, sCocv, sYw, inp, optfl] = nk_PerfPreprocessSpatial( Y, Yocv, Cocv, inp, paramfl, kbin)
global PREPROC
sYocv = []; sCocv = []; sYw = [];
%Eventually, apply spatial operations on image data
if isfield(paramfl,'PREPROC') && isfield(paramfl.PREPROC,'SPATIAL') && paramfl.PREPROC.SPATIAL.cubetype>1
... |
22517f22f50b942858e7f55f6b55c4e06ed1ef405ffd838e78fc95c37265f107 | MATLAB | 5,930 | 186 | function [x, infos] = nmf_dtpp(V, rank, in_options)
% Orthogonal multiplicative upates (MU) for non-negative matrix factorization (DTPP).
%
% The problem of interest is defined as
%
% min || V - WSH ||_F^2,
% where
% {V, W, H} >= 0, and .
%
% W or H is orthogonal (Un... |
5f36649661c981cbd71b99bde572af28049ff3330a5ee80b4beb61e83f8ac5ec | MATLAB | 5,930 | 215 | function [eigvector, elapse] = KSR(options,Responses,K)
% KSR: Kernel Spectral Regression
%
% [eigvector, elapse] = KSR(options,Responses,K)
%
% Input:
% K - Kernel matrix.
% Responses - response vectors. Each column is a response vector
%
% options - S... |
cd57a8a656e5c9004ee4102d15b190b4f3d013d3f8f640d5a5e0bae50b0183a8 | MATLAB | 5,934 | 181 | function NMFHeatMap(Xout,Aout,Yout,option)
% Draw the heatmap for the result of NMF
% Usuage:
% NMFHeatMap(Xout,Aout,Yout);
% NMFHeatMap(Xout,Aout,Yout,option);
% Xout: matrix, the same as the output "Xout" of function "NMFCluster".
% Aout: matrix, the same as the output "Aout" of function "NMFCluster".
% Yout: matrix,... |
f9e7539baa9f3757a0b816d8024a45b28c18610ea903227501ee8a92223a3ffa | MATLAB | 5,936 | 252 | function p_smoothed = head_imager(varargin)
% Possible usage
% head_imager(x,y,P)
% head_imager(x,y,P,label)
% head_imager(x,y,f,P) % For an explorer
% head_imager(x,y,f,P,label)
% head_imager(x,y,f,P,label,f_secondary,P_secondary)
smooth_hf = false;
plot_secondary = false;
if nargin < 4 || (nargin == 4 && (i... |
86f5d97d1e05c0a757c4b80aa6ca29be441189e74dffc8d30ef296b2d904617e | MATLAB | 5,937 | 187 | function revCor(s)
% get prefs if available
if ispref('revCor','stimSizes')
try stimSizes = getpref('revCor','stimSizes'); catch stimSizes = 10;end
blockSize = getpref('revCor','blockSize');
nTrials = getpref('revCor','nTrials');
nSeconds = getpref('revCor','nSeconds');
nFrames = getpref('revCor','nFrames');
s... |
8fb4214f2c974dfb81a3a1ec797886ba0c1a5a66129591071faccd6ecbf58c9b | MATLAB | 5,940 | 160 | function [ARD_time, MTL_time, flipTime] = flipSens(serial, win, flipInx)
%FLIPSENS Executing Flips for CUE, STOP, and FB presentation
% Detecting photosensor timestamps
% Returning ARD timestamps, MTL serial read time, MTL Fliptime
% FlipInx: 1 - Cue, 2 - Flipped, 3 - , 4 - Stop signal
flipTime = [];
global Sin... |
09bcd9f3a919ffcc16706756bdd3f2304271a1acc8c5ea559f0dcdf7a26ed3e6 | MATLAB | 5,958 | 170 | classdef (Sealed) MClustSettings < handle
% Settings used in MClust
%
% ADR 2012/12
%
% contains parameters that do not change from tetrode cycle to cycle
properties(Constant)
VERSION = MClustVersion();
end
properties(Access = public)
% constants
DEBUG =... |
e941e691464d5ceb1b2605185aaa7d39fb0b16411b7f0deb716d111668a119fc | MATLAB | 5,962 | 207 | function L_ratio_ID_distrib(L_thr,ID_thr)
% L_RATIO_ID_DISTRIB Single- / multi unit sorter
% L_RATIO_ID_DISTRIB(L_thr,ID_thr) extracts the L ratio and ID distribution values
% (calculated by MCLust during the unit clustering process) of
% all detected units. Results are saved as 'L_ratio.mat' and 'ID.mat' ... |
6c1bdfbe5f89b2116290a8ddfd9a25881dcb3847f74a880b7239af913e912d6e | MATLAB | 5,964 | 187 | function [dpi, dpc] = calibrateSize(theScreen, distance, mSize, forceLCD)
% [dpi, dpc] = calibrateSize(theScreen, distance, mSize, forceLCD)
% Helps the user to accurately measure the screen's dots per inch.
%
% Denis Pelli
% 5/28/96 dgp Updated to use new GetMouse.
% 3/20/97 dgp Updated.
% 4/2/97 dgp FlushEvents.
% ... |
daa6370ca8a535c773b51ebaf2692dc5b53a2533c998caec6faeb5aba41f0a40 | MATLAB | 5,968 | 241 | function [W, C, P, Q, L, F] = Logistic_rMTFL(X, Y, rho1, rho2, opts)
%W: model parameters
%C: constant parameters
%P: shared structure
%Q: indicate the outliers
%L: objective value for every iteration
%F: loss value for every iteration
if nargin <4
error('\n Inputs: X, Y, rho1, should be specified!\n');
end
X = ... |
5f0ee6b91577d07f9ce9a7018eddff879b6e88e86f74c4b3665fd0459c8c6826 | MATLAB | 5,969 | 142 | function Prediction = SVR_NFolds_RandomCV_CSelect(Subjects_Data, Subjects_Scores, FoldQuantity, CVRepeatTimes, 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 var... |
9bf7e79663e5452fceb51f36f95e32e2577a55992606980a1039a442efe643ad | MATLAB | 5,978 | 196 | %% plot_SAT_TTS_IQR_errorbars.m
% SAT TTS analysis with interquartile range as error bars.
% Assumes the current folder contains:
% - coarse_summary.csv
% - coarse_instance_tts.csv
%
% Prefix examples: 'uf100 ' => N = 100.
clear; clc;
%% Parameters
scale_factor = 1e-8; % iterations -> seconds
delta_tol = 1e-... |
4ac493759b595430552fd6e9d1d367f09331c46423dceaff593d818d24f2b4d2 | MATLAB | 5,988 | 194 | %==========================================================================
% FORMAT function [M, cases_m] = nk_MatchID(Sid, S, Did, D, act, infind)
%==========================================================================
%
% Inputs:
% -------
% Match two m x n arrays (D, S) according to their m x 1 column vector I... |
4f70c6b69ae1adff1b3d722d5b93265b97d5a7e038f63ae26f06896eab276da4 | MATLAB | 5,991 | 173 | <<<<<<< HEAD
function M = obj_read(filename)
% Read Wavefront OBJ-formatted data from disk
% FORMAT M = obj_read(filename)
%
% filename - OBJ-formatted file name
% M - data structure
%__________________________________________________________________________
%
% Wavefront OBJ Format Specification:
% ht... |
6f93a9dd404b49abeabb8970f1f0624d1b941720d61b05ced579a0c486e65044 | MATLAB | 5,994 | 167 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
8fffafb513e5f15db8916dd95a2a2c0d6a8b0024448c673815e32f716ac03ee3 | MATLAB | 6,002 | 173 | classdef gab_mass < bt.model.template
% This model is set up to fit t0 keeping the prior fixed if the alpha peak is not present
% Note that prepare_for_fit must be used before spectrum() can be used
% Uniform priors and DB initial fit
properties
stab_gamma_prefactor
spec_gamma_prefactor
stab_w
spec_w
... |
d8d318eb08811fffd66cc57b403cb3807d13233272add8c653be3f843a989d86 | MATLAB | 6,022 | 170 | function similarity = jaccard(A,B)
%JACCARD Jaccard similarity coefficient for image segmentation.
%
% SIMILARITY = JACCARD(BW1,BW2) computes the intersection of binary
% images BW1 and BW2 divided by the union of BW1 and BW2, also known as
% the Jaccard index. SIMILARITY is a scalar double in [0,1]. A
% ... |
5097d1383a684e54525145340ebba6fe0ad319dccc3ee7054abdb86e143d2e5e | MATLAB | 6,032 | 136 | function E = efficiency_wei(W, local)
%EFFICIENCY_WEI Global efficiency, local efficiency.
%
% Eglob = efficiency_wei(W);
% Eloc = efficiency_wei(W,2);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The local effi... |
fe878943626407813abb76115b8b18ee6108fc80c2da9bc2c2b26b762a497f07 | MATLAB | 6,053 | 242 | function [vol, M, mr_parms, volsz] = load_mgh(fname,slices,frames,headeronly)
% [vol, M, mr_parms, Mdc, volsz] = load_mgh(fname,<slices>,<frames>,<headeronly>)
%
% fname - path of the mgh file
%
% slices - list of one-based slice numbers to load. All
% slices are loaded if slices is not specified, or
% if slices i... |
ad352486d211d6f59ab77d339c5e4c1dae7a1d1143baf20ca7807401917fac12 | MATLAB | 6,065 | 159 | % Functions used from iViewX toolbox:
% iView('initialize', ivx) - Initializes the connection to the iView X system.
% iViewX_vk('calibrate', ivx) - Runs the eye tracker calibration procedure.
% iViewX_vk('openconnection', ivx) - Opens the data connection for streaming eye data.
% iViewX_vk('setscreensize', ivx) - Info... |
82f8c5bde8ae60529125fb2318a0c61e7fc21a63da0975f9ac97092661acef35 | MATLAB | 6,079 | 234 | %% FUNCTION Least_TGL
% L21 Joint Feature Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + opts.rho_L2 * \|W\|_2^2 + rho1 * \|W\|_{2,1} }
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% rho1: L2,1-norm group Lasso ... |
b650dc1b8fca6d60b4935b70ffb38a9aaf2536fdec7cef026bd9a2160abfed71 | MATLAB | 6,082 | 166 | % Compute h-step Weisfeiler-Lehman edge kernel for a set of graphs
% Author: Nino Shervashidze - nino.shervashidze@tuebingen.mpg.de
% Copyright 2010 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'... |
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