sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
387eb9228ea0ec2c0e8e4851e734f8aa25a247e4f4815bd3d56591a29c0b786f | MATLAB | 2,113 | 56 | function [network, mappedX, reconX] = train_autoencoder(X, layers, noise, max_iter)
%TRAIN_AUTOENCODER Trains an simple autoencoder
%
% [network, mappedX, reconX] = train_encoder(X, layers, noise, max_iter)
%
% Trains up an autoencoder with the structure that is specified in layers.
% The low-dimensional data is ret... |
89c016c96e062b0f78324bdb110f31552d5bc3243042b113d3dafad311a69c1d | MATLAB | 2,113 | 37 | clear all
%%
%Define default parameters
[opt] = ParameterOptions();
%%
%Edit parameters for power spectrum plot
opt.FullWaveform=1; % Option to calculate the full waveform of each signal-forming pathway rather than the condensed dictionary. Required to synthesise the Power Spectrum plot
opt.nPeriods=2; ... |
a0c9b8ff3d51d98a449b8689864833d1c77a19c1be379760eb750b0eec5629b1 | MATLAB | 2,113 | 49 | nConds = 4;
timevector = -0.05:0.001:0.2;
rgb = [0.1 0.4 0.5; 0.7, 0.4, 0.7];
labels = [{'Fast trials'}; {'Slow trials'}];
clear h
f = figure('NumberTitle', 'off', 'color', [1, 1, 1], 'position', [360 50 600 400]); hold on;
xlimToPlot = [-0.05 .2];
for iCond = 1: nConds
meanCond{iCond} = nanmean(fr{iCo... |
cdcd831636d4c683ce35523ab31b8c0956c976ca8656870fe908c69f4dd8d8de | MATLAB | 2,113 | 65 | %% This is a simple demo of Matlab's memmapfile class
% Here we memory map the MNIST digit data.
%%
% This file is from pmtk3.googlecode.com
function memoryMapDemo()
if isOctave()
fprintf('Sorry this demo will only work in Matlab\n');
return
end
%% load the mnist data
[Xtrain, ytrain, Xtest, ytest] = setupMni... |
473d85810dbf67b7c0f43da0b60610b3120bf918ccd2ded42cd5d0d58f0b1ad0 | MATLAB | 2,115 | 87 | function [vec,r,c]=getTri(mat,lu)
% getTri -- extract upper triangle componet with diagonal from matrix
%
% [Input]
% -mat: square matrix
% -lu: 'lower' or 'upper', default: get lower components
%
% [Output]
% -vec:vector
% -r:row index
% -c:column index
%
%
%
% [note]
% each component was ex... |
64ec599db102b098e9f46821b407c7f3fad000c0b5948ea22e10b92ab5fe82ee | MATLAB | 2,115 | 73 | function orig_coord = get_orig_coord2(coord, matname,PU)
% Determine corresponding co-ordinate in un-normalised image.
% FORMAT orig_coord = get_orig_coord2(coord, matname,PU)
% coord - [x1 y1 z1 ; x2 y2 z2 ; etc] in MNI space (mm).
% matname - File containing transformation information (_sn.mat).
% PU ... |
6757b67f0c68ee47a96a251fb96d2b8bdd8acb068e6d108efc252fe7d18eae92 | MATLAB | 2,115 | 61 | function [cost, grad] = getCostGrad(problem, x, storedb, key)
% Computes the cost function and the gradient at x in one call if possible.
%
% function [cost, grad] = getCostGrad(problem, x)
% function [cost, grad] = getCostGrad(problem, x, storedb)
% function [cost, grad] = getCostGrad(problem, x, storedb, key)
%... |
8927a153dda390b155d1ed1a4cbc1aa1772e3d2be0718f776dce87636bbd55a4 | MATLAB | 2,115 | 79 | function [mask] = image_stack_adjust_median(stack, name, volume_filtering)
% TODO: optimize
%load parameters
parameters;
Nz = length(stack(1,1,:));
for ii=1:Nz
images{ii} = stack(:,:,ii);
end
clear stack;
max3 = @(x) max(max(max(x)));
%% ------------------------------ 3D matrix -----... |
9bd456b80d95ed035d70fdecd65e096c9eca0f0cee8abb8d07a99e91bb2c5980 | MATLAB | 2,115 | 47 | function reduced = reduceids(ids, obj)
% reduiceids Reduce Imaris IDs to a fundamental numerical identifier
%
% Syntax
% ------
% reduced = reduceids(ids)
%
% Description
% -----------
% reduced = classifyids(ids, obj) converts the Imaris object IDs in
% i... |
661726d77424653811941ef549f2ddc1bae92aa70cf6332d1e0f0fa257fbd9a9 | MATLAB | 2,116 | 82 | function [varargout] = VBA_spm_unvec(vX,varargin)
% Unvectorise a vectorised array - a compiled routine
% FORMAT [varargout] = spm_unvec(vX,varargin)
% varargin - numeric, cell or structure array
% vX - spm_vec(X)
%
% i.e. X = spm_unvec(spm_vec(X),X)
% [X1,X2,...] = spm_unvec(spm_vec(X1,X2,...),X... |
b5d66c2beeca676df3f8ea6ccd1e6639bf5699af30d54127f3d5c760963ac15d | MATLAB | 2,116 | 91 | % Field Trip-related test scripts - Synthetic FT data - Configuration.
%
%
% Includes.
% NOTE - Assume the parent called "init" already.
%
%
% Configuration.
%
% Event detection.
% Select the parameter extraction algorithm we want.
% "coarse" gives a poor fit, "amp" takes a while but gives a good fit, and
% "... |
d0a411b29d994bfe4d7e86776aa0d7513ab27c1d03eeb5816406e074a78a699a | MATLAB | 2,116 | 47 | % classdef CBIG_MFMem_rfMRI_estimation_main_ut_test < matlab.unittest.TestCase
% % Written by Xiaolu Kong and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
%
%
% methods (Test)
%
% function Test_Estimation(testCase)
% %% path setti... |
f47d7c29ed3d8b4493e5d32499804de72ea1cfb48d4c0ff50a724d391fee1634 | MATLAB | 2,116 | 60 | function ztabledat = afxConnNetwork(yRoi,lesioned,rois,outPrefix,subject)
% calculation of functional connectivity
fprintf(' Calculating network functional connectivity ... ')
% calculate correlation coefficients and apply fisher transformation
rhoFisher = atanh(corrcoef(yRoi));
% write... |
8a4aa7a2ed514b4556d9da774b390da7a1d1b6348a7e92f12ec6fd96ddcd161e | MATLAB | 2,117 | 67 | function res = innerprod(X,Y)
%INNERPROD Efficient inner product with a ttensor.
%
% R = INNERPROD(X,Y) efficiently computes the inner product between
% two tensors X and Y. If Y is a tensor or sptensor, the inner
% product is computed directly and the computational complexity
% is... If Y is a ktensor, the i... |
b426cfc03736605dd2be1be9ffd14de970fbbcebac1c8265b832755023e66a89 | MATLAB | 2,117 | 45 | function unitnormals = meshnormals(P, t)
% SYNTAX
% unitnormals = meshnormals(P, t)
% DESCRIPTION
% For a manifold mesh P, t, this function returns an array of outer
% normal vectors for each triangle
% Inputs:
% P - Vertex array of the mesh (N x 3)
% t - Facets of the mesh (N x 3)
% Output:
% unitn... |
d62f5178e0b19fa6e4b4ba84e1529590b13250d319d383bed592e597f4adffd5 | MATLAB | 2,117 | 86 | % [mu,S,priors,post,nll,mix] = mygmm(X,k,...)
% Input:
% X: N x D matrix of N D-dimensional data points
% k: number of clusters
% optional:
% 'Replicates' = 1
% 'CovarType' = 'full'
% 'display' = -1
% 'precision' = .0001
% 'ResetCovar' = 1
% 'MaxIters' = 100
% 'Start' = 'furthestfirst'
% 'distance' = 'sqEuclidean'
% 'w... |
6cb09986241488effff65ce6f34bca2402596c0e2246740319ba29481e84fb73 | MATLAB | 2,119 | 53 | function [b,bPath1] = bHistogramOrderFull(PathwaySignal,GwaveOrder,bWeights,bWeightsPos,bLong,b,Loss,GwaveBlock,dt,gamma,bRound)
%%
%Estimate ratio of Longitudinal scaling
Gwave=GwaveEstimate([1,-2],GwaveBlock);
bPath1=sum(cumsum(gamma*Gwave*dt).^2)*dt;
Gwave=GwaveEstimate([1,-3],GwaveBlock);
bPath2=sum(cumsum(gamm... |
2d2d17f979f93c66cdbe50208cfd8c060f6ec4481881e928233f460bdafa6e3a | MATLAB | 2,120 | 67 | function [CC_arterial] = extract_arterial_CC_PET(CC, dim_TOF_c, dynIDIF)
CC_arterial = CC;
numPixels = cellfun(@numel,CC_arterial.PixelIdxList);
[biggest,idx] = sort(numPixels, 'descend');
CC_arterial.PixelIdxList = CC_arterial.PixelIdxList(idx);
% Tolgo le componenti connesse che sono piccole
to_remove = ... |
e7b44783cff141eec388f07be507612a2ed0612b3d59e124d606de62ac3e664b | MATLAB | 2,121 | 86 | function D = tridiag(A, B, C, useSparse)
% Make a tri-diagonal matrix from a matrix or from 3 vectors
%
% D = tridiag(A) sets all elements of A outside the tridiagonal to 0
% If A is sparse, D will also be sparse.
%
% D = tridiag(main, upper, lower) constructs a matrix with these
% vectors on the diagonal and just abo... |
0085677ace4592325744f2cfe4e00c995811b928d7fdff835d836812b5183cdc | MATLAB | 2,122 | 63 | %% Classify the MNIST digits using a one nearest neighbour classifier and Euclidean distance
% See mnistKNNdeno for a simple minded implemenation.
% This demo uses various tricks to scale the method up to large
% data sets.
%%
% PMTKslow
%%
% This file is from pmtk3.googlecode.com
loadData('mnistAll');
if 0
% test ... |
08f82ac8c2b39db66ee2eb7b14d8726c4a885c323060875443332d8e78c209bf | MATLAB | 2,122 | 62 | % Band power Plot Fig. 1G,1H
% Edited by Jerry. 10/27/2023
function [datamean,datastd] = BandPowerPlot_231027...
(BPinput,Title,Yname,Yrange,figsize,SaveDir,alpha,ErrType)
% fig setup
hFig = SetupFigure_220724(figsize);
colors = [10,139,148;130,10,40;134,179,82;242,110,48;...
177,224,123;232,21... |
0d2c3973f93485494af106fea12e2eedbb92e16d8776dfe46be2da78868773b7 | MATLAB | 2,122 | 58 | % time-frequency toolbox for matlab
% written by F. Auger, P. Flandrin, P. Goncalves, O. Lemoine,
% 1996 - 1999, GdR-PRC ISIS, CNRS, France.
%
% signal generation functions:
% amexpo1s, amexpo2s, amgauss, amrect, amtriang
% fmconst, fmhyp, fmlin, fmodany, fmpar, fmpower, fmsin, gdpower
% altes, anaask, anafsk, anabp... |
bd14d0815470a33d78572c933bcd53dd07f2f3df10ac2135072174f99d5b2d1a | MATLAB | 2,122 | 68 | % Script for testing the string array feature of SOFA
%% Test Strings as application-specific variable
% Load some arbritrary HRTFs
hrtf = SOFAload(fullfile(SOFAdbPath, 'database','ari','dtf_nh2.sofa'));
% Add a string array
str={};
for ii=1:hrtf.API.M
str{ii,1}=['String' num2str(round(rand(1,1)*10000))];
end
% SOF... |
e8dd2fa9bb75b44663909a2d994cd3daad6cff38ab6298d4aa78fec423459156 | MATLAB | 2,123 | 65 | %% Test amplitude of stimulus on various aspects of ionic currents in
% ToR-ORd+Land electro-mechanical model
%% Setting parameters
clear
clc
close all
param.bcl = 800; % basic cycle length in ms
param.model = @model_ToRORd_Land; % which model is to be used
param.verbose = true; % printing numbers of beats simulated... |
36e2e02430ab0423ac7c8662ce6e3d65bd5b9d1eb2a9b783a98fd62c0a1883d0 | MATLAB | 2,124 | 100 | % cor_freq_lan.m
% v1.0
%
% calcula la estadistica de las correlaciones, y grafica
% de cada individuo.
%
% 22.11.2009
% Pablo Billeke
%
function [ bs,mbs,p,hc] = cor_freq_lan(LAN,e)
%
for com = 1:length(LAN{1}.freq.cfg.corfr1)
if iscell(LAN)
disp('Chequeando estructura LAN')
else
warning('Solo para LAN con... |
8a8336740a27b32506849a325be8d04d69f676da60837a5f06c19d6d23180b93 | MATLAB | 2,124 | 75 | function [whtM, H] = doMMGPCA(X, comps, rec_type)
M = 1:length(X);
N = size(X{1},2);
V = cellfun(@(x) size(x,1), X);
outs = cell(size(X));
cvx = zeros(N);
for mm = M
cvx_ = cov(X{mm});
cvx = cvx + cvx_./(length(M)*trace(cvx_)/N);
end
% [V,ll] = eig(cvx);
% ll = diag(ll);
% ll = ll(end:-1:1);
% Subject-le... |
3c5e0cfc085423af6f734fa16b2271c50b29a4a6fdbcd32620f428b81944fce0 | MATLAB | 2,125 | 86 | %% genvar
%
% Generate VAR time series data
%
% <matlab:open('genvar.m') code>
%
%% Syntax
%
% [X,E] = genvar(A,E,trunc)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coefficients matrix
% E residuals (single-trial) time seri... |
8da56bc5cffd1557aed80b942eadf441e17c864417418e89d2e375debfeb054e | MATLAB | 2,126 | 65 | function redbluegraphs(x_var,y_var,y_var2)
x=x_var
y=y_var
yred=y_var2
% first graph (blue)
N = size(y,1); % Number of ‘Experiments’ In Data Set
yMean = mean(y); % Mean Of All Experiments At Each Value Of ‘x’
ySEM = std(y)/sqrt(N); ... |
bf9988a127538a64a0a63340332c7e7086fb8bf2a18de50073ccf6695a05da29 | MATLAB | 2,126 | 36 | function [netMat_betas] = multregressionconnectivity(activityMatrix)
%[netMat_betas] = multregressionconnectivity(activityMatrix)
%
%This function computes multiple regression-based functional connectivity. This is equivalent to running a general linear model (GLM) separately for each region, with all other regions' t... |
a8346b16eff12a766c5decbe166141f9897c8f388d02ff407a768be76887b570 | MATLAB | 2,127 | 52 | % SOFA API - demo script
% Copyright (C) 2012-2013 Acoustics Research Institute - Austrian Academy of Sciences
% Licensed under the EUPL, Version 1.1 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "License")
% You may not use this work except in compliance with the ... |
3c4868768b0eddb9f46dc8979ebd89942ea84785f3c5036c3bac85a2b39a38e1 | MATLAB | 2,128 | 79 | function [R2_cv,mP,VP,R2] = PRESS_GLM(X,y,verbose)
% evaluates the PRESS-R2 cross-valuidation metric for a GLM
% function [R2_cv,mP,VP,R2] = PRESS_GLM(X,y,verbose)
% This function evaluates a prediction accuracy metric (PRESS-R2), in the
% context of a leave-one-out cross-validation scheme for a GLM. Note: it is
% base... |
6bfc3a3c2fdc8b795c8d54870327549f97ae4417f1d8aba93d5e706b5af33809 | MATLAB | 2,128 | 53 | function dispMatrixIm( M, varargin )
% Display a Matrix with non-negative entries in image form.
%
% USAGE
% dispMatrixIm( M, [varargin] )
%
% INPUTS
% M - [m x n] arbitrary matrix with non-negative entries
% varargin - additional params (struct or name/value pairs)
% .fStr - ['%1.2f'] format for ... |
70d51407a178cbc1cf2249fcac47b2efba41ce7f95fd68abef020e2741956357 | MATLAB | 2,128 | 76 | %% Compare SVM, RV and kernelized logreg on synthetic 2 class data in 2d
% This file is from pmtk3.googlecode.com
%% Load Data
% Load synthetic data generated from a mixture of 2 Gaussians. Source:
% http://research.microsoft.com/~cmbishop/PRML/webdatasets/datasets.htm
%
% See also hastieSvmLrDemo
loadData('bishop... |
7ebe14b632d03e32e0035daad6e787180557306191e3acd6978d9ecc0f1903be | MATLAB | 2,128 | 57 | function [stg1net, stg2net] = get_custom_two_stage_tracker_nets_ui(mainFigure, stg1nets, stg2nets)
stg1net = [];
stg2net = [];
stg1nets_str = cell(1, numel(stg1nets)) ;
for ndx = 1:numel(stg1nets)
stg1nets_str{ndx} = stg1nets(ndx).displayString;
end
stg2nets_str = cell(1, numel(stg2nets)) ;
for ndx = ... |
9ba7d3eadebc14ecb7157886d5a4b80d36aa614a53a257566e01cfb2e7eb9e96 | MATLAB | 2,128 | 64 | function FigurePooler(FigDir,FigName,dc,PooledFormat,PoolingDir,SubDir)
[upperPath, deepestFolder] = fileparts(FigDir);
if SubDir
if strcmp(PoolingDir(length(PoolingDir)),dc)
PoolingDir=[PoolingDir,deepestFolder];
else
PoolingDir=[PoolingDir,dc,deepestFolder];
... |
a70c2a7b1fc25c4f4a8f1ff60591f8914b02cec668e1d5596448cf07c28bc909 | MATLAB | 2,128 | 48 | %% Compute the amplitude envelope correlation as defined in Hipp et al. 2012 Nature Neuroscience
%
% input: - virtChan_timeSeries: cell array of EEG time series, each cell
% should represent a single epoch/trial
% output: - connMatrix: s x s x e matrix, containing the connectivity
% matrices f... |
60c8e4177b74f4623a5d15c9911f4ea9363b2e45480feb2fa65f7db4f866f8b2 | MATLAB | 2,129 | 94 | %% demo of spectral clustering
%PMTKauthor Wei-Lwun Lu
function spectralClusteringDemo()
setSeed(0);
datatypes = {'circle', 'spiral'};
for i=1:numel(datatypes)
switch datatypes{i}
case 'circle'
data = sample_circle(2);
case 'spiral'
data = sample_spiral();
end
% Kmeans
[centers, mincen... |
c407496b1d30255527a1d0640e45185d08fb8b2aa88c598bdc451e30209bdd4b | MATLAB | 2,129 | 109 | function [A,B,C,D,dim] = VBA_dcmMatrices(options,theta)
% [A,B,C,D,dim] = VBA_dcmMatrices(out,theta)
% DCM parameters are often given in a vector form (like as the output of a
% model inversion usin VBA_NLStateSpaceModel) that is harder to read than
% the original matrix form.
% This function helps reconstructing all t... |
1009ddf82b6a9edb51bd72337bd15594949f7ceffd05972898a5d4fbd2046a0c | MATLAB | 2,130 | 70 | function [mask info] = GetImageStack(filename,param)
% Tries to read all the 2D image files whose file name is 'filename' plus
% an incremental numbering. Also it can read an image stack from Meta
% It returns a 3D matrix of 'int16' type.
% If param.pad is non-zero, it will add zero padding to four sides of each
% 2D i... |
96d85a2c9d9ddcaa3391958433ec3e6322686e48840e81c2befe7663df48f92d | MATLAB | 2,131 | 74 | % Example of 3D non-rigid registration
% using steepest gradient optimizer and grid refinement.
% clean
clear all; close all; clc;
% Add all function paths
addpaths
% Get the volume data
[I1,I2]=get_example_data;
% Show the volume data
showcs3(I1);
showcs3(I2);
% Start b-spline grid dimensions (in ... |
ce6170bbfdce8ad8ec3120f90fcdb7656a6e0d4e7383b65959875fe303380ccf | MATLAB | 2,131 | 71 | function d = computeCohen_d(x1, x2, varargin)
%
% call: d = computeCohen_d(x1, x2, varargin)
%
% EFFECT SIZE of the difference between the two
% means of two samples, x1 and x2 (that are vectors),
% computed as "Cohen's d".
%
% If x1 and x2 can be either two independent or paired
% samples, and should be treated... |
b2b5629b386a366c41e8fa79f52c9991a074d95c29ae4fb07a1cd9053a2f67e1 | MATLAB | 2,132 | 67 | clear all;
%load ../phi/phi_0_25_1089.mat %Load the measurement matrix for measurement rate of 0.25 to sense patches of 33 by 33.
load ../phi/phi_0_04_1089.mat %Load the measurement matrix for measurement rate of 0.04 to sense patches of 33 by 33.
%load ../phi/phi_0_10_1089.mat %Load the measurement matrix for measurem... |
6fa13b2ac68653c76ffea549a991da8d2b346334062d971b67748075f397a568 | MATLAB | 2,133 | 64 | function CBIG_RL2017_Figure7_toy_ex_bi_vs_multi
% CBIG_RL2017_Figure7_toy_ex_bi_vs_multi
%
% This code illustrates the difference between multivariate and bivariate
% approaches to generate surrogate time series. The multivariate approach
% consists in identifying a single multivariate AR model of the original time ... |
878d12222ee5fce821f32be9233c43a5dadfd185c1989a4017a1306fb51da696 | MATLAB | 2,133 | 70 | function decVec = decode_success(data,winsize,stepsize,prctokeep)
%% test
%close all
%T=969;
%sig=zscore(cumsum(randn(T,18)));
%data=0.5*randn(T,18,32)+repmat(sig,1,1,32);
%winsize=25;
%stepsize=25;
%prctokeep=1;
%
% data is in format time x space x subjects
% winsize is a number of length of windo... |
d0473714a63293eb74241995f0c2ac20af34cb2ecc2ac34ec34753256b2e5a2e | MATLAB | 2,133 | 58 | function [corr, avg_corr_sorted, I] = CBIG_ASDf_computeCorrBetweenRuns(inputDir, k)
% [corr, avg_corr_sorted, I] = CBIG_ASDf_computeCorrBetweenRuns(inputDir, k)
%
% This function computes pair-wise correlation between each run (random
% initialization). In addition, for reach run, it computes the average correlation
%... |
3b2fc115864db1f3b98f0495581c9f8cfc06f0fddb51ef7faf72799d081f6b62 | MATLAB | 2,135 | 88 | function [nnmf_res,ss] = run_nnmf( S, niterations, summary_plot )
% function [nnmf_res,ss] = run_nnmf( S, niters, summary_plot )
%
%
if nargin < 3 || isempty( summary_plot )
summary_plot = false;
end
if nargin < 2 || isempty( niterations )
niterations = 10;
end
if summary_plot == true && niterations > 10
... |
a1cd632eb2525c7f07c5043d7cf249da24f109ba61c11675d2f491b52d38865d | MATLAB | 2,135 | 57 | % Adds freqbands to existing master table
% Load required toolboxes
addpath C:\code\spm12
addpath C:\Users\ICN_guest\Charité - Universitätsmedizin Berlin\Interventional Cognitive Neuromodulation - PROJECT ECoG Atlas Connectomics (1)\functions;
addpath C:\code\wjn_toolbox
spm('defaults','eeg')
% Load required variable... |
5ff1eeb8fd47155933667ff2352fcf8db8806f3d74486162d1d9274d39fed550 | MATLAB | 2,136 | 57 | function loc_list_two_channel(data,scatter_size,scatter_num)
global Int
if length(data)~=2
msgbox('Number of files selected should be equal to 2')
else
figure()
set(gcf,'name','Montage Plot','NumberTitle','off','color',[0.1,0.1,0.1],'units','normalized','position',[0.15 0.2 0.7 0.6],'menubar','non... |
cd7878ef6e149243da6f8e1532e3009ebddd8349e7b6d01bdc528291f0dc9abf | MATLAB | 2,136 | 77 | %% Naive bayes classifier applied to text data (bag of words)
%
%%
% This file is from pmtk3.googlecode.com
%% Load data
if 1
loadData('XwindowsDocData'); % 900x600, 2 classes Xwindows vs MSwindows
Xtrain = xtrain; Xtest = xtest;
else
loadData('20news_w100') % 16242x100, 4 classes
X = shuffleRows(doc... |
18b02b6c8cacddbd06503bac7c14b22f141adec909ea5103728a80890d0eb9e7 | MATLAB | 2,137 | 79 | %% Impute missing entries of the 4d Pima Indians data using an MVN
%PMTKauthor Cody Severinski
%%
% This file is from pmtk3.googlecode.com
setSeed(0);
D = loadData('pimatr');
pima = D.data;
pima = pima(:,3:6);
pimaNan = pima;
[n,p] = size(pima);
probMissing = 0.3;
miss = unifrndPMTK(0,1,size(pima));
miss(miss < p... |
3348678334288e553e94f3d9c8c41fab3d761f924d397c11adff47e237b48f75 | MATLAB | 2,137 | 75 | function [recall, precision, th, averagePrecision] = precisionRecallPMTK(confidence, testClass, col)
% Plot precision-recall curve
% [recall, precision, th, averagePrecision] = precisionRecall(confidence, testClass, col)
%
% Input
% confidence: score of the classifier
% testclass: 1 = inlier, 0 = outlier
% ... |
0ea351239f07361806d8c772ecc3407849ded1830e84c7999497b4e4698fb334 | MATLAB | 2,138 | 50 | function compute_graph_measures(params,bidsID,freqBand,connMeasure)
try
load(fullfile(params.ConnectivityPath,[bidsID '_' connMeasure '_' freqBand '.mat']),'connMatrix');
catch
error(['Connectivity matrix ' bidsID '_' connMeasure '_' freqBand ' could not be loaded']);
end
% Threshold the connectivity matrix d... |
3be130b0e7af6f39d2bf14dd168a7d0ea290d9c8405a3da96a899cf4e672d491 | MATLAB | 2,138 | 71 | function [h] = fdr(p, q)
% FDR false discovery rate
%
% Use as
% h = fdr(p, q)
%
% This implements
% Genovese CR, Lazar NA, Nichols T.
% Thresholding of statistical maps in functional neuroimaging using the false discovery rate.
% Neuroimage. 2002 Apr;15(4):870-8.
%
% There are two types of FDR correction (Ben... |
6b6f60fd200983bbe4e54089da1b76fae6e66b0d2eceaff7f1d91fad1d3c622c | MATLAB | 2,138 | 94 | % Anisotropically diffuse the calibration image
% to enhance the corner detection
fprintf(1,'Anisotropic diffusion of the images for corner enhancement (the images have to be loaded in memory)\n');
ker = [1/4 1/2 1/4];
ker2 = conv2(ker,ker);
ker2 = conv2(ker2,ker);
ker2 = conv2(ker2,ker);
if ~exist(['I_' num2str... |
3b03aecf082f6cc42c186256f4e07640f18bd03ec1248f380af4a84ed0212a9a | MATLAB | 2,139 | 62 | function unit = ft_estimate_units(size)
% FT_ESTIMATE_UNITS tries to determine the units of a geometrical object by
% looking at its size and by relating this to the approximate size of the
% human head according to the following table:
% from 0.050 to 0.500 -> meter
% from 0.500 to 5.000 -> decimeter
% fr... |
61c988f4ae3d644dfe87d2a36717e7779324439a1a055cda2ef70e89e1acbb1c | MATLAB | 2,140 | 59 | function [X,keeplocs] = subsampleMatrix( X, maxMegs )
% Samples elements of X so result uses at most maxMegs megabytes of memory.
%
% If X is m+1 dimensional, say of size [d1 x d2 x...x dm x n], each [d1 x
% d2 x...x dm] element is treated as one observation, and X is treated as
% having n such observations. The subsa... |
6e253b3cf0d085ab54a7959568ab46fa1e2762a17ca7610894823f368b302098 | MATLAB | 2,140 | 73 | %{
Copyright (c) 2025 Axion BioSystems, Inc.
Contact: support@axion-biosystems.com
All Rights Reserved
%}
classdef BlockVectorSampleType < uint16
%SampleType Encoding of the type of samples stored by a block vector
%
% Short: Signed 16-bit numbers (little-endian)
%
% Int: Signed 32-b... |
84f273adcf17cb9ad89d857e3481e94c670e01bb6f046d7b2395a5fb05b03e18 | MATLAB | 2,140 | 75 | function th=rotateticklabel(h,rot,demo)
%ROTATETICKLABEL rotates tick labels
% TH=ROTATETICKLABEL(H,ROT) is the calling form where H is a handle to
% the axis that contains the XTickLabels that are to be rotated. ROT is
% an optional parameter that specifies the angle of rotation. The default
% angle is 90. TH ... |
fe4e7caba63c963f93d87f3f0aa7e3522adf389ed3bffb078f6d8452e30d0b85 | MATLAB | 2,140 | 68 | function Image_Crop=SmartCrop_withFill(Image,Crop_XCoords,Crop_YCoords,FillValue)
%
% Image=UltraHighRes_Contrasted_Color_Merge_ROI_Overlay;
% Crop_XCoords=[-100,3000]
% Crop_YCoords=[-400,2700]
% FillValue=[0.5,0.5,0.5]
Crop_XCoords=round(Crop_XCoords);
Crop_YCoords=round(Crop_YCoords);
... |
37cb08d69f3296855e947c7d60a34bb5dc8e66a2135981e6b19edd2e203f6797 | MATLAB | 2,141 | 70 | function model = crf2Train(model, Xnode, Xedge, y, varargin)
% Fit pairwise CRF using ML/MAP estimation with L2 prior
% model = crfTrain(model, y, Xn, Xe)
% y is Ncases*Nnodes (y(i,n) in 1..nStates(n))
% Xn is Ncases*NnodeFeatures*Nnodes
% Xe is Ncases*NedgeFeatures*Nedges
%
% Optional parameters:
% loss : one of
% ... |
b72d563116130c88fe997be8fd582820aa8bef6363c9a737785b8600acbea40f | MATLAB | 2,141 | 58 | clear;clc
% dataPaths{1} = 'D:\InVivo\Shank-Only\2022-05-13_Shank-Only-UCLA\';
dataPaths{1} = 'D:\InVivo\Shank-Only\2022-05-29_Shank-Only-UCLA\';
dataPaths{2} = 'D:\InVivo\Shank-Only\2022-05-31_Shank-Only-UCLA\';
dataPaths{3} = 'D:\InVivo\S1-Opto\2023-01-27_SoCoChR2-S1_Mouse2\6-whisker_230127_150002\';
topElec... |
d408d3d677075cf2fe85a2bb1f4f39754a5d26fc5b6c357f4bbcc7f69d71df32 | MATLAB | 2,141 | 72 | classdef TargetSetVariable < TargetSet
properties
prettyString
prettyCompactString
% fcn with sig iTgts = fcn(labeler,mIdx).
getTargetIndicesHook
id % id for testing for special cases
end
methods
function obj = TargetSetVariable(ps,pcs,fcn,id)
obj.prettyString = ps;
obj.pret... |
d733f35c536ad4ab81c815dad8de8efbf378ab917e0b9186af77ca23d8ac80a6 | MATLAB | 2,141 | 98 | %% Optimization in high dimensions using minFunc
% PMTKslow
%%
% This file is from pmtk3.googlecode.com
function demoMinfuncHighdim()
%methods = {'sd', 'cg', 'bb'};
methods = {'sd', 'cg', 'bb', 'newton', 'newtoncg', 'bfgs', 'lbfgs'};
%methods = {'sd', 'cg', 'bb', 'newton', 'newtoncg', 'bfgs', 'lbfgs', 'newton0', ... |
087626454e738fda3bada8bf2fb2d649f97ce7adc1bcd6a38e9d0a251264b325 | MATLAB | 2,142 | 1 | % TSdisplay DISPLAYS 2-COL TSDATA WITH EVENT CODE NAMES AND LINE NUMBERS
% This function takes TSdata and displays it in the command window with
% additional columns containing row #'s and the event code names, which
% are found using the currently loaded event code set.
%
% Example output:
% Row #: Time: ... |
03ae72308ba417c46f53b7b8d4b06f7d11e69ec0bf21f4271949d9deaf8fbbf2 | MATLAB | 2,143 | 61 | %% Normalize PDF Columns to Unity
% Common Macro for normalization
%
% Treat column vectors as discretely (and homogenously) sampled probability
% distribution functions. Elementwise division of each element by the sum
% of its column.
%
% Because p(x) cannot equal zero, handle negative values of columns either... |
1ede4ab9fb01e4b267748b3f6fbe52132761c258c18fb23b8aae2b6d955cb7fb | MATLAB | 2,143 | 99 | function [ newftdata chandefs ] = wlFT_getChanSubset(oldftdata, chanlist)
% function [ newftdata chandefs ] = wlFT_getChanSubset(oldftdata, chanlist)
%
% This function extracts a subset of channels from within a Field Trip data
% structure. Channels are selected by name (matching names in the "label"
% cell array).
%
... |
375731ebd195aff740b91f1fe568dcc34416081dff0b3868c923d44daea6f479 | MATLAB | 2,143 | 89 | function CBIG_MFMem_rfMRI_run_1000_simulation_testGrp_resLow_example(save_dir_input,FCSC_file_name,parameter_file_name)
%--------------------------------------------------------------------------
% CBIG_MFMem_rfMRI_run_1000_simulation_testGrp_resLow
%
% Input parameters:
%
% save_dir_input: Output file directory... |
dd46cdc58322b2c286cb4e12e3f2ede1c9df3c742e83be618996e884dc803363 | MATLAB | 2,143 | 117 | % crabsort.common
% class that defines the common
% object type
%
% the common object stores information
% that is common to all files in a given data set
% for example, the names of the channels
%
classdef common
properties
y_scales double
delays
data_channel_names cell
show_hide_channels logical
chann... |
31d43543869054a42ee23f3cebc4621a3418585d37ea5bf284816adbb15e0844 | MATLAB | 2,144 | 85 | % 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 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,... |
64b071cf5ae13075ea27170ac8add8ad155663f538d9fd8a851f23828a1216a5 | MATLAB | 2,145 | 88 | function [model, L, Lhist] = linregFitEbChen(X, Y, pp, varargin)
% Evidence procedure (Empirical Bayes) for linear regression
% PMTKauthor Tao Chen
% PMTKurl http://www3.ntu.edu.sg/home/chentao/software.htm
% It estimates alpha (scalar) and beta
% L is log marginal likelihood
% gamma is effective number of paramers
% ... |
65100d76e2431a8bdb041c6345a8ed150df2d9623ff34fe45d3e8db343581958 | MATLAB | 2,145 | 64 | % demo for binary data classification
% This script first simulates binary data (y) under a simple logistic-like
% model, i.e.: p(y=1) = sigmoid(X*b), where X is a feature matrix, and b
% are arbitrary weights. VBA's classifier is then applied to the simulated
% data, and performance is evaluated. In particular, cross-... |
8f170f61132ea652a06584817a2dca230476dc4ea2fe35bcfac19bdcf1d88835 | MATLAB | 2,145 | 49 | function circular_arrow(figHandle, radius, center, arrow_angle, angle, colour, linewidth, arrowhead_angle)
arrow_angle = arrow_angle*(pi/180);
angle = angle*(pi/180);
arrowhead_angle = arrowhead_angle*(pi/180);
th = arrow_angle:angle/100:arrow_angle+angle;
x = center(1);
y = center(2);
r = radius;
xunit = r * cos(t... |
bd899e8fd026d39dd45fc7f2815f984f05c66c5350ef6c262fac6bda56c83fcc | MATLAB | 2,145 | 81 | function [hEllipse,X,Y] = ellipsearcdraw(theta0,theta1,a,b,x0,y0,phi,lineStyle)
%ELLIPSEDRAW can draw an arbitrary ellipse with given parameters.
% The properties of that ellipse plot can be customized
% by setting the ellipse handle.
%
% hEllipse = ellipsearcdraw(theta0,theta1,a,b,x0,y0,phi,lineStyle)
%
% ... |
db711ceab43a4eda81ebd995f8474c816c56cc7654c1175c3c9508052162ffc9 | MATLAB | 2,145 | 61 | function colorcorrection_3DWholeImage(roundnum)
loadParameters;
if params.DO_DOWNSAMPLE
FILEROOT_NAME = sprintf('%s-downsample',params.FILE_BASENAME);
else
FILEROOT_NAME = sprintf('%s',params.FILE_BASENAME);
end
DIRECTORY = params.deconvolutionImagesDir;
OUTPUTDIR = params.colorCorrectionImagesDir;
if exis... |
0831e83b3ffd051ff96cd4782d1909779101a56dc391e72c0d7b44a88c74171d | MATLAB | 2,146 | 48 | %--------------------------------------------------------------------------
% Title: Multi-Layer feature extraction using sparse representation
% Author: [Behrang Fazli Besheli]
% Email: FazliBesheli.Behrang@mayo.edu
% Institution: [Mayo Clinic, Department of Neurosurgery]
% Date: [09/07/2024]
%------------------... |
1895d86d67c8aafd153e14b7d6f489e204672584b28bdcb73ba4f9b65875e02b | MATLAB | 2,146 | 84 | function u = nvecs(X,n,r,opts)
%NVECS Compute the leading mode-n vectors for a ttensor.
%
% U = NVECS(X,n,r) computes the r leading eigenvalues of Xn*Xn'
% (where Xn is the mode-n matricization of X), which provides
% information about the mode-n fibers. In two-dimensions, the r
% leading mode-1 vectors are the... |
531e4b94cbe6e25cbefb8759c77191b13d5a7ae3ef6c896fb6de8effc5c736d5 | MATLAB | 2,146 | 70 | function a = appcoef2_gpu(c,s,varargin)
%APPCOEF2 Extract 2-D approximation coefficients.
% APPCOEF2 computes the approximation coefficients of a
% two-dimensional signal.
%
% A = APPCOEF2(C,S,'wname',N) computes the approximation
% coefficients at level N using the wavelet decomposition
% structure [C,S] (se... |
7b3be85475b1a882613154e5dcd0e37568714384e57378abef0d3dd46d3d4f7e | MATLAB | 2,146 | 80 | function contrasts = staircaseAdjustmentContrastTargetsDprime(pedestal, contrasts, trialacc, trialcatch, faweight)
%
% function staircaseAdjustment(contrasts, discrims)
%
% contrast is [low-contrast(decrement) high-contrast(increment)]
% discrim is [valid-discrim-targetType1(decrement)
% valid-discrim-targetType2(incre... |
2212d6e70a4a08adb96c063762022410c42b5a1f3daf7c4e7fa4edab611ba664 | MATLAB | 2,147 | 50 | function Rsqf = sufTrialRsq( trialCounts, prs)
% sufTrialRsq computes a measure of trial-to-trial consistency (R-squared)
% across different stimulus combinations.
%
% Inputs:
% trialCounts - A matrix of spike counts, where each row corresponds to a
% cell and each column corresponds to a stimulus ... |
417a4ce055ae18fdfff7fee8dfd307edb954da1c0efe8d7f7f2dd96c21ccc59f | MATLAB | 2,147 | 59 | clear all
clc
% Define methods and their counts for machine and deep learning methods
methods = {'Transfer Learning', 'CNN', 'Autoencoders', 'GANs', ...
'U-Net', 'SegNet', 'LSTM', 'Feature Fusion', 'SVM'};
counts = [12, 10, 3, 2, 2, 1, 1, 1, 1];
% Sort counts and methods
[sortedCounts, sortedIdx] ... |
67e3aff46e776766cfb876a05851f16981c58eb458eaf06e6f681c7428a1c200 | MATLAB | 2,147 | 48 | function [dat] = ft_trialfun_example2(cfg)
% FT_TRIALFUN_EXAMPLE2 is an example trial function that detects muscle
% activity in an EMG channel and defines variable length trials from the
% EMG onset up to the EMG offset.
%
% You would use this function as follows
% cfg = [];
% cfg.dataset = string... |
a2baf5080123275eb9417e5c65576a9eea602ecd4d72f46daf85ae56e152c695 | MATLAB | 2,147 | 62 | % RSKELF_PARTIAL_SV Solve using partial recursive skeletonization
% factorization.
%
% Like RSKELF_SV but using a user-supplied function of the form
% Y = SVFUN(X,TRANS) to apply the inverse of the compressed skeleton
% submatrix. More specifically, denote by A the original matrix, and let ... |
ddf5c90a724b40f9080fe9594428458329d65279cf4114a09fe329d57d1ce244 | MATLAB | 2,147 | 54 | % From Yair Altman's findjobj utility (www.undocumentedmatlab.com)
function [jRootPane,contentSize] = getRootPanel(hFig)
try
contentSize = [0,0]; % initialize
jRootPane = hFig;
figName = get(hFig,'name');
if strcmpi(get(hFig,'number'),'on')
figName = regexprep(['Figure '... |
e080e2a7abe1aee793f77b0459213c1f400e150aaf391e87258c9cbffa455290 | MATLAB | 2,147 | 82 | function T = dirfiltersparse( I, C, Nx, Ny, Nz, F )
% DIRFILTERSPARSE Applies a directional filter over a scalar field in sparse mode
% INPUT:
% I - Intput tomogram (scalar field where foreground is bright)
% C - Coordinates of the sparse mask
% Ni - Corrdinate i of the scalar field (normalized) f... |
053d233addb18ffa188d0db8b515821074b37aac6ed437c44ab1c0620a66253a | MATLAB | 2,148 | 157 | function [d_shap,f_shap,d_shap_se,f_shap_se] = get_neural_slope(rat_name)
rat=rat_name(2:end);
rat=str2double(rat);
res=2;
dur=66;
load data/combined_kernels.mat
%%% (generated by combine_neural_kernels.m)
data=data_all_trials;
data.centers=-1:res/100:2;
vecx=0.01:0.02:dur/100;
data.vecx=vecx;
load data/r... |
51e3bd8191d460da99c34876059e290c2b36c540832e15094dec9c0c997e04e2 | MATLAB | 2,148 | 76 | function [stamps, f] = fmf_read_timestamps( filename, f_start, nframes, incr, show_progress )
% [stamps, f] = fmf_read_timestamps( filename, f_start, nframes, incr, show_progress )
%
% reads FlyMovieFormat data from FILENAME
%
% starts with frame F_START (1-based) [defaults to 1], reads NFRAMES [defaults to inf]
... |
5969f21f9c80b50e930b5bba537ae6fee088586512c73ec8dc9ca9ed11f78d3c | MATLAB | 2,148 | 48 | function [dat] = ft_trialfun_example2(cfg)
% FT_TRIALFUN_EXAMPLE2 is an example trial function that detects muscle
% activity in an EMG channel and defines variable length trials from the
% EMG onset up to the EMG offset.
%
% You would use this function as follows
% cfg = [];
% cfg.dataset = string... |
11d710ae2df3690a71c9fea47c54859452fe6b988a82f323b6c09333ba1a7289 | MATLAB | 2,149 | 66 | function [mu, assign, errHist] = kmeansFit(X, K, varargin)
% Hard cluster data using kmeans.
%
%% Inputs
%
% X ... X(i, :) the ith data case
% K ... the number of clusters to fit
%% Outputs
%
% mu ... mu(:, k) is the k'th center
% assign ... assign(i) is the cluster to which data p... |
17725a8c6528f999427615411f1f7fefc4355ba3ad7cf9258b117bc14678afca | MATLAB | 2,149 | 65 | function [vol] = read_ctf_hdm(filename)
% READ_CTF_HDM reads the head volume conductor model from a *.hdm file
%
% vol = read_ctf_hdm(filename)
% Copyright (C) 2003, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free... |
38b45a22e3c08d2f3970536dbf7fc49d20c7ba9384240f9c45d72f56b90f22ed | MATLAB | 2,149 | 74 | %% Compute 3d tracking
addpath netlab
expdir = '/home/mayank/work/PoseEstimationData/Stephen/fly219/fly219_trial1/';
savefile = fullfile(expdir,'Out_3d.mat');
fvidfile = fullfile(expdir,'C002H001S0001','C002H001S0001_c.avi');
svidfile = fullfile(expdir,'C001H001S0001','C001H001S0001_c.avi');
fmat = fullfile(expdir,'C0... |
8515617a5059955db9a80491a377dd96d5116766cdee352287d05573e4facce4 | MATLAB | 2,149 | 77 | function [posterior, out] = demo_stochasticModel ()
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [posterior, out] = demo_stochasticModel ()
% Demo for stochastic system with binomial output
%
% This demo simulates and inverts a model of a stochastic dynamical system,
% which is obser... |
c6ad95326351e7b5d759d33c3b6dca1b13505399641d0c68c0a5111d2a332c1a | MATLAB | 2,149 | 56 | function h_resetZPos3(handles, syncFlag)
global h_img3;
if ~exist('syncFlag','var')||isempty(syncFlag)
syncFlag = 1;
end
[currentInd, handles, currentStruct, currentStructName] = h_getCurrendInd3(handles);
zPos = get(handles.zPosSlider,'Value');
[xlim,ylim,zlim] = h_getLimits3(handles);
zSiz = zlim(... |
1894b6e064182658ccaba28e936352d0e80092e1dc4761183c2e3e0c2b73dc55 | MATLAB | 2,150 | 51 | function [rf0,gz,gzReph,params] = create_rf_gz_pulseq(params,tr_tmp)
% RF pulses and FA depending on sequence type (VASO,ABC) and field strength,
% Values for: Duration, apodization and timeBwProduct, can be modified to
% avoid SAR or other constraints
lims = params.gen.lims;
for i=1:params.gen.n(... |
7f147e05a7ee5805dd93de218bc2f7e3d92daf00898a3209092aed13fbb2ecad | MATLAB | 2,150 | 58 | function [fx] = f_BSL(x,theta,u,in)
% evolution function for a Bayesian sequence-learner (BSL)
% function [fx] = f_BSL(x,theta,u,in)
% BSL is simply tracking the log-odds of P(y_t=1|y_{t-1}), where y is a
% binary outcome. This variable is updated according to a Laplace-Kalman
% filter, yielding 2 sufficient statistics... |
ffcc06f6398607196f84ee0c0144819d771b63213a2bb3502b3e030cb2792344 | MATLAB | 2,150 | 1 | % TSREMOVESESSION Removes a single session from the Experiment Structure
% TSremovesession ( SUBJECT, SESSION )
% Subject - A subject ID number from the Experiment.Subjects array
% Session - An integer index to Experiment.Subject(subject).Session,
% or a date string.
%
% This function wi... |
1d747dc3a94dc5e15984d9b5527545b235c2cbf12e4bbb875c958e8d5b43eb77 | MATLAB | 2,151 | 93 | % Illustrate a HMM with gaussian emissions in 2d
% (Lillypad model)
%close all
clear all
setSeed(0);
K = 3; D = 2;
%model = mkRndGaussHmm(K,D);
mu = 10*[
-1,0;
1,0;
0,1
];
Sigma = zeros(2,2,3);
sf = 10;
Sigma(:,:,1) = sf*[1,1/2;1/2,1];
Sigma(:,:,2) = sf*[1,-1/2;-1/2,1];
Sigma(:,:,3) = sf*[3,0;0,1/2];
A = [0.8 0.1 0... |
9cf3d3d4686b496babb6f8ea4172613e4692bb74b6161c68d22b0c4b3507c685 | MATLAB | 2,151 | 78 | %% Variable Selection Demo
% PMTKslow
%%
% This file is from pmtk3.googlecode.com
function varSelDemo()
methods = {...
@(X,y) larsSelectSubsetCV(X,y, 'nfolds', 5), ...
@(X,y) fbmpCore(X,y,'maxNumSearches',5), ...
@(X,y) ARDwipf(X,y,'maxIter',50)};
methodNames = {'lars', 'bmp', 'ard'};
nmethods = length(... |
d1ae7e37b99a190d59bd7193a66a927ec057d2b91a69b4acc1a417fd802a80eb | MATLAB | 2,151 | 76 | %% MCMC Sampling from a 2d Gaussians
% Compare MH and Gibbs
% We use a N(0, sigma*eye(2)) proposal and see the effect of changing sigma
%%
% This file is from pmtk3.googlecode.com
setSeed(0);
Sigma = [1 -0.5; -0.5 1];
mu = [1; 1];
N = 500;
model.mu = mu;
model.Sigma = Sigma;
marg1.mu = mu(1);
marg1.Sigma = Sigm... |
48cd27af0ac721c3f4e3fb12971a7cfe4acfbfdc230e70ad9ecb82dd8ffba61a | MATLAB | 2,152 | 42 | function q = CBIG_EM_doc_inference_wc(w, paradigm, params)
% q = CBIG_EM_doc_inference_wc(w, paradigm, params)
%
% Update auxillary parameter q in the E-step on each experiment (document). Note that
% compared to CBIG_EM_doc_inference.m, this function takes in an argument w of a different structure
% FORMAT q = CBIG... |
c50b1ef822744476f74208d490f7aaad3201e53523a797bc6e1dc9dcb4317d84 | MATLAB | 2,152 | 66 | function [ha, pos] = tight_subplot(Nh, Nw, gap, marg_h, marg_w)
% FROM FILEEXCHANGE - NEED TO BE SURE TO CREDIT WHEN CODE GOES PUBLIC
% tight_subplot creates "subplot" axes with adjustable gaps and margins
%
% [ha, pos] = tight_subplot(Nh, Nw, gap, marg_h, marg_w)
%
% in: Nh number of axes in hight (verti... |
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