/usr/share/octave/packages/nnet-0.1.13/dividerand.m is in octave-nnet 0.1.13-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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##
##
## 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, or (at your option)
## any later version.
##
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; see the file COPYING. If not, see
## <http://www.gnu.org/licenses/>.
## -*- texinfo -*-
## @deftypefn {Function File} [@var{trainVectors},@var{validationVectors},@var{testVectors},@var{indexOfTrain},@var{indexOfValidation},@var{indexOfTest}] = dividerand (@var{allCases},@var{trainRatio},@var{valRatio},@var{testRatio})
## Divide the vectors in training, validation and test group according to
## the informed ratios
##
##
## @example
##
## [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio
## n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
##
## The ratios are normalized. This way:
##
## dividerand(xx,1,2,3) == dividerand(xx,10,20,30)
##
## @end example
##
## @end deftypefn
function [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidation,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
#
# Divide the vectors in training, validation and test group according to
# the informed ratios
#
# [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio
# n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
#
# The ratios are normalized. This way:
#
# dividerand(xx,1,2,3) == dividerand(xx,10,20,30)
#
## Normalize ratios
total = trainRatio + valRatio + testRatio;
#trainRatio = trainRatio / total; not used
validationRatio = valRatio / total;
testRatio = testRatio / total;
## Calculate the number of cases for each type
numerOfCases = size(allCases,2);
numberOfValidation = floor(validationRatio*numerOfCases);
numberOfTest = floor(testRatio*numerOfCases);
numberOfTrain = numerOfCases - numberOfValidation - numberOfTest;
## Find their indexes
indexOfAll=randperm(numerOfCases);
indexOfValidation=sort(indexOfAll(1:numberOfValidation));
indexOfTest=sort(indexOfAll((1:numberOfTest)+numberOfValidation));
indexOfTrain=sort(indexOfAll((1:numberOfTrain)+numberOfTest+numberOfValidation));
## Return vectors
trainVectors = allCases(:,indexOfTrain);
testVectors = allCases(:,indexOfTest);
validationVectors = allCases(:,indexOfValidation);
endfunction
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