trainingPartitions.m

function varargout = trainingPartitions(numObservations,splits)
%TRAININGPARTITONS Random indices for splitting training data
%   [idx1,...,idxN] = trainingPartitions(numObservations,splits) returns
%   random vectors of indices to help split a data set with the specified
%   number of observations, where SPLITS is a vector of length N of
%   partition sizes that sum to one.
%
%   Example: Get indices for 50%-50% training-test split of 500
%   observations.
%   [idxTrain,idxTest] = trainingPartitions(500,[0.5 0.5])
%
%   Example: Get indices for 80%-10%-10% training, validation, test split
%   of 500 observations. 
%   [idxTrain,idxValidation,idxTest] = trainingPartitions(500,[0.8 0.1 0.1])

arguments
    numObservations (1,1) {mustBePositive}
    splits {mustBeVector,mustBeInRange(splits,0,1,"exclusive"),mustSumToOne}
end

numPartitions = numel(splits);
varargout = cell(1,numPartitions);

idx = randperm(numObservations)
%idx = 1:numObservations
%a = length(idx)


idxEnd = 0;

for i = 1:numPartitions-1
    idxStart = idxEnd + 1;
    idxEnd = idxStart + floor(splits(i)*numObservations) - 1;

    varargout{i} = idx(idxStart:idxEnd);
end

% Last partition.
varargout{end} = idx(idxEnd+1:end);

end

function mustSumToOne(v)
% Validate that value sums to one.

if sum(v,"all") ~= 1
    error("Value must sum to one.")
end

end