40 lines
1.3 KiB
Matlab
40 lines
1.3 KiB
Matlab
function [trainX, valX, testX, trainY, valY, testY] = split_data(X, Y, ratios)
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% SPLIT_DATA Split dataset into train/validation/test sets (stratified)
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%
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% [trainX, valX, testX, trainY, valY, testY] = split(X, Y, ratios)
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%
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% ratios : [trainRatio, valRatio, testRatio] (e.g. [0.6 0.2 0.2])
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%
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% Stratified split ensures class proportions remain consistent.
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if nargin < 3
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ratios = [0.6 0.2 0.2];
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end
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assert(abs(sum(ratios) - 1) < 1e-6, 'Ratios must sum to 1.');
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n = size(X,1);
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classes = unique(Y);
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idxTrain = []; idxVal = []; idxTest = [];
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for c = classes'
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idx = find(Y == c);
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idx = idx(randperm(length(idx))); % randomize within class
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nTrain = round(ratios(1)*length(idx));
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nVal = round(ratios(2)*length(idx));
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idxTrain = [idxTrain; idx(1:nTrain)];
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idxVal = [idxVal; idx(nTrain+1:nTrain+nVal)];
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idxTest = [idxTest; idx(nTrain+nVal+1:end)];
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end
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% Shuffle within each subset to mix classes
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idxTrain = idxTrain(randperm(length(idxTrain)));
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idxVal = idxVal(randperm(length(idxVal)));
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idxTest = idxTest(randperm(length(idxTest)));
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trainX = X(idxTrain,:); trainY = Y(idxTrain);
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valX = X(idxVal,:); valY = Y(idxVal);
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testX = X(idxTest,:); testY = Y(idxTest);
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end
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