# CROSS VALIDATION FOR CHOOSING SIGMA AND LAMBDA
using Random

function CV(X, n)
    # Random permutation for shuffle
    rng = MersenneTwister(1234);
    rndInd = randperm(n)
    mid = floor(Int, n/2)

    # Random shuffle, using the same permutation to conserve correct labels
    Xshuffle, yshuffle = X[rndInd], y[rndInd]

    # Dividing into train and validation set
    Xtrain, ytrain = Xshuffle[1:mid,:], yshuffle[1:mid]
    Xvali, yvali = Xshuffle[mid+1:end,:], yshuffle[mid+1:end]

    # Iterating through different combinations of sigma and lambda
    bestError = Inf
    bestLambda = 0
    bestSigma = 0

    for lambda in 1:100
        for sigma in 1:100
            model = leastSquaresRBFL2(Xtrain,ytrain,lambda,sigma)
            t = size(Xvali,1)
            yhat = model.predict(Xvali)
            testError = sum((yhat - yvali).^2)/t
            if testError < bestError
                bestError = testError
                bestLambda = lambda
                bestSigma = sigma
            end
        end
    end
    return (bestError, bestSigma, bestLambda)
end

show(CV(X,n))