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# to you under the Apache License, Version 2.0 (the
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#
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m_cvlm = function(Matrix[Double] X, Matrix[Double] y, Integer k, Integer icpt = 0, Double reg = 1e-7) return (Matrix[Double] y_predict, Matrix[Double] allbeta) 
{
  M = nrow(X);
  lim = as.integer(M/k);
  y_predict = y;
  allbeta = matrix(0, rows=k, cols=ncol(X));

	for (i in 1:k)
  {
    testS = ifelse(i==1, 1, ((i-1) * lim)+1)
    testE = i * lim;
    testSet = X[testS:testE,];

    if (i == 1) {
      trainSet = X[testE+1:M,];
      trainRes = y[testE+1:M,];
    }
    else {
      trainSet = rbind(X[1:testS-1,], X[testE+1:M,]);
      trainRes = rbind(y[1:testS-1,], y[testE+1:M,]);
    }

    beta = lm(X=trainSet, y=trainRes, icpt=icpt, reg=reg);
    pred = lmpredict(X=testSet, w=beta, icpt=icpt);
    y_predict[testS:testE,] = pred;
    allbeta[i,] = t(beta);
  }
}