//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** @author  John Miller
 *  @version 1.6
 *  @date    Sat Jun 15 14:42:41 EDT 2019
 *  @see     LICENSE (MIT style license file).
 */

package scalation.analytics.par

import scalation.linalgebra.par.{MatrixD, VectorD}

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/** The `PredictorMat` companion object provides a meythod for splitting
 *  a combined data matrix in predictor matrix and a response vector.
 */
object PredictorMatO
{
    //:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    /** Pull out the designated response column from the combined matrix.
     *  When 'col' is negative or the last column, slice out the last column.
     *  @param xy   the combined data and response matrix
     *  @param col  the designated response column to be pulled out
     */
    def pullResponse (xy: MatrixD, col: Int = -1): (MatrixD, VectorD) =
    {
        if (col < 0 || col == xy.dim2-1) (xy.sliceCol (0, xy.dim2-1), xy.col (xy.dim2-1))
        else                             (xy.sliceEx (xy.dim1, col), xy.col (col))
    } // pullResponse

} // PredictorMatO