/*

 * The file is based on the Random-Forest-Matlab at

 * https://github.com/karpathy/Random-Forest-Matlab available under BSD license

 * Copyright (c) <2014>, <Andrej Karpathy>

 * All rights reserved.

 * Redistribution and use in source and binary forms, with or without

 * modification, are permitted provided that the following conditions are met:

 * 1. Redistributions of source code must retain the above copyright notice, this

 *  list of conditions and the following disclaimer.

 * 2. Redistributions in binary form must reproduce the above copyright notice,

 *  this list of conditions and the following disclaimer in the documentation

 *  and/or other materials provided with the distribution.

 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND

 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED

 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE

 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR

 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES

 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;

 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND

 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS

 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

 * The views and conclusions contained in the software and documentation are those

 * of the authors and should not be interpreted as representing official policies,

 * purpose.

 */

#include "RandomForestModel.h"

using namespace rfmodel;

double Model::weakTest(const DataMatrix& X) const {

  // X is NxD

  double final_yhat = 1.0;

  int N = X.num_rows;

#ifdef DEBUG_RF

  if (classifierID == 2.0)

    printf("\nTesting on weakmodel r1[%d], r2[%d], w[%lf,%lf,%lf]\n", r1, r2, w[0], w[1],

           w[2]);

#endif

// Retaining pseudo code for decision stump classifier, distance based learner

// as placeholders to be potentially implemented later

#if 0

  if (classifierID == 1) {

    // Decision stump classifier

      yhat = (double)X(:, model->r) < model->t;

      int* select_cols;

      select_cols = new int[1];

      select_cols[0] = r;

      DataMatrix *yhat_op = MatrixOp::_subset_cols(X, num_rows, num_cols, select_cols,

      1);

      yhat = yhat_op->data;

      final_yhat = MatrixOp::findIndicesLT(yhat,N, t);

    } else

#endif

  if (classifierID == 2) {

    // 2-D linear classifier stump

    int select_cols[2];

    select_cols[0] = r1 - 1;

    select_cols[1] = r2 - 1;

    DataMatrix subset_arr(X.num_rows, 2);

    X.subset_cols(select_cols, 2, subset_arr);

    const DataMatrix ones_arr(N, 1, true);

    DataMatrix combined_arr(subset_arr.num_rows, subset_arr.num_cols + ones_arr.num_rows);

    combined_arr.combine((const DataMatrix)subset_arr, ones_arr);

    const DataMatrix w_arr(w, 3, 1);

    DataMatrix mm(combined_arr.num_rows, w_arr.num_cols);

    mm.matrix_multiply(combined_arr, w_arr);

    double zero = 0.0;

    DataMatrix yhat(mm.num_rows, mm.num_cols);

    mm.findIndicesLT(zero, yhat);

    if (yhat.data.size() == 0)

      final_yhat = 0.0;

    else

      final_yhat = yhat.data[0] + 1;

  }

  return final_yhat;

}