mad_gain.hpp

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#ifndef MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP
#define MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP

#include <mlpack/prereqs.hpp>
#include "utils.hpp"

namespace mlpack {
namespace tree {

class MADGain
{
 public:
  template<bool UseWeights, typename VecType, typename WeightVecType>
  static double Evaluate(const VecType& values,
                         const WeightVecType& weights,
                         const size_t begin,
                         const size_t end)
  {
    double mad = 0.0;

    if (UseWeights)
    {
      double accWeights = 0.0;
      double weightedMean = 0.0;

      WeightedSum(values, weights, begin, end, accWeights, weightedMean);

      // Catch edge case: if there are no weights, the impurity is zero.
      if (accWeights == 0.0)
        return 0.0;

      weightedMean /= accWeights;

      for (size_t i = begin; i < end; ++i)
      {
        mad += weights[i] * (std::abs(values[i] - weightedMean));
      }
      mad /= accWeights;
      }
    else
    {
      double mean = 0.0;
      Sum(values, begin, end, mean);
      mean /= (double) (end - begin);

      mad = arma::accu(arma::abs(values.subvec(begin, end - 1) - mean));
      mad /= (double) (end - begin);
    }

    return -mad;
  }

  template<bool UseWeights, typename VecType, typename WeightVecType>
  static double Evaluate(const VecType& values,
                         const WeightVecType& weights)
  {
    // Corner case: if there are no elements, the impurity is zero.
    if (values.n_elem == 0)
      return 0.0;

    return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
  }

  template<bool UseWeights, typename ResponsesType, typename WeightsType>
  double OutputLeafValue(const ResponsesType& responses,
                         const WeightsType& weights)
  {
    if (UseWeights)
    {
      double accWeights, weightedSum;
      WeightedSum(responses, weights, 0, responses.n_elem, accWeights,
          weightedSum);
      return weightedSum / accWeights;
    }
    else
    {
      double sum;
      Sum(responses, 0, responses.n_elem, sum);
      return sum / responses.n_elem;
    }
  }
};

} // namespace tree
} // namespace mlpack

#endif