Implemented ctors and dtor
This commit is contained in:
Rishabh Garg
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/**
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* @file methods/decision_tree/decision_tree_regressor_impl.hpp
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* @author Rishabh Garg
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*
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* Implementation of decision tree regressor class.
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*
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* mlpack is free software; you may redistribute it and/or modify it under the
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* terms of the 3-clause BSD license. You should have received a copy of the
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* 3-clause BSD license along with mlpack. If not, see
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* http://www.opensource.org/licenses/BSD-3-Clause for more information.
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*/
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#ifndef MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
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#define MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
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#include "decision_tree_regressor.hpp"
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namespace mlpack {
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namespace tree {
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//! Construct, don't train.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor() :
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splitDimension(0),
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dimensionTypeOrMajorityClass(0),
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classProbabilities(numClasses)
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{
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// Initialize utility vector.
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classProbabilities.fill(1.0 / (double) numClasses);
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}
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//! Construct and train without weight.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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MatType data,
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const data::DatasetInfo& datasetInfo,
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LabelsType labels,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const size_t maximumDepth,
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DimensionSelectionType dimensionSelector)
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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// Set the correct dimensionality for the dimension selector.
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dimensionSelector.Dimensions() = tmpData.n_rows;
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// Pass off work to the Train() method.
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arma::rowvec weights; // Fake weights, not used.
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Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
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weights, minimumLeafSize, minimumGainSplit, maximumDepth,
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dimensionSelector);
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}
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//! Construct and train without weight on numeric data.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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MatType data,
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LabelsType labels,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const size_t maximumDepth,
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DimensionSelectionType dimensionSelector)
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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// Set the correct dimensionality for the dimension selector.
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dimensionSelector.Dimensions() = tmpData.n_rows;
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// Pass off work to the Train() method.
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arma::rowvec weights; // Fake weights, not used.
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Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, weights,
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minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
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}
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//! Construct and train with weights.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType, typename WeightsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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MatType data,
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const data::DatasetInfo& datasetInfo,
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LabelsType labels,
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WeightsType weights,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const size_t maximumDepth,
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DimensionSelectionType dimensionSelector,
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const std::enable_if_t<arma::is_arma_type<
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typename std::remove_reference<WeightsType>::type>::value>*)
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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using TrueWeightsType = typename std::decay<WeightsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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TrueWeightsType tmpWeights(std::move(weights));
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// Set the correct dimensionality for the dimension selector.
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dimensionSelector.Dimensions() = tmpData.n_rows;
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// Pass off work to the weighted Train() method.
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Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
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tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
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dimensionSelector);
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}
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//! Construct and train on numeric data with weights.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType, typename WeightsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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MatType data,
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LabelsType labels,
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WeightsType weights,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const size_t maximumDepth,
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DimensionSelectionType dimensionSelector,
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const std::enable_if_t<
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arma::is_arma_type<
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typename std::remove_reference<
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WeightsType>::type>::value>*)
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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using TrueWeightsType = typename std::decay<WeightsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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TrueWeightsType tmpWeights(std::move(weights));
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// Set the correct dimensionality for the dimension selector.
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dimensionSelector.Dimensions() = tmpData.n_rows;
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// Pass off work to the weighted Train() method.
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Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
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minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
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}
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//! Take ownership of another tree and train with weights.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType, typename WeightsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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const DecisionTreeRegressor& other,
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MatType data,
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const data::DatasetInfo& datasetInfo,
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LabelsType labels,
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const size_t numClasses,
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WeightsType weights,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const std::enable_if_t<arma::is_arma_type<
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typename std::remove_reference<WeightsType>::type>::value>*):
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NumericAuxiliarySplitInfo(other),
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CategoricalAuxiliarySplitInfo(other)
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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using TrueWeightsType = typename std::decay<WeightsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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TrueWeightsType tmpWeights(std::move(weights));
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// Pass off work to the weighted Train() method.
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Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
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tmpWeights, minimumLeafSize, minimumGainSplit);
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}
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//! Take ownership of another tree and train with weights.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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template<typename MatType, typename LabelsType, typename WeightsType>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::DecisionTreeRegressor(
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const DecisionTreeRegressor& other,
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MatType data,
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LabelsType labels,
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WeightsType weights,
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const size_t minimumLeafSize,
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const double minimumGainSplit,
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const size_t maximumDepth,
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DimensionSelectionType dimensionSelector,
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const std::enable_if_t<arma::is_arma_type<
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typename std::remove_reference<
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WeightsType>::type>::value>*):
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NumericAuxiliarySplitInfo(other),
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CategoricalAuxiliarySplitInfo(other) // other info does need to copy
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{
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using TrueMatType = typename std::decay<MatType>::type;
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using TrueLabelsType = typename std::decay<LabelsType>::type;
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using TrueWeightsType = typename std::decay<WeightsType>::type;
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// Copy or move data.
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TrueMatType tmpData(std::move(data));
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TrueLabelsType tmpLabels(std::move(labels));
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TrueWeightsType tmpWeights(std::move(weights));
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// Set the correct dimensionality for the dimension selector.
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dimensionSelector.Dimensions() = tmpData.n_rows;
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// Pass off work to the weighted Train() method.
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Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
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minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
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}
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//! Copy another tree.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion
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>::DecisionTreeRegressor(
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const DecisionTreeRegressor& other) :
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NumericAuxiliarySplitInfo(other),
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CategoricalAuxiliarySplitInfo(other),
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splitDimension(other.splitDimension),
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dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
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classProbabilities(other.classProbabilities)
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{
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// Copy each child.
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for (size_t i = 0; i < other.children.size(); ++i)
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children.push_back(new DecisionTreeRegressor(*other.children[i]));
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}
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//! Take ownership of another tree.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion
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>::DecisionTreeRegressor(
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DecisionTreeRegressor&& other) :
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NumericAuxiliarySplitInfo(std::move(other)),
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CategoricalAuxiliarySplitInfo(std::move(other)),
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children(std::move(other.children)),
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splitDimension(other.splitDimension),
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dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
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classProbabilities(std::move(other.classProbabilities))
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{
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// Reset the other object.
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other.classProbabilities.ones(1); // One class, P(1) = 1.
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}
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//! Copy another tree.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>&
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion
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>::operator=(const DecisionTreeRegressor& other)
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{
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if (this == &other)
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return *this; // Nothing to copy.
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// Clean memory if needed.
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for (size_t i = 0; i < children.size(); ++i)
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delete children[i];
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children.clear();
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// Copy everything from the other tree.
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splitDimension = other.splitDimension;
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dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
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classProbabilities = other.classProbabilities;
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// Copy the children.
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for (size_t i = 0; i < other.children.size(); ++i)
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children.push_back(new DecisionTree(*other.children[i]));
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// Copy the auxiliary info.
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NumericAuxiliarySplitInfo::operator=(other);
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CategoricalAuxiliarySplitInfo::operator=(other);
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return *this;
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}
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//! Take ownership of another tree.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>&
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion
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>::operator=(DecisionTreeRegressor&& other)
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{
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if (this == &other)
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return *this; // Nothing to move.
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// Clean memory if needed.
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for (size_t i = 0; i < children.size(); ++i)
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delete children[i];
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children.clear();
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// Take ownership of the other tree's components.
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children = std::move(other.children);
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splitDimension = other.splitDimension;
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dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
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classProbabilities = std::move(other.classProbabilities);
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// Reset the class probabilities of the other object.
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other.classProbabilities.ones(1); // One class, P(1) = 1.
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// Take ownership of the auxiliary info.
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NumericAuxiliarySplitInfo::operator=(std::move(other));
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CategoricalAuxiliarySplitInfo::operator=(std::move(other));
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return *this;
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}
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//! Clean up memory.
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template<typename FitnessFunction,
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template<typename> class NumericSplitType,
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template<typename> class CategoricalSplitType,
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typename DimensionSelectionType,
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bool NoRecursion>
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DecisionTreeRegressor<FitnessFunction,
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NumericSplitType,
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CategoricalSplitType,
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DimensionSelectionType,
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NoRecursion>::~DecisionTreeRegressor()
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{
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for (size_t i = 0; i < children.size(); ++i)
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delete children[i];
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}
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} // namespace tree
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} // namespace mlpack
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#endif
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