270 lines
8.4 KiB
C
270 lines
8.4 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of Willow Garage, Inc. nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*/
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#pragma once
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#include <pcl/common/common.h>
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#include <pcl/ml/dt/decision_tree.h>
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#include <pcl/ml/dt/decision_tree_data_provider.h>
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#include <pcl/ml/feature_handler.h>
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#include <pcl/ml/stats_estimator.h>
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#include <vector>
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namespace pcl {
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/** Trainer for decision trees. */
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template <class FeatureType,
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class DataSet,
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class LabelType,
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class ExampleIndex,
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class NodeType>
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class PCL_EXPORTS DecisionTreeTrainer {
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public:
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/** Constructor. */
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DecisionTreeTrainer();
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/** Destructor. */
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virtual ~DecisionTreeTrainer();
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/** Sets the feature handler used to create and evaluate features.
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*
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* \param[in] feature_handler the feature handler
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*/
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inline void
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setFeatureHandler(
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pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler)
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{
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feature_handler_ = &feature_handler;
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}
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/** Sets the object for estimating the statistics for tree nodes.
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*
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* \param[in] stats_estimator the statistics estimator
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*/
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inline void
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setStatsEstimator(
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pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>& stats_estimator)
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{
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stats_estimator_ = &stats_estimator;
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}
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/** Sets the maximum depth of the learned tree.
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*
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* \param[in] max_tree_depth maximum depth of the learned tree
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*/
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inline void
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setMaxTreeDepth(const std::size_t max_tree_depth)
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{
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max_tree_depth_ = max_tree_depth;
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}
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/** Sets the number of features used to find optimal decision features.
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*
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* \param[in] num_of_features the number of features
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*/
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inline void
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setNumOfFeatures(const std::size_t num_of_features)
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{
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num_of_features_ = num_of_features;
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}
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/** Sets the number of thresholds tested for finding the optimal decision
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* threshold on the feature responses.
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*
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* \param[in] num_of_threshold the number of thresholds
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*/
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inline void
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setNumOfThresholds(const std::size_t num_of_threshold)
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{
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num_of_thresholds_ = num_of_threshold;
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}
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/** Sets the input data set used for training.
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*
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* \param[in] data_set the data set used for training
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*/
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inline void
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setTrainingDataSet(DataSet& data_set)
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{
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data_set_ = data_set;
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}
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/** Example indices that specify the data used for training.
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*
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* \param[in] examples the examples
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*/
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inline void
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setExamples(std::vector<ExampleIndex>& examples)
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{
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examples_ = examples;
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}
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/** Sets the label data corresponding to the example data.
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*
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* \param[in] label_data the label data
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*/
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inline void
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setLabelData(std::vector<LabelType>& label_data)
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{
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label_data_ = label_data;
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}
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/** Sets the minimum number of examples to continue growing a tree.
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*
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* \param[in] n number of examples
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*/
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inline void
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setMinExamplesForSplit(std::size_t n)
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{
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min_examples_for_split_ = n;
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}
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/** Specify the thresholds to be used when evaluating features.
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*
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* \param[in] thres the threshold values
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*/
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void
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setThresholds(std::vector<float>& thres)
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{
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thresholds_ = thres;
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}
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/** Specify the data provider.
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*
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* \param[in] dtdp the data provider that should implement getDatasetAndLabels()
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* function
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*/
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void
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setDecisionTreeDataProvider(
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typename pcl::DecisionTreeTrainerDataProvider<FeatureType,
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DataSet,
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LabelType,
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ExampleIndex,
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NodeType>::Ptr& dtdp)
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{
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decision_tree_trainer_data_provider_ = dtdp;
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}
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/** Specify if the features are randomly generated at each split node.
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*
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* \param[in] b do it or not
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*/
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void
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setRandomFeaturesAtSplitNode(bool b)
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{
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random_features_at_split_node_ = b;
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}
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/** Trains a decision tree using the set training data and settings.
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*
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* \param[out] tree destination for the trained tree
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*/
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void
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train(DecisionTree<NodeType>& tree);
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protected:
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/** Trains a decision tree node from the specified features, label data, and
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* examples.
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*
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* \param[in] features the feature pool used for training
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* \param[in] examples the examples used for training
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* \param[in] label_data the label data corresponding to the examples
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* \param[in] max_depth the maximum depth of the remaining tree
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* \param[out] node the resulting node
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*/
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void
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trainDecisionTreeNode(std::vector<FeatureType>& features,
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std::vector<ExampleIndex>& examples,
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std::vector<LabelType>& label_data,
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std::size_t max_depth,
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NodeType& node);
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/** Creates uniformely distrebuted thresholds over the range of the supplied
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* values.
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*
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* \param[in] num_of_thresholds the number of thresholds to create
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* \param[in] values the values for estimating the expected value range
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* \param[out] thresholds the resulting thresholds
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*/
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static void
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createThresholdsUniform(const std::size_t num_of_thresholds,
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std::vector<float>& values,
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std::vector<float>& thresholds);
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private:
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/** Maximum depth of the learned tree. */
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std::size_t max_tree_depth_;
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/** Number of features used to find optimal decision features. */
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std::size_t num_of_features_;
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/** Number of thresholds. */
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std::size_t num_of_thresholds_;
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/** FeatureHandler instance, responsible for creating and evaluating features. */
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pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>* feature_handler_;
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/** StatsEstimator instance, responsible for gathering stats about a node. */
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pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>* stats_estimator_;
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/** The training data set. */
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DataSet data_set_;
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/** The label data. */
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std::vector<LabelType> label_data_;
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/** The example data. */
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std::vector<ExampleIndex> examples_;
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/** Minimum number of examples to split a node. */
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std::size_t min_examples_for_split_;
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/** Thresholds to be used instead of generating uniform distributed thresholds. */
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std::vector<float> thresholds_;
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/** The data provider which is called before training a specific tree, if pointer is
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* NULL, then data_set_ is used. */
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typename pcl::DecisionTreeTrainerDataProvider<FeatureType,
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DataSet,
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LabelType,
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ExampleIndex,
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NodeType>::Ptr
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decision_tree_trainer_data_provider_;
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/** If true, random features are generated at each node, otherwise, at start of
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* training the tree */
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bool random_features_at_split_node_;
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};
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} // namespace pcl
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#include <pcl/ml/impl/dt/decision_tree_trainer.hpp>
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