decision_tree_evaluator.hpp
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#pragma once
#include <pcl/common/common.h>
#include <pcl/ml/dt/decision_tree.h>
#include <pcl/ml/feature_handler.h>
#include <pcl/ml/stats_estimator.h>
#include <vector>
namespace pcl {
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
DecisionTreeEvaluator()
{}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
~DecisionTreeEvaluator()
{}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
evaluate(pcl::DecisionTree<NodeType>& tree,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
std::vector<ExampleIndex>& examples,
std::vector<LabelType>& label_data)
{
const std::size_t num_of_examples = examples.size();
label_data.resize(num_of_examples);
for (int example_index = 0; example_index < num_of_examples; ++example_index) {
NodeType* node = &(tree.getRoot());
while (node->sub_nodes.size() != 0) {
float feature_result = 0.0f;
unsigned char flag = 0;
unsigned char branch_index = 0;
feature_handler.evaluateFeature(
node->feature, data_set, examples[example_index], feature_result, flag);
stats_estimator.computeBranchIndex(
feature_result, flag, node->threshold, branch_index);
node = &(node->sub_nodes[branch_index]);
}
label_data[example_index] = stats_estimator.getLabelOfNode(*node);
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
evaluateAndAdd(
pcl::DecisionTree<NodeType>& tree,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
std::vector<ExampleIndex>& examples,
std::vector<LabelType>& label_data)
{
const std::size_t num_of_examples = examples.size();
for (int example_index = 0; example_index < num_of_examples; ++example_index) {
NodeType* node = &(tree.getRoot());
while (node->sub_nodes.size() != 0) {
float feature_result = 0.0f;
unsigned char flag = 0;
unsigned char branch_index = 0;
feature_handler.evaluateFeature(
node->feature, data_set, examples[example_index], feature_result, flag);
stats_estimator.computeBranchIndex(
feature_result, flag, node->threshold, branch_index);
node = &(node->sub_nodes[branch_index]);
}
label_data[example_index] += stats_estimator.getLabelOfNode(*node);
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
evaluate(pcl::DecisionTree<NodeType>& tree,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
ExampleIndex example,
NodeType& leave)
{
NodeType* node = &(tree.getRoot());
while (!node->sub_nodes.empty()) {
float feature_result = 0.0f;
unsigned char flag = 0;
unsigned char branch_index = 0;
feature_handler.evaluateFeature(
node->feature, data_set, example, feature_result, flag);
stats_estimator.computeBranchIndex(
feature_result, flag, node->threshold, branch_index);
node = &(node->sub_nodes[branch_index]);
}
leave = *node;
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
getNodes(pcl::DecisionTree<NodeType>& tree,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
std::vector<ExampleIndex>& examples,
std::vector<NodeType*>& nodes)
{
const std::size_t num_of_examples = examples.size();
for (int example_index = 0; example_index < num_of_examples; ++example_index) {
NodeType* node = &(tree.getRoot());
while (node->sub_nodes.size() != 0) {
float feature_result = 0.0f;
unsigned char flag = 0;
unsigned char branch_index = 0;
feature_handler.evaluateFeature(
node->feature, data_set, examples[example_index], feature_result, flag);
stats_estimator.computeBranchIndex(
feature_result, node->threshold, flag, branch_index);
node = &(node->subNodes[branch_index]);
}
nodes.push_back(node);
}
}
} // namespace pcl
pcl::DecisionTreeEvaluator::~DecisionTreeEvaluator
virtual ~DecisionTreeEvaluator()
Destructor.
Definition: decision_tree_evaluator.hpp:64
pcl::DecisionTreeEvaluator::getNodes
void getNodes(pcl::DecisionTree< NodeType > &tree, pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler, pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< NodeType * > &nodes)
Evaluates the specified examples using the supplied tree.
Definition: decision_tree_evaluator.hpp:181
pcl::DecisionTreeEvaluator::DecisionTreeEvaluator
DecisionTreeEvaluator()
Constructor.
Definition: decision_tree_evaluator.hpp:55
pcl::DecisionTreeEvaluator::evaluateAndAdd
void evaluateAndAdd(pcl::DecisionTree< NodeType > &tree, pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler, pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data)
Evaluates the specified examples using the supplied tree and adds the results to the supplied results...
Definition: decision_tree_evaluator.hpp:111
pcl::DecisionTreeEvaluator::evaluate
void evaluate(pcl::DecisionTree< NodeType > &tree, pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler, pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data)
Evaluates the specified examples using the supplied tree.
Definition: decision_tree_evaluator.hpp:74
pcl::DecisionTree
Class representing a decision tree.
Definition: decision_tree.h:49
pcl::DecisionTree::getRoot
NodeType & getRoot()
Returns the root node of the tree.
Definition: decision_tree.h:69
pcl::FeatureHandler
Utility class interface which is used for creating and evaluating features.
Definition: feature_handler.h:49
pcl::FeatureHandler::evaluateFeature
virtual void evaluateFeature(const FeatureType &feature, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< float > &results, std::vector< unsigned char > &flags) const =0
Evaluates a feature on the specified data.
pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex >
pcl::StatsEstimator::getLabelOfNode
virtual LabelDataType getLabelOfNode(NodeType &node) const =0
Returns the label of the specified node.
pcl::StatsEstimator::computeBranchIndex
virtual void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &branch_index) const =0
Computes the branch indices obtained by the specified threshold on the supplied feature evaluation re...
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl
Definition: convolution.h:46