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#ifndef PCL_ML_DT_DECISION_TREE_EVALUATOR_HPP_
#define PCL_ML_DT_DECISION_TREE_EVALUATOR_HPP_

#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>


//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
pcl::DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::DecisionTreeEvaluator ()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
pcl::DecisionTreeEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::~DecisionTreeEvaluator ()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
void
pcl::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 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
pcl::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 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
pcl::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.size () != 0)
    {
      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
pcl::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 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);
  }
}

#endif
