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);
}
}
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;
}
void
pcl::FernEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::evaluate (
pcl::Fern<FeatureType, NodeType> & fern,
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 ();
const size_t num_of_branches = stats_estimator.getNumOfBranches ();
const size_t num_of_features = fern.getNumOfFeatures ();
label_data.resize (num_of_examples);
std::vector<std::vector<float> > results (num_of_features);
std::vector<std::vector<unsigned char> > flags (num_of_features);
std::vector<std::vector<unsigned char> > branch_indices (num_of_features);
for (size_t feature_index = 0; feature_index < num_of_features; ++feature_index)
{
results[feature_index].reserve (num_of_examples);
flags[feature_index].reserve (num_of_examples);
branch_indices[feature_index].reserve (num_of_examples);
feature_handler.evaluateFeature (fern.accessFeature (feature_index), data_set, examples, results[feature_index], flags[feature_index]);
stats_estimator.computeBranchIndices (results[feature_index], flags[feature_index], fern.accessThreshold (feature_index), branch_indices[feature_index]);
}
for (size_t example_index = 0; example_index < num_of_examples; ++example_index)
{
size_t node_index = 0;
for (size_t feature_index = 0; feature_index < num_of_features; ++feature_index)
{
node_index *= num_of_branches;
node_index += branch_indices[feature_index][example_index];
}
label_data[example_index] = stats_estimator.getLabelOfNode (fern[node_index]);
}
}
