fern_trainer.hpp
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#pragma once
namespace pcl {
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
FernTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::FernTrainer()
: fern_depth_(10)
, num_of_features_(1000)
, num_of_thresholds_(10)
, feature_handler_(nullptr)
, stats_estimator_(nullptr)
, data_set_()
, label_data_()
, examples_()
{}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
FernTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::~FernTrainer()
{}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
FernTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::train(
pcl::Fern<FeatureType, NodeType>& fern)
{
const std::size_t num_of_branches = stats_estimator_->getNumOfBranches();
const std::size_t num_of_examples = examples_.size();
// create random features
std::vector<FeatureType> features;
feature_handler_->createRandomFeatures(num_of_features_, features);
// setup fern
fern.initialize(fern_depth_);
// evaluate all features
std::vector<std::vector<float>> feature_results(num_of_features_);
std::vector<std::vector<unsigned char>> flags(num_of_features_);
for (std::size_t feature_index = 0; feature_index < num_of_features_;
++feature_index) {
feature_results[feature_index].reserve(num_of_examples);
flags[feature_index].reserve(num_of_examples);
feature_handler_->evaluateFeature(features[feature_index],
data_set_,
examples_,
feature_results[feature_index],
flags[feature_index]);
}
// iteratively select features and thresholds
std::vector<std::vector<std::vector<float>>> branch_feature_results(
num_of_features_); // [feature_index][branch_index][result_index]
std::vector<std::vector<std::vector<unsigned char>>> branch_flags(
num_of_features_); // [feature_index][branch_index][flag_index]
std::vector<std::vector<std::vector<ExampleIndex>>> branch_examples(
num_of_features_); // [feature_index][branch_index][result_index]
std::vector<std::vector<std::vector<LabelType>>> branch_label_data(
num_of_features_); // [feature_index][branch_index][flag_index]
// - initialize branch feature results and flags
for (std::size_t feature_index = 0; feature_index < num_of_features_;
++feature_index) {
branch_feature_results[feature_index].resize(1);
branch_flags[feature_index].resize(1);
branch_examples[feature_index].resize(1);
branch_label_data[feature_index].resize(1);
branch_feature_results[feature_index][0] = feature_results[feature_index];
branch_flags[feature_index][0] = flags[feature_index];
branch_examples[feature_index][0] = examples_;
branch_label_data[feature_index][0] = label_data_;
}
for (std::size_t depth_index = 0; depth_index < fern_depth_; ++depth_index) {
// get thresholds
std::vector<std::vector<float>> thresholds(num_of_features_);
for (std::size_t feature_index = 0; feature_index < num_of_features_;
++feature_index) {
thresholds.reserve(num_of_thresholds_);
createThresholdsUniform(num_of_thresholds_,
feature_results[feature_index],
thresholds[feature_index]);
}
// compute information gain
int best_feature_index = -1;
float best_feature_threshold = 0.0f;
float best_feature_information_gain = 0.0f;
for (std::size_t feature_index = 0; feature_index < num_of_features_;
++feature_index) {
for (std::size_t threshold_index = 0; threshold_index < num_of_thresholds_;
++threshold_index) {
float information_gain = 0.0f;
for (std::size_t branch_index = 0;
branch_index < branch_feature_results[feature_index].size();
++branch_index) {
const float branch_information_gain =
stats_estimator_->computeInformationGain(
data_set_,
branch_examples[feature_index][branch_index],
branch_label_data[feature_index][branch_index],
branch_feature_results[feature_index][branch_index],
branch_flags[feature_index][branch_index],
thresholds[feature_index][threshold_index]);
information_gain +=
branch_information_gain *
branch_feature_results[feature_index][branch_index].size();
}
if (information_gain > best_feature_information_gain) {
best_feature_information_gain = information_gain;
best_feature_index = static_cast<int>(feature_index);
best_feature_threshold = thresholds[feature_index][threshold_index];
}
}
}
// add feature to the feature list of the fern
fern.accessFeature(depth_index) = features[best_feature_index];
fern.accessThreshold(depth_index) = best_feature_threshold;
// update branch feature results and flags
for (std::size_t feature_index = 0; feature_index < num_of_features_;
++feature_index) {
std::vector<std::vector<float>>& cur_branch_feature_results =
branch_feature_results[feature_index];
std::vector<std::vector<unsigned char>>& cur_branch_flags =
branch_flags[feature_index];
std::vector<std::vector<ExampleIndex>>& cur_branch_examples =
branch_examples[feature_index];
std::vector<std::vector<LabelType>>& cur_branch_label_data =
branch_label_data[feature_index];
const std::size_t total_num_of_new_branches =
num_of_branches * cur_branch_feature_results.size();
std::vector<std::vector<float>> new_branch_feature_results(
total_num_of_new_branches); // [branch_index][example_index]
std::vector<std::vector<unsigned char>> new_branch_flags(
total_num_of_new_branches); // [branch_index][example_index]
std::vector<std::vector<ExampleIndex>> new_branch_examples(
total_num_of_new_branches); // [branch_index][example_index]
std::vector<std::vector<LabelType>> new_branch_label_data(
total_num_of_new_branches); // [branch_index][example_index]
for (std::size_t branch_index = 0;
branch_index < cur_branch_feature_results.size();
++branch_index) {
const std::size_t num_of_examples_in_this_branch =
cur_branch_feature_results[branch_index].size();
std::vector<unsigned char> branch_indices;
branch_indices.reserve(num_of_examples_in_this_branch);
stats_estimator_->computeBranchIndices(cur_branch_feature_results[branch_index],
cur_branch_flags[branch_index],
best_feature_threshold,
branch_indices);
// split results into different branches
const std::size_t base_branch_index = branch_index * num_of_branches;
for (std::size_t example_index = 0;
example_index < num_of_examples_in_this_branch;
++example_index) {
const std::size_t combined_branch_index =
base_branch_index + branch_indices[example_index];
new_branch_feature_results[combined_branch_index].push_back(
cur_branch_feature_results[branch_index][example_index]);
new_branch_flags[combined_branch_index].push_back(
cur_branch_flags[branch_index][example_index]);
new_branch_examples[combined_branch_index].push_back(
cur_branch_examples[branch_index][example_index]);
new_branch_label_data[combined_branch_index].push_back(
cur_branch_label_data[branch_index][example_index]);
}
}
branch_feature_results[feature_index] = new_branch_feature_results;
branch_flags[feature_index] = new_branch_flags;
branch_examples[feature_index] = new_branch_examples;
branch_label_data[feature_index] = new_branch_label_data;
}
}
// set node statistics
// - re-evaluate selected features
std::vector<std::vector<float>> final_feature_results(
fern_depth_); // [feature_index][example_index]
std::vector<std::vector<unsigned char>> final_flags(
fern_depth_); // [feature_index][example_index]
std::vector<std::vector<unsigned char>> final_branch_indices(
fern_depth_); // [feature_index][example_index]
for (std::size_t depth_index = 0; depth_index < fern_depth_; ++depth_index) {
final_feature_results[depth_index].reserve(num_of_examples);
final_flags[depth_index].reserve(num_of_examples);
final_branch_indices[depth_index].reserve(num_of_examples);
feature_handler_->evaluateFeature(fern.accessFeature(depth_index),
data_set_,
examples_,
final_feature_results[depth_index],
final_flags[depth_index]);
stats_estimator_->computeBranchIndices(final_feature_results[depth_index],
final_flags[depth_index],
fern.accessThreshold(depth_index),
final_branch_indices[depth_index]);
}
// - distribute examples to nodes
std::vector<std::vector<LabelType>> node_labels(
0x1 << fern_depth_); // [node_index][example_index]
std::vector<std::vector<ExampleIndex>> node_examples(
0x1 << fern_depth_); // [node_index][example_index]
for (std::size_t example_index = 0; example_index < num_of_examples;
++example_index) {
std::size_t node_index = 0;
for (std::size_t depth_index = 0; depth_index < fern_depth_; ++depth_index) {
node_index *= num_of_branches;
node_index += final_branch_indices[depth_index][example_index];
}
node_labels[node_index].push_back(label_data_[example_index]);
node_examples[node_index].push_back(examples_[example_index]);
}
// - compute and set statistics for every node
const std::size_t num_of_nodes = 0x1 << fern_depth_;
for (std::size_t node_index = 0; node_index < num_of_nodes; ++node_index) {
stats_estimator_->computeAndSetNodeStats(data_set_,
node_examples[node_index],
node_labels[node_index],
fern[node_index]);
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
FernTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
createThresholdsUniform(const std::size_t num_of_thresholds,
std::vector<float>& values,
std::vector<float>& thresholds)
{
// estimate range of values
float min_value = ::std::numeric_limits<float>::max();
float max_value = -::std::numeric_limits<float>::max();
const std::size_t num_of_values = values.size();
for (int value_index = 0; value_index < num_of_values; ++value_index) {
const float value = values[value_index];
if (value < min_value)
min_value = value;
if (value > max_value)
max_value = value;
}
const float range = max_value - min_value;
const float step = range / (num_of_thresholds + 2);
// compute thresholds
thresholds.resize(num_of_thresholds);
for (int threshold_index = 0; threshold_index < num_of_thresholds;
++threshold_index) {
thresholds[threshold_index] = min_value + step * (threshold_index + 1);
}
}
} // namespace pcl
pcl::Fern
Class representing a Fern.
Definition: fern.h:49
pcl::Fern::accessThreshold
float & accessThreshold(const std::size_t threshold_index)
Access operator for thresholds.
Definition: fern.h:186
pcl::Fern::initialize
void initialize(const std::size_t num_of_decisions)
Initializes the fern.
Definition: fern.h:62
pcl::Fern::accessFeature
FeatureType & accessFeature(const std::size_t feature_index)
Access operator for features.
Definition: fern.h:166
pcl::FernTrainer::createThresholdsUniform
static void createThresholdsUniform(const std::size_t num_of_thresholds, std::vector< float > &values, std::vector< float > &thresholds)
Creates uniformely distrebuted thresholds over the range of the supplied values.
Definition: fern_trainer.hpp:300
pcl::FernTrainer::~FernTrainer
virtual ~FernTrainer()
Destructor.
Definition: fern_trainer.hpp:63
pcl::FernTrainer::train
void train(Fern< FeatureType, NodeType > &fern)
Trains a decision tree using the set training data and settings.
Definition: fern_trainer.hpp:72
pcl::FernTrainer::FernTrainer
FernTrainer()
Constructor.
Definition: fern_trainer.hpp:47
pcl
Definition: convolution.h:46