main/cpp/Tree_8hpp_source.html

#pragma once


#include "utils/Types.hpp"


#include "models/Model.hpp"

#include "models/TreeNode.hpp"


namespace ppforest2 {


  struct Tree : public Model {

    static Tree train(TrainingSpec const& training_spec,

                      types::FeatureMatrix const& x,

                      types::ResponseVector const& y,

                      stats::RNG& rng);


    static Tree train(TrainingSpec const& training_spec,

                      types::FeatureMatrix const& x,

                      stats::GroupPartition const& group_spec,

                      stats::RNG& rng);


    TreeNode::Ptr root;


    Tree(TreeNode::Ptr root, TrainingSpec::Ptr training_spec);


    void accept(Model::Visitor& visitor) const override;


    types::Response predict(types::FeatureVector const& data) const override;

    types::ResponseVector predict(types::FeatureMatrix const& data) const override;

    types::FeatureMatrix predict(types::FeatureMatrix const& data, Proportions) const override;


    bool operator==(Tree const& other) const;

    bool operator!=(Tree const& other) const;

  };


}

Model.hpp

TreeNode.hpp

Types.hpp

ppforest2::stats::GroupPartition
Contiguous-block representation of grouped observations.
Definition GroupPartition.hpp:42

ppforest2::stats::RNG
pcg32 RNG
Definition Stats.hpp:19

ppforest2::types::FeatureMatrix
Eigen::Matrix< Feature, Eigen::Dynamic, Eigen::Dynamic > FeatureMatrix
Dynamic-size matrix of feature values.
Definition Types.hpp:24

ppforest2::types::FeatureVector
Eigen::Matrix< Feature, Eigen::Dynamic, 1 > FeatureVector
Dynamic-size column vector of feature values.
Definition Types.hpp:26

ppforest2::types::Response
int Response
Scalar type for group labels (integer).
Definition Types.hpp:21

ppforest2::types::ResponseVector
Eigen::Matrix< Response, Eigen::Dynamic, 1 > ResponseVector
Dynamic-size column vector of group labels.
Definition Types.hpp:29

ppforest2
Definition Benchmark.hpp:22

ppforest2::Model::Visitor
Visitor interface for model dispatch.
Definition Model.hpp:34

ppforest2::Model
Abstract base class for predictive models (trees and forests).
Definition Model.hpp:25

ppforest2::Model::training_spec
TrainingSpec::Ptr training_spec
Training specification used to build this model.
Definition Model.hpp:45

ppforest2::Proportions
Tag type for requesting vote-proportion predictions.
Definition Model.hpp:19

ppforest2::TrainingSpec
Training configuration for projection pursuit trees and forests.
Definition TrainingSpec.hpp:40

ppforest2::TrainingSpec::Ptr
std::shared_ptr< TrainingSpec > Ptr
Definition TrainingSpec.hpp:41

ppforest2::TreeNode::Ptr
std::unique_ptr< TreeNode > Ptr
Definition TreeNode.hpp:20

ppforest2::Tree::root
TreeNode::Ptr root
Root node of the tree.
Definition Tree.hpp:62

ppforest2::Tree::operator==
bool operator==(Tree const &other) const

ppforest2::Tree::predict
types::Response predict(types::FeatureVector const &data) const override
Predict a single observation.

ppforest2::Tree::operator!=
bool operator!=(Tree const &other) const

ppforest2::Tree::Tree
Tree(TreeNode::Ptr root, TrainingSpec::Ptr training_spec)

ppforest2::Tree::predict
types::FeatureMatrix predict(types::FeatureMatrix const &data, Proportions) const override
Predict proportions for a matrix of observations.

ppforest2::Tree::train
static Tree train(TrainingSpec const &training_spec, types::FeatureMatrix const &x, types::ResponseVector const &y, stats::RNG &rng)
Train a tree from a response vector.

ppforest2::Tree::predict
types::ResponseVector predict(types::FeatureMatrix const &data) const override
Predict a matrix of observations.

ppforest2::Tree::accept
void accept(Model::Visitor &visitor) const override
Accept a model visitor (double dispatch).

ppforest2::Tree::train
static Tree train(TrainingSpec const &training_spec, types::FeatureMatrix const &x, stats::GroupPartition const &group_spec, stats::RNG &rng)
Train a tree from a group partition.