Struct stamm::randforest::RandomForestLearnParam

pub struct RandomForestLearnParam<LearnF> where
    LearnF: TreeLearnFunctions,  {
    pub tree_param: TreeParameters,
    pub number_of_trees: usize,
    pub size_of_subset_per_training: usize,
    pub learn_function: LearnF,
}

Parameter describes the way to train a random forest

Fields

tree_param: TreeParameters

parameter used for every tree

number_of_trees: usize

number of trees

size_of_subset_per_training: usize

size of a random training subset used for train one tree

learn_function: LearnF

TreeLearnFunction

Methods

impl<LearnF> RandomForestLearnParam<LearnF> where
    LearnF: TreeLearnFunctions + Copy, 

pub fn new(
    number_of_trees: usize,
    size_of_subset_per_training: usize,
    learnf: LearnF
) -> RandomForestLearnParam<LearnF>

Creates a new RandomForestLearnParam. number_of_trees is the number of trees used in this random forest. Every tree will be trained using a random subset of the training data. size_of_subset_per_training is the size of this subset. learnf is the TreeLearnFunction for every tree

pub fn train_forest(
    self,
    train_set: &[(&LearnF::Data, &LearnF::Truth)]
) -> Option<RandomForest<LearnF::LeafParam, LearnF::PredictFunction>>

Trains a random forest using the ground truth data train_set.

impl<LearnF> RandomForestLearnParam<LearnF> where
    LearnF: TreeLearnFunctions + Copy + Send + Sync,
    LearnF::PredictFunction: Send + Sync,
    LearnF::Truth: Send + Sync,
    LearnF::LeafParam: Send + Sync,
    LearnF::Data: Send + Sync,
    LearnF::Param: Send + Sync, 

pub fn train_forest_parallel(
    self,
    train_set: &[(&LearnF::Data, &LearnF::Truth)]
) -> Option<RandomForest<LearnF::LeafParam, LearnF::PredictFunction>>

Like train_forest but use rayon to parallelize the training.

Trait Implementations