[−][src]Trait opencv::ml::DTrees

pub trait DTrees: StatModel {
    fn as_raw_DTrees(&self) -> *mut c_void;

    fn get_max_categories(&self) -> Result<i32> { ... }

    fn set_max_categories(&mut self, val: i32) -> Result<()> { ... }

    fn get_max_depth(&self) -> Result<i32> { ... }

    fn set_max_depth(&mut self, val: i32) -> Result<()> { ... }

    fn get_min_sample_count(&self) -> Result<i32> { ... }

    fn set_min_sample_count(&mut self, val: i32) -> Result<()> { ... }

    fn get_cv_folds(&self) -> Result<i32> { ... }

    fn set_cv_folds(&mut self, val: i32) -> Result<()> { ... }

    fn get_use_surrogates(&self) -> Result<bool> { ... }

    fn set_use_surrogates(&mut self, val: bool) -> Result<()> { ... }

    fn get_use1_se_rule(&self) -> Result<bool> { ... }

    fn set_use1_se_rule(&mut self, val: bool) -> Result<()> { ... }

    fn get_truncate_pruned_tree(&self) -> Result<bool> { ... }

    fn set_truncate_pruned_tree(&mut self, val: bool) -> Result<()> { ... }

    fn get_regression_accuracy(&self) -> Result<f32> { ... }

    fn set_regression_accuracy(&mut self, val: f32) -> Result<()> { ... }

    fn get_priors(&self) -> Result<Mat> { ... }

    fn set_priors(&mut self, val: &Mat) -> Result<()> { ... }

    fn get_roots(&self) -> Result<VectorOfint> { ... }

    fn get_nodes(&self) -> Result<VectorOfNode> { ... }

    fn get_splits(&self) -> Result<VectorOfSplit> { ... }

    fn get_subsets(&self) -> Result<VectorOfint> { ... }
}

The class represents a single decision tree or a collection of decision trees.

The current public interface of the class allows user to train only a single decision tree, however the class is capable of storing multiple decision trees and using them for prediction (by summing responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this capability to implement decision tree ensembles.

See also

@ref ml_intro_trees

Required methods

fn as_raw_DTrees(&self) -> *mut c_void

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Provided methods

fn get_max_categories(&self) -> Result<i32>

@see setMaxCategories

fn set_max_categories(&mut self, val: i32) -> Result<()>

@copybrief getMaxCategories @see getMaxCategories

fn get_max_depth(&self) -> Result<i32>

@see setMaxDepth

fn set_max_depth(&mut self, val: i32) -> Result<()>

@copybrief getMaxDepth @see getMaxDepth

fn get_min_sample_count(&self) -> Result<i32>

@see setMinSampleCount

fn set_min_sample_count(&mut self, val: i32) -> Result<()>

@copybrief getMinSampleCount @see getMinSampleCount

fn get_cv_folds(&self) -> Result<i32>

@see setCVFolds

fn set_cv_folds(&mut self, val: i32) -> Result<()>

@copybrief getCVFolds @see getCVFolds

fn get_use_surrogates(&self) -> Result<bool>

@see setUseSurrogates

fn set_use_surrogates(&mut self, val: bool) -> Result<()>

@copybrief getUseSurrogates @see getUseSurrogates

fn get_use1_se_rule(&self) -> Result<bool>

@see setUse1SERule

fn set_use1_se_rule(&mut self, val: bool) -> Result<()>

@copybrief getUse1SERule @see getUse1SERule

fn get_truncate_pruned_tree(&self) -> Result<bool>

@see setTruncatePrunedTree

fn set_truncate_pruned_tree(&mut self, val: bool) -> Result<()>

@copybrief getTruncatePrunedTree @see getTruncatePrunedTree

fn get_regression_accuracy(&self) -> Result<f32>

@see setRegressionAccuracy

fn set_regression_accuracy(&mut self, val: f32) -> Result<()>

@copybrief getRegressionAccuracy @see getRegressionAccuracy

fn get_priors(&self) -> Result<Mat>

@see setPriors

fn set_priors(&mut self, val: &Mat) -> Result<()>

@copybrief getPriors @see getPriors

fn get_roots(&self) -> Result<VectorOfint>

Returns indices of root nodes

fn get_nodes(&self) -> Result<VectorOfNode>

Returns all the nodes

all the node indices are indices in the returned vector

fn get_splits(&self) -> Result<VectorOfSplit>

Returns all the splits

all the split indices are indices in the returned vector

fn get_subsets(&self) -> Result<VectorOfint>

Returns all the bitsets for categorical splits

Split::subsetOfs is an offset in the returned vector

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Methods

impl<'_> dyn DTrees + '_[src]

pub fn create() -> Result<PtrOfDTrees>[src]

Creates the empty model

The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load<DTrees>(filename).

pub fn load(filepath: &str, node_name: &str) -> Result<PtrOfDTrees>[src]

Loads and creates a serialized DTrees from a file

Use DTree::save to serialize and store an DTree to disk. Load the DTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters

  • filepath: path to serialized DTree
  • nodeName: name of node containing the classifier

C++ default parameters

  • node_name: String()

Implementors

impl DTrees for PtrOfBoost[src]

impl DTrees for PtrOfDTrees[src]

impl DTrees for PtrOfRTrees[src]

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