Struct opencv::ml::DTrees

pub struct DTrees { /* private fields */ }

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

[ml_intro_trees]

Implementations

impl DTrees

pub fn create() -> Result<Ptr<DTrees>>

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<Ptr<DTrees>>

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()

pub fn load_def(filepath: &str) -> Result<Ptr<DTrees>>

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

Note

This alternative version of [load] function uses the following default values for its arguments:

• node_name: String()

Trait Implementations

impl AlgorithmTrait for DTrees

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for DTrees

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of [write_with_name] function uses the following default values for its arguments:

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

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl Boxed for DTrees

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl DTreesTrait for DTrees

fn as_raw_mut_DTrees(&mut self) -> *mut c_void

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

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than maxCategories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including our implementation) try to find sub-optimal split in this case by clustering all the samples into maxCategories clusters that is some categories are merged together. The clustering is applied only in n > 2-class classification problems for categorical variables with N > max_categories possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases. Default value is 10.

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

The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth. The actual depth may be smaller if the other termination criteria are met (see the outline of the training procedure [ml_intro_trees] “here”), and/or if the tree is pruned. Default value is INT_MAX.

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

If the number of samples in a node is less than this parameter then the node will not be split.

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

If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. Default value is 10.

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

If true then surrogate splits will be built. These splits allow to work with missing data and compute variable importance correctly. Default value is false.

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

If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. Default value is true.

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

If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree. Default value is true.

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

Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further. Default value is 0.01f

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

The array of a priori class probabilities, sorted by the class label value.

impl DTreesTraitConst for DTrees

fn as_raw_DTrees(&self) -> *const c_void

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

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than maxCategories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including our implementation) try to find sub-optimal split in this case by clustering all the samples into maxCategories clusters that is some categories are merged together. The clustering is applied only in n > 2-class classification problems for categorical variables with N > max_categories possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases. Default value is 10.

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

The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth. The actual depth may be smaller if the other termination criteria are met (see the outline of the training procedure [ml_intro_trees] “here”), and/or if the tree is pruned. Default value is INT_MAX.

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

If the number of samples in a node is less than this parameter then the node will not be split.

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

If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. Default value is 10.

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

If true then surrogate splits will be built. These splits allow to work with missing data and compute variable importance correctly. Default value is false.

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

If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. Default value is true.

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

If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree. Default value is true.

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

Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further. Default value is 0.01f

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

The array of a priori class probabilities, sorted by the class label value.

fn get_roots(&self) -> Result<Vector<i32>>

Returns indices of root nodes

fn get_nodes(&self) -> Result<Vector<DTrees_Node>>

Returns all the nodes

fn get_splits(&self) -> Result<Vector<DTrees_Split>>

Returns all the splits

fn get_subsets(&self) -> Result<Vector<i32>>

Returns all the bitsets for categorical splits

impl Debug for DTrees

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for DTrees

fn drop(&mut self)

Executes the destructor for this type.

impl From<Boost> for DTrees

fn from(s: Boost) -> Self

Converts to this type from the input type.

impl From<DTrees> for Algorithm

fn from(s: DTrees) -> Self

Converts to this type from the input type.

impl From<DTrees> for StatModel

fn from(s: DTrees) -> Self

Converts to this type from the input type.

impl From<RTrees> for DTrees

fn from(s: RTrees) -> Self

Converts to this type from the input type.

impl StatModelTrait for DTrees

fn as_raw_mut_StatModel(&mut self) -> *mut c_void

fn train_with_data( &mut self, train_data: &Ptr<TrainData>, flags: i32 ) -> Result<bool>

Trains the statistical model

fn train_with_data_def(&mut self, train_data: &Ptr<TrainData>) -> Result<bool>

Trains the statistical model

fn train( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray ) -> Result<bool>

Trains the statistical model

impl StatModelTraitConst for DTrees

fn as_raw_StatModel(&self) -> *const c_void

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

Returns the number of variables in training samples

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

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

Returns true if the model is trained

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

Returns true if the model is classifier

fn calc_error( &self, data: &Ptr<TrainData>, test: bool, resp: &mut impl ToOutputArray ) -> Result<f32>

Computes error on the training or test dataset

fn predict( &self, samples: &impl ToInputArray, results: &mut impl ToOutputArray, flags: i32 ) -> Result<f32>

Predicts response(s) for the provided sample(s)

fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>

Predicts response(s) for the provided sample(s)

impl TryFrom<DTrees> for Boost

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: DTrees) -> Result<Self>

Performs the conversion.

impl TryFrom<DTrees> for RTrees

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: DTrees) -> Result<Self>

Performs the conversion.

impl TryFrom<StatModel> for DTrees

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: StatModel) -> Result<Self>

Performs the conversion.

impl Send for DTrees