Type Definition opencv::types::PtrOfRTrees

type PtrOfRTrees = Ptr<dyn RTrees>;

Implementations

impl PtrOfRTrees

pub fn as_raw_PtrOfRTrees(&self) -> *const c_void

pub fn as_raw_mut_PtrOfRTrees(&mut self) -> *mut c_void

Trait Implementations

impl AlgorithmTrait for PtrOfRTrees

pub fn as_raw_Algorithm(&self) -> *const c_void

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

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

Clears the algorithm state

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

Stores algorithm parameters in a file storage

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

simplified API for language bindings Stores algorithm parameters in a file storage

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

Reads algorithm parameters from a file storage

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

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

pub 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).

pub 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 DTrees for PtrOfRTrees

pub fn as_raw_DTrees(&self) -> *const c_void

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

pub 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.

pub 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.

pub 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 @ref ml_intro_trees "here"), and/or if the tree is pruned. Default value is INT_MAX.

pub 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 @ref ml_intro_trees "here"), and/or if the tree is pruned. Default value is INT_MAX.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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.

pub 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

pub 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

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

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

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

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

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

Returns indices of root nodes

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

Returns all the nodes

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

Returns all the splits

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

Returns all the bitsets for categorical splits

impl RTrees for PtrOfRTrees

pub fn as_raw_RTrees(&self) -> *const c_void

pub fn as_raw_mut_RTrees(&mut self) -> *mut c_void

pub fn get_calculate_var_importance(&self) -> Result<bool>

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.

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

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.

pub fn get_active_var_count(&self) -> Result<i32>

The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.

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

The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.

pub fn get_term_criteria(&self) -> Result<TermCriteria>

The termination criteria that specifies when the training algorithm stops. Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)

pub fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>

The termination criteria that specifies when the training algorithm stops. Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)

pub fn get_var_importance(&self) -> Result<Mat>

Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.

pub fn get_votes(
    &self,
    samples: &dyn ToInputArray,
    results: &mut dyn ToOutputArray,
    flags: i32
) -> Result<()>

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

pub fn get_oob_error(&self) -> Result<f64>

impl StatModel for PtrOfRTrees

pub fn as_raw_StatModel(&self) -> *const c_void

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

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

Returns the number of variables in training samples

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

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

Returns true if the model is trained

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

Returns true if the model is classifier

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

Trains the statistical model

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

Trains the statistical model

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

Computes error on the training or test dataset

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

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