Struct opencv::types::PtrOfRTrees

pub struct PtrOfRTrees { /* fields omitted */ }

Methods

impl PtrOfRTrees

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

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

Trait Implementations

impl Algorithm for PtrOfRTrees

fn as_raw_Algorithm(&self) -> *mut c_void

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

Clears the algorithm state

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 DTrees for PtrOfRTrees

fn as_raw_DTrees(&self) -> *mut c_void

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

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

Returns all the splits

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

Returns all the bitsets for categorical splits

impl RTrees for PtrOfRTrees

fn as_raw_RTrees(&self) -> *mut c_void

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

@see setCalculateVarImportance

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

@copybrief getCalculateVarImportance @see getCalculateVarImportance

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

@see setActiveVarCount

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

@copybrief getActiveVarCount @see getActiveVarCount

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

@see setTermCriteria

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

@copybrief getTermCriteria @see getTermCriteria

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.

fn get_votes(&self, samples: &Mat, results: &mut Mat, 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. ## Parameters * samples: Array containing the samples for which votes will be calculated. * results: Array where the result of the calculation will be written. * flags: Flags for defining the type of RTrees.

impl StatModel for PtrOfRTrees

fn as_raw_StatModel(&self) -> *mut 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 train_with_data(
    &mut self,
    train_data: &PtrOfTrainData,
    flags: i32
) -> Result<bool>

Trains the statistical model

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

Trains the statistical model

fn calc_error(
    &self,
    data: &PtrOfTrainData,
    test: bool,
    resp: &mut Mat
) -> Result<f32>

Computes error on the training or test dataset

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

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

impl Send for PtrOfRTrees

impl Drop for PtrOfRTrees

fn drop(&mut self)

Executes the destructor for this type.