[−][src]Trait opencv::hub_prelude::Boost

pub trait Boost: DTrees {
    pub fn as_raw_Boost(&self) -> *const c_void;

    pub fn as_raw_mut_Boost(&mut self) -> *mut c_void;

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

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

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

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

    pub fn get_weight_trim_rate(&self) -> Result<f64> { ... }

    pub fn set_weight_trim_rate(&mut self, val: f64) -> Result<()> { ... }
}

Boosted tree classifier derived from DTrees

See also

@ref ml_intro_boost

Required methods

pub fn as_raw_Boost(&self) -> *const c_void[src]

pub fn as_raw_mut_Boost(&mut self) -> *mut c_void[src]

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Provided methods

pub fn get_boost_type(&self) -> Result<i32>[src]

Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.

See also

setBoostType

pub fn set_boost_type(&mut self, val: i32) -> Result<()>[src]

Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.

See also

setBoostType getBoostType

pub fn get_weak_count(&self) -> Result<i32>[src]

The number of weak classifiers. Default value is 100.

See also

setWeakCount

pub fn set_weak_count(&mut self, val: i32) -> Result<()>[src]

The number of weak classifiers. Default value is 100.

See also

setWeakCount getWeakCount

pub fn get_weight_trim_rate(&self) -> Result<f64>[src]

A threshold between 0 and 1 used to save computational time. Samples with summary weight inline formula do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.

See also

setWeightTrimRate

pub fn set_weight_trim_rate(&mut self, val: f64) -> Result<()>[src]

A threshold between 0 and 1 used to save computational time. Samples with summary weight inline formula do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.

See also

setWeightTrimRate getWeightTrimRate

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Implementations

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

pub fn create() -> Result<Ptr<dyn Boost>>[src]

Creates the empty model. Use StatModel::train to train the model, Algorithm::load<Boost>(filename) to load the pre-trained model.

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

Loads and creates a serialized Boost from a file

Use Boost::save to serialize and store an RTree to disk. Load the Boost 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 Boost
  • nodeName: name of node containing the classifier

C++ default parameters

  • node_name: String()

Implementors

impl Boost for PtrOfBoost[src]

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