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

pub trait StatModel: AlgorithmTrait {
    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> { ... }
    pub fn empty(&self) -> Result<bool> { ... }
    pub fn is_trained(&self) -> Result<bool> { ... }
    pub fn is_classifier(&self) -> Result<bool> { ... }
    pub fn train_with_data(
        &mut self, 
        train_data: &Ptr<dyn TrainData>, 
        flags: i32
    ) -> Result<bool> { ... }
    pub fn train(
        &mut self, 
        samples: &dyn ToInputArray, 
        layout: i32, 
        responses: &dyn ToInputArray
    ) -> Result<bool> { ... }
    pub fn calc_error(
        &self, 
        data: &Ptr<dyn TrainData>, 
        test: bool, 
        resp: &mut dyn ToOutputArray
    ) -> Result<f32> { ... }
    pub fn predict(
        &self, 
        samples: &dyn ToInputArray, 
        results: &mut dyn ToOutputArray, 
        flags: i32
    ) -> Result<f32> { ... }
}

Base class for statistical models in OpenCV ML.

Required methods

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

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

Loading content...

Provided methods

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

Returns the number of variables in training samples

`pub fn empty(&self) -> Result<bool>`[src]

`pub fn is_trained(&self) -> Result<bool>`[src]

Returns true if the model is trained

`pub fn is_classifier(&self) -> Result<bool>`[src]

Returns true if the model is classifier

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

Trains the statistical model

Parameters

trainData: training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags: optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

C++ default parameters

flags: 0

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

Trains the statistical model

Parameters

samples: training samples
layout: See ml::SampleTypes.
responses: vector of responses associated with the training samples.

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

Computes error on the training or test dataset

Parameters

data: the training data
test: if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp: the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).