[−][src]Trait opencv::ml::StatModel

pub trait StatModel: AlgorithmTrait {
    fn as_raw_StatModel(&self) -> *mut c_void;

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

    fn empty(&self) -> Result<bool> { ... }

    fn is_trained(&self) -> Result<bool> { ... }

    fn is_classifier(&self) -> Result<bool> { ... }

    fn train_with_data(
        &mut self, 
        train_data: &PtrOfTrainData, 
        flags: i32
    ) -> Result<bool> { ... }

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

    fn calc_error(
        &self, 
        data: &PtrOfTrainData, 
        test: bool, 
        resp: &mut dyn ToOutputArray
    ) -> Result<f32> { ... }

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

Base class for statistical models in OpenCV ML.

Required methods

fn as_raw_StatModel(&self) -> *mut c_void

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Provided methods

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

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

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

Trains the statistical model

Parameters

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

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

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%).

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

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

Parameters

  • samples: The input samples, floating-point matrix
  • results: The optional output matrix of results.
  • flags: The optional flags, model-dependent. See cv::ml::StatModel::Flags.

C++ default parameters

  • results: noArray()
  • flags: 0
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Implementors

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