Trait opencv::prelude::StatModelTraitConst

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pub trait StatModelTraitConst: AlgorithmTraitConst {
    // Required method
    fn as_raw_StatModel(&self) -> *const c_void;

    // Provided methods
    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 calc_error(
        &self,
        data: &Ptr<TrainData>,
        test: bool,
        resp: &mut impl ToOutputArray
    ) -> Result<f32> { ... }
    fn predict(
        &self,
        samples: &impl ToInputArray,
        results: &mut impl ToOutputArray,
        flags: i32
    ) -> Result<f32> { ... }
}
Expand description

Constant methods for crate::ml::StatModel

Required Methods§

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fn as_raw_StatModel(&self) -> *const c_void

Provided Methods§

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

Returns the number of variables in training samples

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

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

Returns true if the model is trained

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

Returns true if the model is classifier

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fn calc_error( &self, data: &Ptr<TrainData>, test: bool, resp: &mut impl 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%).

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fn predict( &self, samples: &impl ToInputArray, results: &mut impl 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

Implementors§