Trait smartcore::api::SupervisedEstimator

pub trait SupervisedEstimator<X, Y, P> {
    pub fn fit(x: &X, y: &Y, parameters: P) -> Result<Self, Failed>
    where
        Self: Sized,
        P: Clone;
}

An estimator for supervised learning, , that provides method fit to learn from data and training values

Required methods

pub fn fit(x: &X, y: &Y, parameters: P) -> Result<Self, Failed> where
    Self: Sized,
    P: Clone, 

Fit a model to a training dataset, estimate model's parameters.

• x - NxM matrix with N observations and M features in each observation.
• y - target training values of size N.
• parameters - hyperparameters of an algorithm

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Implementors

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, RandomForestClassifierParameters> for RandomForestClassifier<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: RandomForestClassifierParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, RandomForestRegressorParameters> for RandomForestRegressor<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: RandomForestRegressorParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, ElasticNetParameters<T>> for ElasticNet<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: ElasticNetParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, LassoParameters<T>> for Lasso<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: LassoParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, LinearRegressionParameters> for LinearRegression<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: LinearRegressionParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, LogisticRegressionParameters> for LogisticRegression<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: LogisticRegressionParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, RidgeRegressionParameters<T>> for RidgeRegression<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: RidgeRegressionParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, BernoulliNBParameters<T>> for BernoulliNB<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: BernoulliNBParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, CategoricalNBParameters<T>> for CategoricalNB<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: CategoricalNBParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, GaussianNBParameters<T>> for GaussianNB<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: GaussianNBParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, MultinomialNBParameters<T>> for MultinomialNB<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: MultinomialNBParameters<T>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, DecisionTreeClassifierParameters> for DecisionTreeClassifier<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: DecisionTreeClassifierParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, DecisionTreeRegressorParameters> for DecisionTreeRegressor<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: DecisionTreeRegressorParameters
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>, D: Distance<Vec<T>, T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, KNNClassifierParameters<T, D>> for KNNClassifier<T, D>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: KNNClassifierParameters<T, D>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>, D: Distance<Vec<T>, T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, KNNRegressorParameters<T, D>> for KNNRegressor<T, D>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: KNNRegressorParameters<T, D>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, SVCParameters<T, M, K>> for SVC<T, M, K>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: SVCParameters<T, M, K>
) -> Result<Self, Failed>

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, SVRParameters<T, M, K>> for SVR<T, M, K>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: SVRParameters<T, M, K>
) -> Result<Self, Failed>

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