Trait smartcore::api::Predictor

pub trait Predictor<X, Y> {
    pub fn predict(&self, x: &X) -> Result<Y, Failed>;
}

Implements method predict that estimates target value from new data

Required methods

pub fn predict(&self, x: &X) -> Result<Y, Failed>

Estimate target values from new data.

• x - NxM matrix with N observations and M features in each observation.

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Implementors

impl<T: RealNumber, M: Matrix<T>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for KMeans<T>

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

impl<T: RealNumber, M: Matrix<T>, D: Distance<Vec<T>, T>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for DBSCAN<T, D>

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

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