Trait UnsupervisedEstimator

pub trait UnsupervisedEstimator<X, P> {
    // Required method
    fn fit(x: &X, parameters: P) -> Result<Self, Failed>
       where Self: Sized,
             P: Clone;
}

An estimator for unsupervised learning, that provides method fit to learn from data

Required Methods

fn fit(x: &X, 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.
• parameters - hyperparameters of an algorithm

Implementors

impl<T: Number + RealNumber, M: Array2<T>> UnsupervisedEstimator<M, StandardScalerParameters> for StandardScaler<T>

During fit the StandardScaler computes the column means and standard deviation.

impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable<T>> UnsupervisedEstimator<X, PCAParameters> for PCA<T, X>

impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable<T>> UnsupervisedEstimator<X, SVDParameters> for SVD<T, X>

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> UnsupervisedEstimator<X, KMeansParameters> for KMeans<TX, TY, X, Y>

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> UnsupervisedEstimator<X, DBSCANParameters<TX, D>> for DBSCAN<TX, TY, X, Y, D>