Struct smartcore::decomposition::pca::PCA

pub struct PCA<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable<T>> { /* private fields */ }

Principal components analysis algorithm

Implementations

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

pub fn fit(data: &X, parameters: PCAParameters) -> Result<PCA<T, X>, Failed>

Fits PCA to your data.

• data - NxM matrix with N observations and M features in each observation.
• n_components - number of components to keep.
• parameters - other parameters, use Default::default() to set parameters to default values.

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

Run dimensionality reduction for x

• x - KxM data where K is number of observations and M is number of features.

pub fn components(&self) -> &X

Get a projection matrix

Trait Implementations

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

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

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

fn eq(&self, other: &Self) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

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

fn transform(&self, x: &X) -> Result<X, Failed>

Transform data by modifying or filtering it

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

fn fit(x: &X, parameters: PCAParameters) -> Result<Self, Failed>

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