Struct PCA 

pub struct PCA { /* private fields */ }

Principal Component Analysis (PCA)

Reduces dimensionality by projecting data onto principal components (directions of maximum variance).

Time complexity: O(n * d² + d³) where:

• n = number of vectors
• d = original dimensions

Implementations

impl PCA

pub fn new(n_components: usize) -> Self

Create new PCA reducer

Arguments

• n_components - Target number of dimensions

pub fn fit_transform(&mut self, vectors: &[Vec<f32>]) -> Result<Vec<Vec<f32>>>

Fit PCA model and transform data

pub fn fit(&mut self, vectors: &[Vec<f32>]) -> Result<()>

Fit PCA model to data

pub fn transform(&self, vectors: &[Vec<f32>]) -> Result<Vec<Vec<f32>>>

Transform data using fitted PCA model

pub fn inverse_transform(&self, reduced: &[Vec<f32>]) -> Result<Vec<Vec<f32>>>

Inverse transform (reconstruct original space)

pub fn explained_variance_ratio(&self) -> Option<Vec<f32>>

Get explained variance ratio

Trait Implementations

impl Clone for PCA

fn clone(&self) -> PCA

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PCA

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for PCA

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for PCA

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.