Struct FactorAnalysis 

pub struct FactorAnalysis { /* private fields */ }

Factor Analysis for dimensionality reduction

Factor Analysis assumes observed data is generated from a linear model with latent factors plus Gaussian noise with feature-specific variances.

Example

use scirs2_transform::reduction::factor_analysis::FactorAnalysis;
use scirs2_core::ndarray::Array2;

let data = Array2::<f64>::zeros((100, 10));
let mut fa = FactorAnalysis::new(3);
let result = fa.fit_transform(&data).expect("fit_transform failed");
println!("Loadings shape: {:?}", result.loadings.shape());
println!("Scores shape: {:?}", result.scores.shape());

Implementations

impl FactorAnalysis

pub fn new(n_factors: usize) -> Self

Creates a new FactorAnalysis instance

Arguments

• n_factors - Number of latent factors to extract

pub fn with_rotation(self, rotation: RotationMethod) -> Self

Set the rotation method

pub fn with_max_iter(self, max_iter: usize) -> Self

Set the maximum number of EM iterations

pub fn with_tol(self, tol: f64) -> Self

Set the convergence tolerance

pub fn with_promax_power(self, power: usize) -> Self

Set the promax power parameter

pub fn with_center(self, center: bool) -> Self

Set whether to center the data

pub fn fit_transform<S>( &mut self, x: &ArrayBase<S, Ix2>, ) -> Result<FactorAnalysisResult>

where S: Data, S::Elem: Float + NumCast,

Fit the factor analysis model and return results

Arguments

• x - Input data, shape (n_samples, n_features)

Returns

• Result<FactorAnalysisResult> - Factor analysis results

pub fn loadings(&self) -> Option<&Array2<f64>>

Returns the factor loadings

pub fn uniquenesses(&self) -> Option<&Array1<f64>>

Returns the uniquenesses

pub fn scree_plot_data<S>( x: &ArrayBase<S, Ix2>, use_correlation: bool, ) -> Result<ScreePlotData>

where S: Data, S::Elem: Float + NumCast,

Compute scree plot data from a data matrix

Computes eigenvalues of the covariance (or correlation) matrix, which can be used for scree plots to determine the number of factors.

Arguments

• x - Input data, shape (n_samples, n_features)
• use_correlation - If true, use the correlation matrix instead of covariance

Returns

• Result<ScreePlotData> - Eigenvalues and variance proportions

Trait Implementations

impl Clone for FactorAnalysis

fn clone(&self) -> FactorAnalysis

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for FactorAnalysis

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

Formats the value using the given formatter.