Struct DiffusionMaps 

pub struct DiffusionMaps<S = Untrained> { /* private fields */ }

Diffusion Maps

Diffusion Maps is a nonlinear dimensionality reduction technique that captures the intrinsic geometry of data through the eigenfunctions of a Markov chain. It constructs a diffusion process on the data and uses the eigenvectors of the diffusion operator to embed the data.

Parameters

• n_components - Number of coordinates for the manifold
• diffusion_time - Diffusion time parameter (t)
• epsilon - Bandwidth parameter for Gaussian kernel
• alpha - Normalization parameter (0 for symmetric, 1 for row-stochastic)
• eigen_solver - The eigensolver to use
• random_state - Random state for reproducibility
• n_jobs - Number of parallel jobs

Examples

use sklears_manifold::DiffusionMaps;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let x = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0]];

let dm = DiffusionMaps::new()
    .n_components(2)
    .epsilon(1.0);
let fitted = dm.fit(&x.view(), &()).unwrap();
let embedded = fitted.transform(&x.view()).unwrap();

Implementations

impl DiffusionMaps<Untrained>

pub fn new() -> Self

Create a new DiffusionMaps instance

pub fn n_components(self, n_components: usize) -> Self

Set the number of components

pub fn diffusion_time(self, diffusion_time: usize) -> Self

Set the diffusion time

pub fn epsilon(self, epsilon: f64) -> Self

Set the epsilon parameter

pub fn alpha(self, alpha: f64) -> Self

Set the alpha parameter

pub fn eigen_solver(self, eigen_solver: &str) -> Self

Set the eigen solver

pub fn random_state(self, random_state: Option<u64>) -> Self

Set the random state

pub fn n_jobs(self, n_jobs: Option<i32>) -> Self

Set the number of jobs

impl DiffusionMaps<DiffusionMapsTrained>

pub fn embedding(&self) -> &Array2<f64>

Get the embedding

pub fn eigenvalues(&self) -> &Array2<f64>

Get the eigenvalues

pub fn eigenvectors(&self) -> &Array2<f64>

Get the eigenvectors

pub fn affinity_matrix(&self) -> &Array2<f64>

Get the affinity matrix

pub fn epsilon(&self) -> f64

Get the epsilon parameter used

Trait Implementations

impl<S: Clone> Clone for DiffusionMaps<S>

fn clone(&self) -> DiffusionMaps<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for DiffusionMaps<S>

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

Formats the value using the given formatter.

impl Default for DiffusionMaps<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for DiffusionMaps<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ()> for DiffusionMaps<Untrained>

type Fitted = DiffusionMaps<DiffusionMapsTrained>

The fitted model type

fn fit(self, x: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for DiffusionMaps<DiffusionMapsTrained>

fn transform(&self, _x: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>>

Transform the input data