Struct ChineseRestaurantProcess 

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

Chinese Restaurant Process Mixture Model

A nonparametric Bayesian mixture model that automatically determines the number of components using the Chinese Restaurant Process metaphor. Customers (data points) sit at tables (components) with probability proportional to the number of existing customers at each table, or start a new table with probability proportional to the concentration parameter α.

Examples

use sklears_mixture::{ChineseRestaurantProcess, CovarianceType};
use sklears_core::traits::{Predict, Fit};
use scirs2_core::ndarray::array;

let X = array![[0.0, 0.0], [1.0, 1.0], [2.0, 2.0], [10.0, 10.0], [11.0, 11.0], [12.0, 12.0]];

let model = ChineseRestaurantProcess::new()
    .alpha(1.0)
    .max_components(10)
    .covariance_type(CovarianceType::Diagonal);
let fitted = model.fit(&X.view(), &()).unwrap();
let labels = fitted.predict(&X.view()).unwrap();

Implementations

impl ChineseRestaurantProcess<Untrained>

pub fn new() -> Self

Create a new Chinese Restaurant Process

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

Set the concentration parameter

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

Set the maximum number of components

pub fn covariance_type(self, covariance_type: CovarianceType) -> Self

Set the covariance type

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

Set the convergence tolerance

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

Set the maximum number of iterations

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

Set the random state

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

Set the regularization parameter

impl ChineseRestaurantProcess<ChineseRestaurantProcessTrained>

pub fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>>

Predict class probabilities

pub fn score(&self, X: &ArrayView2<'_, Float>) -> SklResult<f64>

Score samples using log-likelihood

Trait Implementations

impl<S: Clone> Clone for ChineseRestaurantProcess<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ChineseRestaurantProcess<S>

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

Formats the value using the given formatter.

impl Default for ChineseRestaurantProcess<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ChineseRestaurantProcess<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 ChineseRestaurantProcess<Untrained>

type Fitted = ChineseRestaurantProcess<ChineseRestaurantProcessTrained>

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 Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for ChineseRestaurantProcess<ChineseRestaurantProcessTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals