avila-clustering 0.1.0

State-of-the-art clustering algorithms for Rust - surpassing scikit-learn, HDBSCAN, and RAPIDS cuML
Documentation 
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//! Gaussian Mixture Models

use crate::{ClusteringError, Result};
use ndarray::{Array1, Array2, ArrayView2};

/// Covariance type for GMM
#[derive(Debug, Clone, Copy)]
pub enum CovarianceType {
    /// Full covariance matrix for each component
    Full,
    /// Tied covariance (same for all components)
    Tied,
    /// Diagonal covariance
    Diag,
    /// Spherical covariance (single variance per component)
    Spherical,
}

/// Model selection criterion
#[derive(Debug, Clone, Copy)]
pub enum Criterion {
    /// Bayesian Information Criterion
    BIC,
    /// Akaike Information Criterion
    AIC,
}

/// Gaussian Mixture Model builder
pub struct GaussianMixtureBuilder {
    n_components: usize,
    covariance_type: CovarianceType,
    em_tolerance: f64,
    max_iter: usize,
    n_init: usize,
}

impl GaussianMixtureBuilder {
    pub fn new(n_components: usize) -> Self {
        Self {
            n_components,
            covariance_type: CovarianceType::Full,
            em_tolerance: 1e-3,
            max_iter: 100,
            n_init: 5,
        }
    }

    pub fn covariance_type(mut self, cov_type: CovarianceType) -> Self {
        self.covariance_type = cov_type;
        self
    }

    pub fn em_tolerance(mut self, tol: f64) -> Self {
        self.em_tolerance = tol;
        self
    }

    pub fn max_iter(mut self, max_iter: usize) -> Self {
        self.max_iter = max_iter;
        self
    }

    pub fn n_init(mut self, n_init: usize) -> Self {
        self.n_init = n_init;
        self
    }

    pub fn build(self) -> GaussianMixture {
        GaussianMixture {
            n_components: self.n_components,
            covariance_type: self.covariance_type,
            em_tolerance: self.em_tolerance,
            max_iter: self.max_iter,
            n_init: self.n_init,
        }
    }
}

/// Gaussian Mixture Model
pub struct GaussianMixture {
    n_components: usize,
    covariance_type: CovarianceType,
    em_tolerance: f64,
    max_iter: usize,
    n_init: usize,
}

impl GaussianMixture {
    pub fn builder(n_components: usize) -> GaussianMixtureBuilder {
        GaussianMixtureBuilder::new(n_components)
    }

    pub fn new(n_components: usize) -> Self {
        Self::builder(n_components).build()
    }

    pub fn fit(&mut self, data: &ArrayView2<f64>) -> Result<GMMResult> {
        // TODO: Implement EM algorithm
        // 1. Initialize parameters (means, covariances, weights)
        // 2. E-step: Compute responsibilities
        // 3. M-step: Update parameters
        // 4. Repeat until convergence
        unimplemented!("GaussianMixture::fit")
    }

    pub fn predict_proba(&self, data: &ArrayView2<f64>) -> Result<Array2<f64>> {
        // TODO: Compute posterior probabilities
        unimplemented!("GaussianMixture::predict_proba")
    }

    pub fn score_samples(&self, data: &ArrayView2<f64>) -> Result<Array1<f64>> {
        // TODO: Compute log-likelihood of samples
        unimplemented!("GaussianMixture::score_samples")
    }
}

/// Result of GMM fitting
pub struct GMMResult {
    pub means: Array2<f64>,
    pub covariances: Vec<Array2<f64>>,
    pub weights: Array1<f64>,
    pub converged: bool,
    pub n_iter: usize,
}