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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//! Cluster validation metrics

use crate::metrics::distance::Metric;
use crate::{ClusteringError, Result};
use ndarray::{Array1, Array2, ArrayView1, ArrayView2};

/// Silhouette score (internal metric)
pub fn silhouette_score(
    data: &ArrayView2<f64>,
    labels: &ArrayView1<usize>,
    metric: &Metric,
) -> Result<f64> {
    // TODO: Implement silhouette score
    // For each sample: s = (b - a) / max(a, b)
    // where a = mean distance to same cluster, b = mean distance to nearest cluster
    unimplemented!("silhouette_score")
}

/// Davies-Bouldin index (internal metric, lower is better)
pub fn davies_bouldin_score(data: &ArrayView2<f64>, labels: &ArrayView1<usize>) -> Result<f64> {
    // TODO: Implement Davies-Bouldin index
    unimplemented!("davies_bouldin_score")
}

/// Calinski-Harabasz index (internal metric, higher is better)
pub fn calinski_harabasz_score(data: &ArrayView2<f64>, labels: &ArrayView1<usize>) -> Result<f64> {
    // TODO: Implement Calinski-Harabasz index
    // Ratio of between-cluster to within-cluster dispersion
    unimplemented!("calinski_harabasz_score")
}

/// Dunn index (internal metric, higher is better)
pub fn dunn_index(
    data: &ArrayView2<f64>,
    labels: &ArrayView1<usize>,
    metric: &Metric,
) -> Result<f64> {
    // TODO: Implement Dunn index
    // Ratio of minimum inter-cluster distance to maximum intra-cluster distance
    unimplemented!("dunn_index")
}

/// Adjusted Rand Index (external metric)
pub fn adjusted_rand_index(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement ARI
    unimplemented!("adjusted_rand_index")
}

/// Adjusted Mutual Information (external metric)
pub fn adjusted_mutual_info(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement AMI
    unimplemented!("adjusted_mutual_info")
}

/// Normalized Mutual Information (external metric)
pub fn normalized_mutual_info(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement NMI
    unimplemented!("normalized_mutual_info")
}

/// Fowlkes-Mallows score (external metric)
pub fn fowlkes_mallows_score(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement Fowlkes-Mallows score
    unimplemented!("fowlkes_mallows_score")
}

/// Homogeneity score (external metric)
pub fn homogeneity_score(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement homogeneity score
    unimplemented!("homogeneity_score")
}

/// Completeness score (external metric)
pub fn completeness_score(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement completeness score
    unimplemented!("completeness_score")
}

/// V-measure score (external metric)
pub fn v_measure_score(
    true_labels: &ArrayView1<usize>,
    pred_labels: &ArrayView1<usize>,
) -> Result<f64> {
    // TODO: Implement V-measure score
    // Harmonic mean of homogeneity and completeness
    unimplemented!("v_measure_score")
}

/// Clustering stability analysis
pub fn clustering_stability(
    data: &ArrayView2<f64>,
    algorithm: &dyn ClusteringAlgorithm,
    n_bootstraps: usize,
) -> Result<f64> {
    // TODO: Implement stability analysis using bootstrap sampling
    unimplemented!("clustering_stability")
}

/// Trait for clustering algorithms (for stability analysis)
pub trait ClusteringAlgorithm {
    fn fit_predict(&self, data: &ArrayView2<f64>) -> Result<Array1<usize>>;
}