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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//! Hierarchical clustering

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

/// Linkage criterion for hierarchical clustering
#[derive(Debug, Clone, Copy)]
pub enum Linkage {
    /// Single linkage (minimum distance)
    Single,
    /// Complete linkage (maximum distance)
    Complete,
    /// Average linkage
    Average,
    /// Ward's minimum variance method
    Ward,
    /// Centroid linkage
    Centroid,
}

/// Hierarchical clustering (Agglomerative)
pub struct AgglomerativeClustering {
    n_clusters: Option<usize>,
    linkage: Linkage,
    distance_threshold: Option<f64>,
    compute_full_tree: bool,
}

impl AgglomerativeClustering {
    pub fn builder() -> AgglomerativeClusteringBuilder {
        AgglomerativeClusteringBuilder::new()
    }

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

    pub fn fit(&self, data: &ArrayView2<f64>) -> Result<HierarchicalResult> {
        // TODO: Implement agglomerative clustering
        // 1. Compute pairwise distances
        // 2. Build dendrogram using linkage criterion
        // 3. Cut tree at n_clusters or distance_threshold
        unimplemented!("AgglomerativeClustering::fit")
    }
}

pub struct AgglomerativeClusteringBuilder {
    n_clusters: Option<usize>,
    linkage: Linkage,
    distance_threshold: Option<f64>,
    compute_full_tree: bool,
}

impl AgglomerativeClusteringBuilder {
    pub fn new() -> Self {
        Self {
            n_clusters: None,
            linkage: Linkage::Ward,
            distance_threshold: None,
            compute_full_tree: false,
        }
    }

    pub fn n_clusters(mut self, n: Option<usize>) -> Self {
        self.n_clusters = n;
        self
    }

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

    pub fn distance_threshold(mut self, threshold: Option<f64>) -> Self {
        self.distance_threshold = threshold;
        self
    }

    pub fn compute_full_tree(mut self, compute: bool) -> Self {
        self.compute_full_tree = compute;
        self
    }

    pub fn build(self) -> AgglomerativeClustering {
        AgglomerativeClustering {
            n_clusters: self.n_clusters,
            linkage: self.linkage,
            distance_threshold: self.distance_threshold,
            compute_full_tree: self.compute_full_tree,
        }
    }
}

/// Result of hierarchical clustering
pub struct HierarchicalResult {
    pub labels: Array1<usize>,
    dendrogram: Option<Dendrogram>,
}

impl HierarchicalResult {
    pub fn labels(&self) -> &Array1<usize> {
        &self.labels
    }

    pub fn dendrogram(&self) -> Result<&Dendrogram> {
        self.dendrogram.as_ref().ok_or_else(|| {
            ClusteringError::InvalidParameter("compute_full_tree was not enabled".to_string())
        })
    }
}

/// Dendrogram representation
pub struct Dendrogram {
    // TODO: Define dendrogram structure
}

impl Dendrogram {
    pub fn plot(&self) -> DendrogramPlot {
        // TODO: Return plot builder
        unimplemented!("Dendrogram::plot")
    }
}

pub struct DendrogramPlot;

impl DendrogramPlot {
    pub fn save(&self, path: &str) -> Result<()> {
        // TODO: Save plot to file
        unimplemented!("DendrogramPlot::save")
    }
}