Unsupervised learning and clustering algorithms for ToRSh

This crate provides PyTorch-compatible clustering algorithms built on top of the SciRS2 ecosystem, offering high-performance implementations of popular clustering methods with extensive customization options.

Key Features

• K-Means Clustering: Classic centroid-based clustering with multiple initialization strategies
• Hierarchical Clustering: Agglomerative and divisive clustering methods
• DBSCAN: Density-based clustering for arbitrary-shaped clusters
• Gaussian Mixture Models: Probabilistic clustering with EM algorithm
• Spectral Clustering: Graph-based clustering using eigendecomposition
• Evaluation Metrics: Silhouette score, adjusted rand index, and more

SciRS2 Integration

All clustering algorithms are built on scirs2-cluster foundation:

• Leverages scirs2-core for random number generation and array operations
• Uses scirs2-stats for statistical computations
• Integrates with scirs2-metrics for clustering evaluation
• Employs scirs2-linalg for linear algebra operations

Example Usage

use torsh_cluster::prelude::*;
use torsh_tensor::Tensor;

// Create sample data
let data = Tensor::randn(&[100, 2])?;

// Perform K-means clustering
let kmeans = KMeans::new(3)
    .max_iters(100)
    .tolerance(1e-4);

let result = kmeans.fit(&data)?;
println!("Cluster centers: {:?}", result.centroids);
println!("Labels: {:?}", result.labels);