ferrolearn-cluster 0.1.2

Clustering algorithms for the ferrolearn ML framework
Documentation

ferrolearn-cluster

Clustering algorithms for the ferrolearn machine learning framework.

Algorithms

Model Description
KMeans K-Means with k-Means++ initialization and parallel assignment via Rayon
MiniBatchKMeans Mini-batch variant of K-Means for large datasets
DBSCAN Density-based clustering — discovers clusters of arbitrary shape
AgglomerativeClustering Hierarchical clustering (Ward, complete, average, single linkage)
GaussianMixture Gaussian Mixture Models via EM (full, tied, diagonal, spherical covariance)
MeanShift Non-parametric mode-seeking clustering
SpectralClustering Graph Laplacian eigenmap clustering
OPTICS Ordering Points To Identify the Clustering Structure

Example

use ferrolearn_cluster::{KMeans, FittedKMeans};
use ferrolearn_core::{Fit, Predict};
use ndarray::array;

let x = array![
    [1.0_f64, 2.0], [1.5, 1.8], [1.2, 2.2],
    [5.0, 6.0], [5.5, 5.8], [5.2, 6.2],
];

let model = KMeans::<f64>::new(2).with_max_iter(100);
let fitted = model.fit(&x, &()).unwrap();

// Assign new points to clusters
let labels = fitted.predict(&x).unwrap();

// Get distances to each centroid
let distances = fitted.transform(&x).unwrap();

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.