third_party/rusty-machine/examples/k-means_generating_cluster.rs

extern crate rusty_machine;
extern crate rand;

use rusty_machine::linalg::{Matrix, BaseMatrix};
use rusty_machine::learning::k_means::KMeansClassifier;
use rusty_machine::learning::UnSupModel;

use rand::thread_rng;
use rand::distributions::IndependentSample;
use rand::distributions::normal::Normal;

fn generate_data(centroids: &Matrix<f64>,
                 points_per_centroid: usize,
                 noise: f64)
                 -> Matrix<f64> {
    assert!(centroids.cols() > 0, "Centroids cannot be empty.");
    assert!(centroids.rows() > 0, "Centroids cannot be empty.");
    assert!(noise >= 0f64, "Noise must be non-negative.");
    let mut raw_cluster_data = Vec::with_capacity(centroids.rows() * points_per_centroid *
                                                  centroids.cols());

    let mut rng = thread_rng();
    let normal_rv = Normal::new(0f64, noise);

    for _ in 0..points_per_centroid {
        // Generate points from each centroid
        for centroid in centroids.row_iter() {
            // Generate a point randomly around the centroid
            let mut point = Vec::with_capacity(centroids.cols());
            for feature in centroid.iter() {
                point.push(feature + normal_rv.ind_sample(&mut rng));
            }

            // Push point to raw_cluster_data
            raw_cluster_data.extend(point);
        }
    }

    Matrix::new(centroids.rows() * points_per_centroid,
                centroids.cols(),
                raw_cluster_data)
}

fn main() {
    println!("K-Means clustering example:");

    const SAMPLES_PER_CENTROID: usize = 2000;

    println!("Generating {0} samples from each centroids:",
             SAMPLES_PER_CENTROID);
    // Choose two cluster centers, at (-0.5, -0.5) and (0, 0.5).
    let centroids = Matrix::new(2, 2, vec![-0.5, -0.5, 0.0, 0.5]);
    println!("{}", centroids);

    // Generate some data randomly around the centroids
    let samples = generate_data(&centroids, SAMPLES_PER_CENTROID, 0.4);

    // Create a new model with 2 clusters
    let mut model = KMeansClassifier::new(2);

    // Train the model
    println!("Training the model...");
    // Our train function returns a Result<(), E>
    model.train(&samples).unwrap();

    let centroids = model.centroids().as_ref().unwrap();
    println!("Model Centroids:\n{:.3}", centroids);

    // Predict the classes and partition into
    println!("Classifying the samples...");
    let classes = model.predict(&samples).unwrap();
    let (first, second): (Vec<usize>, Vec<usize>) = classes.data().iter().partition(|&x| *x == 0);

    println!("Samples closest to first centroid: {}", first.len());
    println!("Samples closest to second centroid: {}", second.len());
}