k-means-plot.rs

extern crate rusty_machine;
extern crate rand;
extern crate gnuplot;

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;

use gnuplot::*;

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.iter_rows() {
            // 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();

    println!("Plotting the samples...");
    let mut first_rows: Matrix<f64> = Matrix::new(0, 2, vec![]);
    let mut second_rows: Matrix<f64> = Matrix::new(0, 2, vec![]);
    for (i, x) in samples.iter_rows().enumerate() {
        if classes.data()[i] == 0 {
            first_rows = first_rows.vcat(&Matrix::new(1, 2, x));
        } else {
            second_rows = second_rows.vcat(&Matrix::new(1, 2, x));
        }

    }
    let mut fg = Figure::new();
    let xs1 = first_rows.select_cols(&[0]).into_vec();
    let ys1 = first_rows.select_cols(&[1]).into_vec();
    let xs2 = second_rows.select_cols(&[0]).into_vec();
    let ys2 = second_rows.select_cols(&[1]).into_vec();
    fg.axes2d()
      .points(xs1, ys1, &[Caption("Points1"), PointSymbol('x'), Color("#008000"), PointSize(0.5)])
      .points(xs2, ys2, &[Caption("Points2"), PointSymbol('o'), Color("#ff4000"), PointSize(0.5)])
      .set_title("Plot", &[]);
    fg.set_terminal("pngcairo", "/tmp/fg1.png");
    fg.show();

    println!("Finish! Created /tmp/fg1.png")
}