A machine learning model example in Java: prediction of f(x) = x/pi

ML.java

/*
 * Me and #root:ari.lt people on Matrix are messing aroung with
 * Java and I went from Hello World to Fibonacci, to Machine Learning.
 * Enjoy
 *
 * License: Unlicense
 */

import java.util.Random;

public class ML {
    public static double rate = 0.001;
    public static int epochs  = 1024;

    public static double loss(double[][] t, double p0) {
        double l;
        int idx;

        l = 0.0;

        for (idx = 0; idx < t.length; ++idx) {
            double in  = t[idx][0];
            double out = t[idx][1];

            double res = in * p0; /* f(x) = x * p0 */

            double delta = res - out;

            System.out.printf("[%f] Expected %f => %f, got %f%n", l, in, out,
                              res);

            l += delta * delta;
        }

        return l / t.length;
    }

    /* Returns an estimate of a deriv(loss), more accurate as e approaches 0,
     * although slower learning */
    public static double loss_deriv(double[][] t, double p0, double e) {
        return (loss(t, p0 + e) - loss(t, p0)) / e;
    }

    public static void main(String[] args) {
        int idx;
        double p0;
        double[][] t = new double[16][2];
        double e;

        Random rand = new Random();

        e = 0.001;

        System.out.println("Doing epic training data generation...");

        for (idx = 0; idx < t.length; ++idx) {
            t[idx][0] = rand.nextDouble() * 20.0;
            t[idx][1] = t[idx][0] / Math.PI;

            System.out.printf("%d: %f => %f%n", idx, t[idx][0], t[idx][1]);
        }

        p0 = rand.nextDouble() * 10.0;

        System.out.printf("Learning for %d epochs...", epochs);

        for (idx = 0; idx < epochs; ++idx) {
            System.out.printf("%d: p0 = %f (loss = %f), e = %f%n", idx, p0,
                              loss(t, p0), e);

            p0 += e;
            e -= rate * loss_deriv(t, p0, e);
        }

        System.out.printf("Final: p0 = %f (loss = %f), e = %f%n", p0,
                          loss(t, p0), e);

        for (idx = 1; idx <= 16; ++idx) {
            System.out.printf("%d / pi = %f (expected %f)%n", idx, idx * p0,
                              idx / Math.PI);
        }
    }
}