package de.lmu.ifi.dbs.elki.math.statistics.dependence;

/*
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures

 Copyright (C) 2015
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
 (at your option) any later version.

 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU Affero General Public License for more details.

 You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

import static org.junit.Assert.assertEquals;

import java.util.Arrays;
import java.util.Random;

import org.junit.Test;

import de.lmu.ifi.dbs.elki.JUnit4Test;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.ArrayLikeUtil;

/**
 * Validate correlation by comparing to manual computation.
 * 
 * @author Erich Schubert
 * @since 0.7.0
 */
public class MutualInformationEquiwidthDependenceMeasureTest implements JUnit4Test {
  double[][] data = { //
  { 1, 2, 3, 4 }, //
  { 1, 3, 5, 7 }, //
  { 4, 3, 2, 1 }, //
  { 1, 4, 2, 3 }, //
  { 1, 0, 0, 1 }, //
  { 0, 1, 1, 1 }, //
  };

  // Due to ties in last example, quantization yields:
  // X [1, 0] 1
  // Y [1, 2] 3
  // M [2, 2] 4
  final static double HH = (.5 * Math.log(4. / 3) + .25 * Math.log(2.) + .25 * Math.log(2. / 3)) * MathUtil.ONE_BY_LOG2;

  // Diagonal: [1, 3]
  final static double H4 = (.75 * Math.log(4. / 3) + .25 * Math.log(4.)) * MathUtil.ONE_BY_LOG2;

  double[][] manual = { //
  { 1. }, //
  { 1., 1. }, //
  { 1., 1., 1. }, //
  { 0., 0., 0., 1. }, //
  { 0., 0., 0., 0., 1 }, //
  { HH, HH, HH, HH, HH, H4 }, //
  };

  @Test
  public void testMI() {
    DependenceMeasure mi = MutualInformationEquiwidthDependenceMeasure.STATIC;
    // Single computations
    for(int i = 0; i < data.length; i++) {
      for(int j = 0; j <= i; j++) {
        double co = mi.dependence(data[i], data[j]);
        assertEquals("MI does not match for " + i + "," + j, manual[i][j], co, 1e-7);
      }
    }
    // Bulk computation
    double[] mat = mi.dependence(ArrayLikeUtil.DOUBLEARRAYADAPTER, Arrays.asList(data));
    for(int i = 0, c = 0; i < data.length; i++) {
      for(int j = 0; j < i; j++) {
        double co = mat[c++];
        assertEquals("MI does not match for " + i + "," + j, manual[i][j], co, 1e-7);
      }
    }
  }

  @Test
  public void testUniform() {
    int len = 10000;
    DependenceMeasure mi = MutualInformationEquiwidthDependenceMeasure.STATIC;
    double[] data1 = new double[len], data2 = new double[len];
    Random r = new Random(0);
    for(int i = 0; i < len; i++) {
      data1[i] = r.nextDouble();
      data2[i] = r.nextDouble();
    }
    // These values are only regression tests...
    // Our implementation will use 100 bins, and rescale via
    // log2(100.) = 6.6438561897747244!
    assertEquals("Self-MI1", 0.999, mi.dependence(data1, data1), 1e-3);
    assertEquals("Self-MI2", 0.999, mi.dependence(data2, data2), 1e-3);
    assertEquals("MI", 0.1235388559, mi.dependence(data1, data2), 1e-8);
  }
}