/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math4.legacy.ml.clustering;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException;
import org.apache.commons.math4.legacy.exception.NullArgumentException;
import org.apache.commons.math4.legacy.ml.distance.CanberraDistance;
import org.apache.commons.math4.legacy.ml.distance.DistanceMeasure;
import org.apache.commons.rng.simple.RandomSource;
import org.apache.commons.rng.UniformRandomProvider;
import org.hamcrest.CoreMatchers;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test cases for FuzzyKMeansClusterer.
 *
 * @since 3.3
 */
public class FuzzyKMeansClustererTest {

    @Test
    public void testCluster() {
        final List<DoublePoint> points = new ArrayList<>();

        // create 10 data points: [1], ... [10]
        for (int i = 1; i <= 10; i++) {
            final DoublePoint p = new DoublePoint(new double[] { i } );
            points.add(p);
        }

        final FuzzyKMeansClusterer<DoublePoint> transformer =
                new FuzzyKMeansClusterer<>(3, 2.0);
        final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);

        // we expect 3 clusters:
        //   [1], [2], [3]
        //   [4], [5], [6], [7]
        //   [8], [9], [10]
        final List<DoublePoint> clusterOne = Arrays.asList(points.get(0), points.get(1), points.get(2));
        final List<DoublePoint> clusterTwo = Arrays.asList(points.get(3), points.get(4), points.get(5), points.get(6));
        final List<DoublePoint> clusterThree = Arrays.asList(points.get(7), points.get(8), points.get(9));

        boolean cluster1Found = false;
        boolean cluster2Found = false;
        boolean cluster3Found = false;
        Assert.assertEquals(3, clusters.size());
        for (final Cluster<DoublePoint> cluster : clusters) {
            if (cluster.getPoints().containsAll(clusterOne)) {
                cluster1Found = true;
            }
            if (cluster.getPoints().containsAll(clusterTwo)) {
                cluster2Found = true;
            }
            if (cluster.getPoints().containsAll(clusterThree)) {
                cluster3Found = true;
            }
        }
        Assert.assertTrue(cluster1Found);
        Assert.assertTrue(cluster2Found);
        Assert.assertTrue(cluster3Found);
    }

    @Test(expected = MathIllegalArgumentException.class)
    public void testTooSmallFuzzynessFactor() {
        new FuzzyKMeansClusterer<DoublePoint>(3, 1.0);
    }

    @Test(expected = NullArgumentException.class)
    public void testNullDataset() {
        final FuzzyKMeansClusterer<DoublePoint> clusterer = new FuzzyKMeansClusterer<>(3, 2.0);
        clusterer.cluster(null);
    }

    @Test
    public void testGetters() {
        final DistanceMeasure measure = new CanberraDistance();
        final UniformRandomProvider random = RandomSource.MT_64.create();
        final FuzzyKMeansClusterer<DoublePoint> clusterer =
                new FuzzyKMeansClusterer<>(3, 2.0, 100, measure, 1e-6, random);

        Assert.assertEquals(3, clusterer.getK());
        Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6);
        Assert.assertEquals(100, clusterer.getMaxIterations());
        Assert.assertEquals(1e-6, clusterer.getEpsilon(), 1e-12);
        Assert.assertThat(clusterer.getDistanceMeasure(), CoreMatchers.is(measure));
        Assert.assertThat(clusterer.getRandomGenerator(), CoreMatchers.is(random));
    }

    @Test
    public void testSingleCluster() {
        final List<DoublePoint> points = new ArrayList<>();
        points.add(new DoublePoint(new double[] { 1, 1 }));

        final FuzzyKMeansClusterer<DoublePoint> transformer =
                new FuzzyKMeansClusterer<>(1, 2.0);
        final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);

        Assert.assertEquals(1, clusters.size());
    }

    @Test
    public void testClusterCenterEqualsPoints() {
        final List<DoublePoint> points = new ArrayList<>();
        points.add(new DoublePoint(new double[] { 1, 1 }));
        points.add(new DoublePoint(new double[] { 1.00001, 1.00001 }));
        points.add(new DoublePoint(new double[] { 2, 2 }));
        points.add(new DoublePoint(new double[] { 3, 3 }));

        final FuzzyKMeansClusterer<DoublePoint> transformer =
                new FuzzyKMeansClusterer<>(3, 2.0);
        final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);

        Assert.assertEquals(3, clusters.size());
    }

}