/*
    * 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.neuralnet.sofm;
  
  import org.junit.Test;
  import org.junit.Assert;
  
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.simple.RandomSource;
  
  import org.apache.commons.math4.neuralnet.DistanceMeasure;
  import org.apache.commons.math4.neuralnet.EuclideanDistance;
  import org.apache.commons.math4.neuralnet.FeatureInitializer;
  import org.apache.commons.math4.neuralnet.FeatureInitializerFactory;
  import org.apache.commons.math4.neuralnet.MapRanking;
  import org.apache.commons.math4.neuralnet.Network;
  import org.apache.commons.math4.neuralnet.Neuron;
  import org.apache.commons.math4.neuralnet.OffsetFeatureInitializer;
  import org.apache.commons.math4.neuralnet.UpdateAction;
  import org.apache.commons.math4.neuralnet.oned.NeuronString;
  
  /**
   * Tests for {@link KohonenUpdateAction} class.
   */
  public class KohonenUpdateActionTest {
      private final UniformRandomProvider rng = RandomSource.SPLIT_MIX_64.create();
  
      /*
       * Test assumes that the network is
       *
       *  0-----1-----2
       */
      @Test
      public void testUpdate() {
          final FeatureInitializer init
              = new OffsetFeatureInitializer(FeatureInitializerFactory.uniform(rng, 0, 0.1));
          final FeatureInitializer[] initArray = {init};
  
          final int netSize = 3;
          final Network net = new NeuronString(netSize, false, initArray).getNetwork();
          final DistanceMeasure dist = new EuclideanDistance();
          final LearningFactorFunction learning
              = LearningFactorFunctionFactory.exponentialDecay(1, 0.1, 100);
          final NeighbourhoodSizeFunction neighbourhood
              = NeighbourhoodSizeFunctionFactory.exponentialDecay(3, 1, 100);
          final UpdateAction update = new KohonenUpdateAction(dist, learning, neighbourhood);
          final MapRanking rank = new MapRanking(net, dist);
  
          // The following test ensures that, after one "update",
          // 1. when the initial learning rate equal to 1, the best matching
          //    neuron's features are mapped to the input's features,
          // 2. when the initial neighbourhood is larger than the network's size,
          //    all neuron's features get closer to the input's features.
  
          final double[] features = new double[] {0.3};
          final double[] distancesBefore = getDistances(net, dist, features);
          final Neuron bestBefore = rank.rank(features, 1).get(0);
  
          // Initial distance from the best match is larger than zero.
          Assert.assertTrue(dist.applyAsDouble(bestBefore.getFeatures(), features) >= 0.2);
  
          update.update(net, features);
  
          final double[] distancesAfter = getDistances(net, dist, features);
          final Neuron bestAfter = rank.rank(features, 1).get(0);
  
          Assert.assertEquals(bestBefore, bestAfter);
          // Distance is now zero.
          Assert.assertEquals(0, dist.applyAsDouble(bestAfter.getFeatures(), features), 1e-16);
  
          for (int i = 0; i < netSize; i++) {
              // All distances have decreased.
              Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);
          }
      }
  
      /**
       * Gets the distance of each Neuron to the specified features.
       * Distances are returned ordered by the Neuron ID.
       *
       * @param net Network
       * @param dist Distance measure
       * @param features Feature vector
       * @return the distances
      */
     private static double[] getDistances(Network net,
                                          DistanceMeasure dist,
                                          double[] features) {
         return net.getNeurons()
                   .stream()
                   .sorted((a, b) -> Long.compare(a.getIdentifier(), b.getIdentifier()))
                   .mapToDouble(n -> dist.applyAsDouble(n.getFeatures(), features))
                   .toArray();
     }
 }