/*
    * 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.oned;
  
  import java.util.ArrayList;
  import java.util.Collection;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.simple.RandomSource;
  
  import org.apache.commons.math4.neuralnet.FeatureInitializer;
  import org.apache.commons.math4.neuralnet.FeatureInitializerFactory;
  import org.apache.commons.math4.neuralnet.Network;
  import org.apache.commons.math4.neuralnet.Neuron;
  
  /**
   * Tests for {@link NeuronString} and {@link Network} functionality for
   * a one-dimensional network.
   */
  public class NeuronStringTest {
      private final UniformRandomProvider rng = RandomSource.SPLIT_MIX_64.create();
      private final FeatureInitializer init = FeatureInitializerFactory.uniform(rng, 0, 2);
  
      /*
       * Test assumes that the network is
       *
       *  0-----1-----2-----3
       */
      @Test
      public void testSegmentNetwork() {
          final FeatureInitializer[] initArray = {init};
          final NeuronString line = new NeuronString(4, false, initArray);
          Assert.assertFalse(line.isWrapped());
  
          final Network net = line.getNetwork();
          Collection<Neuron> neighbours;
  
          // Neuron 0.
          neighbours = net.getNeighbours(net.getNeuron(0));
          for (long nId : new long[] {1}) {
              Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
          }
          // Ensures that no other neurons is in the neighbourhood set.
          Assert.assertEquals(1, neighbours.size());
  
          // Neuron 1.
          neighbours = net.getNeighbours(net.getNeuron(1));
          for (long nId : new long[] {0, 2}) {
              Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
          }
          // Ensures that no other neurons is in the neighbourhood set.
          Assert.assertEquals(2, neighbours.size());
  
          // Neuron 2.
          neighbours = net.getNeighbours(net.getNeuron(2));
          for (long nId : new long[] {1, 3}) {
              Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
          }
          // Ensures that no other neurons is in the neighbourhood set.
          Assert.assertEquals(2, neighbours.size());
  
          // Neuron 3.
          neighbours = net.getNeighbours(net.getNeuron(3));
          for (long nId : new long[] {2}) {
              Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
          }
          // Ensures that no other neurons is in the neighbourhood set.
          Assert.assertEquals(1, neighbours.size());
      }
  
      /*
       * Test assumes that the network is
       *
       *  0-----1-----2-----3
       */
      @Test
      public void testCircleNetwork() {
          final FeatureInitializer[] initArray = {init};
          final NeuronString line = new NeuronString(4, true, initArray);
          Assert.assertTrue(line.isWrapped());
  
         final Network net = line.getNetwork();
         Collection<Neuron> neighbours;
 
         // Neuron 0.
         neighbours = net.getNeighbours(net.getNeuron(0));
         for (long nId : new long[] {1, 3}) {
             Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
         }
         // Ensures that no other neurons is in the neighbourhood set.
         Assert.assertEquals(2, neighbours.size());
 
         // Neuron 1.
         neighbours = net.getNeighbours(net.getNeuron(1));
         for (long nId : new long[] {0, 2}) {
             Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
         }
         // Ensures that no other neurons is in the neighbourhood set.
         Assert.assertEquals(2, neighbours.size());
 
         // Neuron 2.
         neighbours = net.getNeighbours(net.getNeuron(2));
         for (long nId : new long[] {1, 3}) {
             Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
         }
         // Ensures that no other neurons is in the neighbourhood set.
         Assert.assertEquals(2, neighbours.size());
 
         // Neuron 3.
         neighbours = net.getNeighbours(net.getNeuron(3));
         for (long nId : new long[] {0, 2}) {
             Assert.assertTrue(neighbours.contains(net.getNeuron(nId)));
         }
         // Ensures that no other neurons is in the neighbourhood set.
         Assert.assertEquals(2, neighbours.size());
     }
 
     /*
      * Test assumes that the network is
      *
      *  0-----1-----2-----3-----4
      */
     @Test
     public void testGetNeighboursWithExclude() {
         final FeatureInitializer[] initArray = {init};
         final Network net = new NeuronString(5, true, initArray).getNetwork();
         final Collection<Neuron> exclude = new ArrayList<>();
         exclude.add(net.getNeuron(1));
         final Collection<Neuron> neighbours = net.getNeighbours(net.getNeuron(0),
                                                                 exclude);
         Assert.assertTrue(neighbours.contains(net.getNeuron(4)));
         Assert.assertEquals(1, neighbours.size());
     }
 }