/*
* 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.math3.ml.neuralnet.oned;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.util.ArrayList;
import java.util.Collection;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory;
import org.apache.commons.math3.ml.neuralnet.Network;
import org.apache.commons.math3.ml.neuralnet.Neuron;
import org.junit.Assert;
import org.junit.Test;
/**
* Tests for {@link NeuronString} and {@link Network} functionality for
* a one-dimensional network.
*/
public class NeuronStringTest {
final FeatureInitializer init = FeatureInitializerFactory.uniform(0, 2);
/*
* Test assumes that the network is
*
* 0-----1-----2-----3
*/
@Test
public void testSegmentNetwork() {
final FeatureInitializer[] initArray = { init };
final Network net = new NeuronString(4, false, initArray).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 neihbourhood 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 neihbourhood 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 neihbourhood 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 neihbourhood 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 Network net = new NeuronString(4, true, initArray).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 neihbourhood 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 neihbourhood 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 neihbourhood 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 neihbourhood 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<Neuron>();
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());
}
@Test
public void testSerialize()
throws IOException,
ClassNotFoundException {
final FeatureInitializer[] initArray = { init };
final NeuronString out = new NeuronString(4, false, initArray);
final ByteArrayOutputStream bos = new ByteArrayOutputStream();
final ObjectOutputStream oos = new ObjectOutputStream(bos);
oos.writeObject(out);
final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
final ObjectInputStream ois = new ObjectInputStream(bis);
final NeuronString in = (NeuronString) ois.readObject();
for (Neuron nOut : out.getNetwork()) {
final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());
// Same values.
final double[] outF = nOut.getFeatures();
final double[] inF = nIn.getFeatures();
Assert.assertEquals(outF.length, inF.length);
for (int i = 0; i < outF.length; i++) {
Assert.assertEquals(outF[i], inF[i], 0d);
}
// Same neighbours.
final Collection<Neuron> outNeighbours = out.getNetwork().getNeighbours(nOut);
final Collection<Neuron> inNeighbours = in.getNetwork().getNeighbours(nIn);
Assert.assertEquals(outNeighbours.size(), inNeighbours.size());
for (Neuron oN : outNeighbours) {
Assert.assertTrue(inNeighbours.contains(in.getNetwork().getNeuron(oN.getIdentifier())));
}
}
}
}