/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.networks.structure;
import java.util.Arrays;
import org.encog.ml.MLEncodable;
import org.encog.ml.MLMethod;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.networks.BasicNetwork;
/**
* This class will extract the "long term memory" of a neural network, that is
* the weights and bias values into an array. This array can be used to view the
* neural network as a linear array of doubles. These values can then be
* modified and copied back into the neural network. This is very useful for
* simulated annealing, as well as genetic algorithms.
*
* @author jheaton
*
*/
public final class NetworkCODEC {
/**
* Error message.
*/
private final static String ERROR = "This machine learning method cannot be encoded:";
/**
* Use an array to populate the memory of the neural network.
*
* @param array
* An array of doubles.
* @param network
* The network to encode.
*/
public static void arrayToNetwork(final double[] array,
final MLMethod network) {
if (network instanceof MLEncodable) {
((MLEncodable) network).decodeFromArray(array);
return;
}
throw new NeuralNetworkError(NetworkCODEC.ERROR
+ network.getClass().getName());
}
/**
* Determine if the two neural networks are equal. Uses exact precision
* required by Arrays.equals.
*
* @param network1
* The first network.
* @param network2
* The second network.
* @return True if the two networks are equal.
*/
public static boolean equals(final BasicNetwork network1,
final BasicNetwork network2) {
final double[] array1 = NetworkCODEC.networkToArray(network1);
final double[] array2 = NetworkCODEC.networkToArray(network2);
if (array1.length != array2.length) {
return false;
}
return Arrays.equals(array1, array2);
}
/**
* Determine if the two neural networks are equal.
*
* @param network1
* The first network.
* @param network2
* The second network.
* @param precision
* How many decimal places to check.
* @return True if the two networks are equal.
*/
public static boolean equals(final BasicNetwork network1,
final BasicNetwork network2, final int precision) {
final double[] array1 = NetworkCODEC.networkToArray(network1);
final double[] array2 = NetworkCODEC.networkToArray(network2);
if (array1.length != array2.length) {
return false;
}
final double test = Math.pow(10.0, precision);
if (Double.isInfinite(test) || (test > Long.MAX_VALUE)) {
throw new NeuralNetworkError("Precision of " + precision
+ " decimal places is not supported.");
}
for (int i = 0; i < array1.length; i++) {
final long l1 = (long) (array1[i] * test);
final long l2 = (long) (array2[i] * test);
if (l1 != l2) {
return false;
}
}
return true;
}
/**
* Determine the network size.
* @param network The network.
* @return The size.
*/
public static int networkSize(final MLMethod network) {
if (network instanceof MLEncodable) {
return ((MLEncodable) network).encodedArrayLength();
}
throw new NeuralNetworkError(NetworkCODEC.ERROR
+ network.getClass().getName());
}
/**
* Convert to an array. This is used with some training algorithms that
* require that the "memory" of the neuron(the weight and bias values) be
* expressed as a linear array.
*
* @param network
* The network to encode.
* @return The memory of the neuron.
*/
public static double[] networkToArray(final MLMethod network) {
final int size = NetworkCODEC.networkSize(network);
if (network instanceof MLEncodable) {
final double[] encoded = new double[size];
((MLEncodable) network).encodeToArray(encoded);
return encoded;
}
throw new NeuralNetworkError(NetworkCODEC.ERROR
+ network.getClass().getName());
}
/**
* Private constructor.
*/
private NetworkCODEC() {
}
}