/*
* 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.freeform;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.ml.BasicML;
import org.encog.ml.MLClassification;
import org.encog.ml.MLContext;
import org.encog.ml.MLEncodable;
import org.encog.ml.MLError;
import org.encog.ml.MLRegression;
import org.encog.ml.MLResettable;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.neural.freeform.basic.BasicActivationSummationFactory;
import org.encog.neural.freeform.basic.BasicFreeformConnectionFactory;
import org.encog.neural.freeform.basic.BasicFreeformLayerFactory;
import org.encog.neural.freeform.basic.BasicFreeformNeuronFactory;
import org.encog.neural.freeform.factory.FreeformConnectionFactory;
import org.encog.neural.freeform.factory.FreeformLayerFactory;
import org.encog.neural.freeform.factory.FreeformNeuronFactory;
import org.encog.neural.freeform.factory.InputSummationFactory;
import org.encog.neural.freeform.task.ConnectionTask;
import org.encog.neural.freeform.task.NeuronTask;
import org.encog.neural.networks.BasicNetwork;
import org.encog.util.EngineArray;
import org.encog.util.obj.ObjectCloner;
import org.encog.util.simple.EncogUtility;
/**
* Implements a freefrom neural network. A freeform neural network can represent
* much more advanced structures than the flat networks that the Encog
* BasicNetwork implements. However, while freeform networks are more advanced
* than the BasicNetwork, they are also much slower.
*
* Freeform networks allow just about any neuron to be connected to another
* neuron. You can have neuron layers if you want, but they are not required.
*
*/
public class FreeformNetwork extends BasicML implements MLContext, Cloneable,
MLRegression, MLEncodable, MLResettable, MLClassification, MLError {
/**
* The serial ID.
*/
private static final long serialVersionUID = 1L;
/**
* Construct an Elmann recurrent neural network.
*
* @param input
* The input count.
* @param hidden1
* The hidden count.
* @param output
* The output count.
* @param af
* The activation function.
* @return The newly created network.
*/
public static FreeformNetwork createElman(final int input,
final int hidden1, final int output, final ActivationFunction af) {
final FreeformNetwork network = new FreeformNetwork();
final FreeformLayer inputLayer = network.createInputLayer(input);
final FreeformLayer hiddenLayer1 = network.createLayer(hidden1);
final FreeformLayer outputLayer = network.createOutputLayer(output);
network.connectLayers(inputLayer, hiddenLayer1, af, 1.0, false);
network.connectLayers(hiddenLayer1, outputLayer, af, 1.0, false);
network.createContext(hiddenLayer1, hiddenLayer1);
network.reset();
return network;
}
/**
* Create a feedforward freeform neural network.
*
* @param input
* The input count.
* @param hidden1
* The first hidden layer count, zero if none.
* @param hidden2
* The second hidden layer count, zero if none.
* @param output
* The output count.
* @param af
* The activation function.
* @return The newly crated network.
*/
public static FreeformNetwork createFeedforward(final int input,
final int hidden1, final int hidden2, final int output,
final ActivationFunction af) {
final FreeformNetwork network = new FreeformNetwork();
FreeformLayer lastLayer = network.createInputLayer(input);
FreeformLayer currentLayer;
if (hidden1 > 0) {
currentLayer = network.createLayer(hidden1);
network.connectLayers(lastLayer, currentLayer, af, 1.0, false);
lastLayer = currentLayer;
}
if (hidden2 > 0) {
currentLayer = network.createLayer(hidden2);
network.connectLayers(lastLayer, currentLayer, af, 1.0, false);
lastLayer = currentLayer;
}
currentLayer = network.createOutputLayer(output);
network.connectLayers(lastLayer, currentLayer, af, 1.0, false);
network.reset();
return network;
}
/**
* The input layer.
*/
private FreeformLayer inputLayer;
/**
* The output layer.
*/
private FreeformLayer outputLayer;
/**
* The connection factory.
*/
private final FreeformConnectionFactory connectionFactory = new BasicFreeformConnectionFactory();
/**
* The layer factory.
*/
private final FreeformLayerFactory layerFactory = new BasicFreeformLayerFactory();
/**
* The neuron factory.
*/
private final FreeformNeuronFactory neuronFactory = new BasicFreeformNeuronFactory();
/**
* The input summation factory.
*/
private final InputSummationFactory summationFactory = new BasicActivationSummationFactory();
/**
* Default constructor. Typically should not be directly used.
*/
public FreeformNetwork() {
}
/**
* Craete a freeform network from a basic network.
*
* @param network
* The basic network to use.
*/
public FreeformNetwork(final BasicNetwork network) {
if (network.getLayerCount() < 2) {
throw new FreeformNetworkError(
"The BasicNetwork must have at least two layers to be converted.");
}
// handle each layer
FreeformLayer previousLayer = null;
FreeformLayer currentLayer;
for (int currentLayerIndex = 0; currentLayerIndex < network
.getLayerCount(); currentLayerIndex++) {
// create the layer
currentLayer = this.layerFactory.factor();
// Is this the input layer?
if (this.inputLayer == null) {
this.inputLayer = currentLayer;
}
// Add the neurons for this layer
for (int i = 0; i < network.getLayerNeuronCount(currentLayerIndex); i++) {
// obtain the summation object.
InputSummation summation = null;
if (previousLayer != null) {
summation = this.summationFactory.factor(network
.getActivation(currentLayerIndex));
}
// add the new neuron
currentLayer.add(this.neuronFactory.factorRegular(summation));
}
// Fully connect this layer to previous
if (previousLayer != null) {
connectLayersFromBasic(network, currentLayerIndex - 1,
previousLayer, currentLayerIndex, currentLayer,
currentLayerIndex, false);
}
// Add the bias neuron
// The bias is added after connections so it has no inputs
if (network.isLayerBiased(currentLayerIndex)) {
final FreeformNeuron biasNeuron = this.neuronFactory
.factorRegular(null);
biasNeuron.setBias(true);
biasNeuron.setActivation(network
.getLayerBiasActivation(currentLayerIndex));
currentLayer.add(biasNeuron);
}
// update previous layer
previousLayer = currentLayer;
currentLayer = null;
}
// finally, set the output layer.
this.outputLayer = previousLayer;
}
/**
* {@inheritDoc}
*/
@Override
public double calculateError(final MLDataSet data) {
return EncogUtility.calculateRegressionError(this, data);
}
/**
* {@inheritDoc}
*/
@Override
public int classify(final MLData input) {
final MLData output = compute(input);
return EngineArray.maxIndex(output.getData());
}
/**
* {@inheritDoc}
*/
@Override
public void clearContext() {
performNeuronTask(new NeuronTask() {
@Override
public void task(final FreeformNeuron neuron) {
if (neuron instanceof FreeformContextNeuron) {
neuron.setActivation(0);
}
}
});
}
/**
* Return a clone of this neural network. Including structure, weights and
* bias values. This is a deep copy.
*
* @return A cloned copy of the neural network.
*/
@Override
public Object clone() {
final BasicNetwork result = (BasicNetwork) ObjectCloner.deepCopy(this);
return result;
}
/**
* {@inheritDoc}
*/
@Override
public MLData compute(final MLData input) {
// Allocate result
final MLData result = new BasicMLData(this.outputLayer.size());
// Copy the input
for (int i = 0; i < input.size(); i++) {
this.inputLayer.setActivation(i, input.getData(i));
}
// Request calculation of outputs
for (int i = 0; i < this.outputLayer.size(); i++) {
final FreeformNeuron outputNeuron = this.outputLayer.getNeurons()
.get(i);
outputNeuron.performCalculation();
result.setData(i, outputNeuron.getActivation());
}
updateContext();
return result;
}
/**
* Connect two layers. These layers will be connected with a TANH activation
* function in a non-recurrent way. A bias activation of 1.0 will be used,
* if needed.
*
* @param source
* The source layer.
* @param target
* The target layer.
*/
public void connectLayers(final FreeformLayer source,
final FreeformLayer target) {
connectLayers(source, target, new ActivationTANH(), 1.0, false);
}
/**
* Connect two layers.
*
* @param source
* The source layer.
* @param target
* The target layer.
* @param theActivationFunction
* The activation function to use.
* @param biasActivation
* The bias activation to use.
* @param isRecurrent
* True, if this is a recurrent connection.
*/
public void connectLayers(final FreeformLayer source,
final FreeformLayer target,
final ActivationFunction theActivationFunction,
final double biasActivation, final boolean isRecurrent) {
// create bias, if requested
if (biasActivation > Encog.DEFAULT_DOUBLE_EQUAL) {
// does the source already have a bias?
if (source.hasBias()) {
throw new FreeformNetworkError(
"The source layer already has a bias neuron, you cannot create a second.");
}
final FreeformNeuron biasNeuron = this.neuronFactory
.factorRegular(null);
biasNeuron.setActivation(biasActivation);
biasNeuron.setBias(true);
source.add(biasNeuron);
}
// create connections
for (final FreeformNeuron targetNeuron : target.getNeurons()) {
// create the summation for the target
InputSummation summation = targetNeuron.getInputSummation();
// do not create a second input summation
if (summation == null) {
summation = this.summationFactory.factor(theActivationFunction);
targetNeuron.setInputSummation(summation);
}
// connect the source neurons to the target neuron
for (final FreeformNeuron sourceNeuron : source.getNeurons()) {
final FreeformConnection connection = this.connectionFactory
.factor(sourceNeuron, targetNeuron);
sourceNeuron.addOutput(connection);
targetNeuron.addInput(connection);
}
}
}
/**
* Connect two layers, assume bias activation of 1.0 and non-recurrent
* connection.
*
* @param source
* The source layer.
* @param target
* The target layer.
* @param theActivationFunction
* The activation function.
*/
public void ConnectLayers(final FreeformLayer source,
final FreeformLayer target,
final ActivationFunction theActivationFunction) {
connectLayers(source, target, theActivationFunction, 1.0, false);
}
/**
* Connect layers from a BasicNetwork. Used internally only.
*
* @param network
* The BasicNetwork.
* @param fromLayerIdx
* The from layer index.
* @param source
* The from layer.
* @param sourceIdx
* The source index.
* @param target
* The target.
* @param targetIdx
* The target index.
* @param isRecurrent
* True, if this is recurrent.
*/
private void connectLayersFromBasic(final BasicNetwork network,
final int fromLayerIdx, final FreeformLayer source,
final int sourceIdx, final FreeformLayer target,
final int targetIdx, final boolean isRecurrent) {
for (int targetNeuronIdx = 0; targetNeuronIdx < target.size(); targetNeuronIdx++) {
for (int sourceNeuronIdx = 0; sourceNeuronIdx < source.size(); sourceNeuronIdx++) {
final FreeformNeuron sourceNeuron = source.getNeurons().get(
sourceNeuronIdx);
final FreeformNeuron targetNeuron = target.getNeurons().get(
targetNeuronIdx);
// neurons with no input (i.e. bias neurons)
if (targetNeuron.getInputSummation() == null) {
continue;
}
final FreeformConnection connection = this.connectionFactory
.factor(sourceNeuron, targetNeuron);
sourceNeuron.addOutput(connection);
targetNeuron.addInput(connection);
final double weight = network.getWeight(fromLayerIdx,
sourceNeuronIdx, targetNeuronIdx);
connection.setWeight(weight);
}
}
}
/**
* Create a context connection, such as those used by Jordan/Elmann.
*
* @param source
* The source layer.
* @param target
* The target layer.
* @return The newly created context layer.
*/
public FreeformLayer createContext(final FreeformLayer source,
final FreeformLayer target) {
final double biasActivation = 0.0;
ActivationFunction activatonFunction = null;
if (source.getNeurons().get(0).getOutputs().size() < 1) {
throw new FreeformNetworkError(
"A layer cannot have a context layer connected if there are no other outbound connections from the source layer. Please connect the source layer somewhere else first.");
}
activatonFunction = source.getNeurons().get(0).getInputSummation()
.getActivationFunction();
// first create the context layer
final FreeformLayer result = this.layerFactory.factor();
for (int i = 0; i < source.size(); i++) {
final FreeformNeuron neuron = source.getNeurons().get(i);
if (neuron.isBias()) {
final FreeformNeuron biasNeuron = this.neuronFactory
.factorRegular(null);
biasNeuron.setBias(true);
biasNeuron.setActivation(neuron.getActivation());
result.add(biasNeuron);
} else {
final FreeformNeuron contextNeuron = this.neuronFactory
.factorContext(neuron);
result.add(contextNeuron);
}
}
// now connect the context layer to the target layer
connectLayers(result, target, activatonFunction, biasActivation, false);
return result;
}
/**
* Create the input layer.
*
* @param neuronCount
* The input neuron count.
* @return The newly created layer.
*/
public FreeformLayer createInputLayer(final int neuronCount) {
if (neuronCount < 1) {
throw new FreeformNetworkError(
"Input layer must have at least one neuron.");
}
this.inputLayer = createLayer(neuronCount);
return this.inputLayer;
}
/**
* Create a hidden layer.
*
* @param neuronCount
* The neuron count.
* @return The newly created layer.
*/
public FreeformLayer createLayer(final int neuronCount) {
if (neuronCount < 1) {
throw new FreeformNetworkError(
"Layer must have at least one neuron.");
}
final FreeformLayer result = this.layerFactory.factor();
// Add the neurons for this layer
for (int i = 0; i < neuronCount; i++) {
result.add(this.neuronFactory.factorRegular(null));
}
return result;
}
/**
* Create the output layer.
*
* @param neuronCount
* The neuron count.
* @return The newly created output layer.
*/
public FreeformLayer createOutputLayer(final int neuronCount) {
if (neuronCount < 1) {
throw new FreeformNetworkError(
"Output layer must have at least one neuron.");
}
this.outputLayer = createLayer(neuronCount);
return this.outputLayer;
}
/**
* {@inheritDoc}
*/
@Override
public void decodeFromArray(final double[] encoded) {
int index = 0;
final Set visited = new HashSet();
final List queue = new ArrayList();
// first copy outputs to queue
for (final FreeformNeuron neuron : this.outputLayer.getNeurons()) {
queue.add(neuron);
}
while (queue.size() > 0) {
// pop a neuron off the queue
final FreeformNeuron neuron = queue.get(0);
queue.remove(0);
visited.add(neuron);
// find anymore neurons and add them to the queue.
if (neuron.getInputSummation() != null) {
for (final FreeformConnection connection : neuron
.getInputSummation().list()) {
connection.setWeight(encoded[index++]);
final FreeformNeuron nextNeuron = connection.getSource();
if (!visited.contains(nextNeuron)) {
queue.add(nextNeuron);
}
}
}
}
}
/**
* {@inheritDoc}
*/
@Override
public int encodedArrayLength() {
int result = 0;
final Set visited = new HashSet();
final List queue = new ArrayList();
// first copy outputs to queue
for (final FreeformNeuron neuron : this.outputLayer.getNeurons()) {
queue.add(neuron);
}
while (queue.size() > 0) {
// pop a neuron off the queue
final FreeformNeuron neuron = queue.get(0);
queue.remove(0);
visited.add(neuron);
// find anymore neurons and add them to the queue.
if (neuron.getInputSummation() != null) {
for (final FreeformConnection connection : neuron
.getInputSummation().list()) {
result++;
final FreeformNeuron nextNeuron = connection.getSource();
if (!visited.contains(nextNeuron)) {
queue.add(nextNeuron);
}
}
}
}
return result;
}
/**
* {@inheritDoc}
*/
@Override
public void encodeToArray(final double[] encoded) {
int index = 0;
final Set visited = new HashSet();
final List queue = new ArrayList();
// first copy outputs to queue
for (final FreeformNeuron neuron : this.outputLayer.getNeurons()) {
queue.add(neuron);
}
while (queue.size() > 0) {
// pop a neuron off the queue
final FreeformNeuron neuron = queue.get(0);
queue.remove(0);
visited.add(neuron);
// find anymore neurons and add them to the queue.
if (neuron.getInputSummation() != null) {
for (final FreeformConnection connection : neuron
.getInputSummation().list()) {
encoded[index++] = connection.getWeight();
final FreeformNeuron nextNeuron = connection.getSource();
if (!visited.contains(nextNeuron)) {
queue.add(nextNeuron);
}
}
}
}
}
/**
* {@inheritDoc}
*/
@Override
public int getInputCount() {
return this.inputLayer.sizeNonBias();
}
/**
* {@inheritDoc}
*/
@Override
public int getOutputCount() {
return this.outputLayer.sizeNonBias();
}
/**
* @return The output layer.
*/
public FreeformLayer getOutputLayer() {
return this.outputLayer;
}
/**
* Perform the specified connection task. This task will be performed over
* all connections.
*
* @param task
* The connection task.
*/
public void performConnectionTask(final ConnectionTask task) {
final Set visited = new HashSet();
for (final FreeformNeuron neuron : this.outputLayer.getNeurons()) {
performConnectionTask(visited, neuron, task);
}
}
/**
* Perform the specified connection task.
*
* @param visited
* The list of visited neurons.
* @param parentNeuron
* The parent neuron.
* @param task
* The task.
*/
private void performConnectionTask(final Set visited,
final FreeformNeuron parentNeuron, final ConnectionTask task) {
visited.add(parentNeuron);
// does this neuron have any inputs?
if (parentNeuron.getInputSummation() != null) {
// visit the inputs
for (final FreeformConnection connection : parentNeuron
.getInputSummation().list()) {
task.task(connection);
final FreeformNeuron neuron = connection.getSource();
// have we already visited this neuron?
if (!visited.contains(neuron)) {
performConnectionTask(visited, neuron, task);
}
}
}
}
/**
* Perform the specified neuron task. This task will be executed over all
* neurons.
*
* @param task The neuron task to perform.
*/
public void performNeuronTask(final NeuronTask task) {
final Set visited = new HashSet();
for (final FreeformNeuron neuron : this.outputLayer.getNeurons()) {
performNeuronTask(visited, neuron, task);
}
}
/**
* Perform the specified neuron task.
* @param visited The visited list.
* @param parentNeuron The neuron to start with.
* @param task The task to perform.
*/
private void performNeuronTask(final Set visited,
final FreeformNeuron parentNeuron, final NeuronTask task) {
visited.add(parentNeuron);
task.task(parentNeuron);
// does this neuron have any inputs?
if (parentNeuron.getInputSummation() != null) {
// visit the inputs
for (final FreeformConnection connection : parentNeuron
.getInputSummation().list()) {
final FreeformNeuron neuron = connection.getSource();
// have we already visited this neuron?
if (!visited.contains(neuron)) {
performNeuronTask(visited, neuron, task);
}
}
}
}
/**
* {@inheritDoc}
*/
@Override
public void reset() {
reset((int) (System.currentTimeMillis() % Integer.MAX_VALUE));
}
/**
* {@inheritDoc}
*/
@Override
public void reset(final int seed) {
final ConsistentRandomizer randomizer = new ConsistentRandomizer(-1, 1,
seed);
/**
* {@inheritDoc}
*/
performConnectionTask(new ConnectionTask() {
@Override
public void task(final FreeformConnection connection) {
connection.setWeight(randomizer.nextDouble());
}
});
}
/**
* Allocate temp training space.
* @param neuronSize The number of elements to allocate on each neuron.
* @param connectionSize The number of elements to allocate on each connection.
*/
public void tempTrainingAllocate(final int neuronSize,
final int connectionSize) {
performNeuronTask(new NeuronTask() {
@Override
public void task(final FreeformNeuron neuron) {
neuron.allocateTempTraining(neuronSize);
if (neuron.getInputSummation() != null) {
for (final FreeformConnection connection : neuron
.getInputSummation().list()) {
connection.allocateTempTraining(connectionSize);
}
}
}
});
}
/**
* Clear the temp training data.
*/
public void tempTrainingClear() {
performNeuronTask(new NeuronTask() {
@Override
public void task(final FreeformNeuron neuron) {
neuron.clearTempTraining();
if (neuron.getInputSummation() != null) {
for (final FreeformConnection connection : neuron
.getInputSummation().list()) {
connection.clearTempTraining();
}
}
}
});
}
/**
* Update context.
*/
public void updateContext() {
performNeuronTask(new NeuronTask() {
@Override
public void task(final FreeformNeuron neuron) {
neuron.updateContext();
}
});
}
/**
* {@inheritDoc}
*/
@Override
public void updateProperties() {
// not needed
}
}
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