/*
* Encog(tm) Core v2.5 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2010 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.data.basic;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import org.encog.engine.data.EngineData;
import org.encog.neural.data.Indexable;
import org.encog.neural.data.NeuralData;
import org.encog.neural.data.NeuralDataPair;
import org.encog.persist.EncogCollection;
import org.encog.persist.EncogPersistedObject;
import org.encog.persist.Persistor;
import org.encog.persist.persistors.BasicNeuralDataSetPersistor;
import org.encog.util.obj.ObjectCloner;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* neural data in an ArrayList. This class is memory based, so large enough
* datasets could cause memory issues. Many other dataset types extend this
* class.
*
* @author jheaton
*/
public class BasicNeuralDataSet implements EncogPersistedObject, Serializable,
Indexable {
/**
* An iterator to be used with the BasicNeuralDataSet. This iterator does
* not support removes.
*
* @author jheaton
*/
public class BasicNeuralIterator implements Iterator<NeuralDataPair> {
/**
* The index that the iterator is currently at.
*/
private int currentIndex = 0;
/**
* Is there more data for the iterator to read?
*
* @return Returns true if there is more data to read.
*/
public boolean hasNext() {
return this.currentIndex < BasicNeuralDataSet.this.data.size();
}
/**
* Read the next item.
*
* @return The next item.
*/
public NeuralDataPair next() {
if (!hasNext()) {
return null;
}
return BasicNeuralDataSet.this.data.get(this.currentIndex++);
}
/**
* Removes are not supported.
*/
public void remove() {
if (BasicNeuralDataSet.this.logger.isErrorEnabled()) {
BasicNeuralDataSet.this.logger
.error("Called remove, unsupported operation.");
}
throw new UnsupportedOperationException();
}
}
/**
* The serial id.
*/
private static final long serialVersionUID = -2279722928570071183L;
/**
* The logging object.
*/
private final transient Logger logger = LoggerFactory.getLogger(this
.getClass());
/**
* The Encog collection this object belongs to, or null if none.
*/
private EncogCollection encogCollection;
/**
* The data held by this object.
*/
private List<NeuralDataPair> data = new ArrayList<NeuralDataPair>();
/**
* The description for this object.
*/
private String description;
/**
* The name for this object.
*/
private String name;
/**
* Default constructor.
*/
public BasicNeuralDataSet() {
}
/**
* Construct a data set from an input and idea array.
*
* @param input
* The input into the neural network for training.
* @param ideal
* The ideal output for training.
*/
public BasicNeuralDataSet(final double[][] input, final double[][] ideal) {
if (ideal != null) {
for (int i = 0; i < input.length; i++) {
final BasicNeuralData inputData = new BasicNeuralData(input[i]);
final BasicNeuralData idealData = new BasicNeuralData(ideal[i]);
this.add(inputData, idealData);
}
} else {
for (final double[] element : input) {
final BasicNeuralData inputData = new BasicNeuralData(element);
this.add(inputData);
}
}
}
/**
* Construct a data set from an already created list. Mostly used to
* duplicate this class.
*
* @param data
* The data to use.
*/
public BasicNeuralDataSet(final List<NeuralDataPair> data) {
this.data = data;
}
/**
* Add input to the training set with no expected output. This is used for
* unsupervised training.
*
* @param data
* The input to be added to the training set.
*/
public void add(final NeuralData data) {
this.data.add(new BasicNeuralDataPair(data));
}
/**
* Add input and expected output. This is used for supervised training.
*
* @param inputData
* The input data to train on.
* @param idealData
* The ideal data to use for training.
*/
public void add(final NeuralData inputData, final NeuralData idealData) {
final NeuralDataPair pair = new BasicNeuralDataPair(inputData,
idealData);
this.data.add(pair);
}
/**
* Add a neural data pair to the list.
*
* @param inputData
* A NeuralDataPair object that contains both input and ideal
* data.
*/
public void add(final NeuralDataPair inputData) {
this.data.add(inputData);
}
/**
* @return A cloned copy of this object.
*/
@Override
public Object clone() {
return ObjectCloner.deepCopy(this);
}
/**
* Close this data set.
*/
public void close() {
// nothing to close
}
/**
* Create a persistor for this object.
*
* @return A persistor for this object.
*/
public Persistor createPersistor() {
return new BasicNeuralDataSetPersistor();
}
/**
* Get the data held by this container.
*
* @return the data
*/
public List<NeuralDataPair> getData() {
return this.data;
}
/**
* @return the description
*/
public String getDescription() {
return this.description;
}
/**
* Get the size of the ideal dataset. This is obtained from the first item
* in the list.
*
* @return The size of the ideal data.
*/
public int getIdealSize() {
if (this.data.isEmpty()) {
return 0;
}
final NeuralDataPair first = this.data.get(0);
if (first.getIdeal() == null) {
return 0;
}
return first.getIdeal().size();
}
/**
* Get the size of the input dataset. This is obtained from the first item
* in the list.
*
* @return The size of the input data.
*/
public int getInputSize() {
if (this.data.isEmpty()) {
return 0;
}
final NeuralDataPair first = this.data.get(0);
return first.getInput().size();
}
/**
* @return the name
*/
public String getName() {
return this.name;
}
/**
* Get a record by index into the specified pair.
*
* @param index
* The index to read.
* @param pair
* The pair to hold the data.
*/
public void getRecord(final long index, final EngineData pair) {
final NeuralDataPair source = this.data.get((int) index);
pair.setInputArray(source.getInputArray());
if (pair.getIdealArray() != null) {
pair.setIdealArray(source.getIdealArray());
}
}
/**
* @return The total number of records in the file.
*/
public long getRecordCount() {
return this.data.size();
}
/**
* Determine if this neural data set is supervied. All of the pairs should
* be either supervised or not, so simply check the first pair. If the list
* is empty then assume unsupervised.
*
* @return True if supervised.
*/
public boolean isSupervised() {
if (this.data.size() == 0) {
return false;
}
return this.data.get(0).isSupervised();
}
/**
* Create an iterator for this collection.
*
* @return An iterator to access this collection.
*/
public Iterator<NeuralDataPair> iterator() {
final BasicNeuralIterator result = new BasicNeuralIterator();
return result;
}
/**
* Create an additional data set. It will use the same list.
*
* @return The additional data set.
*/
public Indexable openAdditional() {
return new BasicNeuralDataSet(this.data);
}
/**
* @param data
* the data to set
*/
public void setData(final List<NeuralDataPair> data) {
this.data = data;
}
/**
* @param description
* the description to set
*/
public void setDescription(final String description) {
this.description = description;
}
/**
* @param name
* the name to set
*/
public void setName(final String name) {
this.name = name;
}
/**
* @return The collection this Encog object belongs to, null if none.
*/
public EncogCollection getCollection() {
return this.encogCollection;
}
/**
* Set the Encog collection that this object belongs to.
*/
public void setCollection(EncogCollection collection) {
this.encogCollection = collection;
}
}