/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.ml.data.basic;
import java.io.Serializable;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.util.Format;
/**
* A basic implementation of the MLDataPair interface. This implementation
* simply holds and input and ideal MLData object.
*
* For supervised training both input and ideal should be specified.
*
* For unsupervised training the input property should be valid, however the
* ideal property should contain null.
*
* @author jheaton
*
*/
public class BasicMLDataPair implements MLDataPair, Serializable {
/**
* The serial ID.
*/
private static final long serialVersionUID = -9068229682273861359L;
/**
* The significance.
*/
private double significance = 1.0;
/**
* Create a new data pair object of the correct size for the machine
* learning method that is being trained. This object will be passed to the
* getPair method to allow the data pair objects to be copied to it.
*
* @param inputSize
* The size of the input data.
* @param idealSize
* The size of the ideal data.
* @return A new data pair object.
*/
public static MLDataPair createPair(final int inputSize,
final int idealSize) {
MLDataPair result;
if (idealSize > 0) {
result = new BasicMLDataPair(new BasicMLData(inputSize),
new BasicMLData(idealSize));
} else {
result = new BasicMLDataPair(new BasicMLData(inputSize));
}
return result;
}
/**
* The the expected output from the machine learning method, or null for
* unsupervised training.
*/
private final MLData ideal;
/**
* The training input to the machine learning method.
*/
private final MLData input;
/**
* Construct the object with only input. If this constructor is used, then
* unsupervised training is being used.
*
* @param theInput
* The input to the machine learning method.
*/
public BasicMLDataPair(final MLData theInput) {
this.input = theInput;
this.ideal = null;
}
/**
* Construct a BasicMLDataPair class with the specified input and ideal
* values.
*
* @param theInput
* The input to the machine learning method.
* @param theIdeal
* The expected results from the machine learning method.
*/
public BasicMLDataPair(final MLData theInput, final MLData theIdeal) {
this.input = theInput;
this.ideal = theIdeal;
}
/**
* {@inheritDoc}
*/
@Override
public final MLData getIdeal() {
return this.ideal;
}
/**
* {@inheritDoc}
*/
@Override
public final double[] getIdealArray() {
if (this.ideal == null) {
return null;
}
return this.ideal.getData();
}
/**
* {@inheritDoc}
*/
@Override
public final MLData getInput() {
return this.input;
}
/**
* {@inheritDoc}
*/
@Override
public final double[] getInputArray() {
return this.input.getData();
}
/**
* {@inheritDoc}
*/
@Override
public final boolean isSupervised() {
return this.ideal != null;
}
/**
* {@inheritDoc}
*/
@Override
public final void setIdealArray(final double[] data) {
this.ideal.setData(data);
}
/**
* {@inheritDoc}
*/
@Override
public final void setInputArray(final double[] data) {
this.input.setData(data);
}
/**
* {@inheritDoc}
*/
@Override
public final String toString() {
final StringBuilder builder = new StringBuilder("[");
builder.append(this.getClass().getSimpleName());
builder.append(":");
builder.append("Input:");
builder.append(getInput());
builder.append("Ideal:");
builder.append(getIdeal());
builder.append(",");
builder.append("Significance:");
builder.append(Format.formatPercent(this.significance));
builder.append("]");
return builder.toString();
}
/**
* {@inheritDoc}
*/
public double getSignificance() {
return significance;
}
/**
* {@inheritDoc}
*/
public void setSignificance(double significance) {
this.significance = significance;
}
}