/*
* 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.engine.data;
/**
* Training data is stored in two ways, depending on if the data is for
* supervised, or unsupervised training.
*
* For unsupervised training just an input value is provided, and the ideal
* output values are null.
*
* For supervised training both input and the expected ideal outputs are
* provided.
*
* This interface abstracts classes that provide a holder for both of these two
* data items.
*
* @author jheaton
*/
public interface EngineData {
/**
* @return The ideal data that the neural network should produce for the
* specified input.
*/
double[] getIdealArray();
/**
* @return The input that the neural network
*/
double[] getInputArray();
/**
* Set the ideal data, the desired output.
*
* @param data
* The ideal data.
*/
void setIdealArray(double[] data);
/**
* Set the input.
*
* @param data
* The input.
*/
void setInputArray(double[] data);
/**
* @return True if this training pair is supervised. That is, it has both
* input and ideal data.
*/
boolean isSupervised();
}