/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.util.normalize.target;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.data.basic.BasicNeuralData;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.util.normalize.DataNormalization;
/**
* Store the normalized data to a neural data set.
*/
public class NormalizationStorageNeuralDataSet implements NormalizationStorage {
/**
* The input count.
*/
private int inputCount;
/**
* The ideal count.
*/
private int idealCount;
/**
* The data set to add to.
*/
private MLDataSet dataset;
public NormalizationStorageNeuralDataSet()
{
}
/**
* Construct a new NeuralDataSet based on the parameters specified.
*
* @param inputCount The input count.
* @param idealCount The output count.
*/
public NormalizationStorageNeuralDataSet(final int inputCount,
final int idealCount) {
this.inputCount = inputCount;
this.idealCount = idealCount;
this.dataset = new BasicNeuralDataSet();
}
/**
* Construct a normalized neural storage class to hold data.
*
* @param dataset
* The data set to store to. This uses an existing data set.
*/
public NormalizationStorageNeuralDataSet(final MLDataSet dataset) {
this.dataset = dataset;
this.inputCount = this.dataset.getInputSize();
this.idealCount = this.dataset.getIdealSize();
}
/**
* Not needed for this storage type.
*/
public void close() {
}
/**
* Not needed for this storage type.
*/
public void open(DataNormalization norm) {
}
/**
* Write an array.
*
* @param data
* The data to write.
* @param inputCount
* How much of the data is input.
*/
public void write(final double[] data, final int inputCount) {
if (this.idealCount == 0) {
final BasicNeuralData inputData = new BasicNeuralData(data);
this.dataset.add(inputData);
} else {
final BasicNeuralData inputData = new BasicNeuralData(
this.inputCount);
final BasicNeuralData idealData = new BasicNeuralData(
this.idealCount);
int index = 0;
for (int i = 0; i < this.inputCount; i++) {
inputData.setData(i, data[index++]);
}
for (int i = 0; i < this.idealCount; i++) {
idealData.setData(i, data[index++]);
}
this.dataset.add(inputData, idealData);
}
}
/**
* @return The dataset used.
*/
public MLDataSet getDataset() {
return dataset;
}
}