/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.samples.stockmarket;
import java.util.Vector;
import org.neuroph.core.learning.SupervisedTrainingElement;
import org.neuroph.core.learning.TrainingElement;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.util.TrainingSetImport;
/**
* The part of simple stock market components, easy to use
* stock market interface for neural network. Provides method to import stock training set froman array
*
* @author Valentin Steinhauer <valentin.steinhauer@t-online.de>
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class StockTrainingSetImport extends TrainingSetImport {
/**
* Creates and returns training set for stock market prediction using the provided data from array
* @param values an array containing stock data
* @param inputsCount training element (neural net) inputs count
* @param outputsCount training element (neural net) ouputs count
* @return training set with stock data
*/
public static TrainingSet importFromArray(double[] values, int inputsCount, int outputsCount) {
TrainingSet trainingSet = new TrainingSet(inputsCount, outputsCount);
for (int i = 0; i < values.length - inputsCount; i++) {
Vector<Double> inputs = new Vector<Double>();
for (int j = i; j < i + inputsCount; j++) {
inputs.add(values[j]);
}
Vector<Double> outputs = new Vector<Double>();
if (outputsCount > 0 && i + inputsCount + outputsCount <= values.length) {
for (int j = i + inputsCount; j < i + inputsCount + outputsCount; j++) {
outputs.add(values[j]);
}
if (outputsCount > 0) {
trainingSet.addElement(new SupervisedTrainingElement(inputs, outputs));
} else {
trainingSet.addElement(new TrainingElement(inputs));
}
}
}
return trainingSet;
}
}