/**
* 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 org.neuroph.core.learning.SupervisedTrainingElement;
import org.neuroph.core.learning.TrainingSet;
/**
* Creates training set from the given data.
* See http://neuroph.sourceforge.net/tutorials/StockMarketPredictionTutorial.html
* @author Dr.V.Steinhauer
*/
public class TrainingData {
private String[] valuesRow;
private TrainingSet trainingSet = new TrainingSet();
private double normalizer = 10000.0D;
private double minlevel = 0.0D;
public TrainingData() {
}
public TrainingData(String[] valuesRow) {
this.setValuesRow(valuesRow);
}
public String[] getValuesRow() {
return valuesRow;
}
public void setValuesRow(String[] valuesRow) {
this.valuesRow = valuesRow;
}
public double getNormalizer() {
return normalizer;
}
public void setNormalizer(double normolizer) {
this.normalizer = normolizer;
}
public TrainingSet getTrainingSet() {
int length = valuesRow.length;
if (length < 5) {
System.out.println("valuesRow.length < 5");
return null;
}
try {
for (int i = 0; i + 4 < valuesRow.length; i++) {
String[] s1 = valuesRow[i].split(",");
String[] s2 = valuesRow[i + 1].split(",");
String[] s3 = valuesRow[i + 2].split(",");
String[] s4 = valuesRow[i + 3].split(",");
String[] s5 = valuesRow[i + 4].split(",");
double d1 = (Double.parseDouble(s1[1]) - minlevel) / normalizer;
double d2 = (Double.parseDouble(s2[1]) - minlevel) / normalizer;
double d3 = (Double.parseDouble(s3[1]) - minlevel) / normalizer;
double d4 = (Double.parseDouble(s4[1]) - minlevel) / normalizer;
double d5 = (Double.parseDouble(s5[1]) - minlevel) / normalizer;
System.out.println(i + " " + d1 + " " + d2 + " " + d3 + " " + d4 + " ->" + d5);
trainingSet.addElement(new SupervisedTrainingElement(new double[]{d1, d2, d3, d4}, new double[]{d5}));
}
} catch (Exception e) {
System.out.println(e.getMessage());
return null;
}
return trainingSet;
}
public void setTrainingSet(TrainingSet trainingSet) {
this.trainingSet = trainingSet;
}
}