/*
* 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.util.simple;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.util.EngineArray;
import org.encog.util.ObjectPair;
import org.encog.util.csv.CSVFormat;
import org.encog.util.csv.ReadCSV;
public class TrainingSetUtil {
/**
* Load a CSV file into a memory dataset.
* @param format The CSV format to use.
* @param filename The filename to load.
* @param headers True if there is a header line.
* @param inputSize The input size. Input always comes first in a file.
* @param idealSize The ideal size, 0 for unsupervised.
* @return A NeuralDataSet that holds the contents of the CSV file.
*/
public static MLDataSet loadCSVTOMemory(CSVFormat format,
String filename, boolean headers, int inputSize, int idealSize) {
MLDataSet result = new BasicMLDataSet();
ReadCSV csv = new ReadCSV(filename, headers, format);
while (csv.next()) {
MLData input = null;
MLData ideal = null;
int index = 0;
input = new BasicMLData(inputSize);
for (int i = 0; i < inputSize; i++) {
double d = csv.getDouble(index++);
input.setData(i, d);
}
if (idealSize > 0) {
ideal = new BasicMLData(idealSize);
for (int i = 0; i < idealSize; i++) {
double d = csv.getDouble(index++);
ideal.setData(i, d);
}
}
MLDataPair pair = new BasicMLDataPair(input, ideal);
result.add(pair);
}
return result;
}
public static ObjectPair<double[][], double[][]> trainingToArray(
MLDataSet training) {
int length = (int)training.getRecordCount();
double[][] a = new double[length][training.getInputSize()];
double[][] b = new double[length][training.getIdealSize()];
int index = 0;
for (MLDataPair pair : training) {
EngineArray.arrayCopy(pair.getInputArray(), a[index]);
EngineArray.arrayCopy(pair.getIdealArray(), b[index]);
index++;
}
return new ObjectPair<double[][], double[][]>(a, b);
}
}