/*
* 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.benchmark;
import org.encog.mathutil.randomize.generate.LinearCongruentialRandom;
import org.encog.ml.data.MLData;
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;
/**
* Class used to generate random training sets. This will always generate
* the same number outputs, as it always uses the same seed values. This
* allows for the consistent results needed by the benchmark.
*/
public final class RandomTrainingFactory {
/**
* Generate a random training set.
*
* @param seed
* The seed value to use, the same seed value will always produce
* the same results.
* @param count
* How many training items to generate.
* @param inputCount
* How many input numbers.
* @param idealCount
* How many ideal numbers.
* @param min
* The minimum random number.
* @param max
* The maximum random number.
* @return The random training set.
*/
public static BasicMLDataSet generate(final long seed,
final int count, final int inputCount,
final int idealCount, final double min, final double max) {
LinearCongruentialRandom rand =
new LinearCongruentialRandom(seed);
final BasicMLDataSet result = new BasicMLDataSet();
for (int i = 0; i < count; i++) {
final MLData inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++) {
inputData.setData(j, rand.nextDouble(min, max));
}
final MLData idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++) {
idealData.setData(j, rand.nextDouble(min, max));
}
final BasicMLDataPair pair = new BasicMLDataPair(inputData,
idealData);
result.add(pair);
}
return result;
}
/**
* Generate random training into a training set.
* @param training The training set to generate into.
* @param seed The seed to use.
* @param count How much data to generate.
* @param min The low random value.
* @param max The high random value.
*/
public static void generate(final MLDataSet training,
final long seed,
final int count,
final double min, final double max) {
LinearCongruentialRandom rand
= new LinearCongruentialRandom(seed);
int inputCount = training.getInputSize();
int idealCount = training.getIdealSize();
for (int i = 0; i < count; i++) {
final MLData inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++) {
inputData.setData(j, rand.nextDouble(min, max));
}
final MLData idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++) {
idealData.setData(j, rand.nextDouble(min, max));
}
final BasicMLDataPair pair = new BasicMLDataPair(inputData,
idealData);
training.add(pair);
}
}
/**
* Private constructor.
*/
private RandomTrainingFactory() {
}
}