/*
* Encog(tm) Examples 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.examples.neural.benchmark;
import java.io.File;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.data.buffer.BufferedNeuralDataSet;
import org.encog.util.Format;
import org.encog.util.benchmark.Evaluate;
import org.encog.util.benchmark.RandomTrainingFactory;
public class BinaryVsMemory {
private static int evalMemory()
{
final BasicMLDataSet training = RandomTrainingFactory.generate(1000,
10000, 10, 10, -1, 1);
final long start = System.currentTimeMillis();
final long stop = start + (10*Evaluate.MILIS);
int record = 0;
MLDataPair pair = BasicMLDataPair.createPair(10, 10);
int iterations = 0;
while( System.currentTimeMillis()<stop ) {
iterations++;
training.getRecord(record++, pair);
if( record>=training.getRecordCount() )
record = 0;
}
System.out.println("In 10 seconds, the memory dataset read " +
Format.formatInteger( iterations) + " records.");
return iterations;
}
private static int evalBinary()
{
File file = new File("temp.egb");
final BasicMLDataSet training = RandomTrainingFactory.generate(1000,
10000, 10, 10, -1, 1);
// create the binary file
file.delete();
BufferedNeuralDataSet training2 = new BufferedNeuralDataSet(file);
training2.load(training);
final long start = System.currentTimeMillis();
final long stop = start + (10*Evaluate.MILIS);
int record = 0;
MLDataPair pair = BasicMLDataPair.createPair(10, 10);
int iterations = 0;
while( System.currentTimeMillis()<stop ) {
iterations++;
training2.getRecord(record++, pair);
if( record>=training2.getRecordCount() )
record = 0;
}
System.out.println("In 10 seconds, the disk(binary) dataset read " +
Format.formatInteger( iterations) + " records.");
file.delete();
return iterations;
}
public static void main(String[] args)
{
int memory = evalMemory();
int binary = evalBinary();
System.out.println( "Memory is " + Format.formatInteger(memory/binary) + " times the speed of disk.");
}
}