/*
* Artificial Intelligence for Humans
* Volume 3: Deep Learning and Neural Networks
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
*
* Copyright 2014-2015 by Jeff Heaton
*
* 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 com.heatonresearch.aifh.examples.timeseries;
import au.com.bytecode.opencsv.CSVReader;
import com.heatonresearch.aifh.ann.BasicLayer;
import com.heatonresearch.aifh.ann.BasicNetwork;
import com.heatonresearch.aifh.ann.activation.ActivationLinear;
import com.heatonresearch.aifh.ann.activation.ActivationReLU;
import com.heatonresearch.aifh.ann.train.BackPropagation;
import com.heatonresearch.aifh.examples.learning.SimpleLearn;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.general.data.TimeSeriesUtil;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.Reader;
import java.util.List;
public class PredictSunspots extends SimpleLearn {
public int INPUT_WINDOW = 10;
public List<BasicData> loadSunspots() throws IOException {
// Obtain file
final InputStream istream = this.getClass().getResourceAsStream("/sunspots.csv");
if( istream==null ) {
System.out.println("Cannot access data set, make sure the resources are available.");
System.exit(1);
}
final Reader reader = new InputStreamReader(istream);
final CSVReader csv = new CSVReader(reader);
csv.readNext();
// determine how many entries in file
int sunspotCount = 0;
while ( csv.readNext() != null) {
sunspotCount++;
}
System.out.println("Sunspot count:" + sunspotCount);
// allocate array to hold file
double[][] dataset = new double[sunspotCount][1];
// read file
final InputStream istream2 = this.getClass().getResourceAsStream("/sunspots.csv");
final Reader reader2 = new InputStreamReader(istream2);
final CSVReader csv2 = new CSVReader(reader2);
csv2.readNext();
String[] nextLine;
int idx = 0;
while ((nextLine = csv2.readNext()) != null) {
double ssn = Double.parseDouble(nextLine[3]);
dataset[idx++][0] = ssn;
}
// timseries encode
List<BasicData> result = TimeSeriesUtil.slidingWindow(dataset, this.INPUT_WINDOW,1,new int[]{0},new int[]{0});
return result;
}
public void process() {
try {
List<BasicData> trainingData = loadSunspots();
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true, this.INPUT_WINDOW));
network.addLayer(new BasicLayer(new ActivationReLU(),true,50));
network.addLayer(new BasicLayer(new ActivationLinear(),false,1));
network.finalizeStructure();
network.reset();
final BackPropagation train = new BackPropagation(network, trainingData, 1e-9, 0.5);
train.setBatchSize(0);
performIterations(train, 100000, 650, true);
query(network,trainingData);
} catch(Throwable t) {
t.printStackTrace();
}
}
public static void main(String[] args) {
PredictSunspots prg = new PredictSunspots();
prg.process();
}
}