/*
* Encog(tm) Java Examples v3.4
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-examples
*
* Copyright 2008-2016 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:
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package org.encog.examples.neural.predict.sunspot;
import java.text.NumberFormat;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.temporal.TemporalDataDescription;
import org.encog.ml.data.temporal.TemporalMLDataSet;
import org.encog.ml.data.temporal.TemporalPoint;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.Train;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.EngineArray;
import org.encog.util.arrayutil.NormalizeArray;
public class PredictSunspot {
public final static double[] SUNSPOTS = {
0.0262, 0.0575, 0.0837, 0.1203, 0.1883, 0.3033,
0.1517, 0.1046, 0.0523, 0.0418, 0.0157, 0.0000,
0.0000, 0.0105, 0.0575, 0.1412, 0.2458, 0.3295,
0.3138, 0.2040, 0.1464, 0.1360, 0.1151, 0.0575,
0.1098, 0.2092, 0.4079, 0.6381, 0.5387, 0.3818,
0.2458, 0.1831, 0.0575, 0.0262, 0.0837, 0.1778,
0.3661, 0.4236, 0.5805, 0.5282, 0.3818, 0.2092,
0.1046, 0.0837, 0.0262, 0.0575, 0.1151, 0.2092,
0.3138, 0.4231, 0.4362, 0.2495, 0.2500, 0.1606,
0.0638, 0.0502, 0.0534, 0.1700, 0.2489, 0.2824,
0.3290, 0.4493, 0.3201, 0.2359, 0.1904, 0.1093,
0.0596, 0.1977, 0.3651, 0.5549, 0.5272, 0.4268,
0.3478, 0.1820, 0.1600, 0.0366, 0.1036, 0.4838,
0.8075, 0.6585, 0.4435, 0.3562, 0.2014, 0.1192,
0.0534, 0.1260, 0.4336, 0.6904, 0.6846, 0.6177,
0.4702, 0.3483, 0.3138, 0.2453, 0.2144, 0.1114,
0.0837, 0.0335, 0.0214, 0.0356, 0.0758, 0.1778,
0.2354, 0.2254, 0.2484, 0.2207, 0.1470, 0.0528,
0.0424, 0.0131, 0.0000, 0.0073, 0.0262, 0.0638,
0.0727, 0.1851, 0.2395, 0.2150, 0.1574, 0.1250,
0.0816, 0.0345, 0.0209, 0.0094, 0.0445, 0.0868,
0.1898, 0.2594, 0.3358, 0.3504, 0.3708, 0.2500,
0.1438, 0.0445, 0.0690, 0.2976, 0.6354, 0.7233,
0.5397, 0.4482, 0.3379, 0.1919, 0.1266, 0.0560,
0.0785, 0.2097, 0.3216, 0.5152, 0.6522, 0.5036,
0.3483, 0.3373, 0.2829, 0.2040, 0.1077, 0.0350,
0.0225, 0.1187, 0.2866, 0.4906, 0.5010, 0.4038,
0.3091, 0.2301, 0.2458, 0.1595, 0.0853, 0.0382,
0.1966, 0.3870, 0.7270, 0.5816, 0.5314, 0.3462,
0.2338, 0.0889, 0.0591, 0.0649, 0.0178, 0.0314,
0.1689, 0.2840, 0.3122, 0.3332, 0.3321, 0.2730,
0.1328, 0.0685, 0.0356, 0.0330, 0.0371, 0.1862,
0.3818, 0.4451, 0.4079, 0.3347, 0.2186, 0.1370,
0.1396, 0.0633, 0.0497, 0.0141, 0.0262, 0.1276,
0.2197, 0.3321, 0.2814, 0.3243, 0.2537, 0.2296,
0.0973, 0.0298, 0.0188, 0.0073, 0.0502, 0.2479,
0.2986, 0.5434, 0.4215, 0.3326, 0.1966, 0.1365,
0.0743, 0.0303, 0.0873, 0.2317, 0.3342, 0.3609,
0.4069, 0.3394, 0.1867, 0.1109, 0.0581, 0.0298,
0.0455, 0.1888, 0.4168, 0.5983, 0.5732, 0.4644,
0.3546, 0.2484, 0.1600, 0.0853, 0.0502, 0.1736,
0.4843, 0.7929, 0.7128, 0.7045, 0.4388, 0.3630,
0.1647, 0.0727, 0.0230, 0.1987, 0.7411, 0.9947,
0.9665, 0.8316, 0.5873, 0.2819, 0.1961, 0.1459,
0.0534, 0.0790, 0.2458, 0.4906, 0.5539, 0.5518,
0.5465, 0.3483, 0.3603, 0.1987, 0.1804, 0.0811,
0.0659, 0.1428, 0.4838, 0.8127
};
public final static int STARTING_YEAR = 1700;
public final static int WINDOW_SIZE = 30;
public final static int TRAIN_START = WINDOW_SIZE;
public final static int TRAIN_END = 259;
public final static int EVALUATE_START = 260;
public final static int EVALUATE_END = SUNSPOTS.length-1;
/**
* This really should be lowered, I am setting it to a level here that will
* train in under a minute.
*/
public final static double MAX_ERROR = 0.01;
private double[] normalizedSunspots;
private double[] closedLoopSunspots;
public void normalizeSunspots(double lo, double hi) {
NormalizeArray norm = new NormalizeArray();
norm.setNormalizedHigh( hi);
norm.setNormalizedLow( lo);
// create arrays to hold the normalized sunspots
normalizedSunspots = norm.process(SUNSPOTS);
closedLoopSunspots = EngineArray.arrayCopy(normalizedSunspots);
}
public MLDataSet generateTraining()
{
TemporalMLDataSet result = new TemporalMLDataSet(WINDOW_SIZE,1);
TemporalDataDescription desc = new TemporalDataDescription(
TemporalDataDescription.Type.RAW,true,true);
result.addDescription(desc);
for(int year = TRAIN_START;year<TRAIN_END;year++)
{
TemporalPoint point = new TemporalPoint(1);
point.setSequence(year);
point.setData(0, this.normalizedSunspots[year]);
result.getPoints().add(point);
}
result.generate();
return result;
}
public BasicNetwork createNetwork()
{
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(WINDOW_SIZE));
network.addLayer(new BasicLayer(10));
network.addLayer(new BasicLayer(1));
network.getStructure().finalizeStructure();
network.reset();
return network;
}
public void train(BasicNetwork network,MLDataSet training)
{
final Train train = new ResilientPropagation(network, training);
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while(train.getError() > MAX_ERROR);
}
public void predict(BasicNetwork network)
{
NumberFormat f = NumberFormat.getNumberInstance();
f.setMaximumFractionDigits(4);
f.setMinimumFractionDigits(4);
System.out.println("Year\tActual\tPredict\tClosed Loop Predict");
for(int year=EVALUATE_START;year<EVALUATE_END;year++)
{
// calculate based on actual data
MLData input = new BasicMLData(WINDOW_SIZE);
for(int i=0;i<input.size();i++)
{
input.setData(i,this.normalizedSunspots[(year-WINDOW_SIZE)+i]);
}
MLData output = network.compute(input);
double prediction = output.getData(0);
this.closedLoopSunspots[year] = prediction;
// calculate "closed loop", based on predicted data
for(int i=0;i<input.size();i++)
{
input.setData(i,this.closedLoopSunspots[(year-WINDOW_SIZE)+i]);
}
output = network.compute(input);
double closedLoopPrediction = output.getData(0);
// display
System.out.println((STARTING_YEAR+year)
+"\t"+f.format(this.normalizedSunspots[year])
+"\t"+f.format(prediction)
+"\t"+f.format(closedLoopPrediction)
);
}
}
public void run()
{
normalizeSunspots(0.1,0.9);
BasicNetwork network = createNetwork();
MLDataSet training = generateTraining();
train(network,training);
predict(network);
}
public static void main(String args[])
{
PredictSunspot sunspot = new PredictSunspot();
sunspot.run();
}
}