/*
* Encog(tm) Examples v2.4
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
*
* Copyright 2008-2010 by Heaton Research Inc.
*
* Released under the LGPL.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
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*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
*
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package org.encog.examples.neural.forest.feedforward;
import java.io.File;
import org.encog.engine.StatusReportable;
import org.encog.normalize.DataNormalization;
import org.encog.normalize.input.InputField;
import org.encog.normalize.input.InputFieldCSV;
import org.encog.normalize.output.OutputField;
import org.encog.normalize.output.OutputFieldDirect;
import org.encog.normalize.output.OutputFieldRangeMapped;
import org.encog.normalize.output.nominal.OutputEquilateral;
import org.encog.normalize.output.nominal.OutputOneOf;
import org.encog.normalize.segregate.IntegerBalanceSegregator;
import org.encog.normalize.segregate.index.IndexSampleSegregator;
import org.encog.normalize.target.NormalizationStorageCSV;
public class GenerateData implements StatusReportable {
public void buildOutputOneOf(DataNormalization norm, InputField coverType)
{
OutputOneOf outType = new OutputOneOf(0.9,0.1);
outType.addItem(coverType, 1);
outType.addItem(coverType, 2);
outType.addItem(coverType, 3);
outType.addItem(coverType, 4);
outType.addItem(coverType, 5);
outType.addItem(coverType, 6);
outType.addItem(coverType, 7);
norm.addOutputField(outType, true);
}
public void buildOutputEquilateral(DataNormalization norm, InputField coverType)
{
OutputEquilateral outType = new OutputEquilateral(0.9,0.1);
outType.addItem(coverType, 1);
outType.addItem(coverType, 2);
outType.addItem(coverType, 3);
outType.addItem(coverType, 4);
outType.addItem(coverType, 5);
outType.addItem(coverType, 6);
outType.addItem(coverType, 7);
norm.addOutputField(outType, true);
}
public void copy(File source,File target,int start,int stop,int size)
{
InputField inputField[] = new InputField[55];
DataNormalization norm = new DataNormalization();
norm.setReport(this);
norm.setTarget(new NormalizationStorageCSV(target));
for(int i=0;i<55;i++)
{
inputField[i] = new InputFieldCSV(true,source,i);
norm.addInputField(inputField[i]);
OutputField outputField = new OutputFieldDirect(inputField[i]);
norm.addOutputField(outputField);
}
// load only the part we actually want, i.e. training or eval
IndexSampleSegregator segregator2 = new IndexSampleSegregator(start,stop,size);
norm.addSegregator(segregator2);
norm.process();
}
public void narrow(File source,File target,int field, int count)
{
InputField inputField[] = new InputField[55];
DataNormalization norm = new DataNormalization();
norm.setReport(this);
norm.setTarget(new NormalizationStorageCSV(target));
for(int i=0;i<55;i++)
{
inputField[i] = new InputFieldCSV(true,source,i);
norm.addInputField(inputField[i]);
OutputField outputField = new OutputFieldDirect(inputField[i]);
norm.addOutputField(outputField);
}
IntegerBalanceSegregator segregator = new IntegerBalanceSegregator(inputField[field],count);
norm.addSegregator(segregator);
norm.process();
System.out.println("Samples per tree type:");
System.out.println(segregator.dumpCounts());
}
public void step1()
{
System.out.println("Step 1: Generate training and evaluation files");
System.out.println("Generate training file");
copy(Constant.COVER_TYPE_FILE,Constant.TRAINING_FILE,0,2,4); // take 3/4
System.out.println("Generate evaluation file");
copy(Constant.COVER_TYPE_FILE,Constant.EVALUATE_FILE,3,3,4); // take 1/4
}
public void step2()
{
System.out.println("Step 2: Balance training to have the same number of each tree");
narrow(Constant.TRAINING_FILE,Constant.BALANCE_FILE,54,3000);
}
public DataNormalization step3(boolean useOneOf)
{
System.out.println("Step 3: Normalize training data");
InputField inputElevation;
InputField inputAspect;
InputField inputSlope;
InputField hWater;
InputField vWater;
InputField roadway;
InputField shade9;
InputField shade12;
InputField shade3;
InputField firepoint;
InputField[] wilderness = new InputField[4];
InputField[] soilType = new InputField[40];
InputField coverType;
DataNormalization norm = new DataNormalization();
norm.setReport(this);
norm.setTarget(new NormalizationStorageCSV(Constant.NORMALIZED_FILE));
norm.addInputField(inputElevation = new InputFieldCSV(true,Constant.BALANCE_FILE,0));
norm.addInputField(inputAspect = new InputFieldCSV(true,Constant.BALANCE_FILE,1));
norm.addInputField(inputSlope = new InputFieldCSV(true,Constant.BALANCE_FILE,2));
norm.addInputField(hWater = new InputFieldCSV(true,Constant.BALANCE_FILE,3));
norm.addInputField(vWater = new InputFieldCSV(true,Constant.BALANCE_FILE,4));
norm.addInputField(roadway = new InputFieldCSV(true,Constant.BALANCE_FILE,5));
norm.addInputField(shade9 = new InputFieldCSV(true,Constant.BALANCE_FILE,6));
norm.addInputField(shade12 = new InputFieldCSV(true,Constant.BALANCE_FILE,7));
norm.addInputField(shade3 = new InputFieldCSV(true,Constant.BALANCE_FILE,8));
norm.addInputField(firepoint = new InputFieldCSV(true,Constant.BALANCE_FILE,9));
for(int i=0;i<4;i++)
{
norm.addInputField(wilderness[i]=new InputFieldCSV(true,Constant.BALANCE_FILE,10+i));
}
for(int i=0;i<40;i++)
{
norm.addInputField(soilType[i]=new InputFieldCSV(true,Constant.BALANCE_FILE,14+i));
}
norm.addInputField(coverType=new InputFieldCSV(false,Constant.BALANCE_FILE,54));
norm.addOutputField(new OutputFieldRangeMapped(inputElevation,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(inputAspect,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(inputSlope,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(hWater,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(vWater,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(roadway,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(shade9,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(shade12,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(shade3,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(firepoint,0.1,0.9));
for(int i=0;i<40;i++)
{
norm.addOutputField(new OutputFieldDirect(soilType[i]));
}
if( useOneOf )
buildOutputOneOf(norm,coverType);
else
buildOutputEquilateral(norm,coverType);
norm.process();
return norm;
}
public void report(int total, int current, String message) {
System.out.println( current + "/" + total + " " + message );
}
}