/*
* 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.normalize;
import junit.framework.TestCase;
import org.encog.NullStatusReportable;
import org.encog.util.normalize.input.InputField;
import org.encog.util.normalize.input.InputFieldArray2D;
import org.encog.util.normalize.output.OutputFieldRangeMapped;
import org.encog.util.normalize.segregate.IntegerBalanceSegregator;
import org.encog.util.normalize.segregate.RangeSegregator;
import org.encog.util.normalize.segregate.Segregator;
import org.encog.util.normalize.segregate.index.IndexRangeSegregator;
import org.encog.util.normalize.segregate.index.IndexSampleSegregator;
import org.encog.util.normalize.target.NormalizationStorageArray2D;
import org.junit.Assert;
public class TestSegregate extends TestCase {
public static final double[][] ARRAY_2D = { {1.0,2.0,3.0,4.0,5.0},
{1.0,2.0,3.0,4.0,5.0},
{1.0,2.0,3.0,4.0,5.0},
{1.0,2.0,3.0,4.0,5.0},
{1.0,2.0,3.0,4.0,5.0},
{2.0,2.0,3.0,4.0,5.0} };
private DataNormalization createIntegerBalance() {
InputField a,b;
double[][] arrayOutput = new double[3][2];
NormalizationStorageArray2D target = new NormalizationStorageArray2D(arrayOutput);
DataNormalization norm = new DataNormalization();
norm.setReport(new NullStatusReportable());
norm.setTarget(target);
norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
norm.addSegregator(new IntegerBalanceSegregator(a,2));
return norm;
}
private void check(DataNormalization norm, int req) {
Segregator s = norm.getSegregators().get(0);
double[][] arrayOutput = ((NormalizationStorageArray2D)norm.getStorage()).getArray();
Assert.assertEquals(req, arrayOutput.length);
}
public void testIntegerBalance()
{
DataNormalization norm = createIntegerBalance();
norm.process();
check(norm,3);
}
private DataNormalization createRangeSegregate()
{
InputField a,b;
double[][] arrayOutput = new double[1][2];
RangeSegregator s;
NormalizationStorageArray2D target = new NormalizationStorageArray2D(arrayOutput);
DataNormalization norm = new DataNormalization();
norm.setReport(new NullStatusReportable());
norm.setTarget(target);
norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
norm.addSegregator(s = new RangeSegregator(a,false));
s.addRange(2, 2, true);
return norm;
}
public void testRangeSegregate()
{
DataNormalization norm = createRangeSegregate();
norm.process();
check(norm,1);
}
private DataNormalization createSampleSegregate()
{
InputField a,b;
double[][] arrayOutput = new double[6][2];
NormalizationStorageArray2D target = new NormalizationStorageArray2D(arrayOutput);
DataNormalization norm = new DataNormalization();
norm.setReport(new NullStatusReportable());
norm.setTarget(target);
norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
norm.addSegregator(new IndexSampleSegregator(0,3,2));
return norm;
}
public void testSampleSegregate()
{
DataNormalization norm = createSampleSegregate();
norm.process();
check(norm,6);
}
public DataNormalization createIndexSegregate()
{
InputField a,b;
double[][] arrayOutput = new double[6][2];
NormalizationStorageArray2D target = new NormalizationStorageArray2D(arrayOutput);
DataNormalization norm = new DataNormalization();
norm.setReport(new NullStatusReportable());
norm.setTarget(target);
norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
norm.addSegregator(new IndexRangeSegregator(0,3));
return norm;
}
public void testIndexSegregate()
{
DataNormalization norm = createIndexSegregate();
norm.process();
check(norm,6);
}
}