/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* 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:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.data.versatile.normalizers;
import org.encog.EncogError;
import org.encog.ml.data.MLData;
import org.encog.ml.data.versatile.columns.ColumnDefinition;
/**
* Normalize to one-of-n for nominal values. For example, "one", "two", "three"
* becomes 1,0,0 and 0,1,0 and 0,0,1 etc. Assuming 0 and 1 were the min/max.
*/
public class OneOfNNormalizer implements Normalizer {
private static final long serialVersionUID = 1L;
/**
* The normalized low.
*/
private double normalizedLow;
/**
* The normalized high.
*/
private double normalizedHigh;
/**
* Construct the normalizer.
* @param theNormalizedLow The normalized low.
* @param theNormalizedHigh The normalized high.
*/
public OneOfNNormalizer(double theNormalizedLow, double theNormalizedHigh) {
this.normalizedLow = theNormalizedLow;
this.normalizedHigh = theNormalizedHigh;
}
@Override
public boolean equals(Object obj) {
boolean result;
if ( obj instanceof OneOfNNormalizer ) {
OneOfNNormalizer that = (OneOfNNormalizer) obj;
result = Double.valueOf( this.normalizedHigh ).equals( that.normalizedHigh )
&& Double.valueOf( this.normalizedLow ).equals( that.normalizedLow );
} else {
result = false;
}
return result;
}
/**
* {@inheritDoc}
*/
@Override
public int outputSize(ColumnDefinition colDef) {
return colDef.getClasses().size();
}
/**
* {@inheritDoc}
*/
@Override
public int normalizeColumn(ColumnDefinition colDef, String value,
double[] outputData, int outputColumn) {
for (int i = 0; i < colDef.getClasses().size(); i++) {
double d = this.normalizedLow;
if (colDef.getClasses().get(i).equals(value)) {
d = this.normalizedHigh;
}
outputData[outputColumn + i] = d;
}
return outputColumn + colDef.getClasses().size();
}
/**
* {@inheritDoc}
*/
@Override
public String denormalizeColumn(ColumnDefinition colDef, MLData data,
int dataColumn) {
double bestValue = Double.NEGATIVE_INFINITY;
int bestIndex = 0;
for (int i = 0; i < data.size(); i++) {
double d = data.getData(dataColumn + i);
if (d > bestValue) {
bestValue = d;
bestIndex = i;
}
}
return colDef.getClasses().get(bestIndex);
}
/**
* {@inheritDoc}
*/
@Override
public int normalizeColumn(ColumnDefinition colDef, double value,
double[] outputData, int outputColumn) {
throw new EncogError(
"Can't use a one-of-n normalizer on a continuous value: "
+ value);
}
}