/*
* 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 an ordinal into a specific range. An ordinal is a string value that
* has order. For example "first grade", "second grade", ... "freshman", ...,
* "senior". These values are mapped to an increasing index.
*/
public class RangeOrdinal implements Normalizer {
private static final long serialVersionUID = 1L;
/**
* The low range of the normalized data.
*/
private double normalizedLow;
/**
* The high range of the normalized data.
*/
private double normalizedHigh;
public RangeOrdinal(double theNormalizedLow, double theNormalizedHigh) {
this.normalizedLow = theNormalizedLow;
this.normalizedHigh = theNormalizedHigh;
}
@Override
public boolean equals(Object obj) {
boolean result;
if ( obj instanceof RangeOrdinal ) {
RangeOrdinal that = (RangeOrdinal) 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 1;
}
/**
* {@inheritDoc}
*/
@Override
public int normalizeColumn(ColumnDefinition colDef, String theValue,
double[] outputData, int outputColumn) {
// Find the index of the ordinal
int v = colDef.getClasses().indexOf(theValue);
if (v == -1) {
throw new EncogError("Unknown ordinal: " + theValue);
}
double high = colDef.getClasses().size();
double value = v;
double result = (value / high)
* (this.normalizedHigh - this.normalizedLow)
+ this.normalizedLow;
// typically caused by a number that should not have been normalized
// (i.e. normalization or actual range is infinitely small.
if (Double.isNaN(result)) {
result = ((this.normalizedHigh - this.normalizedLow) / 2)
+ this.normalizedLow;
}
outputData[outputColumn] = result;
return outputColumn + 1;
}
/**
* {@inheritDoc}
*/
@Override
public int normalizeColumn(ColumnDefinition colDef, double value,
double[] outputData, int outputColumn) {
throw new EncogError(
"Can't ordinal range-normalize a continuous value: " + value);
}
/**
* {@inheritDoc}
*/
@Override
public String denormalizeColumn(ColumnDefinition colDef, MLData data,
int dataColumn) {
double high = colDef.getClasses().size();
double low = 0;
double value = data.getData(dataColumn);
final double result = ((low - high) * value - this.normalizedHigh * low + high
* this.normalizedLow)
/ (this.normalizedLow - this.normalizedHigh);
// typically caused by a number that should not have been normalized
// (i.e. normalization or actual range is infinitely small.
if (Double.isNaN(result)) {
return colDef.getClasses().get(0);
}
return colDef.getClasses().get((int) result);
}
}