/*
* ARX: Powerful Data Anonymization
* Copyright 2012 - 2017 Fabian Prasser, Florian Kohlmayer and contributors
*
* 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.
*/
package org.deidentifier.arx.metric.v2;
import org.deidentifier.arx.metric.InformationLoss;
/**
* This class implements an information loss which can be represented as a
* decimal number per quasi-identifier. As an aggregate function, the geometric mean
* is applied. To handle zero values while not violating guarantees required for pruning
* based on lower bounds, 1d is added to every individual value and 1d is subtracted from the
* final result.
*
* @author Fabian Prasser
* @author Florian Kohlmayer
*/
public class ILMultiDimensionalGeometricMean extends AbstractILMultiDimensionalReduced {
/** SVUID. */
private static final long serialVersionUID = 621501985571033348L;
/**
* Creates a new instance.
*
* @param values
* @param weights
*/
ILMultiDimensionalGeometricMean(final double[] values,
final double[] weights) {
super(values, weights);
}
@Override
public InformationLoss<double[]> clone() {
return new ILMultiDimensionalGeometricMean(getValues(),
getWeights());
}
@Override
protected double getAggregate() {
double[] values = getValues();
double[] weights = getWeights();
double result = 1.0d;
for (int i = 0; i < values.length; i++) {
result *= Math.pow((values[i] * weights[i] + 1d), 1.0d / (double) values.length);
}
return result - 1d;
}
}