/*
* 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;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional;
import org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional;
import org.deidentifier.arx.metric.v2.MetricMDHeight;
import org.deidentifier.arx.metric.v2.MetricMDNMPrecision;
import org.deidentifier.arx.metric.v2.MetricMDNUEntropyPrecomputed;
import org.deidentifier.arx.metric.v2.QualityMetadata;
import org.deidentifier.arx.metric.v2.__MetricV2;
/**
* This class implements an abstract base class for information loss.
*
* @author Fabian Prasser
* @author Florian Kohlmayer
* @param <T>
*/
public abstract class InformationLoss<T> implements Comparable<InformationLoss<?>>, Serializable {
/** SVUID */
private static final long serialVersionUID = -5347658129539223333L;
/**
* Converter method, converting information loss from version 1 to information loss from version 2,
* if necessary.
*
* @param loss
* @param metric
* @param minLevel
* @param maxLevel
* @return
*/
public static InformationLoss<?> createInformationLoss(InformationLoss<?> loss, Metric<?> metric, int minLevel, int maxLevel) {
Metric<?> _metric = Metric.createMetric(metric, minLevel, maxLevel);
if (loss instanceof InformationLossDefault){
if (_metric instanceof AbstractMetricSingleDimensional) {
return __MetricV2.createILSingleDimensional(((InformationLossDefault)loss).getValue());
} else if (_metric instanceof AbstractMetricMultiDimensional) {
if (_metric instanceof MetricMDNUEntropyPrecomputed) {
return __MetricV2.createILMultiDimensionalSum(((InformationLossDefault)loss).getValue());
} else if (_metric instanceof MetricMDHeight) {
return __MetricV2.createILMultiDimensionalSum(((InformationLossDefault)loss).getValue());
} else if (_metric instanceof MetricMDNMPrecision) {
return __MetricV2.createILMultiDimensionalArithmeticMean(((InformationLossDefault)loss).getValue());
}
}
}
// Default
return loss;
}
/** Metadata */
private List<QualityMetadata<?>> metadata = new ArrayList<QualityMetadata<?>>();
/**
* Creates a new instance
*/
protected InformationLoss(){
// Protected
}
/**
* Adds new metadata
*
* @param metadata
* @return
*/
protected void addMetadata(QualityMetadata<?> metadata) {
// Backwards compatibility
if (this.metadata == null) {
this.metadata = new ArrayList<QualityMetadata<?>>();
}
this.metadata.add(metadata);
}
/**
* Returns a clone of this object.
*
* @return
*/
@Override
public abstract InformationLoss<T> clone();
/**
* Compares the loss to the other.
*
* @param other
* @return
*/
public abstract int compareTo(InformationLoss<?> other);
@Override
public abstract boolean equals(Object obj);
/**
* Adds new metadata
*
* @param metadata
* @return
*/
public List<QualityMetadata<?>> getMetadata() {
// Backwards compatibility
if (this.metadata == null) {
return new ArrayList<QualityMetadata<?>>();
} else {
return new ArrayList<QualityMetadata<?>>(this.metadata);
}
}
/**
* Returns the value
*
* @return
*/
public abstract T getValue();
@Override
public abstract int hashCode();
/**
* Retains the maximum of this and other.
*
* @param other
*/
public abstract void max(InformationLoss<?> other);
/**
* Retains the minimum of this and other.
*
* @param other
*/
public abstract void min(InformationLoss<?> other);
/**
* Returns the value relative to the other instance.
*
* @param min
* @param max
* @return
*/
public abstract double relativeTo(InformationLoss<?> min, InformationLoss<?> max);
/**
* Returns a string representation.
*
* @return
*/
public abstract String toString();
}