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MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task.

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/*
 * ConfidenceCoverage.java
 * (this file is part of MYRA)
 * 
 * Copyright 2008-2015 Fernando Esteban Barril Otero
 * 
 * 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 myra.classification.rule.function;

import myra.Cost.Maximise;
import myra.classification.rule.ClassificationRule;
import myra.datamining.Dataset;
import myra.datamining.Dataset.Instance;

/**
 * The ConfidenceCoverage class represents a rule quality function
 * based on the Confidence (Precision) and Confidence measure. The quality of a
 * rule is equivalent to Confidence + Coverage.
 * 
 * @author Fernando Esteban Barril Otero
 */
public class ConfidenceCoverage extends ClassificationRuleFunction {
    @Override
    public Maximise evaluate(Dataset dataset,
                             ClassificationRule rule,
                             Instance[] instances) {
        int[] cFrequency = rule.covered();
        int[] uFrequency = rule.uncovered();

        int covered = 0;
        int total = 0;

        for (int i = 0; i < cFrequency.length; i++) {
            covered += cFrequency[i];
            total += (cFrequency[i] + uFrequency[i]);
        }

        int predicted = rule.getConsequent().value();
        double confidence = cFrequency[predicted] / (double) covered;
        double coverage = cFrequency[predicted] / (double) total;

        return new Maximise(confidence + coverage);
    }
}