com.rapidminer.operator.learner.clustering.clusterer.WekaCluster.java Source code

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Here is the source code for com.rapidminer.operator.learner.clustering.clusterer.WekaCluster.java

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/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.learner.clustering.clusterer;

import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.operator.AbstractModel;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.WekaInstancesAdaptor;
import com.rapidminer.tools.WekaTools;

import weka.clusterers.Clusterer;
import weka.core.Instance;
import weka.core.Instances;

/**
 * A Weka clusterer which can be used to cluster examples. It is generated by a WekaClusterer.
 * 
 * @author Ingo Mierswa
 * @version $Id: WekaCluster.java,v 1.5 2008/05/09 19:22:49 ingomierswa Exp $
 */
public class WekaCluster extends AbstractModel {

    private static final long serialVersionUID = -8901173604075912094L;

    /** The used Weka clusterer. */
    private final Clusterer clusterer;

    public WekaCluster(ExampleSet exampleSet, Clusterer clusterer) {
        super(exampleSet);
        this.clusterer = clusterer;
    }

    public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
        log("Converting to Weka instances.");
        Instances instances = WekaTools.toWekaInstances(exampleSet, "ClusterInstances",
                WekaInstancesAdaptor.CLUSTERING);
        log("Applying Weka clusterer.");
        int i = 0;
        Attribute clusterAtt = exampleSet.getAttributes().getCluster();
        if (clusterAtt == null)
            clusterAtt = Tools.createSpecialAttribute(exampleSet, Attributes.CLUSTER_NAME, Ontology.NOMINAL);
        Iterator<Example> r = exampleSet.iterator();
        while (r.hasNext()) {
            Example e = r.next();
            Instance instance = instances.instance(i++);
            applyModelForInstance(instance, e, clusterAtt);
        }

        return exampleSet;
    }

    /**
     * Clusters ervery weka instance and sets the result as cluster index of the current example.
     */
    public void applyModelForInstance(Instance instance, Example e, Attribute clusterAtt) throws OperatorException {
        int cluster = -1;
        try {
            cluster = clusterer.clusterInstance(instance);
        } catch (Exception exc) {
            throw new UserError(null, exc, 905, new Object[] { clusterer, exc.toString() });
        }
        e.setValue(clusterAtt, "cluster" + cluster);
    }

    public String toString() {
        return clusterer.toString();
    }
}