/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 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.validation.clustering;

import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.clustering.ClusterModel;
import com.rapidminer.operator.performance.EstimatedPerformance;
import com.rapidminer.operator.performance.PerformanceCriterion;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.PassThroughOrGenerateRule;
import com.rapidminer.operator.ports.metadata.SimplePrecondition;

/**
 * This operator does actually not compute a performance criterion but simply provides the number 
 * of clusters as a value.
 * 
 * @author Cedric Copy, Timm Euler, Ingo Mierswa, Michael Wurst
 * 
 */
public class ClusterNumberEvaluator extends Operator {
	
    private int numberOfClusters;

    private InputPort clusterModelInput = getInputPorts().createPort("cluster model", ClusterModel.class);
    private OutputPort clusterModelOutput = getOutputPorts().createPort("cluster model");
    private InputPort performanceInput = getInputPorts().createPort("performance");
    private OutputPort performanceOutput = getOutputPorts().createPort("performance");
    
    /**
     * Constructor for ClusterNumberEvaluator.
     */
    public ClusterNumberEvaluator(OperatorDescription description) {
        super(description);
        
		performanceInput.addPrecondition(new SimplePrecondition(performanceInput, new MetaData(PerformanceVector.class), false));
        getTransformer().addRule(new PassThroughOrGenerateRule(performanceInput, performanceOutput, new MetaData(PerformanceVector.class)));
        getTransformer().addPassThroughRule(clusterModelInput, clusterModelOutput);
        
        addValue(new ValueDouble("clusternumber", "The number of clusters.", false) {
            @Override
			public double getDoubleValue() {
                return numberOfClusters;
            }
        });
    }

    @Override
    public boolean shouldAutoConnect(OutputPort port) {
    	if (port == clusterModelOutput) {
    		return getParameterAsBoolean("keep_cluster_model");
    	} else {
    		return super.shouldAutoConnect(port);
    	}
    }

    @Override
	public void doWork() throws OperatorException {
        ClusterModel model = clusterModelInput.getData();
       
        this.numberOfClusters = model.getNumberOfClusters();

        int numItems = 0;
        for (int i = 0; i < model.getNumberOfClusters(); i++)
            numItems += model.getCluster(i).getNumberOfExamples();

        PerformanceVector performance = performanceInput.getDataOrNull();        

        if (performance == null)
            performance = new PerformanceVector();

        PerformanceCriterion pc = new EstimatedPerformance("Number of clusters",model.getNumberOfClusters(), 1, true);
        performance.addCriterion(pc);
        pc = new EstimatedPerformance("Cluster Number Index", 1.0 - (((double) model.getNumberOfClusters()) / ((double) numItems)), numItems, false);
        performance.addCriterion(pc);

        clusterModelOutput.deliver(model);
        performanceOutput.deliver(performance);        
    }
}