/*
 *    EvaluateClustering.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet (abifet@cs.waikato.ac.nz)
 *
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 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 General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package tr.gov.ulakbim.jDenetX.tasks;

import tr.gov.ulakbim.jDenetX.clusterers.AbstractClusterer;
import tr.gov.ulakbim.jDenetX.core.ObjectRepository;
import tr.gov.ulakbim.jDenetX.evaluation.LearningCurve;
import tr.gov.ulakbim.jDenetX.gui.visualization.BatchCmd;
import tr.gov.ulakbim.jDenetX.options.ClassOption;
import tr.gov.ulakbim.jDenetX.options.FileOption;
import tr.gov.ulakbim.jDenetX.options.IntOption;
import tr.gov.ulakbim.jDenetX.streams.clustering.RandomRBFGeneratorEvents;

public class EvaluateClustering extends MainTask {

    @Override
    public String getPurposeString() {
        return "Evaluates a clusterer on a stream by testing then training with each example in sequence.";
    }

    private static final long serialVersionUID = 1L;

    public ClassOption learnerOption = new ClassOption("learner", 'l',
            "Clusterer to train.", AbstractClusterer.class, "clustream.Clustream");

    public ClassOption streamOption = new ClassOption("stream", 's',
            "Stream to learn from.", RandomRBFGeneratorEvents.class,
            "RandomRBFGeneratorEvents");

    public IntOption instanceLimitOption = new IntOption("instanceLimit", 'i',
            "Maximum number of instances to test/train on  (-1 = no limit).",
            100000, -1, Integer.MAX_VALUE);

    public IntOption measureCollectionTypeOption = new IntOption(
            "measureCollectionType", 'm',
            "Type of measure collection", 0, 0,
            Integer.MAX_VALUE);

    /*public ClassOption evaluatorOption = new ClassOption("evaluator", 'e',
             "Performance evaluation method.",
             LearningPerformanceEvaluator.class,
             "BasicClusteringPerformanceEvaluator");*/

    /*public IntOption timeLimitOption = new IntOption("timeLimit", 't',
             "Maximum number of seconds to test/train for (-1 = no limit).", -1,
             -1, Integer.MAX_VALUE);

     public IntOption sampleFrequencyOption = new IntOption("sampleFrequency",
             'f',
             "How many instances between samples of the learning performance.",
             100000, 0, Integer.MAX_VALUE);

     public IntOption maxMemoryOption = new IntOption("maxMemory", 'b',
             "Maximum size of model (in bytes). -1 = no limit.", -1, -1,
             Integer.MAX_VALUE);

     public IntOption memCheckFrequencyOption = new IntOption(
             "memCheckFrequency", 'q',
             "How many instances between memory bound checks.", 100000, 0,
             Integer.MAX_VALUE);*/

    public FileOption outputFileOption = new FileOption("outputFile", 'd',
            "File to output intermediate csv results to.", "outputClustering.csv", "csv", true);

    public Class<?> getTaskResultType() {
        return LearningCurve.class;
    }

    @Override
    protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) {

        BatchCmd.runBatch((RandomRBFGeneratorEvents) getPreparedClassOption(this.streamOption),
                (AbstractClusterer) getPreparedClassOption(this.learnerOption),
                (int) measureCollectionTypeOption.getValue(),
                (int) this.instanceLimitOption.getValue(),
                (String) outputFileOption.getValue());

        LearningCurve learningCurve = new LearningCurve(
                "learning evaluation instances");
        //System.out.println(learner.toString());
        return learningCurve;
    }

}