/*
 *  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.functions.kernel;

import java.util.Iterator;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.Kernel;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.svm.SVMInterface;
import com.rapidminer.operator.learner.functions.kernel.logistic.KLR;
import com.rapidminer.parameter.ParameterType;


/**
 * This is the Java implementation of <em>myKLR</em> by Stefan Rüping.
 * myKLR is a tool for large scale kernel logistic regression based on the
 * algorithm of Keerthi/etal/2003 and the code of mySVM.
 * 
 * @rapidminer.index KLR
 * @author Ingo Mierswa
 * @version $Id: MyKLRLearner.java,v 1.8 2008/05/09 19:23:01 ingomierswa Exp $
 */
public class MyKLRLearner extends AbstractMySVMLearner {

	public MyKLRLearner(OperatorDescription description) {
		super(description);
	}

	public boolean supportsCapability(LearnerCapability lc) {
		if (lc == LearnerCapability.NUMERICAL_ATTRIBUTES)
			return true;
		if (lc == LearnerCapability.BINOMINAL_CLASS)
			return true;
		return false;
	}

	public AbstractMySVMModel createSVMModel(ExampleSet exampleSet, SVMExamples sVMExamples, Kernel kernel, int kernelType) {
		return new MyKLRModel(exampleSet, sVMExamples, kernel, kernelType);
	}

	public SVMInterface createSVM(Attribute label, Kernel kernel, SVMExamples sVMExamples, com.rapidminer.example.ExampleSet rapidMinerExamples) throws OperatorException {
		if (!label.isNominal())
			throw new UserError(this, 101, new Object[] { "MyKLR", label.getName() });
		return new KLR(this);
	}
    
    public List<ParameterType> getParameterTypes() {
        List<ParameterType> types = super.getParameterTypes();
        
        // important: myKLR does not support determinition of the value C!
        Iterator<ParameterType> p = types.iterator();
        while (p.hasNext()) {
            ParameterType type = p.next();
            if (type.getKey().equals(PARAMETER_C)) {
                type.setDefaultValue(Double.valueOf(1.0d));
            }
        }

        return types;
    }
}