/*
 *  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.learner.functions.kernel;

import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;


/** 
 *  A model learned by the GPLearner. 
 *  
 *  @author Piotr Kasprzak, Ingo Mierswa
 */
public class GPModel extends KernelModel {
	
	private static final long serialVersionUID = 6094706651995436944L;
	
	private com.rapidminer.operator.learner.functions.kernel.gaussianprocess.Model model = null;
	
	public GPModel(ExampleSet exampleSet, com.rapidminer.operator.learner.functions.kernel.gaussianprocess.Model model) {
		super(exampleSet);	
		this.model = model;		
	}
    
    @Override
	public boolean isClassificationModel() {
        return getLabel().isNominal();
    }
    
	@Override
	public double getBias() {
		return 0;
	}

	@Override
	public SupportVector getSupportVector(int index) {
		return null;
	}
	
    @Override
	public double getAlpha(int index) {
        return Double.NaN;
    }
    
    @Override
	public String getId(int index) {
        return null;
    }
    
    @Override
	public int getNumberOfSupportVectors() {
        return this.model.getNumberOfBasisVectors();
    }
    
    @Override
	public int getNumberOfAttributes() {
        return this.model.getInputDim();
    }
    
    @Override
	public double getAttributeValue(int exampleIndex, int attributeIndex) {
        return this.model.getBasisVectorValue(exampleIndex, attributeIndex);
    }
    
    @Override
	public String getClassificationLabel(int index) {
        return "?";
    }
    
    @Override
	public double getRegressionLabel(int index) {
        return Double.NaN;
    }
    
    @Override
	public double getFunctionValue(int index) {
        return model.applyToVector(this.model.getBasisVector(index));
    }
        
    @Override
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
    	Iterator<Example> i = exampleSet.iterator();
    	while (i.hasNext()) {
    	    Example e = i.next();
    	    double functionValue = model.applyToVector(RVMModel.makeInputVector(e));
    	    if (getLabel().isNominal()) {
    	        if (functionValue > 0)
    	            e.setValue(predictedLabel, getLabel().getMapping().getPositiveIndex());
    	        else
    	            e.setValue(predictedLabel, getLabel().getMapping().getNegativeIndex());
    	        // set confidence to numerical prediction, such that can be scaled later
    	        e.setConfidence(predictedLabel.getMapping().getPositiveString(), 1.0d / (1.0d + java.lang.Math.exp(-functionValue)));
    	        e.setConfidence(predictedLabel.getMapping().getNegativeString(), 1.0d / (1.0d + java.lang.Math.exp(functionValue)));
    	    } else {
    	        e.setValue(predictedLabel, functionValue);
    	    }
    	}
    	return exampleSet;
    }
}