/*
 *  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;

import java.util.Iterator;

import liblinear.FeatureNode;
import liblinear.Linear;
import liblinear.Model;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.FastExample2SparseTransform;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Tools;

/**
 * This is the model of the fast margin learner which learns a linear SVM in linear time.
 * 
 * @author Ingo Mierswa
 */
public class FastMarginModel extends PredictionModel {

	private static final long serialVersionUID = 7701199447666181333L;

	private Model linearModel;
	
	private boolean useBias;
	
	private String[] attributeConstructions;
	
	public FastMarginModel(ExampleSet headerSet, Model linearModel, boolean useBias) {
		super(headerSet);
		this.linearModel = linearModel;
		this.useBias = useBias;
		this.attributeConstructions = com.rapidminer.example.Tools.getRegularAttributeConstructions(headerSet);
	}
	
	@Override
	public String getName() {
		return "Fast Linear Classification";	
	}
	
	@Override
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		FastExample2SparseTransform ripper = new FastExample2SparseTransform(exampleSet);		
		Attribute label = getLabel();

		Attribute[] confidenceAttributes = null;
		if (label.isNominal() && label.getMapping().size() >= 2) {
			confidenceAttributes = new Attribute[linearModel.label.length];
			for (int j = 0; j < linearModel.label.length; j++) {
				String labelName = label.getMapping().mapIndex(linearModel.label[j]);
				confidenceAttributes[j] = exampleSet.getAttributes().getSpecial(Attributes.CONFIDENCE_NAME + "_" +  labelName);
			}
		}
        
		Iterator<Example> i = exampleSet.iterator();
		while (i.hasNext()) {
			Example e = i.next();

			// set prediction
			FeatureNode[] currentNodes = FastLargeMargin.makeNodes(e, ripper, this.useBias);

			double predictedClass = Linear.predict(linearModel, currentNodes);
			e.setValue(predictedLabel, predictedClass);

			// use simple calculation for binary cases...
			if (label.getMapping().size() == 2) { 
				double[] functionValues = new double[linearModel.nr_class];
				Linear.predictValues(linearModel, currentNodes, functionValues);
				double prediction = functionValues[0];
				if ((confidenceAttributes != null) && (confidenceAttributes.length > 0)) {
					e.setValue(confidenceAttributes[0], 1.0d / (1.0d + java.lang.Math.exp(-prediction)));
					if (confidenceAttributes.length > 1) {
						e.setValue(confidenceAttributes[1], 1.0d / (1.0d + java.lang.Math.exp(prediction)));
					}
				}
			}

		}
		return exampleSet;
	}

	@Override
	public String toString() {
		StringBuffer result = new StringBuffer();
		boolean first = true;
		for (int i = 0; i < this.attributeConstructions.length; i++) {
			result.append(getCoefficientString(linearModel.w[i], first) + " * " + attributeConstructions[i] + Tools.getLineSeparator());
			first = false;
		}
		if (this.useBias)
			result.append(getCoefficientString(linearModel.w[linearModel.w.length - 1], first));
		return result.toString();
	}
	
	private String getCoefficientString(double coefficient, boolean first) {
		if (!first) {
			if (coefficient >= 0)
				return "+ " + Tools.formatNumber(Math.abs(coefficient));
			else
				return "- " + Tools.formatNumber(Math.abs(coefficient));
		} else {
			if (coefficient >= 0)
				return "  " + Tools.formatNumber(Math.abs(coefficient));
			else
				return "- " + Tools.formatNumber(Math.abs(coefficient));
		}
	}
}