/*
 *  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.subgroups.hypothesis;

import java.io.Serializable;
import java.util.Collection;
import java.util.LinkedHashMap;

import com.rapidminer.example.Example;
import com.rapidminer.operator.learner.subgroups.utility.UtilityFunction;
import com.rapidminer.tools.Tools;

/**
 * A rule for subgroup discovery.
 * 
 * @author Tobias Malbrecht
 */
public class Rule implements Serializable {
	
	private static final long serialVersionUID = 1L;

	Hypothesis hypothesis;
	
	Literal prediction;
	
	LinkedHashMap<UtilityFunction, Double> utilityMap = new LinkedHashMap<UtilityFunction, Double>();
	
	public Rule(Hypothesis hypothesis, Literal prediction) {
		this.hypothesis = hypothesis;
		this.prediction = prediction;
	}
	
	public boolean applicable(Example example) {
		return hypothesis.applicable(example);
	}
	
	public double getCoveredWeight() {
		return hypothesis.getCoveredWeight();
	}

	public double getPositiveWeight() {
		return hypothesis.getPositiveWeight();
	}
	
	public double getNegativeWeight() {
		return hypothesis.getCoveredWeight() - hypothesis.getPositiveWeight();
	}
	
	public double getPredictionWeight() {
		if (predictsPositive()) {
			return getPositiveWeight();
		} else {
			return getNegativeWeight();
		}
	}
	
	public boolean predictsPositive() {
		return (prediction.getValue() == prediction.getAttribute().getMapping().getPositiveIndex());
	}
	
	public double getPrediction() {
		return prediction.getValue();
	}
	
	public Hypothesis getHypothesis() {
		return hypothesis;
	}

	public void setUtility(UtilityFunction function, double utility) {
		utilityMap.put(function, utility);
	}

	public double getUtility(Class<? extends UtilityFunction> functionClass) {
		for (UtilityFunction function : utilityMap.keySet()) {
			if (function.getClass().equals(functionClass)) {
				return utilityMap.get(function);
			}
		}
		return Double.NaN;
	}
	
	public UtilityFunction getUtilityFunction(Class<? extends UtilityFunction> functionClass) {
		for (UtilityFunction function : utilityMap.keySet()) {
			if (function.getClass().equals(functionClass)) {
				return function;
			}
		}
		return null;
	}
	
	public Collection<UtilityFunction> getUtilityFunctions() {
		return utilityMap.keySet();
	}
	
	@Override
	public boolean equals(Object object) {
		if (object == null) {
			return false;
		}
		if (this.getClass() != object.getClass()) {
			return false;
		}
		Rule otherRule = (Rule) object;
		return (hypothesis.equals(otherRule.hypothesis) && prediction.equals(otherRule.prediction));
	}

	private String utilityString() {
		StringBuffer stringBuffer = new StringBuffer("[");
		stringBuffer.append("Pos=" + getPositiveWeight() + ", ");
		stringBuffer.append("Neg=" + getNegativeWeight() + ", ");
		stringBuffer.append("Size=" + getCoveredWeight() + ", ");
		for (UtilityFunction function : utilityMap.keySet()) {
			stringBuffer.append(function.getAbbreviation() + "=" + Tools.formatIntegerIfPossible(utilityMap.get(function)) + ", ");
		}
		stringBuffer.subSequence(0, stringBuffer.length() - 2);
		stringBuffer.append("]");
		return stringBuffer.toString();
	}
	
	public String toStringScored() {
		return toString() + "  " + utilityString();
	}
	
	@Override
	public String toString() {
		return hypothesis + " --> " + prediction;
	}
	
	public Hypothesis getPremise() {
		return hypothesis;
	}
	
	public Literal getConclusion() {
		return prediction;
	}
}