/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.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;

import java.util.Iterator;
import java.util.LinkedHashSet;
import java.util.LinkedList;

import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.SimplePredictionModel;
import com.rapidminer.operator.learner.subgroups.hypothesis.Rule;
import com.rapidminer.operator.learner.subgroups.utility.UtilityFunction;
import com.rapidminer.tools.Tools;


/**
 * A model consisting of rules which are scored by utility values.
 * Only the best rule (according to its utility) is used for prediction
 * at the moment.
 * 
 * @author Tobias Malbrecht
 */
public class RuleSet extends SimplePredictionModel implements Iterable<Rule> {

	private boolean predictUncoveredRules = false;

	private static final long serialVersionUID = -47885282272818733L;

	private LinkedList<Rule> rules = null;

	private LinkedHashSet<UtilityFunction> utilityFunctions = null;

	public RuleSet(ExampleSet exampleSet) {
		super(exampleSet);
		rules = new LinkedList<Rule>();
		utilityFunctions = new LinkedHashSet<UtilityFunction>();
	}

	public void addRule(Rule rule) {
		rules.add(rule);
		utilityFunctions.addAll(rule.getUtilityFunctions());
	}

	public Rule getRule(int index) {
		return rules.get(index);
	}

	public int getNumberOfRules() {
		return rules.size();
	}

	public Iterator<Rule> iterator() {
		return rules.iterator();
	}

	public LinkedList<Rule> getPositiveRules() {
		LinkedList<Rule> positiveRules = new LinkedList<Rule>();
		for (Rule rule : this) {
			if (rule.predictsPositive()) {
				positiveRules.add(rule);
			}
		}
		return positiveRules;
	}

	public LinkedList<Rule> getNegativeRules() {
		LinkedList<Rule> negativeRules = new LinkedList<Rule>();
		for (Rule rule : this) {
			if (!rule.predictsPositive()) {
				negativeRules.add(rule);
			}
		}
		return negativeRules;
	}

	public int size() {
		return getNumberOfRules();
	}

	@Override
	public double predict(Example example) throws OperatorException {
		for (Rule rule : rules) {
			if (rule.applicable(example)) {
				return (rule.getPrediction());
			}
		}
		return (predictUncoveredRules ? example.getAttributes().getLabel().getMapping().getNegativeIndex() : Double.NaN);
	}

	public UtilityFunction[] getUtilityFunctions() {
		UtilityFunction[] functions = new UtilityFunction[utilityFunctions.size()];
		functions = utilityFunctions.toArray(functions);
		return functions;
	}

	@Override
	public String toString() {
		StringBuffer stringBuffer = new StringBuffer();
		int i = 0;
		for (Rule rule : rules) {
			if (i < 10) {
				stringBuffer.append(rule.toStringScored());
				stringBuffer.append(Tools.getLineSeparator());
			}
			i++;
		}
		if (i > 10) {
			stringBuffer.append(Tools.getLineSeparators(2));
			stringBuffer.append("... and " + (i-10) + " more rules!");
		}
		return stringBuffer.toString();
	}
}