/*
 *  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.bayes;

import java.util.Collection;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.learner.UpdateablePredictionModel;
import com.rapidminer.tools.math.distribution.Distribution;


/**
 * DistributionModel is a model for learners which estimate distributions 
 * of attribute values from example sets like NaiveBayes.
 * 
 * Predictions are calculated as product of the conditional probabilities
 * for all attributes times the class probability.
 * 
 * The basic learning concept is to simply count occurances of classes
 * and attribute values. This means no propabilities are calculated during
 * the learning step. This is only done before output. Optionally, this
 * calculation can apply a Laplace correction which means in particular
 * that zero probabilities are avoided which would hide information
 * in distributions of other attributes.
 * 
 * @author Tobias Malbrecht
 */
public abstract class DistributionModel extends UpdateablePredictionModel {

	private static final long serialVersionUID = -402827845291958569L;

	public DistributionModel(ExampleSet exampleSet) {
		super(exampleSet);
	}

	public abstract String[] getAttributeNames();

	public abstract int getNumberOfAttributes();

	public abstract double getLowerBound(int attributeIndex);

	public abstract double getUpperBound(int attributeIndex);

	public abstract boolean isDiscrete(int attributeIndex);

	public abstract Collection<Integer> getClassIndices();

	public abstract int getNumberOfClasses();

	public abstract String getClassName(int index);

	public abstract Distribution getDistribution(int classIndex, int attributeIndex);

	@Override
	public abstract ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel);

}