/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 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;

import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.performance.PerformanceVector;

/**
 * A <tt>Learner</tt> is an operator that encapsulates the learning step of a
 * machine learning method. Some Learners may be capable of estimating the
 * performance of the generated model. In that case, they additionally return a
 * {@link com.rapidminer.operator.performance.PerformanceVector}. Furthermore
 * some learner can calculate weights of the used attributes which can also be
 * delivered.
 * 
 * @author Ingo Mierswa
 * @version $Id: Learner.java,v 1.4 2008/05/09 19:23:25 ingomierswa Exp $
 */
public interface Learner {

	/**
	 * Trains a model. This method should be called by apply() and is
	 * implemented by subclasses.
	 */
	public Model learn(ExampleSet exampleSet) throws OperatorException;

	/** Returns the name of the learner. */
	public String getName();

	/**
	 * Checks for Learner capabilities. Should return true if the given
	 * capability is supported.
	 */
	public boolean supportsCapability(LearnerCapability capability);

	/**
	 * Most learners will return false since they are not able to estimate the
	 * learning performance. However, if a learning scheme is able to calculate
	 * the performance (e.g. Xi-Alpha estimation of a SVM) it should return
	 * true.
	 */
	public boolean shouldEstimatePerformance();

	/**
	 * Most learners should throw an exception if they are not able to estimate
	 * the learning performance. However, if a learning scheme is able to
	 * calculate the performance (e.g. Xi-Alpha estimation of a SVM) it should
	 * return a performance vector containing the estimated performance.
	 */
	public PerformanceVector getEstimatedPerformance() throws OperatorException;

	/**
	 * Most learners will return false since they are not able to calculate
	 * attribute weights. However, if a learning scheme is able to calculate
	 * weights (e.g. the normal vector of a SVM) it should return true.
	 */
	public boolean shouldCalculateWeights();

	/**
	 * Most learners should throw an exception if they are not able to calculate
	 * attribute weights. However, if a learning scheme is able to calculate
	 * weights (e.g. the normal vector of a SVM) it should return an
	 * AttributeWeights object.
	 */
	public AttributeWeights getWeights(ExampleSet eSet) throws OperatorException;
}