/**
 * Copyright (c) 2007-2014 The LIBLINEAR Project. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification, are permitted
 * provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this list of conditions
 * and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice, this list of
 * conditions and the following disclaimer in the documentation and/or other materials provided with
 * the distribution.
 *
 * 3. Neither name of copyright holders nor the names of its contributors may be used to endorse or
 * promote products derived from this software without specific prior written permission.
 *
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR
 * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
 * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
 * OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
 * THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package de.bwaldvogel.liblinear;

import java.util.HashMap;
import java.util.Map;


public enum SolverType {

	/**
	 * L2-regularized logistic regression (primal)
	 *
	 * (fka L2_LR)
	 */
	L2R_LR(0, true, false),

	/**
	 * L2-regularized L2-loss support vector classification (dual)
	 *
	 * (fka L2LOSS_SVM_DUAL)
	 */
	L2R_L2LOSS_SVC_DUAL(1, false, false),

	/**
	 * L2-regularized L2-loss support vector classification (primal)
	 *
	 * (fka L2LOSS_SVM)
	 */
	L2R_L2LOSS_SVC(2, false, false),

	/**
	 * L2-regularized L1-loss support vector classification (dual)
	 *
	 * (fka L1LOSS_SVM_DUAL)
	 */
	L2R_L1LOSS_SVC_DUAL(3, false, false),

	/**
	 * multi-class support vector classification by Crammer and Singer
	 */
	MCSVM_CS(4, false, false),

	/**
	 * L1-regularized L2-loss support vector classification
	 *
	 * @since 1.5
	 */
	L1R_L2LOSS_SVC(5, false, false),

	/**
	 * L1-regularized logistic regression
	 *
	 * @since 1.5
	 */
	L1R_LR(6, true, false),

	/**
	 * L2-regularized logistic regression (dual)
	 *
	 * @since 1.7
	 */
	L2R_LR_DUAL(7, true, false),

	/**
	 * L2-regularized L2-loss support vector regression (dual)
	 *
	 * @since 1.91
	 */
	L2R_L2LOSS_SVR(11, false, true),

	/**
	 * L2-regularized L1-loss support vector regression (dual)
	 *
	 * @since 1.91
	 */
	L2R_L2LOSS_SVR_DUAL(12, false, true),

	/**
	 * L2-regularized L2-loss support vector regression (primal)
	 *
	 * @since 1.91
	 */
	L2R_L1LOSS_SVR_DUAL(13, false, true),

	;

	private final boolean logisticRegressionSolver;
	private final boolean supportVectorRegression;
	private final int id;

	private SolverType(int id, boolean logisticRegressionSolver, boolean supportVectorRegression) {
		this.id = id;
		this.logisticRegressionSolver = logisticRegressionSolver;
		this.supportVectorRegression = supportVectorRegression;
	}

	private static Map<Integer, SolverType> SOLVERS_BY_ID = new HashMap<Integer, SolverType>();
	static {
		for (SolverType solverType : SolverType.values()) {
			SolverType old = SOLVERS_BY_ID.put(Integer.valueOf(solverType.getId()), solverType);
			if (old != null) {
				throw new Error("duplicate solver type ID: " + solverType.getId());
			}
		}
	}

	public int getId() {
		return id;
	}

	public static SolverType getById(int id) {
		SolverType solverType = SOLVERS_BY_ID.get(Integer.valueOf(id));
		if (solverType == null) {
			throw new RuntimeException("found no solvertype for id " + id);
		}
		return solverType;
	}

	/**
	 * @since 1.9
	 */
	public boolean isLogisticRegressionSolver() {
		return logisticRegressionSolver;
	}

	/**
	 * @since 1.91
	 */
	public boolean isSupportVectorRegression() {
		return supportVectorRegression;
	}
}