/**
 * Copyright (c) 2000-2007 Chih-Chung Chang and Chih-Jen Lin
 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 gate.learning.learners.svm;
public class svm_parameter implements Cloneable,java.io.Serializable
{
	/* svm_type */
	public static final int C_SVC = 0;
	public static final int NU_SVC = 1;
	public static final int ONE_CLASS = 2;
	public static final int EPSILON_SVR = 3;
	public static final int NU_SVR = 4;

	/* kernel_type */
	public static final int LINEAR = 0;
	public static final int POLY = 1;
	public static final int RBF = 2;
	public static final int SIGMOID = 3;
	public static final int PRECOMPUTED = 4;

	public int svm_type;
	public int kernel_type;
	public int degree;	// for poly
	public double gamma;	// for poly/rbf/sigmoid
	public double coef0;	// for poly/sigmoid

	// these are for training only
	public double cache_size; // in MB
	public double eps;	// stopping criteria
	public double C;	// for C_SVC, EPSILON_SVR and NU_SVR
	public int nr_weight;		// for C_SVC
	public int[] weight_label;	// for C_SVC
	public double[] weight;		// for C_SVC
	public double nu;	// for NU_SVC, ONE_CLASS, and NU_SVR
	public double p;	// for EPSILON_SVR
	public int shrinking;	// use the shrinking heuristics
	public int probability; // do probability estimates

	public Object clone() 
	{
		try 
		{
			return super.clone();
		} catch (CloneNotSupportedException e) 
		{
			return null;
		}
	}

}