/*
 *  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.tools.math.smoothing;

import java.util.LinkedList;
import java.util.List;

import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterHandler;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;

/**
 * This class provides functionality in order to create SmoothingKernels 
 * in operators in a parameter depended way.
 * @author Sebastian Land
 */
public class SmoothingKernels {

	public static final String PARAMETER_SMOOTHING_KERNEL = "smoothing_kernel";
	
	public static final String[] KERNEL_NAMES = new String[] {
		"Rectangular",
		"Triangular",
		"Epanechnikov",
		"Bisquare",
		"Tricube",
		"Triweight",
		"Gaussian",
		"Exponential",
		"McLain"
	};
	
	public static final Class<?>[] KERNEL_CLASSES = new Class[] {
		RectangularSmoothingKernel.class,
		TriangularSmoothingKernel.class,
		EpanechnikovSmoothingKernel.class,
		BisquareSmoothingKernel.class,
		TricubeSmoothingKernel.class,
		TriweightSmoothingKernel.class,
		GaussianSmoothingKernel.class,
		ExponentialSmoothingKernel.class,
		McLainSmoothingKernel.class
	};
	
	public static final List<ParameterType> getParameterTypes(ParameterHandler handler) {
		List<ParameterType> types = new LinkedList<ParameterType>();
		ParameterType type = new ParameterTypeCategory(PARAMETER_SMOOTHING_KERNEL, "Determines which kernel type is used to calculate the weights of distant examples.", KERNEL_NAMES, 5);
		type.setExpert(false);
		types.add(type);
		return types;
	}
	
	public static final SmoothingKernel createKernel(ParameterHandler handler) throws OperatorException {
		int chosenKernel = handler.getParameterAsInt(PARAMETER_SMOOTHING_KERNEL); 
		try {
			return (SmoothingKernel) KERNEL_CLASSES[chosenKernel].newInstance();
		} catch (InstantiationException e) {
			throw new OperatorException("Could not instanciate distance measure " + KERNEL_NAMES[chosenKernel]);
		} catch (IllegalAccessException e) {
			throw new OperatorException("Could not instanciate distance measure " + KERNEL_NAMES[chosenKernel]);
		}
	}
}