/**
* Copyright (C) 2001-2017 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 com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterHandler;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import java.util.LinkedList;
import java.util.List;
/**
* 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]);
}
}
}