/* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.commons.math.optimization.fitting; import java.io.Serializable; import org.apache.commons.math.analysis.UnivariateRealFunction; import org.apache.commons.math.exception.DimensionMismatchException; import org.apache.commons.math.exception.util.LocalizedFormats; import org.apache.commons.math.exception.ZeroException; import org.apache.commons.math.exception.NullArgumentException; /** * The derivative of {@link GaussianFunction}. Specifically: * <p> * <tt>f'(x) = (-b / (d^2)) * (x - c) * exp(-((x - c)^2) / (2*(d^2)))</tt> * <p> * Notation key: * <ul> * <li><tt>x^n</tt>: <tt>x</tt> raised to the power of <tt>n</tt> * <li><tt>exp(x)</tt>: <i>e</i><tt>^x</tt> * </ul> * * @since 2.2 * @version $Revision: 1037327 $ $Date: 2010-11-20 21:57:37 +0100 (sam. 20 nov. 2010) $ */ public class GaussianDerivativeFunction implements UnivariateRealFunction, Serializable { /** Serializable version identifier. */ private static final long serialVersionUID = -6500229089670174766L; /** Parameter b of this function. */ private final double b; /** Parameter c of this function. */ private final double c; /** Square of the parameter d of this function. */ private final double d2; /** * Constructs an instance with the specified parameters. * * @param b <tt>b</tt> parameter value * @param c <tt>c</tt> parameter value * @param d <tt>d</tt> parameter value * * @throws IllegalArgumentException if <code>d</code> is 0 */ public GaussianDerivativeFunction(double b, double c, double d) { if (d == 0.0) { throw new ZeroException(); } this.b = b; this.c = c; this.d2 = d * d; } /** * Constructs an instance with the specified parameters. * * @param parameters <tt>b</tt>, <tt>c</tt>, and <tt>d</tt> parameter values * * @throws IllegalArgumentException if <code>parameters</code> is null, * <code>parameters</code> length is not 3, or if * <code>parameters[2]</code> is 0 */ public GaussianDerivativeFunction(double[] parameters) { if (parameters == null) { throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY); } if (parameters.length != 3) { throw new DimensionMismatchException(3, parameters.length); } if (parameters[2] == 0.0) { throw new ZeroException(); } this.b = parameters[0]; this.c = parameters[1]; this.d2 = parameters[2] * parameters[2]; } /** {@inheritDoc} */ public double value(double x) { final double xMc = x - c; return (-b / d2) * xMc * Math.exp(-(xMc * xMc) / (2.0 * d2)); } }