001 /*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements. See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License. You may obtain a copy of the License at
008 *
009 * http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017
018 package org.apache.commons.math.optimization.fitting;
019
020 import java.io.Serializable;
021
022 import org.apache.commons.math.analysis.UnivariateRealFunction;
023 import org.apache.commons.math.exception.DimensionMismatchException;
024 import org.apache.commons.math.exception.util.LocalizedFormats;
025 import org.apache.commons.math.exception.ZeroException;
026 import org.apache.commons.math.exception.NullArgumentException;
027
028 /**
029 * The derivative of {@link GaussianFunction}. Specifically:
030 * <p>
031 * <tt>f'(x) = (-b / (d^2)) * (x - c) * exp(-((x - c)^2) / (2*(d^2)))</tt>
032 * <p>
033 * Notation key:
034 * <ul>
035 * <li><tt>x^n</tt>: <tt>x</tt> raised to the power of <tt>n</tt>
036 * <li><tt>exp(x)</tt>: <i>e</i><tt>^x</tt>
037 * </ul>
038 *
039 * @since 2.2
040 * @version $Revision: 1037327 $ $Date: 2010-11-20 21:57:37 +0100 (sam. 20 nov. 2010) $
041 */
042 public class GaussianDerivativeFunction implements UnivariateRealFunction, Serializable {
043
044 /** Serializable version identifier. */
045 private static final long serialVersionUID = -6500229089670174766L;
046
047 /** Parameter b of this function. */
048 private final double b;
049
050 /** Parameter c of this function. */
051 private final double c;
052
053 /** Square of the parameter d of this function. */
054 private final double d2;
055
056 /**
057 * Constructs an instance with the specified parameters.
058 *
059 * @param b <tt>b</tt> parameter value
060 * @param c <tt>c</tt> parameter value
061 * @param d <tt>d</tt> parameter value
062 *
063 * @throws IllegalArgumentException if <code>d</code> is 0
064 */
065 public GaussianDerivativeFunction(double b, double c, double d) {
066 if (d == 0.0) {
067 throw new ZeroException();
068 }
069 this.b = b;
070 this.c = c;
071 this.d2 = d * d;
072 }
073
074 /**
075 * Constructs an instance with the specified parameters.
076 *
077 * @param parameters <tt>b</tt>, <tt>c</tt>, and <tt>d</tt> parameter values
078 *
079 * @throws IllegalArgumentException if <code>parameters</code> is null,
080 * <code>parameters</code> length is not 3, or if
081 * <code>parameters[2]</code> is 0
082 */
083 public GaussianDerivativeFunction(double[] parameters) {
084 if (parameters == null) {
085 throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY);
086 }
087 if (parameters.length != 3) {
088 throw new DimensionMismatchException(3, parameters.length);
089 }
090 if (parameters[2] == 0.0) {
091 throw new ZeroException();
092 }
093 this.b = parameters[0];
094 this.c = parameters[1];
095 this.d2 = parameters[2] * parameters[2];
096 }
097
098 /** {@inheritDoc} */
099 public double value(double x) {
100 final double xMc = x - c;
101 return (-b / d2) * xMc * Math.exp(-(xMc * xMc) / (2.0 * d2));
102 }
103
104 }