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/*
* 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));
}
}
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