MATH-514
Removed "ParametricGaussianFunction" (superseded by "Gaussian.Parametric" in package "analysis.function"). git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1072207 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski
parent
04bbc3fccf
commit
f2e551b8de
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@ -43,7 +43,6 @@ import org.apache.commons.math.exception.NullArgumentException;
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* </ul>
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* </ul>
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*
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*
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* @see GaussianDerivativeFunction
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* @see GaussianDerivativeFunction
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* @see ParametricGaussianFunction
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* @since 2.2
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* @since 2.2
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* @version $Revision$ $Date$
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* @version $Revision$ $Date$
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*/
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*/
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@ -1,143 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.optimization.fitting;
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import java.io.Serializable;
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import org.apache.commons.math.exception.DimensionMismatchException;
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import org.apache.commons.math.exception.util.LocalizedFormats;
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import org.apache.commons.math.exception.ZeroException;
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import org.apache.commons.math.exception.NullArgumentException;
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import org.apache.commons.math.analysis.ParametricUnivariateRealFunction;
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/**
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* A Gaussian function. Specifically:
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* <p>
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* {@code f(x) = a + b*exp(-((x - c)^2 / (2*d^2)))}
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* <p>
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* The parameters have the following meaning:
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* <ul>
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* <li>{@code a} is a constant offset that shifts {@code f(x)} up or down
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* <li>{@code b} is the height of the peak
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* <li>{@code c} is the position of the center of the peak
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* <li>{@code d} is related to the FWHM by {@code FWHM = 2*sqrt(2*ln(2))*d}
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* </ul>
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* Notation key:
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* <ul>
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* <li>{@code x^n}: {@code x} raised to the power of {@code n}
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* <li>{@code exp(x)}: e<sup>x</sup>
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* <li>{@code sqrt(x)}: square root of {@code x}
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* <li>{@code ln(x)}: natural logarithm of {@code x}
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* </ul>
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* References:
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* <ul>
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* <li><a href="http://en.wikipedia.org/wiki/Gaussian_function">Wikipedia:
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* Gaussian function</a>
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* </ul>
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*
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* @since 2.2
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* @version $Revision$ $Date$
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*/
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public class ParametricGaussianFunction implements ParametricUnivariateRealFunction, Serializable {
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/** Serializable version Id. */
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private static final long serialVersionUID = -3875578602503903233L;
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/**
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* Computes value of function {@code f(x)} for the specified {@code x} and
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* parameters {@code a}, {@code b}, {@code c}, and {@code d}.
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*
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* @param x Value at which to compute the function.
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* @return {@code f(x)}.
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* @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}.
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* @throws NullArgumentException if {@code parameters} is {@code null}.
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* @throws DimensionMismatchException if the size of {@code parameters} is
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* not 4.
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* @throws ZeroException if {@code parameters[3]} is 0.
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*/
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public double value(double x, double[] parameters) {
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validateParameters(parameters);
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final double a = parameters[0];
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final double b = parameters[1];
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final double c = parameters[2];
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final double d = parameters[3];
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final double xMc = x - c;
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return a + b * Math.exp(-xMc * xMc / (2.0 * (d * d)));
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}
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/**
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* Computes the gradient vector for a four variable version of the function
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* where the parameters, {@code a}, {@code b}, {@code c}, and {@code d},
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* are considered the variables, not {@code x}. That is, instead of
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* computing the gradient vector for the function {@code f(x)} (which would
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* just be the derivative of {@code f(x)} with respect to {@code x} since
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* it's a one-dimensional function), computes the gradient vector for the
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* function {@code f(a, b, c, d) = a + b*exp(-((x - c)^2 / (2*d^2)))}
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* treating the specified {@code x} as a constant.
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* <p>
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* The components of the computed gradient vector are the partial
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* derivatives of {@code f(a, b, c, d)} with respect to each variable.
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* That is, the partial derivative of {@code f(a, b, c, d)} with respect to
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* {@code a}, the partial derivative of {@code f(a, b, c, d)} with respect
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* to {@code b}, the partial derivative of {@code f(a, b, c, d)} with
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* respect to {@code c}, and the partial derivative of {@code f(a, b, c,
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* d)} with respect to {@code d}.
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*
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* @param x Value to be used as constant in {@code f(x, a, b, c, d)}.
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* @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}.
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* @return the gradient vector of {@code f(a, b, c, d)}.
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* @throws NullArgumentException if {@code parameters} is {@code null}.
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* @throws DimensionMismatchException if the size of {@code parameters} is
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* not 4.
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* @throws ZeroException if {@code parameters[3]} is 0.
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*/
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public double[] gradient(double x, double[] parameters) {
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validateParameters(parameters);
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final double b = parameters[1];
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final double c = parameters[2];
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final double d = parameters[3];
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final double xMc = x - c;
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final double d2 = d * d;
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final double exp = Math.exp(-xMc * xMc / (2 * d2));
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final double f = b * exp * xMc / d2;
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return new double[] { 1.0, exp, f, f * xMc / d };
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}
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/**
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* Validates parameters to ensure they are appropriate for the evaluation of
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* the {@code value} and {@code gradient} methods.
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*
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* @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}.
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* @throws NullArgumentException if {@code parameters} is {@code null}.
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* @throws DimensionMismatchException if the size of {@code parameters} is
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* not 4.
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* @throws ZeroException if {@code parameters[3]} is 0.
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*/
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private void validateParameters(double[] parameters) {
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if (parameters == null) {
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throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY);
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}
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if (parameters.length != 4) {
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throw new DimensionMismatchException(4, parameters.length);
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}
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if (parameters[3] == 0) {
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throw new ZeroException();
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}
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}
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}
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@ -52,6 +52,10 @@ The <action> type attribute can be add,update,fix,remove.
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If the output is not quite correct, check for invisible trailing spaces!
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If the output is not quite correct, check for invisible trailing spaces!
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-->
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-->
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<release version="3.0" date="TBD" description="TBD">
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<release version="3.0" date="TBD" description="TBD">
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<action dev="erans" type="fix" issue="MATH-514">
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Removed "ParametricGaussianFunction" (in package "optimization.fitting");
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functionality moved to class "Gaussian" (in package "analysis.function").
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</action>
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<action dev="erans" type="update" issue="MATH-512" due-to="Ole Ersoy">
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<action dev="erans" type="update" issue="MATH-512" due-to="Ole Ersoy">
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Refactored "GaussianFitter" (in package "optimization.fitting").
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Refactored "GaussianFitter" (in package "optimization.fitting").
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The class now really fits a Gaussian function (whereas previously it was
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The class now really fits a Gaussian function (whereas previously it was
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@ -1,158 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.optimization.fitting;
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import org.apache.commons.math.exception.MathUserException;
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import org.apache.commons.math.exception.ZeroException;
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import org.apache.commons.math.exception.MathIllegalArgumentException;
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import org.apache.commons.math.optimization.OptimizationException;
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import org.apache.commons.math.optimization.fitting.CurveFitter;
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import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer;
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import org.junit.Test;
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import static org.junit.Assert.assertEquals;
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/**
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* Tests {@link ParametricGaussianFunction}.
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*
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* @since 2.2
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* @version $Revision$ $Date$
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*/
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public class ParametricGaussianFunctionTest {
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/** Dataset 1 used by some test cases. */
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protected static final double[][] DATASET1 = new double[][] {
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{4.0254623, 531026.0},
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{4.02804905, 664002.0},
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{4.02934242, 787079.0},
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{4.03128248, 984167.0},
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{4.03386923, 1294546.0},
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{4.03580929, 1560230.0},
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{4.03839603, 1887233.0},
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{4.0396894, 2113240.0},
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{4.04162946, 2375211.0},
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{4.04421621, 2687152.0},
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{4.04550958, 2862644.0},
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{4.04744964, 3078898.0},
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{4.05003639, 3327238.0},
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{4.05132976, 3461228.0},
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{4.05326982, 3580526.0},
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{4.05585657, 3576946.0},
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{4.05779662, 3439750.0},
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{4.06038337, 3220296.0},
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{4.06167674, 3070073.0},
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{4.0636168, 2877648.0},
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{4.06620355, 2595848.0},
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{4.06749692, 2390157.0},
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{4.06943698, 2175960.0},
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{4.07202373, 1895104.0},
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{4.0733171, 1687576.0},
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{4.07525716, 1447024.0},
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{4.0778439, 1130879.0},
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{4.07978396, 904900.0},
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{4.08237071, 717104.0},
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{4.08366408, 620014.0}
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};
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/**
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* Using not-so-good initial parameters.
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*
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* @throws OptimizationException in the event of a test case error
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* @throws MathUserException in the event of a test case error
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*/
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@Test
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public void testFit01()
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throws OptimizationException, MathUserException {
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CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer());
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addDatasetToCurveFitter(DATASET1, fitter);
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double[] parameters = fitter.fit(new ParametricGaussianFunction(),
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new double[] {8.64753e3, 3.483323e6, 4.06322, 1.946857e-2});
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assertEquals(99200.94715858076, parameters[0], 1e-4);
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assertEquals(3410515.221897707, parameters[1], 1e-4);
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assertEquals(4.054928275257894, parameters[2], 1e-4);
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assertEquals(0.014609868499860, parameters[3], 1e-4);
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}
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/**
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* Using eye-balled guesses for initial parameters.
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*
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* @throws OptimizationException in the event of a test case error
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* @throws MathUserException in the event of a test case error
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*/
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@Test
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public void testFit02()
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throws OptimizationException, MathUserException {
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CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer());
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addDatasetToCurveFitter(DATASET1, fitter);
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double[] parameters = fitter.fit(new ParametricGaussianFunction(),
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new double[] {500000.0, 3500000.0, 4.055, 0.025479654});
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assertEquals(99200.81836264656, parameters[0], 1e-4);
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assertEquals(3410515.327151986, parameters[1], 1e-4);
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assertEquals(4.054928275377392, parameters[2], 1e-4);
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assertEquals(0.014609869119806, parameters[3], 1e-4);
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}
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/**
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* The parameters array is null.
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*
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* @throws MathUserException in the event of a test case error
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*/
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@Test(expected=MathIllegalArgumentException.class)
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public void testValue01() throws MathUserException {
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ParametricGaussianFunction f = new ParametricGaussianFunction();
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f.value(0.0, null);
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}
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/**
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* The parameters array length is not 4.
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*
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* @throws MathUserException in the event of a test case error
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*/
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@Test(expected=MathIllegalArgumentException.class)
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public void testValue02() throws MathUserException {
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ParametricGaussianFunction f = new ParametricGaussianFunction();
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f.value(0.0, new double[] {0.0, 1.0});
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}
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/**
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* The parameters d is 0.
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*
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* @throws MathUserException in the event of a test case error
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*/
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@Test(expected=ZeroException.class)
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public void testValue03() throws MathUserException {
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ParametricGaussianFunction f = new ParametricGaussianFunction();
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f.value(0.0, new double[] {0.0, 1.0, 1.0, 0.0});
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}
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/**
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* Adds the specified points to specified <code>CurveFitter</code> instance.
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*
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* @param points data points where first dimension is a point index and
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* second dimension is an array of length two representing the point
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* with the first value corresponding to X and the second value
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* corresponding to Y
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* @param fitter fitter to which the points in <code>points</code> should be
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* added as observed points
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*/
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protected static void addDatasetToCurveFitter(double[][] points,
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CurveFitter fitter) {
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for (int i = 0; i < points.length; i++) {
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fitter.addObservedPoint(points[i][0], points[i][1]);
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}
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}
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}
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