| /* |
| * 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.math3.fitting; |
| |
| import org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer; |
| import org.apache.commons.math3.analysis.ParametricUnivariateFunction; |
| import org.apache.commons.math3.util.FastMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| @Deprecated |
| public class CurveFitterTest { |
| @Test |
| public void testMath303() { |
| LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); |
| CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer); |
| fitter.addObservedPoint(2.805d, 0.6934785852953367d); |
| fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d); |
| fitter.addObservedPoint(1.655d, 0.9474675497289684); |
| fitter.addObservedPoint(1.725d, 0.9013594835804194d); |
| |
| ParametricUnivariateFunction sif = new SimpleInverseFunction(); |
| |
| double[] initialguess1 = new double[1]; |
| initialguess1[0] = 1.0d; |
| Assert.assertEquals(1, fitter.fit(sif, initialguess1).length); |
| |
| double[] initialguess2 = new double[2]; |
| initialguess2[0] = 1.0d; |
| initialguess2[1] = .5d; |
| Assert.assertEquals(2, fitter.fit(sif, initialguess2).length); |
| } |
| |
| @Test |
| public void testMath304() { |
| LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); |
| CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer); |
| fitter.addObservedPoint(2.805d, 0.6934785852953367d); |
| fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d); |
| fitter.addObservedPoint(1.655d, 0.9474675497289684); |
| fitter.addObservedPoint(1.725d, 0.9013594835804194d); |
| |
| ParametricUnivariateFunction sif = new SimpleInverseFunction(); |
| |
| double[] initialguess1 = new double[1]; |
| initialguess1[0] = 1.0d; |
| Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14); |
| |
| double[] initialguess2 = new double[1]; |
| initialguess2[0] = 10.0d; |
| Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14); |
| } |
| |
| @Test |
| public void testMath372() { |
| LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); |
| CurveFitter<ParametricUnivariateFunction> curveFitter = new CurveFitter<ParametricUnivariateFunction>(optimizer); |
| |
| curveFitter.addObservedPoint( 15, 4443); |
| curveFitter.addObservedPoint( 31, 8493); |
| curveFitter.addObservedPoint( 62, 17586); |
| curveFitter.addObservedPoint(125, 30582); |
| curveFitter.addObservedPoint(250, 45087); |
| curveFitter.addObservedPoint(500, 50683); |
| |
| ParametricUnivariateFunction f = new ParametricUnivariateFunction() { |
| public double value(double x, double ... parameters) { |
| double a = parameters[0]; |
| double b = parameters[1]; |
| double c = parameters[2]; |
| double d = parameters[3]; |
| |
| return d + ((a - d) / (1 + FastMath.pow(x / c, b))); |
| } |
| |
| public double[] gradient(double x, double ... parameters) { |
| double a = parameters[0]; |
| double b = parameters[1]; |
| double c = parameters[2]; |
| double d = parameters[3]; |
| |
| double[] gradients = new double[4]; |
| double den = 1 + FastMath.pow(x / c, b); |
| |
| // derivative with respect to a |
| gradients[0] = 1 / den; |
| |
| // derivative with respect to b |
| // in the reported (invalid) issue, there was a sign error here |
| gradients[1] = -((a - d) * FastMath.pow(x / c, b) * FastMath.log(x / c)) / (den * den); |
| |
| // derivative with respect to c |
| gradients[2] = (b * FastMath.pow(x / c, b - 1) * (x / (c * c)) * (a - d)) / (den * den); |
| |
| // derivative with respect to d |
| gradients[3] = 1 - (1 / den); |
| |
| return gradients; |
| |
| } |
| }; |
| |
| double[] initialGuess = new double[] { 1500, 0.95, 65, 35000 }; |
| double[] estimatedParameters = curveFitter.fit(f, initialGuess); |
| |
| Assert.assertEquals( 2411.00, estimatedParameters[0], 500.00); |
| Assert.assertEquals( 1.62, estimatedParameters[1], 0.04); |
| Assert.assertEquals( 111.22, estimatedParameters[2], 0.30); |
| Assert.assertEquals(55347.47, estimatedParameters[3], 300.00); |
| Assert.assertTrue(optimizer.getRMS() < 600.0); |
| } |
| |
| private static class SimpleInverseFunction implements ParametricUnivariateFunction { |
| |
| public double value(double x, double ... parameters) { |
| return parameters[0] / x + (parameters.length < 2 ? 0 : parameters[1]); |
| } |
| |
| public double[] gradient(double x, double ... doubles) { |
| double[] gradientVector = new double[doubles.length]; |
| gradientVector[0] = 1 / x; |
| if (doubles.length >= 2) { |
| gradientVector[1] = 1; |
| } |
| return gradientVector; |
| } |
| } |
| } |