}
@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);
}