如何使用Math Commons CurveFitter将函数拟合到一组数据？有人告诉我使用带有LevenbergMarquardtOptimizer和ParametricUnivariateFunction的CurveFitter,但我不知道在ParametricUnivariateFunction梯度和值方法中要写什么.另外,在写完之后,如何获得拟合的函数参数？我的功能：
public static double fnc(double t,double a,double b,double c){
return a * Math.pow(t,b) * Math.exp(-c * t);
}
解决方法
所以,这是一个老问题,但我最近遇到了同样的问题,最终不得不深入研究邮件列表和Apache Commons Math源代码来解决这个问题.
这个API的文档记录非常少,但是在当前版本的Apache Common Math(3.3)中,有两个部分,假设你有一个带有多个参数的变量：拟合的函数(实现ParametricUnivariateFunction)和曲线拟合器(它扩展了AbstractCurveFitter).
适合的功能
>公共双值(双t,双…参数)
>你的等式.这是您放置fnc逻辑的地方.
> public double [] gradient(double t,double …参数)
>根据每个参数返回上述偏导数的数组.如果(像我一样)你的微积分生锈了,但是任何好的计算机代数系统都可以计算出这些值,This calculator可能会有所帮助.
曲线钳工
>受保护的LeastSquaresProblem getProblem(集合< WeightedObservedPoint>点)
>设置一堆样板垃圾,并返回最小二乘问题供装配工使用.
总而言之,这是您特定情况下的示例解决方案：
import java.util.*;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.linear.DiagonalMatrix;
class MyFunc implements ParametricUnivariateFunction {
public double value(double t,double... parameters) {
return parameters[0] * Math.pow(t,parameters[1]) * Math.exp(-parameters[2] * t);
}
// Jacobian matrix of the above. In this case,this is just an array of
// partial derivatives of the above function,with one element for each parameter.
public double[] gradient(double t,double... parameters) {
final double a = parameters[0];
final double b = parameters[1];
final double c = parameters[2];
return new double[] {
Math.exp(-c*t) * Math.pow(t,b),a * Math.exp(-c*t) * Math.pow(t,b) * Math.log(t),a * (-Math.exp(-c*t)) * Math.pow(t,b+1)
};
}
}
public class MyFuncFitter extends AbstractCurveFitter {
protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points) {
final int len = points.size();
final double[] target = new double[len];
final double[] weights = new double[len];
final double[] initialGuess = { 1.0,1.0,1.0 };
int i = 0;
for(WeightedObservedPoint point : points) {
target[i] = point.getY();
weights[i] = point.getWeight();
i += 1;
}
final AbstractCurveFitter.TheoreticalValuesFunction model = new
AbstractCurveFitter.TheoreticalValuesFunction(new MyFunc(),points);
return new LeastSquaresBuilder().
maxEvaluations(Integer.MAX_VALUE).
maxIterations(Integer.MAX_VALUE).
start(initialGuess).
target(target).
weight(new DiagonalMatrix(weights)).
model(model.getModelFunction(),model.getModelFunctionJacobian()).
build();
}
public static void main(String[] args) {
MyFuncFitter fitter = new MyFuncFitter();
ArrayList<WeightedObservedPoint> points = new ArrayList<WeightedObservedPoint>();
// Add points here; for instance,WeightedObservedPoint point = new WeightedObservedPoint(1.0,1.0);
points.add(point);
final double coeffs[] = fitter.fit(points);
System.out.println(Arrays.toString(coeffs));
}
}
