Apache Commons Math3学习笔记(2) - 多项式曲线拟合(转)

时间:2022-10-31 18:54:28

多项式曲线拟合:org.apache.commons.math3.fitting.PolynomialCurveFitter类。

用法示例代码:

  1. // ... 创建并初始化输入数据:
  2. double[] x = new double[...];
  3. double[] y = new double[...];
  4. 将原始的x-y数据序列合成带权重的观察点数据序列:
  5. WeightedObservedPoints points = new WeightedObservedPoints();
  6. // 将x-y数据元素调用points.add(x[i], y[i])加入到观察点序列中
  7. // ...
  8. PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);   // degree 指定多项式阶数
  9. double[] result = fitter.fit(points.toList());   // 曲线拟合,结果保存于双精度数组中,由常数项至最高次幂系数排列

首先要准备好待拟合的曲线数据x和y,这是两个double数组,然后把这两个数组合并到WeightedObservedPoints对象实例中,可以调用WeightedObservedPoints.add(x[i], y[i])将x和y序列中的数据逐个添加到观察点序列对象中。随后创建PolynomialCurveFitter对象,创建时要指定拟合多项式的阶数,注意阶数要选择适当,不是越高越好,否则拟合误差会很大。最后调用PolynomialCurveFitter的fit方法即可完成多项式曲线拟合,fit方法的参数通过WeightedObservedPoints.toList()获得。拟合结果通过一个double数组返回,按元素顺序依次是常数项、一次项、二次项、……。

完整的演示代码如下:

package fitting;

import org.apache.commons.math3.fitting.PolynomialCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoints; import java.util.ArrayList;
import java.util.List; public class TimeCostCalculator {
public static void main(String[] args) throws Exception {
TimeCostCalculator tcc = new TimeCostCalculator();
double timeCost = tcc.calcTimeCost(new CalcCurveFitting());
System.out.println("--------------------------------------------------------------------------");
System.out.println("time cost is: " + timeCost + "s");
System.out.println("--------------------------------------------------------------------------");
} /**
* 计算指定对象的运行时间开销。
*
* @param curveFitting 指定被测对象。
* @return 返回sub.run的时间开销,单位为s。
* @throws Exception
*/
public double calcTimeCost(CurveFitting curveFitting) throws Exception {
List<Object> params = curveFitting.getParams();
long startTime = System.nanoTime();
Object result = curveFitting.run(params);
long stopTime = System.nanoTime();
curveFitting.printResult(result);
System.out.println("start: " + startTime + " / stop: " + stopTime);
return 1.0e-9 * (stopTime - startTime);
} } interface CurveFitting {
public List<Object> getParams(); public Object run(List<Object> params) throws Exception; public void printResult(Object result);
} class CalcCurveFitting implements CurveFitting { private WeightedObservedPoints points; private final int degree = 5; // 阶数 public CalcCurveFitting() {
int arrayLength = 200000;
System.out.println(String.format("本算例用于计算多项式曲线拟合。正在初始化计算数据(%s点,%s阶......", arrayLength, degree));
double[] inputDataX = new double[arrayLength];
// inputDataX = new double[] {1, 2, 3, 4, 5, 6, 7};
double[] inputDataY = new double[inputDataX.length];
double[] factor = new double[degree + 1]; // N阶多项式会有N+1个系数,其中之一为常数项
for (int index = 0; index < factor.length; index++) {
factor[index] = index + 1;
}
for (int index = 0; index < inputDataY.length; index++) {
inputDataX[index] = index * 0.00001;
inputDataY[index] = calcPoly(inputDataX[index], factor); // y = sum(x[n) * fact[n])
// System.out.print(inputDataY[index] + ", ");
}
points = new WeightedObservedPoints();
for (int index = 0; index < inputDataX.length; index++) {
points.add(inputDataX[index], inputDataY[index]);
}
System.out.println("init completely");
} @Override
public List<Object> getParams() {
List<Object> params = new ArrayList<Object>();
params.add(points);
return params;
} @Override
public Object run(List<Object> params) throws Exception {
PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);
WeightedObservedPoints points = (WeightedObservedPoints) params.get(0);
double[] result = fitter.fit(points.toList());
return result;
} @Override
public void printResult(Object result) {
for (double data : (double[]) result) {
System.out.println(data);
}
} private double calcPoly(double x, double[] factor) {
double y = 0;
for (int deg = 0; deg < factor.length; deg++) {
y += Math.pow(x, deg) * factor[deg];
}
return y;
} }

http://blog.csdn.net/kingfox/article/details/44118319