Python实现分段线性插值-创新互联

本文实例为大家分享了Python实现分段线性插值的具体代码，供大家参考，具体内容如下

函数：

算法

这个算法不算难。甚至可以说是非常简陋。但是在代码实现上却比之前的稍微麻烦点。主要体现在分段上。

图像效果

代码

import numpy as np
from sympy import *
import matplotlib.pyplot as plt


def f(x):
 return 1 / (1 + x ** 2)


def cal(begin, end):
 by = f(begin)
 ey = f(end)
 I = (n - end) / (begin - end) * by + (n - begin) / (end - begin) * ey
 return I


def calnf(x):
 nf = []
 for i in range(len(x) - 1):
  nf.append(cal(x[i], x[i + 1]))
 return nf


def calf(f, x):
 y = []
 for i in x:
  y.append(f.subs(n, i))
 return y


def nfSub(x, nf):
 tempx = np.array(range(11)) - 5
 dx = []
 for i in range(10):
  labelx = []
  for j in range(len(x)):
   if x[j] >= tempx[i] and x[j] < tempx[i + 1]:
    labelx.append(x[j])
   elif i == 9 and x[j] >= tempx[i] and x[j] <= tempx[i + 1]:
    labelx.append(x[j])
  dx = dx + calf(nf[i], labelx)
 return np.array(dx)


def draw(nf):
 plt.rcParams['font.sans-serif'] = ['SimHei']
 plt.rcParams['axes.unicode_minus'] = False
 x = np.linspace(-5, 5, 101)
 y = f(x)
 Ly = nfSub(x, nf)
 plt.plot(x, y, label='原函数')
 plt.plot(x, Ly, label='分段线性插值函数')
 plt.xlabel('x')
 plt.ylabel('y')
 plt.legend()

 plt.savefig('1.png')
 plt.show()


def lossCal(nf):
 x = np.linspace(-5, 5, 101)
 y = f(x)
 Ly = nfSub(x, nf)
 Ly = np.array(Ly)
 temp = Ly - y
 temp = abs(temp)
 print(temp.mean())


if __name__ == '__main__':
 x = np.array(range(11)) - 5
 y = f(x)

 n, m = symbols('n m')
 init_printing(use_unicode=True)

 nf = calnf(x)
 draw(nf)
 lossCal(nf)