Python | Scipy integrate . romberg()方法
原文:https://www.geesforgeks.org/python-scipy-integrate-romberg-method/
借助**scipy.integrate.romberg()**方法，我们可以用scipy.integrate.romberg()方法得到一个可调用函数从极限 a 到极限 b 的龙贝格积分。
语法:
scipy.integrate.romberg(func, a, b)返回:返回一个可调用函数的龙贝格积分值。
示例#1 :
在这个示例中，我们可以看到，通过使用scipy.integrate.romberg()方法，我们能够通过使用scipy.integrate.romberg()方法获得可调用函数从极限 a 到极限 b 的龙贝格积分。
# import numpy and scipy.integrate
import numpy as np
from scipy import integrate
gfg = lambda x: np.exp(-x**2)
# using scipy.integrate.romberg()
geek = integrate.romberg(gfg, 0, 3, show = True)
print(geek)
输出:
```py Romberg integration of
from [0, 3] Steps StepSize Results 1 3.000000 1.500185 2 1.500000 0.908191 0.710860 4 0.750000 0.886180 0.878843 0.890042 8 0.375000 0.886199 0.886206 0.886696 0.886643 16 0.187500 0.886205 0.886207 0.886207 0.886200 0.886198 32 0.093750 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207 64 0.046875 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207 128 0.023438 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207 0.886207
The final result is 0.8862073482595311 after 129 function evaluations.
```
例 2 :
# import numpy and scipy.integrate
import numpy as np
from scipy import integrate
gfg = lambda x: np.exp(-x**2) + 1 / np.sqrt(np.pi)
# using scipy.integrate.romberg()
geek = integrate.romberg(gfg, 1, 2, show = True)
print(geek)
输出:
```py Romberg integration of
from [1, 2] Steps StepSize Results 1 1.000000 0.757287 2 0.500000 0.713438 0.698822 4 0.250000 0.702909 0.699400 0.699438 8 0.125000 0.700310 0.699444 0.699447 0.699447 16 0.062500 0.699663 0.699447 0.699447 0.699447 0.699447 32 0.031250 0.699501 0.699447 0.699447 0.699447 0.699447 0.699447
The final result is 0.6994468414978009 after 33 function evaluations.
```
