python中power的用法_Python numpy.float_power()用法及代码示例
numpy.float_power(arr1，arr2，out = None，其中= True，强制转换=“ same_kind”，order =“ K”，dtype = None)：
来自第一个数组的数组元素被提升为来自第二个元素的元素的幂(所有情况都逐个元素发生)。 arr1和arr2必须具有相同的形状。
float_power与幂函数的不同之处在于，将整数float16和float32提升为float64的最小精度为float64，因此结果始终是不精确的。此函数将为负幂返回可用结果，而对于+ ve幂则很少溢出。
参数：
arr1 :[array_like]Input array or object which works as base.
arr2 :[array_like]Input array or object which works as exponent.
out :[ndarray, optional]Output array with same dimensions as Input array,
placed with result.
**kwargs :Allows you to pass keyword variable length of argument to a function.
It is used when we want to handle named argument in a function.
where :[array_like, optional]True value means to calculate the universal
functions(ufunc) at that position, False value means to leave the
value in the output alone.
返回：
An array with elements of arr1 raised to exponents in arr2
代码1：将arr1提升为arr2
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = [2, 2, 2, 2, 2]
arr2 = [2, 3, 4, 5, 6]
print ("arr1 : ", arr1)
print ("arr1 : ", arr2)
# output_array
out = np.float_power(arr1, arr2)
print ("\nOutput array : ", out)
输出：
arr1 : [2, 2, 2, 2, 2]
arr1 : [2, 3, 4, 5, 6]
Output array : [ 4. 8. 16. 32. 64.]
代码2：将arr1的元素提高到指数2
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = np.arange(8)
exponent = 2
print ("arr1 : ", arr1)
# output_array
out = np.float_power(arr1, exponent)
print ("\nOutput array : ", out)
输出：
arr1 : [0 1 2 3 4 5 6 7]
Output array : [ 0. 1. 4. 9. 16. 25. 36. 49.]
代码3：如果arr2具有-ve元素，则float_power处理结果
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = [2, 2, 2, 2, 2]
arr2 = [2, -3, 4, -5, 6]
print ("arr1 : ", arr1)
print ("arr2 : ", arr2)
# output_array
out = np.float_power(arr1, arr2)
print ("\nOutput array : ", out)
输出：
arr1 : [2, 2, 2, 2, 2]
arr2 : [2, -3, 4, -5, 6]
Output array : [ 4.00000000e+00 1.25000000e-01 1.60000000e+01
3.12500000e-02 6.40000000e+01]