Python 中的 numpy.float_power()
numpy.float_power(arr1，arr2，out = None，其中= True，casting = 'same_kind '，order = 'K '，dtype = None) : 第一个数组中的数组元素被提升为第二个元素中的元素的幂(都是按元素进行的)。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，整数 float16 和 float32 被提升为 float64 的最小精度的浮点数，因此结果总是不精确的。这个函数将返回负幂的可用结果，对于+ve 幂很少溢出。
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 : arr1 升至 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 arr1 的元素提升到指数 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 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]
