Python中的 numpy.apply_along_axis()
numpy.apply_along_axis()函数帮助我们将所需的函数应用于给定数组的一维切片。
1d_func(ar, *args) :适用于一维数组,其中ar是arr沿轴的一维切片。
句法 :
numpy.apply_along_axis(1d_func, axis, array, *args, **kwargs)
参数 :
1d_func : the required function to perform over 1D array. It can only be applied in
1D slices of input array and that too along a particular axis.
axis : required axis along which we want input array to be sliced
array : Input array to work on
*args : Additional arguments to 1D_function
**kwargs : Additional arguments to 1D_function
*args 和 **kwargs 实际上是什么?
这两个都允许你传递一个变量号。函数的参数。
*args :允许向函数发送非关键字可变长度参数列表。
Python
# Python Program illustrating
# use of *args
args = [3, 8]
a = list(range(*args))
print("use of args : \n ", a)
Python
# Python Program illustrating
# use of **kwargs
def test_args_kwargs(in1, in2, in3):
print ("in1:", in1)
print ("in2:", in2)
print ("in3:", in3)
kwargs = {"in3": 1, "in2": "No.","in1":"geeks"}
test_args_kwargs(**kwargs)
Python
# Python Program illustrating
# apply_along_axis() in NumPy
import numpy as geek
# 1D_func is "geek_fun"
def geek_fun(a):
# Returning the sum of elements at start index and at last index
# inout array
return (a[0] + a[-1])
arr = geek.array([[1,2,3],
[4,5,6],
[7,8,9]])
'''
-> [1,2,3] <- 1 + 7
[4,5,6] 2 + 8
-> [7,8,9] <- 3 + 9
'''
print("axis=0 : ", geek.apply_along_axis(geek_fun, 0, arr))
print("\n")
''' | |
[1,2,3] 1 + 3
[4,5,6] 4 + 6
[7,8,9] 7 + 9
^ ^
'''
print("axis=1 : ", geek.apply_along_axis(geek_fun, 1, arr))
Python
# Python Program illustrating
# apply_along_axis() in NumPy
import numpy as geek
geek_array = geek.array([[8,1,7],
[4,3,9],
[5,2,6]])
# using pre-defined sorted function as 1D_func
print("Sorted as per axis 1 : \n", geek.apply_along_axis(sorted, 1, geek_array))
print("\n")
print("Sorted as per axis 0 : \n", geek.apply_along_axis(sorted, 0, geek_array))
输出 :
use of args :
[3, 4, 5, 6, 7]
**kwargs:允许您将参数的关键字可变长度传递给函数。当我们要处理函数中的命名参数时使用它。
Python
# Python Program illustrating
# use of **kwargs
def test_args_kwargs(in1, in2, in3):
print ("in1:", in1)
print ("in2:", in2)
print ("in3:", in3)
kwargs = {"in3": 1, "in2": "No.","in1":"geeks"}
test_args_kwargs(**kwargs)
输出 :
in1: geeks
in2: No.
in3: 1
代码 1:解释使用 numpy.apply_along_axis() 的Python代码。
Python
# Python Program illustrating
# apply_along_axis() in NumPy
import numpy as geek
# 1D_func is "geek_fun"
def geek_fun(a):
# Returning the sum of elements at start index and at last index
# inout array
return (a[0] + a[-1])
arr = geek.array([[1,2,3],
[4,5,6],
[7,8,9]])
'''
-> [1,2,3] <- 1 + 7
[4,5,6] 2 + 8
-> [7,8,9] <- 3 + 9
'''
print("axis=0 : ", geek.apply_along_axis(geek_fun, 0, arr))
print("\n")
''' | |
[1,2,3] 1 + 3
[4,5,6] 4 + 6
[7,8,9] 7 + 9
^ ^
'''
print("axis=1 : ", geek.apply_along_axis(geek_fun, 1, arr))
输出 :
axis=0 : [ 8 10 12]
axis=1 : [ 4 10 16]
代码 2:在 NumPy Python中使用 apply_along_axis() 进行排序
Python
# Python Program illustrating
# apply_along_axis() in NumPy
import numpy as geek
geek_array = geek.array([[8,1,7],
[4,3,9],
[5,2,6]])
# using pre-defined sorted function as 1D_func
print("Sorted as per axis 1 : \n", geek.apply_along_axis(sorted, 1, geek_array))
print("\n")
print("Sorted as per axis 0 : \n", geek.apply_along_axis(sorted, 0, geek_array))
输出 :
Sorted as per axis 1 :
[[1 7 8]
[3 4 9]
[2 5 6]]
Sorted as per axis 0 :
[[4 1 6]
[5 2 7]
[8 3 9]]
笔记 :
这些代码不会在在线 IDE 上运行。因此,请在您的系统上运行它们以探索其工作原理。