arrays - 在 numpy 数组中搜索 numpy 数组

Question

我需要查找一个 numpy 数组是否在另一个 numpy 数组中，但它似乎与 python 列表的工作方式不同。我试图在 numpy 文档和互联网中搜索这个问题，但没有回答。这是一个例子:


将 numpy 导入为 np

m1=np.array([[1,2,3],[5,3,4]])
m2=np.array([5,4,3])
m1 中的 m2
真的
m3=[[1,2,3],[5,3,4]]
m4=[5,4,3]
立方米立方米
错误的

在 numpy 中我得到 True 但在 Python 列表中我得到 False。是否有任何 numpy 函数可以使这项工作正常进行？

谢谢。

Answer 1

要获得与列表的 in 相同的行为，您可以这样做:

any(np.all(row == m2) for row in m1)

这会在 python 中对行进行循环，这并不理想，但它应该可以工作。

要了解 numpy in 发生了什么，这里是对 in 语义的描述 from Robert Kern on the numpy mailing list :

It dates back to Numeric's semantics for bool(some_array), which would be True if any of the elements were nonzero. Just like any other iterable container in Python, x in y will essentially do

for row in y:
   if x == row:
       return True
return False

Iterate along the first axis of y and compare by boolean equality. In Numeric/numpy's case, this comparison is broadcasted. So that's why [3,6,4] works, because there is one row where 3 is in the first column. [4,2,345] doesn't work because the 4 and the 2 are not in those columns.

Probably, this should be considered a mistake during the transition to numpy's semantics of having bool(some_array) raise an exception. scalar in array should probably work as-is for an ND array, but there are several different possible semantics for array in array that should be explicitly spelled out, much like bool(some_array).