返回满足特定条件的多维数组的子集？

Question

基本上取一个多维数组并检查数组的第3列是否是第一列和第二列的斜边。这是我到目前为止的代码。我在 cmets 中添加了一些内容，以便更好地了解正在发生的事情。

import numpy
import random

def triple(mm):
    mm=np.asanyarray(mm) # I think this is how we specify that the paremeter should be an array, however,
    # it's possible the parameter isn't just mm, it could be 'j', 'qw', 'mm1', whatever. I'm not sure
    # how to work that while specifying the parameter must be array.
assert mm.ndim == 2 # we want mm or w/e the name of the parameter to be a 1 multidimensional array
assert mm.shape[1] == 3 # we want 3 columns, with any number of rows 
    x = mm[:,0]
    y = mm[:,1]
    z = mm[:,2] # 3rd column is to be checked to see if its's the hypotenuse of 1st & 2nd columns   
    zz = np.hypot(x,y)
    condition = np.any(z) == np.any(zz)
    return np.array([condition, mm]) # I'm not sure how to specify it here, that we want the function to return a subset
        # of the original multi-dim array, where the 3rd column is in fact the hypotenuse of the first and 
        # second columns. And we want to exclude the rows that don't satisfy this condition.

我想检查一下：

mm = np.array([[5,5,5],[5,12,13],[3,4,5],[5,11,21],[8,15,17]])
triple(mm)

但我得到的错误是：

ValueError: setting an array element with a sequence.

我不确定我设置的“条件”是否是解决此问题的正确方法，所以有人可以帮助我朝着正确的方向前进吗？ 随时要求更多说明。

Answer 1

一个向量化的方法是：

此计算 x²+y²-z² 在每一行 (axis=1)

In [1]: goods=(mm*mm*[1,1,-1]).sum(axis=1)==0


In [2]: goods
Out[2]: array([False,  True,  True, False,  True], dtype=bool)

还有boolean indexing :

In [3]: mm[goods]
Out[3]: 
array([[ 5, 12, 13],
       [ 3,  4,  5],
       [ 8, 15, 17]])

您的条件不好：如果zz 和z 不是空向量，则np.any(z) == np.any(zz) 为真。

np.array([condition, mm]) 在这里是 np.array ([ True, [[5....]] ]) ，它会触发错误消息。