numpy：安排输入和输出向量的正确方法

Question

I am trying to create a numpy array with 2 columns and multiple rows. The first column is meant to represent input vector of size 3. The 2nd column is meant to represent output vector of size 2.

arr = np.array([
    [np.array([1,2,3]), np.array([1,0])]
    [np.array([4,5,6]), np.array([0,1])]
])

I was expecting: arr[:, 0].shape to return (2, 3), but it returns (2, )

What is the proper way to arrange input and output vectors into a matrix using numpy?

Answer 1

If you are sure the elements in each column have the same size/length, you can select and then stack the result using numpy.row_stack :

np.row_stack(arr[:,0]).shape
# (2, 3)

np.row_stack(arr[:,1]).shape
# (2, 2)

Answer 2

So, the code

arr = np.array([
[np.array([1,2,3]), np.array([1,0])],
[np.array([4,5,6]), np.array([0,1])]
])

Creates an object array, indexing the first column gives you back two rows with one object in each, which accounts for the size. To get what you want you'd need to wrap it in something like

np.vstack(arr[:, 0])

Which creates an array out of the objects in the first column. This isn't very convenient, it would make more sense to me to store these in a dictionary, something like

io = {'in': np.array([[1,2,3],[4,5,6]]),
'out':np.array([[1,0], [0,1]])
}

A structured array gives you a bit of both. Creation is a bit tricky, for the example given,

arr = np.array([
    (1,2,3), (1,0)),
    ((4,5,6), (0,1)) ],
    dtype=[('in', '3int64'), ('out', '2float64')])

Creates a structured array with fields in and out , consisting of 3 integers and 2 floats respectively. Rows can be accessed as usual,

In[73]: arr[0]
Out[74]: ([1, 2, 3], [ 1.,  0.])

Or by the field name

In [73]: arr['in']
Out[73]: 
array([[1, 2, 3],
   [4, 5, 6]])

The numpy manual has many more details ( https://docs.scipy.org/doc/numpy-1.13.0/user/basics.rec.html ). I can't add any details as I've been intending to use them in a project for some time, but haven't.