I am trying to understand what exactly happens in the following snippet?
>>> a = np.random.randn(3,3,3)
>>> a
array([[[ 1.17565688, 0.58223235, -0.41813242],
[-0.16933573, 0.24205104, 1.37286476],
[-0.58120365, 0.32970027, -0.1521039 ]],
[[ 1.18393152, 0.00254526, 1.67234901],
[ 0.72494527, 0.72414755, 0.32974478],
[-1.2290148 , 1.18013258, -0.61498214]],
[[-0.38574517, -0.46385622, 0.06616913],
[ 0.26560153, 0.61720524, 0.03528806],
[ 0.66292143, -0.57724826, -0.33810831]]])
>>> np.concatenate(a, 1)
array([[ 1.17565688, 0.58223235, -0.41813242, 1.18393152, 0.00254526,
1.67234901, -0.38574517, -0.46385622, 0.06616913],
[-0.16933573, 0.24205104, 1.37286476, 0.72494527, 0.72414755,
0.32974478, 0.26560153, 0.61720524, 0.03528806],
[-0.58120365, 0.32970027, -0.1521039 , -1.2290148 , 1.18013258,
-0.61498214, 0.66292143, -0.57724826, -0.33810831]])
What operations are exactly happening? Is it splitting array on the given dimension and then concatenating?
Thank You!
The signature
numpy.concatenate((a1, a2, ...), axis=0, out=None)
implies that the input is 'unpacked' on the first axis.
To illustrate:
In [40]: arr = np.arange(24).reshape(2,3,4)
In [41]: a1,a2 = arr
In [42]: a1
Out[42]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [43]: a2
Out[43]:
array([[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]])
In [44]: np.concatenate(arr, axis=1)
Out[44]:
array([[ 0, 1, 2, 3, 12, 13, 14, 15],
[ 4, 5, 6, 7, 16, 17, 18, 19],
[ 8, 9, 10, 11, 20, 21, 22, 23]])
In [45]: np.concatenate((a1,a2), axis=1)
Out[45]:
array([[ 0, 1, 2, 3, 12, 13, 14, 15],
[ 4, 5, 6, 7, 16, 17, 18, 19],
[ 8, 9, 10, 11, 20, 21, 22, 23]])