numpy.stack和numpy.concatenate的區別

　　在使用numpy進行矩陣運算的時候踩到的坑，緣由是不能正確區分numpy.concatenate和numpy.stack在功能上的差別。

　　先說numpy.concatenate，直接看文檔：

numpy.concatenate((a1, a2, ...), axis=0, out=None)

Join a sequence of arrays along an existing axis.

Parameters

a1, a2, … :  sequence of array_like

The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).

axis :  int, optional

The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.

out :  ndarray, optional

If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

Returns

res :  ndarray

The concatenated array.

　　重點在這一句：在一個已經存在的維度上鍊接數組列。可見numpy.concatenate能夠同時鏈接好幾個數組，而且不會生成新的維度: along an existing axis。示例以下：

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])

　　

　　再說numpy.stack:

numpy.stack(arrays, axis=0, out=None)

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

New in version 1.10.0.

Parameters

arrays :  sequence of array_like

Each array must have the same shape.

axis :  int, optional

The axis in the result array along which the input arrays are stacked.

out :  ndarray, optional

If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.

Returns

stacked :  ndarray

The stacked array has one more dimension than the input arrays.

　　和concatenate不一樣的是，stack Joins a sequence of arrays along a new axis.也就是說stack會生成一個新的維度。並且stack適用的條件很強，數組序列必須所有有相同的shape。用例子來講明，使用最多的大概是在第0維stack：

>>> arrays = [np.random.randn(3, 4) for _ in range(10)]    # arrays是一個長度爲10的List，每個元素都是(3,4)的ndarray
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)

　　一個清晰的區別是返回的數組比輸入數組多了一維。