将numpy.ndarray的列表转换为矩阵以执行乘法

Question

i have vectors of this form :

test=np.linspace(0,1,10)

i want to stack them horizontally in order to make a matrix . problem is that i define them in a loop so the first stack is between an empty matrix and the first column vector , which gives the following error:

ValueError: all the input arrays must have same number of dimensions

bottom line - i have a for loop that with every iteration creates a vector p1 and i want to add it to a final matrix of the form : [p1 p2 p3 p4] which i could then do matrix operations on such as multiplying by the transposed etc

Answer 1

If you've got a list of 1D arrays that you want horizontally stacked, you could convert them all to column first, but it's probably easier to just vertically stack them and then transpose:

In [6]: vector_list = [np.linspace(0, 1, 10) for _ in range(3)]

In [7]: np.vstack(vector_list).T
Out[7]:
array([[0.        , 0.        , 0.        ],
       [0.11111111, 0.11111111, 0.11111111],
       [0.22222222, 0.22222222, 0.22222222],
       [0.33333333, 0.33333333, 0.33333333],
       [0.44444444, 0.44444444, 0.44444444],
       [0.55555556, 0.55555556, 0.55555556],
       [0.66666667, 0.66666667, 0.66666667],
       [0.77777778, 0.77777778, 0.77777778],
       [0.88888889, 0.88888889, 0.88888889],
       [1.        , 1.        , 1.        ]])

Answer 2

How did you get this dimension error? What does empty array have to do with it?

A list of arrays of the same length:

In [610]: alist = [np.linspace(0,1,6), np.linspace(10,11,6)]                                          
In [611]: alist                                                                                       
Out[611]: 
[array([0. , 0.2, 0.4, 0.6, 0.8, 1. ]),
 array([10. , 10.2, 10.4, 10.6, 10.8, 11. ])]

Several ways of making an array from them:

In [612]: np.array(alist)                                                                             
Out[612]: 
array([[ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ],
       [10. , 10.2, 10.4, 10.6, 10.8, 11. ]])

In [614]: np.stack(alist)                                                                             
Out[614]: 
array([[ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ],
       [10. , 10.2, 10.4, 10.6, 10.8, 11. ]])

If you want to join them in columns, you can transpose one of the above, or use:

In [615]: np.stack(alist, axis=1)                                                                     
Out[615]: 
array([[ 0. , 10. ],
       [ 0.2, 10.2],
       [ 0.4, 10.4],
       [ 0.6, 10.6],
       [ 0.8, 10.8],
       [ 1. , 11. ]])

np.column_stack is also handy.

In newer numpy versions you can do:

In [617]: np.linspace((0,10),(1,11),6)                                                                
Out[617]: 
array([[ 0. , 10. ],
       [ 0.2, 10.2],
       [ 0.4, 10.4],
       [ 0.6, 10.6],
       [ 0.8, 10.8],
       [ 1. , 11. ]])

You don't specify how you create the 'empty array' and how you attempt to stack. I can't exactly recreate the error message (full traceback would have helped). But given that message did you check the number of dimensions of the inputs? Did they match?

Array stacking in a loop is tricky. You have to pay close attention to the shapes, especially of the initial 'empty' array. There isn't a close analog to the empty list [] . np.array([]) is 1d with shape (1,). np.empty((0,6)) is 2d with shape (0,6). Also all the stacking functions create a new array with each call (non operate in-place), so they are inefficient (compared to list append).