The numpy.dot docstring says:
For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of a and the second-to-last of b
But it doesn't illustrate how numpy.dot calculate 1-D array with 2-D array.So how does Numpy handle 1-D array(vector) with 2-D array(matrix)?
I have make some test:
In [27]: a
Out[27]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [28]: b
Out[28]: array([0, 1, 2])
In [29]: np.dot(a,b)
Out[29]: array([ 5, 14, 23])
In [30]: np.dot(a, b.reshape(-1,1))
Out[30]:
array([[ 5],
[14],
[23]])
In [31]: np.dot(a, b.reshape(-1,1)).ravel() # same as np.dot(a,b)
Out[31]: array([ 5, 14, 23])
In [32]: np.dot(b,a)
Out[32]: array([15, 18, 21])
In [33]: np.dot(b.reshape(1,-1), a)
Out[33]: array([[15, 18, 21]])
In [34]: np.dot(b.reshape(1,-1), a).ravel() # same as np.dot(b,a)
Out[34]: array([15, 18, 21])
The above tests indecate that numpy.dot can handle 1-D array with 2-D array. Is it right?