, b)

Dot product of two arrays.

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:

dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters :

a : array_like

First argument.

b : array_like

Second argument.

Returns :

output : ndarray

Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned.

Raises :

ValueError :

If the last dimension of a is not the same size as the second-to-last dimension of b.

See also

Complex-conjugating dot product.
Sum products over arbitrary axes.


>>>, 4)

Neither argument is complex-conjugated:

>>>[2j, 3j], [2j, 3j])

For 2-D arrays it’s the matrix product:

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>>, b)
array([[4, 1],
       [2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>>, b)[2,3,2,1,2,2]
>>> sum(a[2,3,2,:] * b[1,2,:,2])

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