Dot product of two arrays.
For 2D arrays it is equivalent to matrix multiplication, and for 1D 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 secondtolast of b:
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters:  a : array_like
b : array_like


Returns:  output : ndarray

Raises:  ValueError :

Examples
>>> np.dot(3, 4)
12
Neither argument is complexconjugated:
>>> np.dot([2j, 3j], [2j, 3j])
(13+0j)
For 2D arrays it’s the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, 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))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128