# [SciPy-User] Dot product of two arrays of vectors

Alexander Kalinin alec.kalinin@gmail....
Thu Oct 4 08:26:13 CDT 2012

```Could you, please, explain me more about matrix_multiply? I tried the
following:

>>> import numpy.core.umath_tests as ut
>>> ut.matrix_multiply.signature
'(m,n),(n,p)->(m,p)'
>>>

So, I see the the matrix_multiply is the usual matrix product.

Sincerely,
Alexander

On Thu, Oct 4, 2012 at 3:43 PM, Robert Cimrman <cimrman3@ntc.zcu.cz> wrote:

> On 10/04/2012 01:25 PM, Alexander Kalinin wrote:
> > Hello, SciPy,
> >
> > Could you, please, explain me, what is the most standard way in NumPy to
> > calculate a dot product of two arrays of vectors, like in MatLab? For
> > example, consider two numpy arrays of vectors:
> >
> > a = np.array([[1, 2, 3], [4, 5, 6]])
> > b = np.array([[3, 2, 1], [6, 5, 4]])
> >
> > For the cross product we have convenient function numpy.cross:
> >>>> np.cross(a, b)
> > array([[ -4,   8,  -4],
> >         [-10,  20, -10]])
> >
> > But the numpy.dot product for the arrays of vectors do the matrix
> > multiplication:
> >>>> np.dot(a, b)
> > Traceback (most recent call last):
> >    File "<stdin>", line 1, in <module>
> > ValueError: objects are not aligned
> >
> > Yes, I can emulate the dot product code like:
> >
> > np.sum(a * b, axis = 1).reshape(-1, 1)
> > but may be there is exist more standard way to do the dot product?
>
> You could try using:
>
> from numpy.core.umath_tests import matrix_multiply
>
> if your numpy is recent enough.
>
> Cheers,
> r.
>
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