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

[Robert Cimrman]

On 10/04/2012 03:26 PM, Alexander Kalinin wrote:
> 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.

Yes, but the important part is the "on last two dimensions" part of the docstring:

In [5]: a = np.ones((5, 2))

In [6]: b = 2 * a

In [7]: a
Out[7]:
array([[ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.]])

In [8]: b
Out[8]:
array([[ 2.,  2.],
        [ 2.,  2.],
        [ 2.,  2.],
        [ 2.,  2.],
        [ 2.,  2.]])

In [17]: matrix_multiply(a[:, None, :], b[:, :, None]).squeeze()
Out[17]: array([ 4.,  4.,  4.,  4.,  4.])

r.

> Sincerely,
> Alexander
>
> On Thu, Oct 4, 2012 at 3:43 PM, Robert Cimrman <cimrman3 at 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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>
>
>
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