Adding to an old discussion thread (see below) ... an implementation
of the proposed functionality:
from numpy import rollaxis
def moveaxis(a, i, j = 0):
"""
move axis i of array a to position j
"""
n = a.ndim
i = i if i >= 0 else i + n
if j > i:
return rollaxis(a,
>
> >> It's supposed to take 2 matrixes, each (1004, 13) and do element-wise
> >> multiply,
> >> then sum over axis 0.
> >>
>
> Can I use tensordot to do what I want?
No. In your case I'd just do (a*b.conj()).sum(0). (Assuming that a and b
are arrays, not matrices).
It is most helpful to think
On 02/15/2013 06:29 PM, Robert Kern wrote:
> On Fri, Feb 15, 2013 at 3:58 PM, Juan Luis Cano wrote:
>> Hello all, I have a brief question about the general purpose of
>> Numpy-discussion. I suscribed a month ago more or less to keep an eye on
>> NumPy development (and perhaps contributing eventual
On Fri, Feb 15, 2013 at 3:58 PM, Juan Luis Cano wrote:
> Hello all, I have a brief question about the general purpose of
> Numpy-discussion. I suscribed a month ago more or less to keep an eye on
> NumPy development (and perhaps contributing eventually), for I saw that
> some decisions about the r
Bradley M. Froehle wrote:
> Hi Neal:
>
> The tensordot part:
> np.tensordot (a, b.conj(), ((0,),(0,))
>
> is returning a (13, 13) array whose [i, j]-th entry is sum( a[k, i] *
> b.conj()[k, j] for k in xrange(1004) ).
>
> -Brad
>
>
> The print statement outputs this:
>>
>> (1004, 13) (100
Hi Neal:
The tensordot part:
np.tensordot (a, b.conj(), ((0,),(0,))
is returning a (13, 13) array whose [i, j]-th entry is sum( a[k, i] *
b.conj()[k, j] for k in xrange(1004) ).
-Brad
The print statement outputs this:
>
> (1004, 13) (1004, 13) (13,) (13, 13)
>
> The correct output should b
Hello all, I have a brief question about the general purpose of
Numpy-discussion. I suscribed a month ago more or less to keep an eye on
NumPy development (and perhaps contributing eventually), for I saw that
some decisions about the roadmap were discussed here, but 90 % of the
emails that arri
In the following code
c = np.multiply (a, b.conj())
d = np.abs (np.sum (c, axis=0)/rows)
d2 = np.abs (np.tensordot (a, b.conj(), ((0,),(0,)))/rows)
print a.shape, b.shape, d.shape, d2.shape
The 1st compute steps, where I do multiply and then sum g
Hi all,
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The Python
