On Sun, Jun 8, 2008 at 3:54 AM, Anne Archibald
<[EMAIL PROTECTED]> wrote:
> 2008/6/7 Robert Kern <[EMAIL PROTECTED]>:
>> On Sat, Jun 7, 2008 at 14:37, Ondrej Certik <[EMAIL PROTECTED]> wrote:
>>> Hi,
>>>
>>> what is the current plan with array and matrix with regard of calculating
>>>
>>> sin(A)
>>
On Sat, Jun 7, 2008 at 20:54, Anne Archibald <[EMAIL PROTECTED]> wrote:
> For consistency, it makes a certain amount of sense to have sin(A)
> compute a matrix sine, since A**n computes a matrix power. But looking
> at the matrix exponential, I see that we have several implementations,
> none of w
2008/6/7 Robert Kern <[EMAIL PROTECTED]>:
> On Sat, Jun 7, 2008 at 14:37, Ondrej Certik <[EMAIL PROTECTED]> wrote:
>> Hi,
>>
>> what is the current plan with array and matrix with regard of calculating
>>
>> sin(A)
>>
>> ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach
>> (eleme
On Sat, Jun 7, 2008 at 14:37, Ondrej Certik <[EMAIL PROTECTED]> wrote:
> Hi,
>
> what is the current plan with array and matrix with regard of calculating
>
> sin(A)
>
> ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach
> (elementwise for array and Q*sin(D)*Q^T for matrix) the wa
Hi,
what is the current plan with array and matrix with regard of calculating
sin(A)
? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach
(elementwise for array and Q*sin(D)*Q^T for matrix) the way to go?
We are solving the same problem in SymPy:
http://groups.google.com/group/
Re-hi,
thanks for all the comments. I have re-tried with
X = nm.random.rand( 1, 3 ) and the times (in seconds) were:
428.588043213 # scipy.dot, array
445.045716047 # numpy.dot, array
519.489458799 # scipy.dot, matrix
513.328601122 # numpy.dot, matrix
The scipy.dot and numpy.dot performs the
On 5/21/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
On 5/21/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
>
> Nils Wagner wrote:
> > Robert Cimrman wrote:
> >> I have come to a case where using a matrix would be easier than an
> >> array. The code uses lots of dot products, so I tested s
On 5/21/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
Nils Wagner wrote:
> Robert Cimrman wrote:
>> I have come to a case where using a matrix would be easier than an
>> array. The code uses lots of dot products, so I tested scipy.dot()
>> performance with the code below and found that the arr
Nils Wagner wrote:
> Robert Cimrman wrote:
>> I have come to a case where using a matrix would be easier than an
>> array. The code uses lots of dot products, so I tested scipy.dot()
>> performance with the code below and found that the array version is much
>> faster (about 3 times for the given s
Robert Cimrman wrote:
> I have come to a case where using a matrix would be easier than an
> array. The code uses lots of dot products, so I tested scipy.dot()
> performance with the code below and found that the array version is much
> faster (about 3 times for the given shape). What is the reason
I have come to a case where using a matrix would be easier than an
array. The code uses lots of dot products, so I tested scipy.dot()
performance with the code below and found that the array version is much
faster (about 3 times for the given shape). What is the reason for this?
Or is something wro
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