On Mon, May 23, 2011 at 3:27 PM, Bruce Southey wrote:
> On 05/23/2011 02:02 PM, Robert Kern wrote:
>> On Mon, May 23, 2011 at 13:33, wrote:
>>> I have a function in two versions, one vectorized, one with loop
>>>
>>> the vectorized function gets all randn variables in one big array
>>> rvs = dis
On Mon, May 23, 2011 at 12:34 PM, wrote:
> Obviously I was working by columns, using a transpose worked, but
> rewriting to axis=1 instead of axis=0 which should be more efficient
> since I had almost all calculations by columns, I needed
> params = map(lambda x: np.expand_dims(x, 1), params)
>
On Mon, May 23, 2011 at 3:02 PM, Robert Kern wrote:
> On Mon, May 23, 2011 at 13:33, wrote:
>> I have a function in two versions, one vectorized, one with loop
>>
>> the vectorized function gets all randn variables in one big array
>> rvs = distr.rvs(args, **{'size':(nobs, nrep)})
>>
>> the loo
On 05/23/2011 02:02 PM, Robert Kern wrote:
> On Mon, May 23, 2011 at 13:33, wrote:
>> I have a function in two versions, one vectorized, one with loop
>>
>> the vectorized function gets all randn variables in one big array
>> rvs = distr.rvs(args, **{'size':(nobs, nrep)})
>>
>> the looping versio
On Mon, May 23, 2011 at 13:33, wrote:
> I have a function in two versions, one vectorized, one with loop
>
> the vectorized function gets all randn variables in one big array
> rvs = distr.rvs(args, **{'size':(nobs, nrep)})
>
> the looping version has:
> for irep in xrange(nrep):
> rvs
On Mon, May 23, 2011 at 11:42 AM, Keith Goodman wrote:
> On Mon, May 23, 2011 at 11:33 AM, wrote:
>> I have a function in two versions, one vectorized, one with loop
>>
>> the vectorized function gets all randn variables in one big array
>> rvs = distr.rvs(args, **{'size':(nobs, nrep)})
>>
>> t
On Mon, May 23, 2011 at 11:33 AM, wrote:
> I have a function in two versions, one vectorized, one with loop
>
> the vectorized function gets all randn variables in one big array
> rvs = distr.rvs(args, **{'size':(nobs, nrep)})
>
> the looping version has:
> for irep in xrange(nrep):
>
I have a function in two versions, one vectorized, one with loop
the vectorized function gets all randn variables in one big array
rvs = distr.rvs(args, **{'size':(nobs, nrep)})
the looping version has:
for irep in xrange(nrep):
rvs = distr.rvs(args, **{'size':nobs})
the rest should
I realized if I re-build numpy it takes the interpreter compile options or you
can edit site.cfg.
I actually solved the issue by getting the newest svn copy of numpy. It must be
something with 1.6b2. The newest SVN has no issues with either library and
calls to sum or prod. I was getting memor
