On Thu, Sep 26, 2013 at 6:42 PM, Nathaniel Smith wrote:
> On 26 Sep 2013 21:59, "Faraz Mirzaei" wrote:
>>
>> Thanks Josef and Nathaniel for your responses.
>>
>> In the application that I have, I don't use the correlation coefficient
>> matrix as a whole (so I don't care if it is PSD or not). I s
On 26 Sep 2013 21:59, "Faraz Mirzaei" wrote:
>
> Thanks Josef and Nathaniel for your responses.
>
> In the application that I have, I don't use the correlation coefficient
matrix as a whole (so I don't care if it is PSD or not). I simply read the
off-diagonal elements for pair-wise correlation coe
Thanks Josef and Nathaniel for your responses.
In the application that I have, I don't use the correlation coefficient
matrix as a whole (so I don't care if it is PSD or not). I simply read the
off-diagonal elements for pair-wise correlation coefficients. I use the
pairwise correlation coefficient
xrange should be more memory efficient than range:
http://stackoverflow.com/questions/135041/should-you-always-favor-xrange-over-range
Replacing arrays with lists is probably a bad idea for a lot of reasons.
You'll lose nice vectorization of simple operations, and all of numpy's
other benefits.
On Thu, Sep 26, 2013 at 7:19 PM, Josè Luis Mietta <
joseluismie...@yahoo.com.ar> wrote:
>
> Hi experts!
>
> I wanna use less RAM memory in my Monte Carlo simulations. In my
algorithm I use numpy arrays and xrange() function.
> I hear that I can reduce RAM used in my lagorithm if I do the next:
>
>
Hi experts!
I wanna use less RAM memory in my Monte Carlo simulations. In my algorithm I
use numpy arrays and xrange() function.
I hear that I can reduce RAM used in my lagorithm if I do the next:
1) replace xrange() for range().
2) replace numpya arrays for python lists
3) use reset() funct
On 26 Sep 2013 17:32, wrote:
>
> On Thu, Sep 26, 2013 at 7:35 AM, Nathaniel Smith wrote:
> > By textbook I mean, users expect corrcoef to use this formula, which
> > is printed in every textbook:
> >
https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#For_a_sample
> > The
On Thu, Sep 26, 2013 at 7:35 AM, Nathaniel Smith wrote:
> On Thu, Sep 26, 2013 at 11:51 AM, wrote:
>> On Thu, Sep 26, 2013 at 4:21 AM, Nathaniel Smith wrote:
>>> If you want a proper self-consistent correlation/covariance matrix, then
>>> pairwise deletion just makes no sense period, I don't se
On Thu, Sep 26, 2013 at 11:51 AM, wrote:
> On Thu, Sep 26, 2013 at 4:21 AM, Nathaniel Smith wrote:
>> If you want a proper self-consistent correlation/covariance matrix, then
>> pairwise deletion just makes no sense period, I don't see how postprocessing
>> can help.
>
> clipping to [-1, 1] and
On Thu, Sep 26, 2013 at 6:51 AM, wrote:
> On Thu, Sep 26, 2013 at 4:21 AM, Nathaniel Smith wrote:
>> If you want a proper self-consistent correlation/covariance matrix, then
>> pairwise deletion just makes no sense period, I don't see how postprocessing
>> can help.
>
> clipping to [-1, 1] and f
On Thu, Sep 26, 2013 at 4:21 AM, Nathaniel Smith wrote:
> If you want a proper self-consistent correlation/covariance matrix, then
> pairwise deletion just makes no sense period, I don't see how postprocessing
> can help.
clipping to [-1, 1] and finding the nearest positive semi-definite matrix.
If you want a proper self-consistent correlation/covariance matrix, then
pairwise deletion just makes no sense period, I don't see how
postprocessing can help.
If you want a matrix of correlations, then pairwise deletion makes sense.
It's an interesting point that arguably the current ma.corrcoef
On 25 September 2013 19:41, David Goldsmith wrote:
> "the 'angles' that describe the position undergo a random walk [actually,
> it would seem that they don't, since they too fail the
> varying-as-white-noise test], so the particle tends to move in the same
> direction over short intervals"--is t
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