Hi
This is my function. It serves an HTML page after the calculations. I'm
connecting to a MSSQL DB using pyodbc.
def CAPM(self,client):
r=self.r
cds="1590"
bm="20559"
d1 = []
v1 = []
v2 = []
print"Parsing GET Params"
param
Update: it appears that the time taken isn't so much on the Data conversion.
The maximum time taken is in CAPM calculation. :( Anyone know why the CAPM
calculation would be faster on Windows?
On Wed, May 19, 2010 at 5:51 PM, Abhijit Bera wrote:
> Hi
>
> This is my function. It serves an HTML pag
Here is an updated bench mark:
Linux
Time taken by DB:0:00:00.226888
Time taken by R:0:00:05.536973
Time taken for vector conversions:0:00:00.001799
Total time taken for return calculation:0:00:00.090062
Total time taken for making Tagged list and Data Frame:0:00:00.015424
Total time taken for mak
Dear Abhijit,
If you think that table.CAPM is the culprit, you could run the call to
such function in R on both platforms using Rprof to check which part
of the function is producing the bottleneck.
Best regards,
Carlos J. Gil Bellosta
http://www.datanalytics.com
2010/5/19 Abhijit Bera :
> Upd
Hi
This problem is fixed. I was running an older kernel 2.6.28. I upgraded to
2.6.32-5 Debian Sid/Squeeze and now the performance is similar.
Regards
Abhijit Bera
On Wed, May 19, 2010 at 6:49 PM, Carlos J. Gil Bellosta <
c...@datanalytics.com> wrote:
> Dear Abhijit,
>
> If you think that table
