On Sat, Nov 6, 2010 at 9:51 PM, qihua wu wrote:
> I used the following command to install the numpy to enable the SSE3
> numpy-1.5.1rc1-win32-superpack-python3.1.exe /arch sse3
The whole point of the super pack installer is to install the most
optimized one possible on your machine. So you should
On 2010-11-06, at 7:46 PM, qihua wu wrote:
> day 1,2,3 have the non-promoted sales, day 4 have the promoted sales, day
> 5,6,7 have the non-promted sales, the output for day 1~7 are all non-promoted
> sales. During the process, we might need to sum all the data for day 1~7, is
> this what you c
Thank David,
the java program takes 3 hours to read data, after read the data into
memory, it takes 4 hours to process/calculate somthing on all these data.
The data is the sale data which contains both promoted sale and non-promoted
sale, the program needs to predict the non-promoted sale: so in
The difference is that dis[k,:] eliminates the first dimension since
you are using a single number as an index, but dis[k:k+1,:] does not
eliminate that dimension.
On Sat, Nov 6, 2010 at 1:24 PM, wrote:
> On Sat, Nov 6, 2010 at 4:14 PM, K. Sun wrote:
>> Thanks a lot. It works! I modify the code
On Sat, Nov 6, 2010 at 4:14 PM, K. Sun wrote:
> Thanks a lot. It works! I modify the code as follows and it runs
> at fast as matlab. By numpy's convention, the input and output
> are all ndarrays. 'route' has to be a (1xN) matrix to produce a
> square matrix in 'route + route.T'.
If you read my
Thanks a lot. It works! I modify the code as follows and it runs
at fast as matlab. By numpy's convention, the input and output
are all ndarrays. 'route' has to be a (1xN) matrix to produce a
square matrix in 'route + route.T'.
def floyd( dis ):
'''Floyd-Wallshall algorithm for shortest path
On 2010-11-06, at 8:51 AM, qihua wu wrote:
> I used the following command to install the numpy to enable the SSE3
> numpy-1.5.1rc1-win32-superpack-python3.1.exe /arch sse3
>
> Then how can I know whether numpy is running with SSE or not?
As far as I know, the only thing that uses SSE/SSE2/SSE3 w
On Sat, Nov 6, 2010 at 3:28 PM, K. Sun wrote:
> Hello,
>
> I wrote the following code with numpy to implement the Floyd-Wallshall
> algorithm to compute the pair-wise shortest path in a undirected weighted
> graph. It is really slow when N ~ 10k, while the same implementation in
> matlab is much f
Hello,
I wrote the following code with numpy to implement the Floyd-Wallshall
algorithm to compute the pair-wise shortest path in a undirected weighted
graph. It is really slow when N ~ 10k, while the same implementation in
matlab is much faster. I am sorry I don't want to run it again to
present
On Sat, Nov 6, 2010 at 11:52 AM, Damien Moore wrote:
> In reply to my own question, the trivial, but massively inefficient solution
> is:
>
> s=StringIO.StringIO('q1,2\nq3,4')
> a=numpy.genfromtxt(s,delimiter=',',converters={0:lambda s:float(s[1:])})
> a1 = numpy.array(a.tolist())
>
> But what I r
Sorry, I got it wrong and ignored the StringIO part.
Lluis
Lluís writes:
> Damien Moore writes:
[...]
>> import numpy, StringIO
>> s=StringIO.StringIO('q1,2\nq3,4')
>> a=numpy.genfromtxt(s,delimiter=',',converters={0:lambda s:float(s[1:])})
>> s=StringIO.StringIO('q1,2\nq3,4')
>> b=numpy.genf
Damien Moore writes:
> Hi List,
> I'm trying to import csv data as a numpy array using genfromtxt. The csv file
> contains mixed data, some floating point, others string codes and dates that I
> want to convert to floating point. The strange thing is that when I use the '
> converters' argument to
In reply to my own question, the trivial, but massively inefficient solution
is:
s=StringIO.StringIO('q1,2\nq3,4')
a=numpy.genfromtxt(s,delimiter=',',converters={0:lambda s:float(s[1:])})
a1 = numpy.array(a.tolist())
But what I really want to do is have genfromtxt do the conversion for me.
Specif
Hi List,
I'm trying to import csv data as a numpy array using genfromtxt. The csv
file contains mixed data, some floating point, others string codes and dates
that I want to convert to floating point. The strange thing is that when I
use the 'converters' argument to convert a subset of the columns
I used the following command to install the numpy to enable the SSE3
numpy-1.5.1rc1-win32-superpack-python3.1.exe /arch sse3
Then how can I know whether numpy is running with SSE or not?
I have a program to process the data from sql server using java to process
600M rows, it takes 7 hours to comp
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