On Wed, Aug 31, 2011 at 3:34 PM, wrote:
> On Wed, Aug 31, 2011 at 3:22 PM, Olivier Delalleau wrote:
>> 2011/8/31 Christopher Jordan-Squire
>>>
>>> On Wed, Aug 31, 2011 at 2:07 PM, Olivier Delalleau wrote:
>>> > You can use:
>>> > 1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7]))
>
On Wed, Aug 31, 2011 at 3:22 PM, Olivier Delalleau wrote:
> 2011/8/31 Christopher Jordan-Squire
>>
>> On Wed, Aug 31, 2011 at 2:07 PM, Olivier Delalleau wrote:
>> > You can use:
>> > 1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7]))
>> >
>> > For your "real" application you'll proba
2011/8/31 Christopher Jordan-Squire
> On Wed, Aug 31, 2011 at 2:07 PM, Olivier Delalleau wrote:
> > You can use:
> > 1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7]))
> >
> > For your "real" application you'll probably want to use a value >1 for
> the
> > first parameter (equal to y
On Wed, Aug 31, 2011 at 2:07 PM, Olivier Delalleau wrote:
> You can use:
> 1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7]))
>
> For your "real" application you'll probably want to use a value >1 for the
> first parameter (equal to your sample size), instead of calling it multiple
> t
You can use:
1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7]))
For your "real" application you'll probably want to use a value >1 for the
first parameter (equal to your sample size), instead of calling it multiple
times.
-=- Olivier
2011/8/31 Christopher Jordan-Squire
> In numpy,
In numpy, is there a way of generating a random integer in a specified
range where the integers in that range have given probabilities? So,
for example, generating a random integer between 1 and 3 with
probabilities [0.1, 0.2, 0.7] for the three integers?
I'd like to know how to do this without re
When I say garbage, I mean in the context of my hypothesis testing when in the
presence of perfect multicollinearity. I advise the user of the combination
that leads to the problem and move on
-Original Message-
From: numpy-discussion-boun...@scipy.org
[mailto:numpy-discussion-bou
On 08/31/2011 12:56 PM, Mark Janikas wrote:
> Right indeed... I have spent a lot of time looking at this and it seems a
> waste of time as the results are garbage anyways when the columns are
> collinear. I am just going to set a threshold, check the condition number,
> continue is satisfied, r
Right indeed... I have spent a lot of time looking at this and it seems a waste
of time as the results are garbage anyways when the columns are collinear. I
am just going to set a threshold, check the condition number, continue is
satisfied, return error/warning if not now, what is too larg
- Original Message -
> From: Chris.Barker
> To: numpy-discussion@scipy.org
> Cc:
> Sent: Wednesday, August 31, 2011 9:08 AM
> Subject: Re: [Numpy-discussion] Numpy performance boost
>
> On 8/31/11 3:58 AM, Dieter Weber wrote:
>> just wanted to show an example of how python3 + numpy comp
On 8/31/11 3:58 AM, Dieter Weber wrote:
> just wanted to show an example of how python3 + numpy compares with just
> python3 and many other languages and language implementations:
> http://shootout.alioth.debian.org/u64q/performance.php?test=mandelbrot#about
hmmm - it would be interesting to see w
Dieter,
thank you for contributing a numpy mandelbrot program - but no thanks
for your "disqualified for doing things differently" comment here.
The benchmarks game has been showing a spectral-norm program based on
numpy as an "interesting alternative" for the last couple of years -
http://shoot
On Wed, Aug 31, 2011 at 9:24 AM, Jean-Baptiste Marquette wrote:
>
> Hi Pierre,
>
>
> On Aug 31, 2011, at 3:40 PM, Jean-Baptiste Marquette wrote:
>
> Traceback (most recent call last):
>
> File "/Users/marquett/workspace/Distort/src/StatsSep.py", line 44, in
>
>
>np.savetxt(Table, StatsAll, d
On Aug 31, 2011, at 4:24 PM, Jean-Baptiste Marquette wrote:
>
> Hi Pierre,
>
>>
>> On Aug 31, 2011, at 3:40 PM, Jean-Baptiste Marquette wrote:
>>> Traceback (most recent call last):
>>> File "/Users/marquett/workspace/Distort/src/StatsSep.py", line 44, in
>>>
>>>np.savetxt(Table, StatsA
Hi Pierre,
>
> On Aug 31, 2011, at 3:40 PM, Jean-Baptiste Marquette wrote:
>> Traceback (most recent call last):
>> File "/Users/marquett/workspace/Distort/src/StatsSep.py", line 44, in
>>
>>np.savetxt(Table, StatsAll, delimiter=' ', fmt=['%15s %.5f %.5f %5d %.4f
>> %.4f'])
>> File
>>
On Aug 31, 2011, at 3:40 PM, Jean-Baptiste Marquette wrote:
> Traceback (most recent call last):
> File "/Users/marquett/workspace/Distort/src/StatsSep.py", line 44, in
>
> np.savetxt(Table, StatsAll, delimiter=' ', fmt=['%15s %.5f %.5f %5d %.4f
> %.4f'])
> File
> "/Library/Frameworks/
Hi Pierre,
Bingo ! That works. I finally coded like:
Stats = [(CatBase, round(stats.mean(Data.Ra), 5),
round(stats.mean(Data.Dec), 5), len(Sep), round(stats.mean(Sep),4),
round(stats.stdev(Sep),4),)]
StatArray = np.array(Stats, dtype=([('Catalog', 'a15'), ('RaMean',
'f
Dear List,
Does anybody knows if there is a python package for simulating LTI
dynamic systems controlled with a model predictive controller? I am
writing some code which does the job, but the math is not super-easy and
i would not like to reinvent the wheel and loose to much time.
I will soon
Hi,
just wanted to show an example of how python3 + numpy compares with just
python3 and many other languages and language implementations:
http://shootout.alioth.debian.org/u64q/performance.php?test=mandelbrot#about
The python3 program using numpy is #6 and you find it with the
"interesting alter
On Aug 31, 2011, at 12:20 PM, Jean-Baptiste Marquette wrote:
>
> Hi Pierre,
>
> Thanks for the guess. Unfortunately, I got the same error:
>
> [('bs3000k.cat', 280.60341, -7.09118, 9480, 0.2057, 0.14)]
> Traceback (most recent call last):
> File "/Users/marquett/workspace/Distort/src/StatsSe
Hi Pierre,
Thanks for the guess. Unfortunately, I got the same error:
[('bs3000k.cat', 280.60341, -7.09118, 9480, 0.2057, 0.14)]
Traceback (most recent call last):
File "/Users/marquett/workspace/Distort/src/StatsSep.py", line 40, in
StatsAll = np.array(np.asarray(Stats), dtype=('a15, f8,
On Tue, 30 Aug 2011 15:48:18 -0700, Mark Janikas wrote:
> Last week I posted a question involving the identification of linear
> dependent columns of a matrix... but now I am finding an interesting
> result based on the linalg.inv() function... sometime I am able to
> invert a matrix that has linea
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