On Thu, 05 Jan 2006 09:47:02 -0600, Robert Kern <[EMAIL PROTECTED]> wrote:
>Bengt Richter wrote:
>> On 4 Jan 2006 12:46:47 -0800, "Raven" <[EMAIL PROTECTED]> wrote:
>
>>>The problem with Stirling's approximation is that I need to calculate
>>>the hypergeometric hence the factorial for numbers with
Bengt Richter wrote:
> On 4 Jan 2006 12:46:47 -0800, "Raven" <[EMAIL PROTECTED]> wrote:
>>The problem with Stirling's approximation is that I need to calculate
>>the hypergeometric hence the factorial for numbers within a large range
>>e.g. choose(14000,170) or choose(5,2)
>
> It seems you are hi
eral other potential
>> targets mentioned--I echo Steven D'Aprano, and ask, are you *sure*
>> the suggestions already offered aren't adequate?
>
>Hi Cameron, my real goal was to calculate the hypergeometric
>distribution. The problem was that the function for hypergeometr
"Raven" <[EMAIL PROTECTED]> writes:
> The problem with Stirling's approximation is that I need to calculate
> the hypergeometric hence the factorial for numbers within a large range
> e.g. choose(14000,170) or choose(5,2)
Stirling's approximation to second order is fairly accurate even at
low valu
uggestions already offered aren't adequate?
Hi Cameron, my real goal was to calculate the hypergeometric
distribution. The problem was that the function for hypergeometric
calculation from scipy uses the scipy.comb function which by default
uses floats so for large numbers comb(n,r) returns inf
In article <[EMAIL PROTECTED]>,
Raven <[EMAIL PROTECTED]> wrote:
>Well, what to say? I am very happy for all the solutions you guys have
>posted :-)
>For Paul:
>I would prefer not to use Stirling's approximation
>
>
>The problem with long integers is that to calculate the hypergeometric
>I need to
Travis E. Oliphant wrote:
> Notice the keyword for the comb function (in scipy) lets you use it to
> compute exact values. SciPy does not just automatically use the long
> integer because this will always slow you down.
>
> comb(N, k, exact=0)
>
> Combinations of N things taken k at a time.
>
>
Raven wrote:
> Thanks Steven for your very interesting post.
>
> This was a critical instance from my problem:
>
>
from scipy import comb
>>>
comb(14354,174)
>
> inf
>
> The scipy.stats.distributions.hypergeom function uses the scipy.comb
> function, so it returned nan since it tries t
Bengt Richter wrote:
> ISTM you wouldn't get zero if you scaled by 10**significant_digits (however
> many
> you require) before dividing. E.g., expected hits per trillion (or septillion
> or whatever)
> expresses probability too. Perhaps that could work in your calculation?
>
> Regards,
> Beng
Bengt Richter wrote:
> ISTM you wouldn't get zero if you scaled by 10**significant_digits (however
> many
> you require) before dividing. E.g., expected hits per trillion (or septillion
> or whatever)
> expresses probability too. Perhaps that could work in your calculation?
>
> Regards,
> Bengt
Scott David Daniels ha scritto:
> You should really look into the timeit module -- you'll get nice
> solid timings slightly easier to tweak.
This seems a very interesting module, I will give it a try as soon as
possible. Thanks Scott.
Ale
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On 2 Jan 2006 03:35:33 -0800, "Raven" <[EMAIL PROTECTED]> wrote:
[...]
>
>The problem with long integers is that to calculate the hypergeometric
>I need to do float division and multiplication because integer division
>returns 0. A solution could be to calculate log(Long_Factorial_Integer)
ISTM yo
Raven wrote:
> ...
> def main():
> t = time.time()
> for i in range(1000):
> r = hypergeometric(6,6,30,6)
> print time.time() - t
>
> t = time.time()
> for i in range(1000):
> r = hypergeometric_gamma(6,6,30,6)
> print time.time() - t
>
> and the result is:
>
> 0.038644790649
Well, what to say? I am very happy for all the solutions you guys have
posted :-)
For Paul:
I would prefer not to use Stirling's approximation
The problem with long integers is that to calculate the hypergeometric
I need to do float division and multiplication because integer division
returns 0.
Steven D'Aprano wrote:
> On Sun, 01 Jan 2006 14:24:39 -0800, Raven wrote:
>
>> Thanks Steven for your very interesting post.
>>
>> This was a critical instance from my problem:
>>
>from scipy import comb
> comb(14354,174)
>> inf
>
> Curious. It wouldn't surprise me if scipy was using flo
On Sun, 01 Jan 2006 14:24:39 -0800, Raven wrote:
> Thanks Steven for your very interesting post.
>
> This was a critical instance from my problem:
>
from scipy import comb
comb(14354,174)
> inf
Curious. It wouldn't surprise me if scipy was using floats, because 'inf'
is usually a float
"Raven" <[EMAIL PROTECTED]> writes:
> Yes I am calculating hundreds of hypergeometric probabilities so I need
> fast calculations
Can you use Stirling's approximation to get the logs of the factorials?
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Thanks Steven for your very interesting post.
This was a critical instance from my problem:
>>>from scipy import comb
>>> comb(14354,174)
inf
The scipy.stats.distributions.hypergeom function uses the scipy.comb
function, so it returned nan since it tries to divide an infinite. I
did not tried to
On Sat, 31 Dec 2005 16:24:02 -0800, Raven wrote:
> Thanks to all of you guys, I could resolve my problem using the
> logarithms as proposed by Robert. I needed to calculate the factorial
> for genomic data, more specifically for the number of genes in the
> human genome i.e. about 30.000 and that
Thanks to all of you guys, I could resolve my problem using the
logarithms as proposed by Robert. I needed to calculate the factorial
for genomic data, more specifically for the number of genes in the
human genome i.e. about 30.000 and that is a big number :-)
I didn't know gmpy
Thanks a lot, real
he hypergeometric using the factorials too, so the problem subsist. Is
> there any other libray or an algorithm to calculate
> the hypergeometric distribution? The statistical package R can handle
> such calculations but I don't want to use python R binding since I wan
On Mon, 26 Dec 2005 12:18:55 -0800, Raven wrote:
> Hi to all, I need to calculate the hpergeometric distribution:
>
>
>choose(r, x) * choose(b, n-x)
> p(x; r,b,n) = -
>choose(r+b, n)
>
> choose(r,x) is the
e factorials too, so the problem subsist. Is
> there any other libray or an algorithm to calculate
> the hypergeometric distribution? The statistical package R can handle
> such calculations but I don't want to use python R binding since I want
> a standalone app.
> Thanks a lot
&g
e factorials too, so the problem subsist. Is
> there any other libray or an algorithm to calculate
> the hypergeometric distribution?
Use logarithms.
Specifically,
from scipy import special
def logchoose(n, k):
lgn1 = special.gammaln(n+1)
lgk1 = special.gammaln(k+1)
lgnk1 = spec
rithm to calculate
the hypergeometric distribution? The statistical package R can handle
such calculations but I don't want to use python R binding since I want
a standalone app.
Thanks a lot
Ale
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