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 hinting at
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 within a large
?
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. and hence the
hypergeometric returns nan
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 values:
, 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 hypergeometric
ISTM that can't have been your real goal -- unless you are e.g. preparing
numeric
tables for publication
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.
If
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 do float
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.
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:
0.0386447906494
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 you
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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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
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
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 to divide an
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 is
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
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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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 floating point
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,
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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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 = special.gammaln(n-k+1)
return lgn1 - (lgnk1 + lgk1)
def
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
Ale
Ale
I had this code lying about if it helps. I don't know if it's even
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
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.
Have you tried with gmpy?
Alex
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