8.0 years ago by

United States

The problem is that with genomic scales the NchooseK operation produces giant numbers that often cause overflows in python. The trick is to use the log-nchoosek operations since this will keep even th elargest numbers within a reasonable range.

You can check out my gist here: git://gist.github.com/1051335.git

or this:

```
from math import log, exp
from mpmath import loggamma
def logchoose(ni, ki):
try:
lgn1 = loggamma(ni+1)
lgk1 = loggamma(ki+1)
lgnk1 = loggamma(ni-ki+1)
except ValueError:
#print ni,ki
raise ValueError
return lgn1 - (lgnk1 + lgk1)
def gauss_hypergeom(X, n, m, N):
"""Returns the probability of drawing X successes of m marked items
in n draws from a bin of N total items."""
assert N >= m, 'Number of items %i must be larger than the number of marked items %i' % (N, m)
assert m >= X, 'Number of marked items %i must be larger than the number of sucesses %i' % (m, X)
assert n >= X, 'Number of draws %i must be larger than the number of sucesses %i' % (n, X)
assert N >= n, 'Number of draws %i must be smaller than the total number of items %i' % (n, N)
r1 = logchoose(m, X)
try:
r2 = logchoose(N-m, n-X)
except ValueError:
return 0
r3 = logchoose(N,n)
return exp(r1 + r2 - r3)
def hypergeo_cdf(X, n, m, N):
assert N >= m, 'Number of items %i must be larger than the number of marked items %i' % (N, m)
assert m >= X, 'Number of marked items %i must be larger than the number of sucesses %i' % (m, X)
assert n >= X, 'Number of draws %i must be larger than the number of sucesses %i' % (n, X)
assert N >= n, 'Number of draws %i must be smaller than the total number of items %i' % (n, N)
assert N-m >= n-X, 'There are more failures %i than unmarked items %i' % (N-m, n-X)
s = 0
for i in range(1, X+1):
s += max(gauss_hypergeom(i, n, m, N), 0.0)
return min(max(s,0.0), 1)
```

Everything should be self-explanitory.

•

link
written
8.0 years ago by
Will ♦ **4.5k**