gers antifx wrote:
> I have to write a LU-decomposition. My Code worked so far but (I want to
> become better:) ) I want to ask you, if I could write this
> LU-decomposition in a better way?
>
> def LU(x):
> L = np.eye((x.shape[0]))
> n = x.shape[0]
> for ii in range(n-1):
> for ll in range(1+ii,n):
> factor = float(x[ll,ii])/x[ii,ii]
> L[ll,ii] = factor
> for kk in range(0+ii,n):
> x[ll,kk] = x[ll,kk] - faktor*x[ii,kk]
> LU = np.dot(L,x)
You want to become better? A good way to find the problems Chris pointed out
quickly is to write unit tests similar to
import unittest
...
from mymodule import LU
class LUTest(unittest.TestCase):
def test_LU(self):
matrix = ...
expected_result = ...
self.assertEqual(LU(matrix), expected_result)
if __name__ == "__main__":
unittest.main()
There's no point making stylistic changes or tweaks to improve performance
on code that doesn't work and doesn't have a well-defined interface yet,
but as a rule of thumb for efficient number-crunching you should try to
replace for loops with numpy's array operations if at all possible.
Google found me
http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.linalg.lu.html
but it's hard to learn from that code as the real meat is a few levels down
and implemented in C.
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