marc magrans de abril wrote:
Hi!
...I have found a good enough solution, although it only works if the
number of patterns (clusters) is not very big:
def classify(f):
THERESHOLD=0.1
patterns={}
for l in enumerate(f):
found = False
for p,c in patterns.items():
Hi!
...I have found a good enough solution, although it only works if the
number of patterns (clusters) is not very big:
def classify(f):
THERESHOLD=0.1
patterns={}
for l in enumerate(f):
found = False
for p,c in patterns.items():
if dist(l,p) < THERESHOLD:
>Find out which pattern is being used on the second iteration and then try it
>on the first iteration. Is it just as slow?
You were right, the second pattern was 1891 bytes but the first was
just 142 :P
I will need to put more thought than I expect in the "small script".
--
http://mail.python.or
Maybe it is a good idea to use Disco (http://discoproject.org/) to
process your data.
Yours faithfully,
Alexander Abushkevich
On Sat, Jan 30, 2010 at 10:36 PM, marc magrans de abril
wrote:
> Dear colleagues,
>
> I was doing a small program to classify log files for a cluster of
> PCs, I just w
Alf P. Steinbach wrote:
* marc magrans de abril:
Dear colleagues,
I was doing a small program to classify log files for a cluster of
PCs, I just wanted to simplify a quite repetitive task in order to
find errors and so.
My first naive implementation was something like:
patterns = []
wh
* marc magrans de abril:
Dear colleagues,
I was doing a small program to classify log files for a cluster of
PCs, I just wanted to simplify a quite repetitive task in order to
find errors and so.
My first naive implementation was something like:
patterns = []
while(logs):
patter
Dear colleagues,
I was doing a small program to classify log files for a cluster of
PCs, I just wanted to simplify a quite repetitive task in order to
find errors and so.
My first naive implementation was something like:
patterns = []
while(logs):
pattern = logs[0]
new_log
