Am 25.12.2010 16:42, schrieb Roy Smith:
I'm processing a stream of N numbers and want to keep track of the K
largest. There's too many numbers in the stream (i.e. N is too large)
to keep in memory at once. K is small (100 would be typical).
> From a theoretical point of view, I should be able to do this in N log K
time. What I'm doing now is essentially:
top = [-1] # Assume all x are>= 0
for x in input():
if x<= top[0]:
continue
top.append(x)
if len(top)> K:
top.sort()
top.pop(0)
I can see pathological cases (say, all input values the same) where
running time would be N K log K, but on average (N>> K and random
distribution of values), this should be pretty close to N.
Is there a better way to do this, either from a theoretical running time
point of view, or just a nicer way to code this in Python?
Here is my version:
l = []
K = 10
while 1:
a = input()
if len(l) == K:
l.remove(min(l))
l=[x for x in l if x < a] + [a] + [x for x in l if x > a]
print l
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