I'm working on a naive K-nearest-neighbors selection criteria for an optical
character recognition problem.
After I build my training set, I test each new image against against the
trained feature vectors and record the scores as follows:
match_vals = [(match_val_1, identifier_a), (match_val_2, identifier_b) ....
] and so on..
then I sort the list so the smallest match_val's appear first (indictating a
strong match, so I may end up with something like this:
[(match_val_291, identifier_b), (match_val_23, identifier_b), (match_val_22,
identifer_k) .... ]
Now, what I would like to do is step through this list and find the
identifier which appears first a K number of times.
Naively, I could make a dict and iterate through the list AND the dict at
the same time and keep a tally, breaking when the criteria is met.
such as:
def getnn(match_vals):
tallies = defaultdict(lambda: 0)
for match_val, ident in match_vals:
tallies[ident] += 1
for ident, tally in tallies.iteritems():
if tally == 5:
return ident
I would think there is a better way to do this. Any ideas?
Cheers!
Chris
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