-def Pwords(words):
- """The Naive Bayes log probability of a sequence of words.
- """
- return sum(Pw[w] for w in words)
-
-class Pdist(dict):
- """A probability distribution estimated from counts in datafile.
- Values are stored and returned as log probabilities.
- """
- def __init__(self, data=[], estimate_of_missing=None):
- data1, data2 = itertools.tee(data)
- self.total = sum([int(d[1]) for d in data1])
- for key, count in data2:
- self[key] = log10(int(count) / self.total)
- self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
- def __missing__(self, key):
- return self.estimate_of_missing(key, self.total)
-
-def datafile(name, sep='\t'):
- """Read key,value pairs from file.
- """
- with open(name, 'r') as f:
- for line in f:
- yield line.split(sep)
-
-def avoid_long_words(key, N):
- """Estimate the probability of an unknown word.
- """
- return -log10((N * 10**(len(key) - 2)))
-
-# N = 1024908267229 ## Number of tokens
-
-Pw = Pdist(datafile('count_1w.txt'), avoid_long_words)
-