-# import re, string, random, glob, operator, heapq
-import string
-import collections
-from math import log10
+import language_models
+import sys
+from functools import lru_cache
+sys.setrecursionlimit(1000000)
-def memo(f):
- "Memoize function f."
- table = {}
- def fmemo(*args):
- if args not in table:
- table[args] = f(*args)
- return table[args]
- fmemo.memo = table
- return fmemo
-
-@memo
+@lru_cache()
def segment(text):
"""Return a list of words that is the best segmentation of text.
"""
if not text: return []
- candidates = ([first]+segment(rem) for first,rem in splits(text))
- return max(candidates, key=Pwords)
+ candidates = ([first]+segment(rest) for first,rest in splits(text))
+ return max(candidates, key=language_models.Pwords)
def splits(text, L=20):
"""Return a list of all possible (first, rest) pairs, len(first)<=L.
return [(text[:i+1], text[i+1:])
for i in range(min(len(text), L))]
-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):
- self.total = sum([int(d[1]) for d in data])
- for key, count in data:
- self[key] = log10(int(count) / self.total)
- self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
- def __call__(self, key):
- if key in self:
- return self[key]
- else:
- 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)