Merge pull request #4564 from tyomitch/patch-1
[build] simplify makeqstrdata heuristic
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commit
e54e5e3575
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@ -12,7 +12,6 @@ from __future__ import print_function
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import re
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import sys
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from math import log
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import collections
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import gettext
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import os.path
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@ -167,7 +166,7 @@ def compute_huffman_coding(translations, compression_filename):
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sum_len = 0
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while True:
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# Until the dictionary is filled to capacity, use a heuristic to find
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# the best "word" (2- to 9-gram) to add to it.
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# the best "word" (3- to 9-gram) to add to it.
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#
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# The TextSplitter allows us to avoid considering parts of the text
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# that are already covered by a previously chosen word, for example
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@ -179,32 +178,25 @@ def compute_huffman_coding(translations, compression_filename):
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for t in texts:
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for (found, word) in extractor.iter_words(t):
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if not found:
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for substr in iter_substrings(word, minlen=2, maxlen=9):
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for substr in iter_substrings(word, minlen=3, maxlen=9):
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counter[substr] += 1
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# Score the candidates we found. This is an empirical formula only,
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# chosen for its effectiveness.
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scores = sorted(
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((s, (len(s) - 1) ** log(max(occ - 2, 1)), occ) for (s, occ) in counter.items()),
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((s, (len(s) - 1) ** (occ + 4)) for (s, occ) in counter.items() if occ > 4),
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key=lambda x: x[1],
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reverse=True,
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)
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# Do we have a "word" that occurred 5 times and got a score of at least
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# 5? Horray. Pick the one with the highest score.
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word = None
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for (s, score, occ) in scores:
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if occ < 5:
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continue
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if score < 5:
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break
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word = s
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# Pick the one with the highest score.
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if not scores:
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break
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word = scores[0][0]
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# If we can successfully add it to the dictionary, do so. Otherwise,
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# we've filled the dictionary to capacity and are done.
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if not word:
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break
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if sum_len + len(word) - 2 > max_words_len:
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break
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if len(words) == max_words:
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