Worked on Enigma, mainly changing how the notch positions are handled

[cipher-training.git] / language_models.py

diff --git a/language_models.py b/language_models.py
index f1877b70d8169ee12372cf028e284f4d14f6183b..644338eda837e845095353ed882b7daf048cf1d0 100644 (file)
--- a/language_models.py
+++ b/language_models.py
@@ -9,8 +9,9 @@ import collections
 import unicodedata
 import itertools
 from math import log10
+import os 
 
-unaccent_specials = ''.maketrans({"’": "'"})
+unaccent_specials = ''.maketrans({"’": "'", '“': '"', '”': '"'})
 
 def letters(text):
     """Remove all non-alphabetic characters from a text
@@ -60,7 +61,7 @@ def sanitise(text):
 def datafile(name, sep='\t'):
     """Read key,value pairs from file.
     """
-    with open(name, 'r') as f:
+    with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), name), 'r') as f:
         for line in f:
             splits = line.split(sep)
             yield [splits[0], int(splits[1])]
@@ -74,7 +75,7 @@ normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
 english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
 normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
 
-with open('words.txt', 'r') as f:
+with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'words.txt'), 'r') as f:
     keywords = [line.rstrip() for line in f]