bigrams

testing.py

@@ -3,7 +3,7 @@ import sys
 from math import log
 import regex as re
 
-def get_prob(bigrams, unigrams):
+def count_prob(bigrams, unigrams):
     prob = (bigrams + 1.0) / (unigrams + 1)
     if prob > 1.0:
         return 1.0
@@ -25,9 +25,9 @@ def main():
             pre_ngram = tuple(left_word + [word])
             post_ngram = tuple([word] + right_word)
             #print(pre_ngram)
-            pre_ngram_prob = get_prob(ngrams[2].get(pre_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
+            pre_ngram_prob = count_prob(ngrams[2].get(pre_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
             #if pre_ngram_prob>0:
-            post_ngram_prob = get_prob(ngrams[2].get(post_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
+            post_ngram_prob = count_prob(ngrams[2].get(post_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
 
             probabilities.append((word, pre_ngram_prob * post_ngram_prob))
         probabilities = sorted(probabilities, key=lambda t: t[1], reverse=True)[:50]

train.py

@@ -1,37 +1,57 @@
-#!/usr/bin/python3
-
-import sys
-import regex as re
 import pickle
+import sys
+from math import log
+import regex as re
 
-
+def count_prob(bigrams, unigrams):
-def into_words(sentence):
+    prob = (bigrams + 1.0) / (unigrams + 1)
-    return re.findall(r'\p{P}|[^\p{P}\s]+', sentence.lower())
+    if prob > 1.0:
-
+        return 1.0
+    else:
+        return prob
 
 def main():
-    ngrams = {1: {}, 2: {}}
+    ngrams = pickle.load(open('ngrams_2.pkl', 'rb'))
-    lowest_ngram = 1
+    vocabulary_size = len(ngrams[1])
-    highest_ngram = 2
+    for line in sys.stdin:
+        words = re.findall(r'.*\t.*\t.* (.*?)\t(.*?) ', line.lower())[0]
+        #print(words)
+        left_word = [str(words[0])]
+        right_word = [str(words[1])]
+
+        probabilities = []
+        for word in ngrams[1].keys():
+            word = str(word[0])
+            pre_ngram = tuple(left_word + [word])
+            post_ngram = tuple([word] + right_word)
+            #print(pre_ngram)
+            pre_ngram_prob = count_prob(ngrams[2].get(pre_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
+            #if pre_ngram_prob>0:
+            post_ngram_prob = count_prob(ngrams[2].get(post_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
+
+            probabilities.append((word, pre_ngram_prob * post_ngram_prob))
+        probabilities = sorted(probabilities, key=lambda t: t[1], reverse=True)[:50]
+        probability = 1.0
+        text = ''
         counter = 0
-    for line in sys.stdin:              #dla kazdej linii z pliku
+        has_log_prob0 = False
-        line = line.split('\t')[4]          # podziel na 4
+        for p in probabilities:
-        tokens = into_words(line)              #na slowa
+            word = p[0]
-        number_of_tokens = len(tokens)          #ile slow?
+            prob = p[1]
-        for n in range(lowest_ngram, highest_ngram+1):  #dla kazdego ngram
+            if counter == 0 and (probability - prob <= 0.0):
-            for i in range(0, number_of_tokens-n+1):   #i tyle ile jest slow -n gram + 1
+                text = word + ':' + str(log(0.95)) + ' :' + str(log(0.05))
-                ngram = tuple(tokens[i:i+n])
+                has_log_prob0 = True
-                if ngram in ngrams[n]:
+                break
-                    ngrams[n][ngram] += 1
+            if counter > 0 and (probability - prob <= 0.0):
-                else:
+                text += ':' + str(log(probability))
-                    ngrams[n][ngram] = 1
+                has_log_prob0 = True
-        if counter % 1000 == 0:
+                break
-            print('counter = ', counter)
+            text += word + ':' + str(log(prob)) + ' '
+            probability -= prob
             counter += 1
-    ngrams[1] = dict(sorted(ngrams[1].items(), key=lambda item: ngrams[1][item[0]], reverse=True)[:1000])
+        if not has_log_prob0:
-    ngrams[2] = dict(sorted(ngrams[2].items(), key=lambda item: ngrams[2][item[0]], reverse=True)[:100000])
+            text += ':' + str(log(0.0001))
-    #ngrams[3] = dict(sorted(ngrams[3].items(), key=lambda item: ngrams[3][item[0]], reverse=True)[:120000])
+        print(text)
-    pickle.dump(ngrams, open('ngrams_2.pkl', 'wb'))
 
 
 if __name__ == '__main__':