kenlm

run2.py

@@ -4,6 +4,7 @@ import regex as re
 import kenlm
 from english_words import english_words_alpha_set
 from nltk import  word_tokenize
+from math import log10
 from pathlib import  Path
 import os
 import numpy as np
@@ -63,31 +64,28 @@ def softmax(x):
     return e_x / e_x.sum(axis=0)
 
 def predict(model, before, after):
-    prob = 0.0
+    best_scores = []
-    best = []
     for word in english_words_alpha_set:
         text = ' '.join([before, word, after])
         text_score = model.score(text, bos=False, eos=False)
-        if len(best) < 12:
+        if len(best_scores) < 12:
-            best.append((word, text_score))
+            best_scores.append((word, text_score))
         else:
             worst_score = None
-            for score in best:
+            for score in best_scores:
                 if not worst_score:
                     worst_score = score
                 else:
                     if worst_score[1] > score[1]:
                         worst_score = score
             if worst_score[1] < text_score:
-                best.remove(worst_score)
+                best_scores.remove(worst_score)
-                best.append((word, text_score))
+                best_scores.append((word, text_score))
-    words = [word[0] for word in best]
+    probs = sorted(best_scores, key=lambda tup: tup[1], reverse=True)
-    probs = [prob[1] for prob in best]
-    probs = softmax(probs)
-    bests = sorted(zip(words, probs), key=lambda x:x[1], reverse=True)
     pred_str = ''
-    for word, prob in bests:
+    for word, prob in probs:
         pred_str += f'{word}:{prob} '
+    pred_str += f':{log10(0.99)}'
     return pred_str
 
 def make_prediction(model, path, result_path):