Add alpha plus smoothing

run.py

@@ -10,21 +10,26 @@ class WordGapPrediction:
     def __init__(self):
         self.tokenizer = RegexpTokenizer(r"\w+")
         self.model = defaultdict(lambda: defaultdict(lambda: 0))
+        self.vocab = set()
+        self.alpha = 0.001
 
     def read_train_data(self, file):
         data = pd.read_csv(file, sep="\t", error_bad_lines=False, index_col=0, header=None)
-        for index, row in data[:140000].iterrows():
+        for index, row in data[:100000].iterrows():
             text = str(row[6]) + ' ' + str(row[7])
             tokens = self.tokenizer.tokenize(text)
             for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
                 if w1 and w2 and w3:
                     self.model[(w2, w3)][w1] += 1
                     self.model[(w1, w2)][w3] += 1
+                    self.vocab.add(w1)
+                    self.vocab.add(w2)
+                    self.vocab.add(w3)
         
         for word_pair in self.model:
             num_n_grams = float(sum(self.model[word_pair].values()))
             for word in self.model[word_pair]:
-                self.model[word_pair][word] /= num_n_grams
+                self.model[word_pair][word] = (self.model[word_pair][word] + self.alpha) / (num_n_grams + self.alpha*len(self.vocab))
 
     def generate_outputs(self, input_file, output_file):
         data = pd.read_csv(input_file, sep='\t', error_bad_lines=False, index_col=0, header=None, quoting=csv.QUOTE_NONE)