Unigram model almost completed

UnigramModel.py

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+from collections import Counter
+
+
+# insert <s> to first and append </s> to end array, return words array
+def get_words_array(sentence):
+    words_in_sent = sentence.split(' ')
+    words_in_sent.insert(0, '<s>')
+    words_in_sent.append('</s>')
+
+    return words_in_sent
+
+
+class UnigramModel():
+
+    def __init__(self, train_pos_set, train_neg_set, lambda_arr, epsilon):
+        self.train_positive_set = train_pos_set
+        self.train_negative_set = train_neg_set
+        self.lambda_arr = lambda_arr  # [h0, h1, h2] => weights for probability
+        self.epsilon = epsilon
+        self.count_unary_train_pos_dict = {}
+        self.count_unary_train_neg_dict = {}
+        self.number_words_in_neg = 0
+        self.number_words_in_pos = 0
+        self.alpha_cut = 2
+
+    def do_alpha_cut(self):
+        for word in self.count_unary_train_pos_dict.keys():
+            if self.count_unary_train_pos_dict[word] <= 2:
+                del self.count_unary_train_pos_dict[word]
+
+        for word in self.count_unary_train_neg_dict.keys():
+            if self.count_unary_train_neg_dict[word] <= 2:
+                del self.count_unary_train_neg_dict[word]
+
+    def create_unary_words_dict(self):
+        for sentence in self.train_positive_set:
+            sentence = get_words_array(sentence)
+            words_in_sent = Counter(sentence)
+            for word in words_in_sent.keys():
+                if word in self.count_unary_train_pos_dict.keys():
+                    self.count_unary_train_pos_dict[word] += 1
+                else:
+                    self.count_unary_train_pos_dict[word] = 1
+
+        for sentence in self.train_negative_set:
+            sentence = get_words_array(sentence)
+            words_in_sent = Counter(sentence)
+            for word in words_in_sent.keys():
+                if word in self.count_unary_train_neg_dict.keys():
+                    self.count_unary_train_neg_dict[word] += 1
+                else:
+                    self.count_unary_train_neg_dict[word] = 1
+
+        self.calculate_number_words()  # to calculate numbers of all words
+
+    # calculate number of all words in dictionary
+    def calculate_number_words(self):
+        sum_in_pos = 0
+        for key, value in self.count_unary_train_pos_dict:
+            sum_in_pos += value
+        sum_in_neg = 0
+        for key, value in self.count_unary_train_neg_dict:
+            sum_in_neg += value
+
+        self.number_words_in_pos = sum_in_pos
+        self.number_words_in_neg = sum_in_neg
+
+    # calculate p(w) = count(w)/M   (M: all words in dictionary)
+    def calculate_unary_probability(self, word, dataset_mode):
+        res = 0
+        if dataset_mode == "positive":
+            if word in self.count_unary_train_pos_dict.keys():
+                res = self.count_unary_train_pos_dict[word] / self.number_words_in_pos
+
+        elif dataset_mode == "negative":
+            if word in self.count_unary_train_neg_dict.keys():
+                res = self.count_unary_train_neg_dict[word] / self.number_words_in_neg
+
+        return res
+
+    # recognize sentence: is positive or negative
+    def recognize_sentence(self, sentence):
+        words_array = get_words_array()