Regression

code_regression.py

@@ -9,6 +9,8 @@ import random
 vocabulary = []
 
 file_to_save = open("test.tsv", "w", encoding='utf-8')
+
+
 def define_vocabulary(file_to_learn_new_words):
     word_counts = {'count': defaultdict(int)}
     with open(file_to_learn_new_words, encoding='utf-8') as in_file:
@@ -19,6 +21,7 @@ def define_vocabulary(file_to_learn_new_words):
                 word_counts['count'][token] += 1
     return word_counts
 
+
 def read_input(file_path):
     read_word_counts = {'count': defaultdict(int)}
     with open(file_path, encoding='utf-8') as in_file:
@@ -29,7 +32,9 @@ def read_input(file_path):
                 read_word_counts['count'][token] += 1
     return read_word_counts
 
+
 def training(vocabulary, read_input, expected):
+    file_to_write = open(expected, 'w+', encoding='utf8')
     learning_rate = 0.00001
     learning_precision = 0.0000001
     weights = []
@@ -46,7 +51,7 @@ def training(vocabulary,read_input,expected):
     # max_iteration=len(vocabulary['count'])+1
     max_iteration = 1000
     delta = 1
-    while (delta>learning_precision and iteration<max_iteration):
+    while delta > learning_precision and iteration < max_iteration:
         d, y = random.choice(list(read_input['count'].items()))  # d-word, y-value of
         y_hat = weights[0]
         i = 0
@@ -54,10 +59,14 @@ def training(vocabulary,read_input,expected):
             if word_d in vocabulary['count'].keys():
                 # print(vocabulary['count'][d])
                 y_hat += weights[vocabulary['count'][word_d]] * readed_words_values[i]
-                delta=abs(y_hat-y)*learning_rate
-                weights[0]=weights[0]-delta
-                i+=i
+                i += 1
+            if y_hat > 0.0:
+                file_to_write.write('1\n')
+            else:
+                file_to_write.write('0\n')
         i = 0
+        delta = (y_hat - y) * learning_rate
+        weights[0] = weights[0] - delta
         for word_w in d:
             if word_w in vocabulary['count'].keys():
                 weights[vocabulary['count'][word_w]] -= readed_words_values[i] * delta
@@ -73,10 +82,16 @@ def training(vocabulary,read_input,expected):
             iteration = 0
             loss_sum = 0.0
         iteration += 1
+        file_to_write.close
+    return weights, vocabulary
+
+
 def main():
     vocabulary = define_vocabulary('train/in.tsv')
     readed_words = read_input('dev-0/in.tsv')
+    readed_words_test_a = read_input('test-A/in.tsv/in.tsv')
     training(vocabulary, readed_words, 'test.tsv')
+    training(vocabulary,readed_words_test_a, 'test_a.tsv')
 
 
 # def cost_function(y_hat,y):
@@ -88,7 +103,6 @@ def main():
 #        loss_sum=0.0
 
 
-
 # def main():
 # --------------- initialization ---------------------------------
 #    vocabulary = define_vocabulary('train/in.tsv')
@@ -126,4 +140,3 @@ def main():
 
 
 main()
-

test.tsv

@@ -0,0 +1,19 @@
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+0
+0
+0
+0
+0
+0
+0
+0
+0