neural net

Author: codeXLinkX

linear_reg.py

@@ -14,23 +14,20 @@ def predict(x, y):
     else:
         file = 'model'
     pickle.dump(regr, open(file, 'wb'))
-    # model = pickle.load(open('model', 'rb'))
-    # y_pred = model.predict(x)
-    # print("Predictions")
-    # print(y_pred)
+
 
 def read_file(arg):
 
     args = len(arg)
 
-    if (args > 1):
+    if args > 1:
         file_name = sys.argv[1]
     else:
         file_name = 'data.csv'
 
     data = pd.read_csv(file_name).squeeze()
 
-    if (args > 2):
+    if args > 2:
         feature_count = int(sys.argv[2])
     else:
         feature_count = len(data.columns)
@@ -48,6 +45,5 @@ def main():
     predict(x, y)
 
 
-
 if __name__ == '__main__':
     main()

neural_net.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import numpy as np
+from sklearn import linear_model
+import sys
+import pickle
+from sklearn.neural_network import MLPClassifier
+
+
+def nn(x, y):
+    clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes = (5, 2), random_state = 1)
+    clf.fit(x, y)
+    if len(sys.argv) > 3:
+        file = sys.argv[3]
+    else:
+        file = 'model'
+    pickle.dump(clf, open(file, 'wb'))
+
+
+def read_file(arg):
+
+    args = len(arg)
+
+    if args > 1:
+        file_name = sys.argv[1]
+    else:
+        file_name = 'data.csv'
+
+    data = pd.read_csv(file_name).squeeze()
+
+    if args > 2:
+        feature_count = int(sys.argv[2])
+    else:
+        feature_count = len(data.columns)
+
+    x = data[data.columns[0:feature_count]]
+    y = data[data.columns[feature_count:]]
+
+    return x, y
+
+
+def main():
+
+    x, y = read_file(sys.argv)
+
+    nn(x, y)
+
+
+if __name__ == '__main__':
+    main()