Modernize Python 2 code to get ready for Python 3

machine_learning/decision_tree.py

@@ -3,6 +3,7 @@ Implementation of a basic regression decision tree.
 Input data set: The input data set must be 1-dimensional with continuous labels.
 Output: The decision tree maps a real number input to a real number output. 
 """
+from __future__ import print_function
 
 import numpy as np
 

machine_learning/gradient_descent.py

@@ -1,6 +1,7 @@
 """
 Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis function.
 """
+from __future__ import print_function
 import numpy
 
 # List of input, output pairs
@@ -106,13 +107,13 @@ def run_gradient_descent():
                           atol=absolute_error_limit, rtol=relative_error_limit):
             break
         parameter_vector = temp_parameter_vector
-    print("Number of iterations:", j)
+    print(("Number of iterations:", j))
 
 
 def test_gradient_descent():
     for i in range(len(test_data)):
-        print("Actual output value:", output(i, 'test'))
-        print("Hypothesis output:", calculate_hypothesis_value(i, 'test'))
+        print(("Actual output value:", output(i, 'test')))
+        print(("Hypothesis output:", calculate_hypothesis_value(i, 'test')))
 
 
 if __name__ == '__main__':

machine_learning/linear_regression.py

@@ -7,6 +7,7 @@ We try to set these Feature weights, over many iterations, so that they best
 fits our dataset. In this particular code, i had used a CSGO dataset (ADR vs
 Rating). We try to best fit a line through dataset and estimate the parameters.
 """
+from __future__ import print_function
 
 import requests
 import numpy as np

machine_learning/perceptron.py

@@ -9,6 +9,7 @@
 	p2 = 1
 
 '''
+from __future__ import print_function
 
 import random
 
@@ -52,7 +53,7 @@ class Perceptron:
             epoch_count = epoch_count + 1
             # if you want controle the epoch or just by erro
             if erro == False:
-                print('\nEpoch:\n',epoch_count)
+                print(('\nEpoch:\n',epoch_count))
                 print('------------------------\n')
             #if epoch_count > self.epoch_number or not erro:
                 break
@@ -66,10 +67,10 @@ class Perceptron:
         y = self.sign(u)
 
         if  y == -1:
-            print('Sample: ', sample)
+            print(('Sample: ', sample))
             print('classification: P1')
         else:
-            print('Sample: ', sample)
+            print(('Sample: ', sample))
             print('classification: P2')
 
     def sign(self, u):

machine_learning/scoring_functions.py

@@ -1,4 +1,4 @@
-import numpy
+import numpy as np
 
 """ Here I implemented the scoring functions.
     MAE, MSE, RMSE, RMSLE are included.