py3 compatible

linear_regression_class/generate_1d.py

@@ -4,13 +4,19 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 
 import numpy as np
 
 N = 100
 with open('data_1d.csv', 'w') as f:
     X = np.random.uniform(low=0, high=100, size=N)
     Y = 2*X + 1 + np.random.normal(scale=5, size=N)
-    for i in xrange(N):
+    for i in range(N):
         f.write("%s,%s\n" % (X[i], Y[i]))
 

linear_regression_class/generate_2d.py

@@ -4,6 +4,12 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 
 import numpy as np
 
@@ -12,5 +18,5 @@
 with open('data_2d.csv', 'w') as f:
     X = np.random.uniform(low=0, high=100, size=(N,2))
     Y = np.dot(X, w) + 1 + np.random.normal(scale=5, size=N)
-    for i in xrange(N):
+    for i in range(N):
         f.write("%s,%s,%s\n" % (X[i,0], X[i,1], Y[i]))

linear_regression_class/generate_poly.py

@@ -4,6 +4,11 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 
 import numpy as np
 
@@ -12,6 +17,6 @@
     X = np.random.uniform(low=0, high=100, size=N)
     X2 = X*X
     Y = 0.1*X2 + X + 3 + np.random.normal(scale=10, size=N)
-    for i in xrange(N):
+    for i in range(N):
         f.write("%s,%s\n" % (X[i], Y[i]))
 

linear_regression_class/gradient_descent.py

@@ -2,6 +2,12 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -14,7 +20,7 @@
 Y = np.array([0]*5 + [1]*5)
 
 # print X so you know what it looks like
-print "X:", X
+print("X:", X)
 
 # won't work!
 # w = np.linalg.solve(X.T.dot(X), X.T.dot(Y))
@@ -23,7 +29,7 @@
 costs = [] # keep track of squared error cost
 w = np.random.randn(D) / np.sqrt(D) # randomly initialize w
 learning_rate = 0.001
-for t in xrange(1000):
+for t in range(1000):
   # update w
   Yhat = X.dot(w)
   delta = Yhat - Y
@@ -37,7 +43,7 @@
 plt.plot(costs)
 plt.show()
 
-print "final w:", w
+print("final w:", w)
 
 # plot prediction vs target
 plt.plot(Yhat, label='prediction')

linear_regression_class/l1_regularization.py

@@ -2,6 +2,12 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -22,7 +28,7 @@
 w = np.random.randn(D) / np.sqrt(D) # randomly initialize w
 learning_rate = 0.001
 l1 = 10.0 # Also try 5.0, 2.0, 1.0, 0.1 - what effect does it have on w?
-for t in xrange(500):
+for t in range(500):
   # update w
   Yhat = X.dot(w)
   delta = Yhat - Y
@@ -36,7 +42,7 @@
 plt.plot(costs)
 plt.show()
 
-print "final w:", w
+print("final w:", w)
 
 # plot our w vs true w
 plt.plot(true_w, label='true w')

linear_regression_class/l2_regularization.py

@@ -4,6 +4,13 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 

linear_regression_class/lr_1d.py

@@ -4,6 +4,12 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 

linear_regression_class/lr_2d.py

@@ -4,6 +4,12 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
@@ -42,4 +48,4 @@
 d1 = Y - Yhat
 d2 = Y - Y.mean()
 r2 = 1 - d1.dot(d1) / d2.dot(d2)
-print "the r-squared is:", r2
+print("the r-squared is:", r2)

linear_regression_class/moore.py

@@ -4,6 +4,13 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 # transistor count from: https://en.wikipedia.org/wiki/Transistor_count
+
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import re
 import numpy as np
 import matplotlib.pyplot as plt

linear_regression_class/overfitting.py

@@ -2,6 +2,11 @@
 # https://deeplearningcourses.com/c/data-science-linear-regression-in-python
 # https://www.udemy.com/data-science-linear-regression-in-python
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -10,7 +15,7 @@
 def make_poly(X, deg):
     n = len(X)
     data = [np.ones(n)]
-    for d in xrange(deg):
+    for d in range(deg):
         data.append(X**(d+1))
     return np.vstack(data).T
 
@@ -53,14 +58,14 @@ def plot_train_vs_test_curves(X, Y, sample=20, max_deg=20):
     Xtrain = X[train_idx]
     Ytrain = Y[train_idx]
 
-    test_idx = [idx for idx in xrange(N) if idx not in train_idx]
+    test_idx = [idx for idx in range(N) if idx not in train_idx]
     # test_idx = np.random.choice(N, sample)
     Xtest = X[test_idx]
     Ytest = Y[test_idx]
 
     mse_trains = []
     mse_tests = []
-    for deg in xrange(max_deg+1):
+    for deg in range(max_deg+1):
         Xtrain_poly = make_poly(Xtrain, deg)
         w = fit(Xtrain_poly, Ytrain)
         Yhat_train = Xtrain_poly.dot(w)

linear_regression_class/systolic.py

@@ -8,6 +8,13 @@
 # X2 = age in years
 # X3 = weight in pounds
 
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -41,9 +48,9 @@ def get_r2(X, Y):
     r2 = 1 - d1.dot(d1) / d2.dot(d2)
     return r2
 
-print "r2 for x2 only:", get_r2(X2only, Y)
-print "r2 for x3 only:", get_r2(X3only, Y)
-print "r2 for both:", get_r2(X, Y)
+print("r2 for x2 only:", get_r2(X2only, Y))
+print("r2 for x3 only:", get_r2(X3only, Y))
+print("r2 for both:", get_r2(X, Y))