python 3

Author: lazyprogrammer

supervised_class/bayes.py

@@ -1,6 +1,12 @@
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
 # This is an example of a Bayes classifier on MNIST data.
+from __future__ import print_function, division
+from future.utils import iteritems
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 
 import numpy as np
 from util import get_data
@@ -9,7 +15,7 @@
 from scipy.stats import multivariate_normal as mvn
 
 class Bayes(object):
-    def fit(self, X, Y, smoothing=10e-3):
+    def fit(self, X, Y, smoothing=1e-2):
         N, D = X.shape
         self.gaussians = dict()
         self.priors = dict()
@@ -30,27 +36,27 @@ def predict(self, X):
         N, D = X.shape
         K = len(self.gaussians)
         P = np.zeros((N, K))
-        for c, g in self.gaussians.iteritems():
+        for c, g in iteritems(self.gaussians):
             mean, cov = g['mean'], g['cov']
             P[:,c] = mvn.logpdf(X, mean=mean, cov=cov) + np.log(self.priors[c])
         return np.argmax(P, axis=1)
 
 
 if __name__ == '__main__':
     X, Y = get_data(10000)
-    Ntrain = len(Y) / 2
+    Ntrain = len(Y) // 2
     Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]
     Xtest, Ytest = X[Ntrain:], Y[Ntrain:]
 
     model = Bayes()
     t0 = datetime.now()
     model.fit(Xtrain, Ytrain)
-    print "Training time:", (datetime.now() - t0)
+    print("Training time:", (datetime.now() - t0))
 
     t0 = datetime.now()
-    print "Train accuracy:", model.score(Xtrain, Ytrain)
-    print "Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)
+    print("Train accuracy:", model.score(Xtrain, Ytrain))
+    print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain))
 
     t0 = datetime.now()
-    print "Test accuracy:", model.score(Xtest, Ytest)
-    print "Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)
+    print("Test accuracy:", model.score(Xtest, Ytest))
+    print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest))

supervised_class/dt.py

@@ -1,6 +1,13 @@
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
 # Decision Tree for continuous-vector input, binary output
+from __future__ import print_function, division
+from future.utils import iteritems
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 from util import get_data, get_xor, get_donut
 from datetime import datetime
@@ -149,7 +156,7 @@ def predict_one(self, x):
     def predict(self, X):
         N = len(X)
         P = np.zeros(N)
-        for i in xrange(N):
+        for i in range(N):
             P[i] = self.predict_one(X[i])
         return P
 
@@ -186,20 +193,20 @@ def score(self, X, Y):
     Y = Y[idx]
 
     # split the data
-    Ntrain = len(Y) / 2
+    Ntrain = len(Y) // 2
     Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]
     Xtest, Ytest = X[Ntrain:], Y[Ntrain:]
 
     model = DecisionTree()
     # model = DecisionTree(max_depth=7)
     t0 = datetime.now()
     model.fit(Xtrain, Ytrain)
-    print "Training time:", (datetime.now() - t0)
+    print("Training time:", (datetime.now() - t0))
 
     t0 = datetime.now()
-    print "Train accuracy:", model.score(Xtrain, Ytrain)
-    print "Time to compute train accuracy:", (datetime.now() - t0)
+    print("Train accuracy:", model.score(Xtrain, Ytrain))
+    print("Time to compute train accuracy:", (datetime.now() - t0))
 
     t0 = datetime.now()
-    print "Test accuracy:", model.score(Xtest, Ytest)
-    print "Time to compute test accuracy:", (datetime.now() - t0)
+    print("Test accuracy:", model.score(Xtest, Ytest))
+    print("Time to compute test accuracy:", (datetime.now() - t0))