update

hmm_class/sites.py

@@ -2,6 +2,8 @@
 # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python
 # http://lazyprogrammer.me
 # Create a Markov model for site data.
+from __future__ import print_function, division
+from future.utils import iteritems
 import numpy as np
 
 transitions = {}
@@ -14,19 +16,19 @@
     row_sums[s] = row_sums.get(s, 0.) + 1
 
 # normalize
-for k, v in transitions.iteritems():
+for k, v in iteritems(transitions):
     s, e = k
     transitions[k] = v / row_sums[s]
 
 # initial state distribution
-print "initial state distribution:"
-for k, v in transitions.iteritems():
+print("initial state distribution:")
+for k, v in iteritems(transitions):
     s, e = k
     if s == '-1':
-        print e, v
+        print(e, v)
 
 # which page has the highest bounce?
-for k, v in transitions.iteritems():
+for k, v in iteritems(transitions):
     s, e = k
     if e == 'B':
-        print "bounce rate for %s: %s" % (s, v)
+        print("bounce rate for %s: %s" % (s, v))