From 3aaa09b330024eb057c033193eaecc3f0f8f076b Mon Sep 17 00:00:00 2001 From: Bob Date: Fri, 24 May 2024 06:03:16 -0400 Subject: [PATCH] update --- hmm_class/sites.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/hmm_class/sites.py b/hmm_class/sites.py index 617863f5..0187e03a 100644 --- a/hmm_class/sites.py +++ b/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))