updating code to Python 3

1 files changed, 11 insertions, 12 deletions

diff --git a/src/algorithms.py b/src/algorithms.py
index 5b07f78..c880a7b 100644
--- a/src/algorithms.py
+++ b/src/algorithms.py
@@ -2,7 +2,6 @@ import numpy as np
 import networkx as nx
 import cascade_creation
 from collections import Counter
-from itertools import izip
 import convex_optimization
 import timeout
 
@@ -15,7 +14,7 @@ def greedy_prediction(G, cascades):
     G_hat = cascade_creation.InfluenceGraph(max_proba=None)
     G_hat.add_nodes_from(G.nodes())
     for node in G_hat.nodes():
-        print node
+        print(node)
         # Avoid cases where infection time is None or 0
         tmp = [cascade for cascade in cascades if cascade.infection_time(node)
                                                                            [0]]
@@ -41,14 +40,14 @@ def recovery_l1obj_l2constraint(G, cascades, floor_cstt, passed_function,
     G_hat = cascade_creation.InfluenceGraph(max_proba=None)
     G_hat.add_nodes_from(G.nodes())
     for node in G_hat.nodes():
-        print node
+        print(node)
         try:
             M, w = cascade_creation.icc_matrixvector_for_node(cascades, node)
             p_node, __ = passed_function(M,w, *args, **kwargs)
             G_hat = cascade_creation.add_edges_from_proba_vector(G=G_hat,
                         p_node=p_node, node=node, floor_cstt=floor_cstt)
         except timeout.TimeoutError:
-            print "TimeoutError, skipping to next node"
+            print("TimeoutError, skipping to next node")
     return G_hat
 
 
@@ -69,14 +68,14 @@ def correctness_measure(G, G_hat, print_values=False):
     f1_score = 2.* tp / (2 * tp + fp + fn)
 
     if print_values:
-        print "False Positives: {}".format(fp)
-        print "False Negatives: {}".format(fn)
-        print "True Positives: {}".format(tp)
-        print "True Negatives: {}".format(tn)
-        print "-------------------------------"
-        print "Precision: {}".format(precision)
-        print "Recall: {}".format(recall)
-        print "F1 score: {}".format(f1_score)
+        print("False Positives: {}".format(fp))
+        print("False Negatives: {}".format(fn))
+        print("True Positives: {}".format(tp))
+        print("True Negatives: {}".format(tn))
+        print("-------------------------------")
+        print("Precision: {}".format(precision))
+        print("Recall: {}".format(recall))
+        print("F1 score: {}".format(f1_score))
 
     return fp, fn, tp, tn