Instead of removing the nodes and edges while iterating over graph, nodes and edges are added to two lists and then removed the entire collection from the graph.

.gitignore

@@ -1,3 +1,7 @@
+
+# Jupyter notebooks
+JupyterNB/
+
 .idea/
 
 visualization/

gng.py

@@ -5,6 +5,9 @@
 import networkx as nx
 import matplotlib.pyplot as plt
 from sklearn import decomposition
+import copy
+import os
+
 
 __authors__ = 'Adrien Guille'
 __email__ = 'adrien.guille@univ-lyon2.fr'
@@ -18,10 +21,13 @@
 
 class GrowingNeuralGas:
 
-    def __init__(self, input_data):
+    def __init__(self, input_data,output_folder):
         self.network = None
+        self.output_folder = output_folder
         self.data = input_data
         self.units_created = 0
+        if not os.path.exists(output_folder):
+            os.makedirs(output_folder)
         plt.style.use('ggplot')
 
     def find_nearest_units(self, observation):
@@ -35,12 +41,21 @@ def find_nearest_units(self, observation):
         return ranking
 
     def prune_connections(self, a_max):
+        listToRemoveE = []
+        listToRemoveU = []
         for u, v, attributes in self.network.edges(data=True):
             if attributes['age'] > a_max:
-                self.network.remove_edge(u, v)
+                listToRemoveE.append([u, v])
         for u in self.network.nodes():
             if self.network.degree(u) == 0:
-                self.network.remove_node(u)
+                listToRemoveU.append(u)
+        try:
+            self.network.remove_edges_from(listToRemoveE)
+            self.network.remove_nodes_from(listToRemoveU)
+        except:
+            print('Error while removing...')
+            print('Edges to remove',listToRemoveE)
+            print('Nodes to remove',listToRemoveU)
 
     def fit_network(self, e_b, e_n, a_max, l, a, d, passes=1, plot_evolution=False):
         # logging variables
@@ -70,7 +85,7 @@ def fit_network(self, e_b, e_n, a_max, l, a, d, passes=1, plot_evolution=False):
                 s_1 = nearest_units[0]
                 s_2 = nearest_units[1]
                 # 3. increment the age of all edges emanating from s_1
-                for u, v, attributes in self.network.edges_iter(data=True, nbunch=[s_1]):
+                for u, v, attributes in self.network.edges(data=True, nbunch=[s_1]):
                     self.network.add_edge(u, v, age=attributes['age']+1)
                 # 4. add the squared distance between the observation and the nearest unit in input space
                 self.network.node[s_1]['error'] += spatial.distance.euclidean(observation, self.network.node[s_1]['vector'])**2
@@ -91,12 +106,13 @@ def fit_network(self, e_b, e_n, a_max, l, a, d, passes=1, plot_evolution=False):
                 steps += 1
                 if steps % l == 0:
                     if plot_evolution:
-                        self.plot_network('visualization/sequence/' + str(sequence) + '.png')
+                        print(str(self.output_folder)+'/' + str(sequence) + '.png')
+                        self.plot_network(str(self.output_folder)+'/' + str(sequence) + '.png')
                     sequence += 1
                     # 8.a determine the unit q with the maximum accumulated error
                     q = 0
                     error_max = 0
-                    for u in self.network.nodes_iter():
+                    for u in self.network.nodes():
                         if self.network.node[u]['error'] > error_max:
                             error_max = self.network.node[u]['error']
                             q = u
@@ -123,13 +139,13 @@ def fit_network(self, e_b, e_n, a_max, l, a, d, passes=1, plot_evolution=False):
                     self.network.node[r]['error'] = self.network.node[q]['error']
                 # 9. decrease all error variables by multiplying them with a constant d
                 error = 0
-                for u in self.network.nodes_iter():
+                for u in self.network.nodes():
                     error += self.network.node[u]['error']
                 accumulated_local_error.append(error)
                 network_order.append(self.network.order())
                 network_size.append(self.network.size())
                 total_units.append(self.units_created)
-                for u in self.network.nodes_iter():
+                for u in self.network.nodes():
                     self.network.node[u]['error'] *= d
                     if self.network.degree(nbunch=[u]) == 0:
                         print(u)
@@ -155,7 +171,7 @@ def plot_network(self, file_path):
         plt.clf()
         plt.scatter(self.data[:, 0], self.data[:, 1])
         node_pos = {}
-        for u in self.network.nodes_iter():
+        for u in self.network.nodes():
             vector = self.network.node[u]['vector']
             node_pos[u] = (vector[0], vector[1])
         nx.draw(self.network, pos=node_pos)