CNR encoding

CNI.py

+'''Compact neighborhood Encoding using Bijection Function'''
+
+import math
+import numpy as np
+
+from builder import *
+neighbor_labels, labels_dict = node_neighbor_labels()
+only_labels = list(set(labels_dict.values()))
+G = build_graph()
+
+    
+def cantor_pairing(x, y):
+    if x > (y + x - 1):
+        return 0
+    if y + x - 1 == 1:
+        return 1
+    st = 1
+    i = 1
+    t = y
+    while t <= (y + x - 1):
+        st *= t
+        t += 1       
+        while (i <= x) and (st % i == 0) and (st > 0):
+            st = st / i
+            i += 1
+    return st
+
+
+def CNR():
+    CNI_Nodes_log = {}
+    CNI_original = {}
+    for node in G.nodes:
+        visited = []
+        CNI_list = []
+        CNI_Sum = 0
+        hop_labels = sorted(neighbor_labels[node])  #Returns Labels of Node neighbors
+        hop_count = np.zeros(len(only_labels))
+        for label in only_labels:
+            if label in hop_labels:
+                hop_count[only_labels.index(label)] = hop_labels.count(label)   #returns counts of unique neighbor labels 
+            else: 0
+        hop_count = [int(n) for n in hop_count]    
+        for i in range(len(only_labels)):
+            visited.append(hop_count.pop(0))
+            CNI_list.append((only_labels[i],sum(visited)))    #Returns the label, hop count sum pair
+        for j in CNI_list:
+            CNI_Sum += cantor_pairing(j[0], j[1])
+        
+        CNI_original[node] = int(CNI_Sum)
+        if CNI_Sum == 1:
+            CNI_Nodes_log[node] = CNI_Sum
+        else:
+            CNI_Nodes_log[node] = math.log(CNI_Sum)
+    return (CNI_original, CNI_Nodes_log)
+
+