initial commit (S=4)

main.py

@@ -0,0 +1,76 @@
+import matplotlib.pyplot as plt
+import networkx as nx
+
+def nn(cluster_no, node_name):
+    return str(cluster_no) + '-' + node_name
+
+def build_cluster(n):
+    g = nx.Graph()
+    nodes = ['ls', 'lu', 'ld', 'm', 'rd', 'ru', 'rs']
+    edges = [('ls', 'lu'), ('ls', 'ld'), ('ld', 'lu'),
+             ('rs', 'ru'), ('rs', 'rd'), ('rd', 'ru'),
+             ('lu', 'm'),  ('ld', 'm'),
+             ('ru', 'm'),  ('rd', 'm')]
+
+    g.add_nodes_from([nn(n, x) for x in nodes])
+    g.add_edges_from([tuple(nn(n, y) for y in x) for x in edges])
+
+    return g
+
+def build_network(n):
+    g = build_cluster(0)
+
+    for i in range(1, n):
+        c = build_cluster(i)
+        g = nx.union(g, c)
+
+        if i % 2:
+            g.add_edge(nn(i, 'lu'), nn(i-1, 'lu'))
+            g.add_edge(nn(i, 'rd'), nn(i-1, 'rd'))
+        else:
+            g.add_edge(nn(i, 'ld'), nn(i-1, 'ld'))
+            g.add_edge(nn(i, 'ru'), nn(i-1, 'ru'))
+
+        g.add_edge(nn(i, 'ls'), nn(i-1, 'rs'))
+        g.add_edge(nn(i, 'rs'), nn(i-1, 'ls'))
+
+    return g
+
+def analyze(G: nx.Graph) -> dict:
+    N = G.number_of_nodes()
+    E = G.number_of_edges()
+    degs = G.degree()
+    s_vals = [s[1] for s in degs]
+    S = max(s_vals)
+    D = nx.diameter(G)
+    D_average = nx.average_shortest_path_length(G)
+    Q = N*D_average/E
+    C = N*D*S
+    return {
+            'N': N,
+            'E': E,
+            'Ступінь': S,
+            'Діаметр': D,
+            'Середній діаметр': D_average,
+            'Топологічний трафік': Q,
+            'Вартість': C,
+            'Graph': G.copy()
+    }
+
+def main():
+    i = 0
+
+    while True:
+        i += 1
+        g = build_network(i)
+
+        if g.number_of_nodes() > 1000:
+            break
+
+        r = analyze(g)
+        print(r)
+        #nx.draw(g, with_labels = True)
+        #plt.show()
+
+if __name__=="__main__":
+    main()