def algorithm_dpa(nodes_n, nodes_m):
graph_V = er.algorithm_er(nodes_m, 1.0)
print "Complete graph", graph_V
for node_i in range(nodes_m, nodes_n):
in_degree_graph = er.compute_in_degrees(graph_V)
print in_degree_graph
in_degree_sum = 0
for key in in_degree_graph:
in_degree_sum += in_degree_graph[key]
print "In-degree sum is", in_degree_sum
V_ = []
while len(V_) < nodes_m:
rnd_node = random.choice(graph_V.keys())
probablity = float(in_degree_graph[rnd_node] + 1.0) / (in_degree_sum + len(graph_V))
print "probablity", probablity
a = random.uniform(0.0, 1.0)
if a > probablity:
if not rnd_node in V_:
V_.append(rnd_node)
graph_V.update({node_i: set([])})
print "V_ is", V_
for v in V_:
graph_V[node_i].add(v)
return graph_V
def algorithm_dpa(nodes_n,nodes_m):
graph_V=er.algorithm_er(nodes_m,1.0)
dpa_object=DPATrial(len(graph_V))
for node_i in range(nodes_m,nodes_n):
#in_degree_graph=er.compute_in_degrees(graph_V)
V_=dpa_object.run_trial(nodes_m)
graph_V.update({node_i:set([])})
print "V_",V_
for v in V_:
graph_V[node_i].add(v)
return graph_V
#print "The returned graph is",algorithm_dpa(27770,13)
