def run_simulation(n, A1, multipartite_graph_matrix, iterations, partitions):
correct_count = 0
for i in range(0,iterations):
A2 = Graph.create_adjacency_list(n,multipartite_graph_matrix) #need to do this as it hasn't updated the internal adjacency list representation
# graph deconvolver with new components
graph_deconvolver = GraphDeconvolver(n,A1,A2)
#convolved graphs
A_matrix = Graph.create_adjacency_matrix(n,A1) + multipartite_graph_matrix
status,problem_value,A1_star,A2_star= graph_deconvolver.deconvolve(A_matrix)
# first check has the correct degree sequence before check multipartite, (needs to be complete)
correct_degree_sequence = check_degree_sequence(partitions,np.round(A2_star)) # check_degree_sequence can't deal with weighted edges so round
if correct_degree_sequence:
adjacency_table = Graph.create_adjacency_table(n,A2_star)
#print('p=',partitions,'\ng=',adjacency_table)
multipartite_graph_recognizer=Multipartite_graph_recognizer(partitions,adjacency_table)
ok=multipartite_graph_recognizer.check()
print('multipartite:',multipartite_graph_recognizer.match)
else:
print('incorrect degree sequence')
ok=False
np.set_printoptions(suppress=True)
print('Problem status: ',status)
cycle = is_cycle(Graph.create_adjacency_list(n,A1_star))
print('Is cycle: ', cycle)
print('A2 correct: ',ok)
if cycle and ok:
correct_count +=1
return correct_count
def run_simulation(n,
multipartite_graph_matrix,
iterations,
partitions,
perturbation_scale=0.1):
correct_count = 0
graph_denoiser = GraphDenoiser(
n, Graph.create_adjacency_list(n, multipartite_graph_matrix))
# vector of small parameters for spectral hull
epsilon_vector = epsilon * np.ones(n)
for i in range(0, iterations):
A_matrix_noisy = perturb_matrix(multipartite_graph_matrix,
perturbation_scale)
status, problem_value, A_recovered = graph_denoiser.denoise(
A_matrix_noisy, epsilon_vector)
# first check has the correct degree sequence before check multipartite, (needs to be complete)
correct_degree_sequence = check_degree_sequence(
partitions, np.round(A_recovered)
) # check_degree_sequence can't deal with weighted edges so round
if correct_degree_sequence:
adjacency_table = Graph.create_adjacency_table(n, A_recovered)
multipartite_graph_recognizer = Multipartite_graph_recognizer(
partitions, adjacency_table)
ok = multipartite_graph_recognizer.check()
print('multipartite:', multipartite_graph_recognizer.match)
else:
print('incorrect degree sequence')
ok = False
if ok:
correct_count += 1
return correct_count
