def run_simulation(n, A1, clebsch_graph, iterations, perturbation_scale=0.1):
correct_count = 0
epsilon_vector = np.zeros(n) # no small parameter
# graph deconvolver with new components
graph_deconvolver = GraphDeconvolver(n, A1, clebsch_graph.adjacency_list)
for i in range(0, iterations):
# perturb matrix (introduce noise to the weights)
clebsch_matrix = perturb_matrix(clebsch_graph.adjacency_matrix,
perturbation_scale)
#convolved graphs
A_matrix = Graph.create_adjacency_matrix(n, A1) + clebsch_matrix
status, problem_value, A1_star, A2_star = graph_deconvolver.deconvolve(
A_matrix, epsilon_vector)
print('Problem status: ', status)
cycle = is_cycle(Graph.create_adjacency_list(n, A1_star))
print('Is cycle: ', cycle)
if cycle:
correct_count += 1
return correct_count
def run_simulation(n,
A1,
clebsch_graph,
iterations,
perturbation_scale=0.1,
epsilon=0):
correct_count = 0
epsilon_vector = epsilon * np.ones(n)
for i in range(0, iterations):
# perturb matrix (introduce noise to the weights)
clebsch_matrix = perturb_matrix(clebsch_graph.adjacency_matrix,
perturbation_scale)
A2 = Graph.create_adjacency_list(
n, clebsch_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) + clebsch_matrix
status, problem_value, A1_star, A2_star = graph_deconvolver.deconvolve(
A_matrix, epsilon_vector)
if status == 'optimal':
print('Problem status: ', status)
cycle = is_cycle(Graph.create_adjacency_list(n, A1_star))
print('Is cycle: ', cycle)
if cycle:
correct_count += 1
return correct_count
def run_simulation(n, cycle, itererations, perturbation_scale=0.1, epsilon=0):
correct_count = 0
graph_denoiser = GraphDenoiser(n, cycle)
A_matrix = Graph.create_adjacency_matrix(n, A)
# vector of small parameters for spectral hull
epsilon_vector = epsilon * np.ones(n)
# adjacency matrix to compare against to check is denoised correctly
#test_clebsch_adjacency_matrix = Graph.create_adjacency_matrix(n,cycle)
for i in range(0, iterations):
# perturb matrix (introduce noise to the weights)
A_matrix_noisy = perturb_matrix(A_matrix, perturbation_scale)
status, problem_value, A_recovered = graph_denoiser.denoise(
A_matrix_noisy, epsilon_vector)
if status == 'optimal':
print('Problem status: ', status)
cycle = is_cycle(Graph.create_adjacency_list(n, A_recovered))
print('Is cycle: ', cycle)
# check for clebsch graph
#cycle = np.allclose(test_clebsch_adjacency_matrix,A_recovered, atol=1e-3)
#print('Is clebsch graph: ', cycle)
if cycle:
correct_count += 1
return correct_count
def run_simulation(n, cycle,itererations, perturbation_scale = 0.1):
correct_count = 0
graph_denoiser = GraphDenoiser(n,cycle)
A_matrix = Graph.create_adjacency_matrix(n,A)
# generate cum sum of mean shift in eigenvalue distribution
for i in range(0,iterations):
# perturb matrix (introduce noise to the weights)
A_matrix_noisy = perturb_matrix(A_matrix,perturbation_scale)
epsilon_vector = calculate_epsilon_vector(A_matrix,A_matrix_noisy)
print
status,problem_value,A_recovered= graph_denoiser.denoise(A_matrix_noisy,epsilon_vector)
print('Problem status: ',status)
cycle = is_cycle(Graph.create_adjacency_list(n,A_recovered))
print('Is cycle: ', cycle)
if cycle:
correct_count +=1
return correct_count
def run_simulation(n, cycle, itererations, perturbation_scale=0.1, epsilon=0):
correct_count = 0
graph_denoiser = GraphDenoiser(n, cycle)
A_matrix = Graph.create_adjacency_matrix(n, A)
# vector of small parameters for spectral hull
epsilon_vector = epsilon * np.ones(n)
for i in range(0, iterations):
# perturb matrix (introduce noise to the weights)
A_matrix_noisy = perturb_matrix(A_matrix, perturbation_scale)
status, problem_value, A_recovered = graph_denoiser.denoise(
A_matrix_noisy, epsilon_vector)
if status == 'optimal':
print('Problem status: ', status)
cycle = is_cycle(Graph.create_adjacency_list(n, A_recovered))
print('Is cycle: ', cycle)
if cycle:
correct_count += 1
return correct_count
def mean_shift_eigenvalue_distribution(graph_matrix, sigma):
n = graph_matrix.shape[0]
means = np.zeros(n)
original_eigenvalues = eigvalsh(graph_matrix)
iterations = 1000
for iteration in range(iterations):
eigenvalues = eigvalsh(perturb_matrix(graph_matrix,
sigma)) - original_eigenvalues
means += (eigenvalues - means) / (1.0 + iteration)
return np.cumsum(means)
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
def run_simulation(n, A1, multipartite_graph_matrix, iterations, partitions, perturbation_scale = 0.1):
correct_count = 0
for i in range(0,iterations):
# perturb matrix (introduce noise to the weights)
graph_matrix = perturb_matrix(n,multipartite_graph_matrix, perturbation_scale)
A2 = Graph.create_adjacency_list(n,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) + 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
(10, 12),
(10, 13),
(11, 13),
(11, 14),
(12, 14),
)
#A_matrix = bicycle(n)
#A = Graph.create_adjacency_list(n,A_matrix)
graph_denoiser = GraphDenoiser(n, A)
# add gaussian noise to cycle
A_matrix = Graph.create_adjacency_matrix(n, A)
A_matrix_noisy = perturb_matrix(A_matrix, 0.2)
epsilon_vector = np.zeros(n)
status, problem_value, A_recovered = graph_denoiser.denoise(
A_matrix_noisy, epsilon_vector)
np.set_printoptions(precision=1e-3, suppress=True)
print('Problem status: ', status)
print('Norm value: ', problem_value)
print('Recovered A: \n', A_recovered)
print('is cycle graph: ',
is_cycle(Graph.create_adjacency_list(n, A_recovered)))
def set_visualiser_attributes(graph_visualiser):
graph_visualiser.A.node_attr['shape'] = 'circle'
