def calculate_clusters(data_file_name,
init_method="kmeans",
max_iterations=10,
max_internal_iterations=10,
use_gpu=False,
config: tf.ConfigProto = tf.ConfigProto()):
start = time.time()
iterations = 0
internal_iterations = 0
loglikelihood_history = []
with tf.device("/gpu:0" if use_gpu else "/cpu:0"):
data = GraphData.load_graph_data(data_file_name)
if init_method == "random":
initial_clusters = find_random_initial_clustering(data)
else:
initial_clusters = find_initial_clustering(data, config)
tau = Tau(data, initial_clusters)
multinomial = Multinomial(data, tau)
markov_chain = MarkovChain(data, tau)
likelihood = Likelihood(data, tau, multinomial, markov_chain)
tau.init(data, multinomial.density, markov_chain.stationary,
markov_chain.trans)
generate_time_end = time.time()
likelihood_result = None
trans_result = None
with tf.Session(config=config) as session:
# init
session.run(tf.global_variables_initializer())
tau.update_taum(session)
multinomial.update_density(session)
previous_log = -1e30
for i in range(max_iterations):
iterations += 1
previous_log_internal = -1e30
for z in range(max_internal_iterations):
internal_iterations += 1
tau.update_tau(session)
new_log = likelihood.calculate(session)
if abs(previous_log_internal - new_log) < 1e-7:
break
loglikelihood_history.append(new_log)
previous_log_internal = new_log
trans_result = markov_chain.update_trans(session)
markov_chain.update_stationary(session)
multinomial.update_density(session)
new_log = likelihood.calculate(session)
likelihood_result = new_log
loglikelihood_history.append(likelihood_result)
if previous_log > new_log:
break
previous_log = new_log
memory_gpu = 0
if use_gpu:
memory_gpu = session.run(
tf.contrib.memory_stats.MaxBytesInUse()) / 1024 / 1024
end = time.time()
membership = __find_element_membership(data, tau)
memory_cpu = psutil.Process().memory_info().rss / 1024 / 1024
results = Results(likelihood_result, trans_result, membership,
initial_clusters)
measurements = Measurements(generate_time_end - start,
end - generate_time_end, memory_cpu,
memory_gpu, iterations, internal_iterations,
loglikelihood_history, data.N)
return results, measurements
