def templateClustering(path, number_clusters, expected_length_clusters,
solver, initial_neighbors, increase_persent,
collect_dynamic_flag, ccore_flag):
result_testing = False
# If phases crosses each other because of random part of the network then we should try again.
for _ in range(0, 6, 1):
sample = read_sample(path)
network = hsyncnet(sample,
number_clusters,
initial_type.EQUIPARTITION,
initial_neighbors,
increase_persent,
ccore=ccore_flag)
analyser = network.process(order=0.997,
solution=solver,
collect_dynamic=collect_dynamic_flag)
clusters = analyser.allocate_clusters(0.1)
if (sum([len(cluster) for cluster in clusters]) !=
sum(expected_length_clusters)):
continue
if (sorted([len(cluster)
for cluster in clusters]) != expected_length_clusters):
if (sorted([len(cluster) for cluster in clusters]) !=
sorted(expected_length_clusters)):
continue
# Unit-test is passed
result_testing = True
break
assert result_testing
def testCreationDeletionByCore(self):
# Crash occurs in case of memory leak
data = read_image(IMAGE_MAP_SAMPLES.IMAGE_WHITE_SEA_SMALL);
for iteration in range(0, 15):
network = hsyncnet(data, 2, ccore = True);
del network;
def templateDynamicLength(path, number_clusters, expected_length,
initial_neighbors, increase_persent,
collect_dynamic_flag, ccore_flag):
sample = read_sample(path)
network = hsyncnet(sample,
number_clusters,
initial_type.EQUIPARTITION,
initial_neighbors,
increase_persent,
ccore=ccore_flag)
analyser = network.process(order=0.995,
solution=solve_type.FAST,
collect_dynamic=collect_dynamic_flag)
assert len(analyser) != 0
if (collect_dynamic_flag is True):
assert len(analyser) >= 1
if (expected_length is None):
assert len(analyser) > 1
else:
assert len(analyser) == expected_length
else:
assert len(analyser) == 1
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, ccore = ccore_flag);
(time, dynamic) = network.process(arg_order, collect_dynamic = arg_collect_dynamic);
clusters = network.get_clusters();
if (arg_collect_dynamic == True):
draw_dynamics(time, dynamic, x_title = "Time", y_title = "Phase", y_lim = [0, 2 * 3.14]);
draw_clusters(sample, clusters);
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, ccore = ccore_flag);
analyser = network.process(arg_order, collect_dynamic = arg_collect_dynamic);
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def testCoreInterfaceIntInputData():
result_testing = False;
for _ in range(10):
hsyncnet_instance = hsyncnet([ [1], [2], [3], [20], [21], [22] ], 2, initial_type.EQUIPARTITION, ccore = True);
analyser = hsyncnet_instance.process();
if (len(analyser.allocate_clusters(0.1)) == 2):
result_testing = True;
break;
assert result_testing;
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, initial_neighbors = int(len(sample) * 0.15), osc_initial_phases = initial_type.EQUIPARTITION, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, arg_order, solve_type.FAST, arg_collect_dynamic);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def templateDynamicLength(path, number_clusters, expected_length, initial_neighbors, increase_persent, collect_dynamic_flag, ccore_flag):
sample = read_sample(path);
network = hsyncnet(sample, number_clusters, initial_type.EQUIPARTITION, initial_neighbors, increase_persent, ccore = ccore_flag);
analyser = network.process(order = 0.995, solution = solve_type.FAST, collect_dynamic = collect_dynamic_flag);
assert len(analyser) != 0;
if (collect_dynamic_flag is True):
assert len(analyser) >= 1;
if (expected_length is None):
assert len(analyser) > 1;
else:
assert len(analyser) == expected_length;
else:
assert len(analyser) == 1;
def template_clustering(file,
number_clusters,
arg_order=0.999,
arg_collect_dynamic=True,
ccore_flag=False):
sample = read_sample(file)
network = hsyncnet(sample, number_clusters, ccore=ccore_flag)
(time, dynamic) = network.process(arg_order,
collect_dynamic=arg_collect_dynamic)
clusters = network.get_clusters()
if (arg_collect_dynamic == True):
draw_dynamics(time,
dynamic,
x_title="Time",
y_title="Phase",
y_lim=[0, 2 * 3.14])
draw_clusters(sample, clusters)
def templateClustering(self, path, number_clusters, expected_length_clusters, solver, ccore_flag):
result_testing = False;
# If phases crosses each other because of random part of the network then we should try again.
for attempt in range(0, 3, 1):
sample = read_sample(path);
network = hsyncnet(sample, number_clusters, initial_type.EQUIPARTITION, ccore = ccore_flag); # EQUIPARTITION - makes test more stable.
analyser = network.process(order = 0.997, solution = solver, collect_dynamic = True);
clusters = analyser.allocate_clusters(0.1);
if (sum([len(cluster) for cluster in clusters]) != sum(expected_length_clusters)):
continue;
if (sorted([len(cluster) for cluster in clusters]) != expected_length_clusters):
if (sorted([len(cluster) for cluster in clusters]) != expected_length_clusters):
continue;
# Unit-test is passed
result_testing = True;
break;
assert result_testing;
def process_hsyncnet(sample):
instance = hsyncnet(sample, CURRENT_CLUSTER_SIZE, initial_type.EQUIPARTITION, CURRENT_CLUSTER_SIZE)
(ticks, _) = timedcall(instance.process, 0.998)
return ticks
