def template_clustering(file,
radius,
order,
show_dyn=False,
show_conn=False,
show_clusters=True,
ena_conn_weight=False,
ccore_flag=False):
sample = read_sample(file)
network = syncnet(sample,
radius,
enable_conn_weight=ena_conn_weight,
ccore=ccore_flag)
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, order,
solve_type.FAST, show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n")
if (show_dyn == True):
draw_dynamics(dyn_time,
dyn_phase,
x_title="Time",
y_title="Phase",
y_lim=[0, 2 * 3.14])
if (show_conn == True):
network.show_network()
if (show_clusters == True):
clusters = network.get_clusters(0.1)
draw_clusters(sample, clusters)
def template_clustering(start_centers, path, tolerance=0.25, ccore=True):
sample = read_sample(path)
kmeans_instance = kmeans(sample, start_centers, tolerance, ccore)
(ticks, result) = timedcall(kmeans_instance.process)
clusters = kmeans_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
draw_clusters(sample, clusters)
def template_clustering(number_clusters, path, ccore=True):
sample = read_sample(path)
hierarchical_instance = hierarchical(sample, number_clusters, ccore)
(ticks, result) = timedcall(hierarchical_instance.process)
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
clusters = hierarchical_instance.get_clusters()
draw_clusters(sample, clusters)
def template_clustering(number_clusters, path, branching_factor = 5, max_node_entries = 5, initial_diameter = 0.0, type_measurement = measurement_type.CENTROID_EUCLIDIAN_DISTANCE, entry_size_limit = 200, ccore = True):
sample = read_sample(path);
birch_instance = birch(sample, number_clusters, branching_factor, max_node_entries, initial_diameter, type_measurement, entry_size_limit, ccore)
(ticks, result) = timedcall(birch_instance.process);
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
clusters = birch_instance.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(start_medoids, path, tolerance = 0.25):
sample = read_sample(path);
kmedoids_instance = kmedoids(sample, start_medoids, tolerance);
(ticks, result) = timedcall(kmedoids_instance.process);
clusters = kmedoids_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
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);
(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 template_clustering(radius, neighb, path, invisible_axes = False, ccore = True):
sample = read_sample(path);
dbscan_instance = dbscan(sample, radius, neighb, ccore);
(ticks, result) = timedcall(dbscan_instance.process);
clusters = dbscan_instance.get_clusters();
noise = dbscan_instance.get_noise();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
draw_clusters(sample, clusters, [], '.', hide_axes = invisible_axes);
def template_clustering(path, radius, cluster_numbers, threshold, draw = True, ccore = True):
sample = read_sample(path);
rock_instance = rock(sample, radius, cluster_numbers, threshold, ccore);
(ticks, result) = timedcall(rock_instance.process);
clusters = rock_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
if (draw == True):
draw_clusters(sample, clusters);
def template_clustering(number_clusters, path, number_represent_points = 5, compression = 0.5, draw = True, ccore_flag = False):
sample = read_sample(path);
cure_instance = cure(sample, number_clusters, number_represent_points, compression, ccore_flag);
(ticks, result) = timedcall(cure_instance.process);
clusters = cure_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
if (draw is True):
if (ccore_flag is True):
draw_clusters(sample, clusters);
else:
draw_clusters(None, clusters);
def template_clustering(start_centers, path, tolerance = 0.025, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore = False):
sample = read_sample(path);
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion, ccore);
(ticks, result) = timedcall(xmeans_instance.process);
clusters = xmeans_instance.get_clusters();
criterion_string = "UNKNOWN";
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION): criterion_string = "BAYESIAN_INFORMATION_CRITERION";
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH): criterion_string = "MINIMUM_NOISELESS_DESCRIPTION_LENGTH";
print("Sample: ", path, "\tExecution time: ", ticks, "Number of clusters: ", len(clusters), criterion_string, "\n");
draw_clusters(sample, clusters);
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = False):
sample = read_sample(file);
network = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag);
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, order, solve_type.FAST, show_dyn);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
if (show_dyn == True):
draw_dynamics(dyn_time, dyn_phase, x_title = "Time", y_title = "Phase", y_lim = [0, 2 * 3.14]);
if (show_conn == True):
network.show_network();
if (show_clusters == True):
clusters = network.get_clusters(0.1);
draw_clusters(sample, clusters);
def template_clustering(radius,
neighb,
path,
invisible_axes=False,
ccore=True):
sample = read_sample(path)
dbscan_instance = dbscan(sample, radius, neighb, ccore)
(ticks, result) = timedcall(dbscan_instance.process)
clusters = dbscan_instance.get_clusters()
noise = dbscan_instance.get_noise()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
draw_clusters(sample, clusters, [], '.', hide_axes=invisible_axes)
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True):
sample = read_sample(file);
network = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, order, solve_type.FAST, show_dyn);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
if (show_dyn == True):
sync_visualizer.show_output_dynamic(analyser);
sync_visualizer.animate_output_dynamic(analyser);
if ( (show_conn == True) and (ccore_flag == False) ):
network.show_network();
if (show_clusters == True):
clusters = analyser.allocate_clusters();
draw_clusters(sample, clusters);
def template_clustering(path_sample, eps, minpts):
sample = read_sample(path_sample)
optics_instance = optics(sample, eps, minpts)
optics_instance.process()
clusters = optics_instance.get_clusters()
noise = optics_instance.get_noise()
draw_clusters(sample, clusters, [], '.')
ordering = optics_instance.get_cluster_ordering()
indexes = [i for i in range(0, len(ordering))]
# visualization of cluster ordering in line with reachability distance.
plt.bar(indexes, ordering)
plt.show()
def template_clustering(path,
radius,
cluster_numbers,
threshold,
draw=True,
ccore=True):
sample = read_sample(path)
rock_instance = rock(sample, radius, cluster_numbers, threshold, ccore)
(ticks, result) = timedcall(rock_instance.process)
clusters = rock_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
if (draw == True):
draw_clusters(sample, clusters)
def template_clustering(path_sample, eps, minpts):
sample = read_sample(path_sample);
optics_instance = optics(sample, eps, minpts);
optics_instance.process();
clusters = optics_instance.get_clusters();
noise = optics_instance.get_noise();
draw_clusters(sample, clusters, [], '.');
ordering = optics_instance.get_cluster_ordering();
indexes = [i for i in range(0, len(ordering))];
# visualization of cluster ordering in line with reachability distance.
plt.bar(indexes, ordering);
plt.show();
def template_clustering(
number_clusters,
path,
branching_factor=5,
max_node_entries=5,
initial_diameter=0.0,
type_measurement=measurement_type.CENTROID_EUCLIDIAN_DISTANCE,
entry_size_limit=200,
ccore=True):
sample = read_sample(path)
birch_instance = birch(sample, number_clusters, branching_factor,
max_node_entries, initial_diameter,
type_measurement, entry_size_limit, ccore)
(ticks, result) = timedcall(birch_instance.process)
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
clusters = birch_instance.get_clusters()
draw_clusters(sample, clusters)
def template_clustering(number_clusters,
path,
number_represent_points=5,
compression=0.5,
draw=True,
ccore_flag=False):
sample = read_sample(path)
cure_instance = cure(sample, number_clusters, number_represent_points,
compression, ccore_flag)
(ticks, result) = timedcall(cure_instance.process)
clusters = cure_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
if (draw is True):
if (ccore_flag is True):
draw_clusters(sample, clusters)
else:
draw_clusters(None, 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)
(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,
map_size,
trust_order,
sync_order=0.999,
show_dyn=False,
show_layer1=False,
show_layer2=False,
show_clusters=True):
# Read sample
sample = read_sample(file)
# Create network
network = syncsom(sample, map_size[0], map_size[1])
# Run processing
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, trust_order,
show_dyn, sync_order)
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n")
# Show dynamic of the last layer.
if (show_dyn == True):
draw_dynamics(dyn_time,
dyn_phase,
x_title="Time",
y_title="Phase",
y_lim=[0, 2 * 3.14])
if (show_clusters == True):
clusters = network.get_som_clusters()
draw_clusters(network.som_layer.weights, clusters)
# Show network stuff.
if (show_layer1 == True):
network.show_som_layer()
if (show_layer2 == True):
network.show_sync_layer()
if (show_clusters == True):
clusters = network.get_clusters()
draw_clusters(sample, clusters)
def template_clustering(
start_centers,
path,
tolerance=0.025,
criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION,
ccore=False):
sample = read_sample(path)
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion,
ccore)
(ticks, result) = timedcall(xmeans_instance.process)
clusters = xmeans_instance.get_clusters()
criterion_string = "UNKNOWN"
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION):
criterion_string = "BAYESIAN_INFORMATION_CRITERION"
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH):
criterion_string = "MINIMUM_NOISELESS_DESCRIPTION_LENGTH"
print("Sample: ", path, "\tExecution time: ", ticks,
"Number of clusters: ", len(clusters), criterion_string, "\n")
draw_clusters(sample, clusters)