def templateAnimateClusteringResultNoFailure(filename, initial_centers, ccore_flag):
sample = read_sample(filename)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer)
kmeans_instance.process()
kmeans_visualizer.animate_cluster_allocation(sample, observer)
def templateCollectEvolution(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer)
kmeans_instance.process()
assertion.le(1, len(observer))
for i in range(len(observer)):
assertion.le(1, len(observer.get_centers(i)))
for center in observer.get_centers(i):
assertion.eq(len(sample[0]), len(center))
assertion.le(1, len(observer.get_clusters(i)))
def templateCollectEvolution(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename);
observer = kmeans_observer();
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer);
kmeans_instance.process();
assertion.le(1, len(observer));
for i in range(len(observer)):
assertion.le(1, len(observer.get_centers(i)));
for center in observer.get_centers(i):
assertion.eq(len(sample[0]), len(center));
assertion.le(1, len(observer.get_clusters(i)));
def template_clustering(start_centers, path, tolerance=0.25, ccore=True):
sample = read_sample(path)
observer = kmeans_observer()
kmeans_instance = kmeans(sample,
start_centers,
tolerance,
ccore,
observer=observer)
(ticks, _) = timedcall(kmeans_instance.process)
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
kmeans_visualizer.show_clusters(sample, clusters, centers, start_centers)
kmeans_visualizer.animate_cluster_allocation(sample, observer)
def template_clustering(start_centers, path, tolerance = 0.25, ccore = False):
sample = read_sample(path)
dimension = len(sample[0])
metric = distance_metric(type_metric.MANHATTAN)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, start_centers, tolerance, ccore, observer=observer, metric=metric)
(ticks, _) = timedcall(kmeans_instance.process)
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clusters, sample)
visualizer.show()
if dimension > 3:
kmeans_visualizer.show_clusters(sample, clusters, centers, start_centers)
kmeans_visualizer.animate_cluster_allocation(sample, observer)
def template_clustering(start_centers, path, tolerance = 0.25, ccore = False):
sample = read_sample(path)
dimension = len(sample[0])
metric = distance_metric(type_metric.MANHATTAN)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, start_centers, tolerance, ccore, observer=observer, metric=metric)
(ticks, _) = timedcall(kmeans_instance.process)
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clusters, sample)
visualizer.show()
if dimension > 3:
kmeans_visualizer.show_clusters(sample, clusters, centers, start_centers)
kmeans_visualizer.animate_cluster_allocation(sample, observer)