Python animate_cluster_allocation Examples

Example #1

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)

Example #2

File: kmeans_templates.py Project: annoviko/pyclustering

    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)

Example #3

    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);

Example #4

File: kmeans_examples.py Project: zgsxwsdxg/pyclustering

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)

Example #5

File: kmeans_examples.py Project: annoviko/pyclustering

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)