def template_clustering(sample_file_path, amount_clusters, initializer, show_animation = False):
sample = read_sample(sample_file_path);
observer = None;
if (show_animation is True):
observer = ema_observer();
initial_means, initial_covariance = ema_initializer(sample, amount_clusters).initialize(initializer);
ema_instance = ema(sample, amount_clusters, initial_means, initial_covariance, observer=observer);
ema_instance.process();
clusters = ema_instance.get_clusters();
covariances = ema_instance.get_covariances();
means = ema_instance.get_centers();
cluster_length = [ len(cluster) for cluster in clusters ];
print("Data '" + sample_file_path + "'");
print("Clusters: " + str(len(clusters)) + ", Length:" + str(cluster_length));
if (observer is True):
ema_visualizer.show_clusters(observer.get_evolution_clusters()[0], sample, observer.get_evolution_covariances()[0], observer.get_evolution_means()[0]);
ema_visualizer.show_clusters(clusters, sample, covariances, means);
if (show_animation is True):
ema_visualizer.animate_cluster_allocation(sample, observer);
def template_clustering(sample_file_path, amount_clusters, initializer, show_animation = False):
sample = read_sample(sample_file_path);
observer = None;
if (show_animation is True):
observer = ema_observer();
initial_means, initial_covariance = ema_initializer(sample, amount_clusters).initialize(initializer);
ema_instance = ema(sample, amount_clusters, initial_means, initial_covariance, observer=observer);
ema_instance.process();
clusters = ema_instance.get_clusters();
covariances = ema_instance.get_covariances();
means = ema_instance.get_centers();
cluster_length = [ len(cluster) for cluster in clusters ];
print("Data '" + sample_file_path + "'");
print("Clusters: " + str(len(clusters)) + ", Length:" + str(cluster_length));
if (observer is True):
ema_visualizer.show_clusters(observer.get_evolution_clusters()[0], sample, observer.get_evolution_covariances()[0], observer.get_evolution_means()[0]);
ema_visualizer.show_clusters(clusters, sample, covariances, means);
if (show_animation is True):
ema_visualizer.animate_cluster_allocation(sample, observer);
def testVisualizerNoFailures(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
means, variances = ema_initializer(sample, 4).initialize(ema_init_type.RANDOM_INITIALIZATION)
observer_instance = ema_observer()
ema_instance = ema(sample, 4, means, variances, observer_instance)
ema_instance.process()
clusters = ema_instance.get_clusters()
means = ema_instance.get_centers()
covariances = ema_instance.get_covariances()
ema_visualizer.show_clusters(clusters, sample, covariances, means)
ema_visualizer.animate_cluster_allocation(sample, observer_instance)
def testVisualizerNoFailures(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
means, variances = ema_initializer(sample, 4).initialize(ema_init_type.RANDOM_INITIALIZATION)
observer_instance = ema_observer()
ema_instance = ema(sample, 4, means, variances, observer_instance)
ema_instance.process()
clusters = ema_instance.get_clusters()
means = ema_instance.get_centers()
covariances = ema_instance.get_covariances()
ema_visualizer.show_clusters(clusters, sample, covariances, means)
ema_visualizer.animate_cluster_allocation(sample, observer_instance)