def template_clustering(path_sample,
eps,
minpts,
amount_clusters=None,
visualize=True,
ccore=False):
sample = read_sample(path_sample)
optics_instance = optics(sample, eps, minpts, amount_clusters, ccore)
(ticks, _) = timedcall(optics_instance.process)
print("Sample: ", path_sample, "\t\tExecution time: ", ticks, "\n")
if (visualize is True):
clusters = optics_instance.get_clusters()
noise = optics_instance.get_noise()
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(noise, sample, marker='x')
visualizer.show()
ordering = optics_instance.get_ordering()
analyser = ordering_analyser(ordering)
ordering_visualizer.show_ordering_diagram(analyser, amount_clusters)
def testClusteringOrderVisualizer(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4);
optics_instance = optics(sample, 6.0, 3, 5);
optics_instance.process();
analyser = ordering_analyser(optics_instance.get_ordering());
ordering_visualizer.show_ordering_diagram(analyser);
def testClusteringOrderVisualizer(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4);
optics_instance = optics(sample, 6.0, 3, 5);
optics_instance.process();
analyser = ordering_analyser(optics_instance.get_ordering());
ordering_visualizer.show_ordering_diagram(analyser, 5);
def main():
output_dir = 'plots_OPTICS'
if not os.path.exists(output_dir): os.makedirs(output_dir)
# OPTICS demo with SDRs
radius = 10.0
neighbors = 2
epsilon_cutoff = 0.3
file_path = os.path.join(
os.getcwd(), 'htm_traces', 'binary_ampl=10.0_mean=0.0_noise=0.0_'
'sp=True_tm=True_tp=False_SDRClassifier.csv')
sdrs, cluster_ids = load_sdrs(file_path)
(cluster_assignments,
sdr_slices) = find_cluster_assignments(sdrs,
cluster_ids,
ignore_noise=True)
sdr_cluster_centroids = get_sdr_cluster_centroids(sdr_slices)
# Project SDRs in 2D for visualization purposes
distance_mat = distance_matrix(sdr_cluster_centroids, euclidian_distance)
sdr_projections = project_in_2D(distance_mat, method='mds')
# OPTICS SDR results
sample_sdr = sdr_projections # or: sdr_cluster_centroids
ordering_sdr, clusters_sdr, noise_sdr = analyze(sample_sdr, radius,
neighbors)
# Plot OPTICS results
sample_sdr = sdr_projections
plot_results(sample_sdr, ordering_sdr, clusters_sdr, noise_sdr, 'SDR',
epsilon_cutoff, output_dir)
# Plot SDR 2D projections
title = '2d_projections'
output_file = os.path.join(output_dir, '%s' % ('%s.png' % title))
plt = plot_2D_projections(title, output_file, cluster_assignments,
sdr_projections)
plt.show()
# OPTICS demo with 2D vectors
radius = 2.0
neighbors = 2
epsilon_cutoff = 0.5
sample_2d = read_sample(
FCPS_SAMPLES.SAMPLE_LSUN) # or: gaussian_clusters(3)
ordering_2d, clusters_2d, noise_2d = analyze(sample_2d, radius, neighbors)
plot_results(sample_2d, ordering_2d, clusters_2d, noise_2d, '2D',
epsilon_cutoff, output_dir)
# Plot input data and clustering structure
ordering_visualizer.show_ordering_diagram(ordering_sdr)
ordering_visualizer.show_ordering_diagram(ordering_2d)
def main():
# OPTICS demo with 2D vectors
radius = 2.0
neighbors = 2
epsilon_cutoff = 0.5
sample_2d = read_sample(
FCPS_SAMPLES.SAMPLE_LSUN) # or: gaussian_clusters(3)
ordering_2d, clusters_2d, noise_2d = analyze(sample_2d, radius, neighbors)
plot_results(sample_2d, ordering_2d, clusters_2d, noise_2d, '2D',
epsilon_cutoff)
# Plot input data and clustering structure
ordering_visualizer.show_ordering_diagram(ordering_2d)
def main():
output_dir = 'plots_OPTICS'
if not os.path.exists(output_dir): os.makedirs(output_dir)
# OPTICS demo with SDRs
radius = 10.0
neighbors = 2
epsilon_cutoff = 0.3
file_path = os.path.join(os.getcwd(), 'htm_traces',
'binary_ampl=10.0_mean=0.0_noise=0.0_'
'sp=True_tm=True_tp=False_SDRClassifier.csv')
sdrs, cluster_ids = load_sdrs(file_path)
(cluster_assignments,
sdr_slices) = find_cluster_assignments(sdrs, cluster_ids, ignore_noise=True)
sdr_cluster_centroids = get_sdr_cluster_centroids(sdr_slices)
# Project SDRs in 2D for visualization purposes
distance_mat = distance_matrix(sdr_cluster_centroids, euclidian_distance)
sdr_projections = project_in_2D(distance_mat, method='mds')
# OPTICS SDR results
sample_sdr = sdr_projections # or: sdr_cluster_centroids
ordering_sdr, clusters_sdr, noise_sdr = analyze(sample_sdr,
radius,
neighbors)
# Plot OPTICS results
sample_sdr = sdr_projections
plot_results(sample_sdr, ordering_sdr, clusters_sdr, noise_sdr,
'SDR', epsilon_cutoff, output_dir)
# Plot SDR 2D projections
title = '2d_projections'
output_file = os.path.join(output_dir, '%s' % ('%s.png' % title))
plt = plot_2D_projections(title, output_file, cluster_assignments,
sdr_projections)
plt.show()
# OPTICS demo with 2D vectors
radius = 2.0
neighbors = 2
epsilon_cutoff = 0.5
sample_2d = read_sample(FCPS_SAMPLES.SAMPLE_LSUN) # or: gaussian_clusters(3)
ordering_2d, clusters_2d, noise_2d = analyze(sample_2d, radius, neighbors)
plot_results(sample_2d, ordering_2d, clusters_2d, noise_2d, '2D',
epsilon_cutoff, output_dir)
# Plot input data and clustering structure
ordering_visualizer.show_ordering_diagram(ordering_sdr)
ordering_visualizer.show_ordering_diagram(ordering_2d)
def optics_temp(self, point_list):
# data = np.array( point_list)
sample = point_list
start = time.time()
optics_instance = optics(sample, 0.5, 6, ccore=True)
optics_instance.process()
clusters = optics_instance.get_clusters()
end = time.time()
print("imte", end - start)
noise = optics_instance.get_noise()
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(noise, sample, marker='x')
visualizer.show()
ordering = optics_instance.get_ordering()
analyser = ordering_analyser(ordering)
ordering_visualizer.show_ordering_diagram(analyser, amount_clusters)
