"""!

@brief Examples of usage and demonstration of abilities of K-Medoids algorithm in cluster analysis.

@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause

"""

from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.kmedoids import kmedoids

from pyclustering.utils import read_sample, calculate_distance_matrix
from pyclustering.utils import timedcall, distance_metric, type_metric


def template_clustering(start_medoids, path, tolerance=0.25, show=True, **kwargs):
    ccore = kwargs.get('ccore', True)
    data_type = kwargs.get('data_type', 'points')

    original_data = read_sample(path)
    sample = original_data
    if data_type == 'distance_matrix':
        sample = calculate_distance_matrix(sample)

    metric = distance_metric(type_metric.EUCLIDEAN_SQUARE, data=sample)

    kmedoids_instance = kmedoids(sample, start_medoids, tolerance, metric=metric, ccore=ccore, data_type=data_type)
    (ticks, result) = timedcall(kmedoids_instance.process)
    
    clusters = kmedoids_instance.get_clusters()
    print("Iterations:", kmedoids_instance.get_iterations())
    print([len(cluster) for cluster in clusters])
    print(clusters)
    medoids = kmedoids_instance.get_medoids()
    print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")

    if show is True:
        visualizer = cluster_visualizer(1)
        visualizer.append_clusters(clusters, original_data, 0)
        visualizer.append_cluster([original_data[index] for index in start_medoids], marker='*', markersize=15)
        visualizer.append_cluster(medoids, data=original_data, marker='*', markersize=15)
        visualizer.show()
    
    return original_data, clusters


def cluster_sample1():
    template_clustering([2, 9], SIMPLE_SAMPLES.SAMPLE_SIMPLE1)

def cluster_sample2():
    template_clustering([3, 12, 20], SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
    
def cluster_sample3():
    template_clustering([4, 12, 25, 37], SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
    
def cluster_sample4():
    template_clustering([4, 15, 30, 40, 50], SIMPLE_SAMPLES.SAMPLE_SIMPLE4)

def cluster_sample5():
    template_clustering([4, 18, 34, 55], SIMPLE_SAMPLES.SAMPLE_SIMPLE5)
        
def cluster_elongate():
    template_clustering([8, 56], SIMPLE_SAMPLES.SAMPLE_ELONGATE)

def cluster_lsun():
    template_clustering([10, 275, 385], FCPS_SAMPLES.SAMPLE_LSUN)

def cluster_target():
    template_clustering([10, 160, 310, 460, 560, 700], FCPS_SAMPLES.SAMPLE_TARGET)

def cluster_two_diamonds():
    template_clustering([10, 650], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)

def cluster_wing_nut():
    template_clustering([19, 823], FCPS_SAMPLES.SAMPLE_WING_NUT)
    
def cluster_chainlink():
    template_clustering([30, 900], FCPS_SAMPLES.SAMPLE_CHAINLINK)
    
def cluster_hepta():
    template_clustering([0, 35, 86, 93, 125, 171, 194], FCPS_SAMPLES.SAMPLE_HEPTA)
    
def cluster_tetra():
    template_clustering([0, 131, 214, 265], FCPS_SAMPLES.SAMPLE_TETRA)

def cluster_atom():
    template_clustering([0, 650], FCPS_SAMPLES.SAMPLE_ATOM)

def cluster_engy_time():
    template_clustering([10, 3000], FCPS_SAMPLES.SAMPLE_ENGY_TIME)


def display_fcps_clustering_results():
    (lsun, lsun_clusters) = template_clustering([10, 275, 385], FCPS_SAMPLES.SAMPLE_LSUN, 0.1, False)
    (target, target_clusters) = template_clustering([10, 160, 310, 460, 560, 700], FCPS_SAMPLES.SAMPLE_TARGET, 0.1, False)
    (two_diamonds, two_diamonds_clusters) = template_clustering([10, 650], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, 0.1, False)
    (wing_nut, wing_nut_clusters) = template_clustering([19, 823], FCPS_SAMPLES.SAMPLE_WING_NUT, 0.1, False)
    (chainlink, chainlink_clusters) = template_clustering([30, 900], FCPS_SAMPLES.SAMPLE_CHAINLINK, 0.1, False)
    (hepta, hepta_clusters) = template_clustering([0, 35, 86, 93, 125, 171, 194], FCPS_SAMPLES.SAMPLE_HEPTA, 0.1, False)
    (tetra, tetra_clusters) = template_clustering([0, 131, 214, 265], FCPS_SAMPLES.SAMPLE_TETRA, 0.1, False)
    (atom, atom_clusters) = template_clustering([0, 650], FCPS_SAMPLES.SAMPLE_ATOM, 0.1, False)
    
    visualizer = cluster_visualizer(8, 4)
    visualizer.append_clusters(lsun_clusters, lsun, 0)
    visualizer.append_clusters(target_clusters, target, 1)
    visualizer.append_clusters(two_diamonds_clusters, two_diamonds, 2)
    visualizer.append_clusters(wing_nut_clusters, wing_nut, 3)
    visualizer.append_clusters(chainlink_clusters, chainlink, 4)
    visualizer.append_clusters(hepta_clusters, hepta, 5)
    visualizer.append_clusters(tetra_clusters, tetra, 6)
    visualizer.append_clusters(atom_clusters, atom, 7)
    visualizer.show()


cluster_sample1()
cluster_sample2()
cluster_sample3()
cluster_sample4()
cluster_sample5()
cluster_elongate()
cluster_lsun()
cluster_target()
cluster_two_diamonds()
cluster_wing_nut()
cluster_chainlink()
cluster_hepta()
cluster_tetra()
cluster_atom()
cluster_engy_time()


display_fcps_clustering_results()
