"""!

@brief Examples of usage and demonstration of abilities of BIRCH algorithm in cluster analysis.

@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2017
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""

from pyclustering.cluster import cluster_visualizer;
from pyclustering.cluster.birch import birch;

from pyclustering.container.cftree import measurement_type;

from pyclustering.utils import read_sample;
from pyclustering.utils import timedcall;

from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES;


def template_clustering(number_clusters, path, branching_factor = 5, max_node_entries = 5, initial_diameter = 0.0, type_measurement = measurement_type.AVERAGE_INTER_CLUSTER_DISTANCE, entry_size_limit = 200, diameter_multiplier = 1.5, show_result = True):
    sample = read_sample(path);

    birch_instance = birch(sample, number_clusters, branching_factor, max_node_entries, initial_diameter, type_measurement, entry_size_limit, diameter_multiplier);
    (ticks, result) = timedcall(birch_instance.process);

    print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");

    clusters = birch_instance.get_clusters();
    
    if (show_result is True):
        visualizer = cluster_visualizer();
        visualizer.append_clusters(clusters, sample);
        visualizer.show();
    
    return (sample, clusters);


def cluster_sample1():
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 5, 5, 0.1, measurement_type.CENTROID_EUCLIDIAN_DISTANCE, 2);      # only two entries available

def cluster_sample2():
    template_clustering(3, SIMPLE_SAMPLES.SAMPLE_SIMPLE2);

def cluster_sample3():
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE3);

def cluster_sample4():
    template_clustering(5, SIMPLE_SAMPLES.SAMPLE_SIMPLE4);
    
def cluster_sample5():
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE5);

def cluster_sample7():
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_SIMPLE7);

def cluster_sample8():
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE8);

def cluster_elongate():
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_ELONGATE);

def cluster_lsun():
    template_clustering(3, FCPS_SAMPLES.SAMPLE_LSUN);
    template_clustering(3, FCPS_SAMPLES.SAMPLE_LSUN, 5, 5, 0.2, measurement_type.CENTROID_MANHATTAN_DISTANCE, 200);         # not correct, but almost good result

def cluster_target():
    template_clustering(6, FCPS_SAMPLES.SAMPLE_TARGET);
    template_clustering(6, FCPS_SAMPLES.SAMPLE_TARGET, 5, 10, 0.5, measurement_type.CENTROID_MANHATTAN_DISTANCE, 200);       # interesting - sliced cake.
    template_clustering(6, FCPS_SAMPLES.SAMPLE_TARGET, 50, 100, 0.5, measurement_type.VARIANCE_INCREASE_DISTANCE, 200);      # interesting - sliced cake.

def cluster_two_diamonds():
    template_clustering(2, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS);

def cluster_wing_nut():
    template_clustering(2, FCPS_SAMPLES.SAMPLE_WING_NUT);
    template_clustering(2, FCPS_SAMPLES.SAMPLE_WING_NUT, 5, 5, 0.1, measurement_type.CENTROID_EUCLIDIAN_DISTANCE, 800);     # not correct, but almost good result

def cluster_chainlink():
    template_clustering(2, FCPS_SAMPLES.SAMPLE_CHAINLINK);

def cluster_hepta():
    template_clustering(7, FCPS_SAMPLES.SAMPLE_HEPTA); 

def cluster_tetra():
    template_clustering(4, FCPS_SAMPLES.SAMPLE_TETRA);    

def cluster_engy_time():
    template_clustering(2, FCPS_SAMPLES.SAMPLE_ENGY_TIME);  # one cluster is allocated
    template_clustering(2, FCPS_SAMPLES.SAMPLE_ENGY_TIME, 10, 10, 0.05, measurement_type.VARIANCE_INCREASE_DISTANCE, 500);  # good result


def experiment_execution_time(ccore = False):
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
    template_clustering(3, SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE3);
    template_clustering(5, SIMPLE_SAMPLES.SAMPLE_SIMPLE4);
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE5);
    template_clustering(2, SIMPLE_SAMPLES.SAMPLE_ELONGATE);
    template_clustering(3, FCPS_SAMPLES.SAMPLE_LSUN);
    template_clustering(6, FCPS_SAMPLES.SAMPLE_TARGET);
    template_clustering(2, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS);
    template_clustering(2, FCPS_SAMPLES.SAMPLE_WING_NUT);
    template_clustering(2, FCPS_SAMPLES.SAMPLE_CHAINLINK);
    template_clustering(7, FCPS_SAMPLES.SAMPLE_HEPTA);
    template_clustering(4, FCPS_SAMPLES.SAMPLE_TETRA);
    template_clustering(2, FCPS_SAMPLES.SAMPLE_ATOM);


def display_fcps_clustering_results():
    (lsun, lsun_clusters, _) = template_clustering(3, FCPS_SAMPLES.SAMPLE_LSUN, show_result = False);
    (target, target_clusters, _) = template_clustering(6, FCPS_SAMPLES.SAMPLE_TARGET, show_result = False);
    (two_diamonds, two_diamonds_clusters, _) = template_clustering(2, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, show_result = False);
    (wing_nut, wing_nut_clusters, _) = template_clustering(2, FCPS_SAMPLES.SAMPLE_WING_NUT, show_result = False);
    (chainlink, chainlink_clusters, _) = template_clustering(2, FCPS_SAMPLES.SAMPLE_CHAINLINK, show_result = False);
    (hepta, hepta_clusters, _) = template_clustering(7, FCPS_SAMPLES.SAMPLE_HEPTA, show_result = False);
    (tetra, tetra_clusters, _) = template_clustering(4, FCPS_SAMPLES.SAMPLE_TETRA, show_result = False);
    (atom, atom_clusters, _) = template_clustering(2, FCPS_SAMPLES.SAMPLE_ATOM, show_result = 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_sample7();
cluster_sample8();
cluster_elongate();
cluster_lsun();
cluster_target();
cluster_two_diamonds();
cluster_wing_nut();
cluster_chainlink();
cluster_hepta();
cluster_tetra();
cluster_engy_time();
 
 
experiment_execution_time(True);    # C++ code + Python env.

display_fcps_clustering_results();