"""!

@brief Examples of usage and demonstration of abilities of X-Means 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.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES;

from pyclustering.cluster import cluster_visualizer;
from pyclustering.cluster.xmeans import xmeans, splitting_type;

from pyclustering.utils import read_sample, timedcall;


def template_clustering(start_centers, path, tolerance = 0.025, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore = False):
    sample = read_sample(path);
    
    xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion, ccore);
    (ticks, result) = timedcall(xmeans_instance.process);
    
    clusters = xmeans_instance.get_clusters();
    centers = xmeans_instance.get_centers();

    criterion_string = "UNKNOWN";
    if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION): criterion_string = "BAYESIAN INFORMATION CRITERION";
    elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH): criterion_string = "MINIMUM NOISELESS DESCRIPTION_LENGTH";
    
    print("Sample: ", path, "\nInitial centers: '", (start_centers is not None), "', Execution time: '", ticks, "', Number of clusters:", len(clusters), ",", criterion_string, "\n");

    visualizer = cluster_visualizer();
    visualizer.set_canvas_title(criterion_string);
    visualizer.append_clusters(clusters, sample);
    visualizer.append_cluster(centers, None, marker = '*');
    visualizer.show();


def cluster_sample1():
    "Start with wrong number of clusters."
    start_centers = [[3.7, 5.5]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample1_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample2():
    "Start with wrong number of clusters."
    start_centers = [[3.5, 4.8], [2.6, 2.5]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample2_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample3():
    "Start with wrong number of clusters."
    start_centers = [[0.2, 0.1], [4.0, 1.0]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample3_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample4():
    start_centers = [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample4_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample5():
    "Start with wrong number of clusters."
    start_centers = [[0.0, 1.0], [0.0, 0.0]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_sample5_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_elongate():
    "Not so applicable for this sample"
    start_centers = [[1.0, 4.5], [3.1, 2.7]];
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_ELONGATE, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_ELONGATE, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_lsun():
    "Not so applicable for this sample"
    start_centers = [[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_LSUN, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_LSUN, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);
    
def cluster_target():
    "Not so applicable for this sample"
    start_centers = [[0.2, 0.2], [0.0, -2.0], [3.0, -3.0], [3.0, 3.0], [-3.0, 3.0], [-3.0, -3.0]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TARGET, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TARGET, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);   

def cluster_two_diamonds():
    "Start with wrong number of clusters."
    start_centers = [[0.8, 0.2]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_two_diamonds_without_initial_centers():
    template_clustering(None, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(None, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);

def cluster_wing_nut():
    start_centers = [[-1.5, 1.5], [1.5, 1.5]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_WING_NUT, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION); 
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_WING_NUT, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);
    
def cluster_chainlink():
    start_centers = [[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_CHAINLINK, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_CHAINLINK, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);  
    
def cluster_hepta():
    "Start with wrong number of clusters."
    start_centers = [[0.0, 0.0, 0.0], [3.0, 0.0, 0.0], [-2.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, -3.0, 0.0], [0.0, 0.0, 2.5]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_HEPTA, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_HEPTA, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);
    
def cluster_tetra():
    start_centers = [[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]];
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TETRA, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION);
    template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TETRA, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH);
    
def experiment_execution_time(ccore_flag = False):
    template_clustering([[3.7, 5.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE1, ccore = ccore_flag);
    template_clustering([[3.5, 4.8], [2.6, 2.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE2, ccore = ccore_flag);
    template_clustering([[0.2, 0.1], [4.0, 1.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE3, ccore = ccore_flag);
    template_clustering([[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE4, ccore = ccore_flag);
    template_clustering([[0.0, 1.0], [0.0, 0.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE5, ccore = ccore_flag);
    template_clustering([[1.0, 4.5], [3.1, 2.7]], SIMPLE_SAMPLES.SAMPLE_ELONGATE, ccore = ccore_flag);
    template_clustering([[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]], FCPS_SAMPLES.SAMPLE_LSUN, ccore = ccore_flag);
    template_clustering([[0.2, 0.2], [0.0, -2.0], [3.0, -3.0], [3.0, 3.0], [-3.0, 3.0], [-3.0, -3.0]], FCPS_SAMPLES.SAMPLE_TARGET, ccore = ccore_flag);
    template_clustering([[0.8, 0.2]], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, ccore = ccore_flag);
    template_clustering([[-1.5, 1.5], [1.5, 1.5]], FCPS_SAMPLES.SAMPLE_WING_NUT, ccore = ccore_flag);
    template_clustering([[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]], FCPS_SAMPLES.SAMPLE_CHAINLINK, ccore = ccore_flag);
    template_clustering([[0.0, 0.0, 0.0], [3.0, 0.0, 0.0], [-2.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, -3.0, 0.0], [0.0, 0.0, 2.5]], FCPS_SAMPLES.SAMPLE_HEPTA, ccore = ccore_flag)
    template_clustering([[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]], FCPS_SAMPLES.SAMPLE_TETRA, ccore = ccore_flag);
    template_clustering([[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]], FCPS_SAMPLES.SAMPLE_ATOM);


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_sample1_without_initial_centers();
cluster_sample2_without_initial_centers();
cluster_sample3_without_initial_centers();
cluster_sample4_without_initial_centers();
cluster_sample5_without_initial_centers();
cluster_two_diamonds_without_initial_centers();
 
experiment_execution_time(False);   # Python code
experiment_execution_time(True);    # C++ code + Python env.
