cluster.py

import numpy as np
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import math

points = np.array([[160 180],
 [ 40 ,  0],
 [100 , 20],
 [140  , 0],
 [180 ,120],
 [100 ,140],
 [ 80 ,100],
 [120  ,40],
 [197 ,197]])


def distance(origin, destination): #found here https://gist.github.com/rochacbruno/2883505
    lat1, lon1 = origin[0],origin[1]
    lat2, lon2 = destination[0],destination[1]
    radius = 6371 # km
    dlat = math.radians(lat2-lat1)
    dlon = math.radians(lon2-lon1)
    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
    d = radius * c

    return d

def create_clusters(number_of_clusters,points):
    kmeans = KMeans(n_clusters=number_of_clusters, random_state=0).fit(points)
    l_array = np.array([[label] for label in kmeans.labels_])
    clusters = np.append(points,l_array,axis=1)
    return clusters

def validate_solution(max_dist,clusters):
    _, __, n_clust = clusters.max(axis=0)
    n_clust = int(n_clust)
    for i in range(n_clust):
        two_d_cluster=clusters[clusters[:,2] == i][:,np.array([True, True, False])]
        if not validate_cluster(max_dist,two_d_cluster):
            return False
        else:
            continue
    return True

def validate_cluster(max_dist,cluster):
    distances = cdist(cluster,cluster, lambda ori,des: int(round(distance(ori,des))))
    print(distances)
    print(30*'-')
    for item in distances.flatten():
        if item > max_dist:
            return False
    return True

if __name__ == '__main__':
    for i in range(2,len(points)):
        print(i)
        print(validate_solution(20,create_clusters(i,points)))