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from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils.metric import distance_metric, type_metric
import matplotlib.pyplot as plt
# metric = distance_metric(type_metric.MINKOWSKI, degree=2)
# print(metric([1,1], [2,2]))
# Load list of points for cluster analysis.
sample = [[1,1,1], [2,2,2],[3,3,3]]
# Set random initial medoids.
initial_medoids = [1, 1 ,1]
# Create instance of K-Medoids algorithm.
kmedoids_instance = kmedoids(sample, initial_medoids)
# Run cluster analysis and obtain results.
kmedoids_instance.process()
centoids = kmedoids_instance.get_medoids()
clusters = kmedoids_instance.get_clusters()
# Show allocated clusters.
for cluster_id in range(len(clusters)):
for index in clusters[cluster_id]:
if(cluster_id == 0):
plt.plot(sample[index][0], sample[index][1], 'ro')
print(sample[index][0])
else:
plt.plot(sample[index][0], sample[index][1], 'bo')
plt.plot(sample[centoids[0]][0], sample[centoids[0]][1], 'bo')
# plt.plot(sample[centoids[1]][0], sample[centoids[1]][1], 'ro')
plt.show()
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