circular-clustering 0.0.4

Adaptation of X means algorithm for circular data

circular_clustering

Adaptation of X means algorithm for circular data

install the package using

pip install circular-clustering

X means algorithm with quantiles

The class CircularXMeansQuantiles contains the X means algorithm for circular data. The use is similar to the clustering algorithms in scipy.

To import it use:

from circular_clustering.circular_x_means_quantiles import CircularXMeansQuantiles

To invoke the class use:

circXmeans = CircularXMeansQuantiles(x, kmax=8, confidence=0.99, use_optimal_k_means=True)

Note that kmax= will fix the maximun number of clusters desired.

To fit the algorithm:

circXmeans.fit()

Centroids are available at circXmeans.centroids, while labels are available at circXmeans.labels

Example of use

import numpy as np
import matplotlib.pyplot as plt

from circular_clustering.circular_x_means_quantiles import CircularXMeansQuantiles


x = np.array([ 1.65824313,  1.36928704,  1.78387914,  1.5872978 ,  0.94256866,  1.26848715,
  1.74052966,  2.24549453,  1.95567787,  1.13242718, -1.69442172, -1.1219816 ,
 -1.24971631, -1.8345882 , -1.86868335, -1.35164974, -1.49251909, -1.60791295,
 -1.32335839, -1.91367625,  0.09918781,  0.06024964, -0.07459622, -0.12782416,
  0.17969188,  0.00629938,  0.27335795, -0.28549939,  0.08032582,  0.30140874])


circXmeans = CircularXMeansQuantiles(x, kmax=8, confidence=0.99, use_optimal_k_means=True)
circXmeans.fit()
centroids = circXmeans.centroids

print("number of clusters found: ", len(circXmeans.cluster_points))


plt.figure(figsize=(5,5))
plt.axes().set_aspect('equal', 'datalim')  # before `plt.show()`
plt.scatter(np.cos(x),np.sin(x))

for c in centroids:
    plt.scatter(np.cos(c),np.sin(c), c="r")

for i in range(len(circXmeans.cluster_points)):
    cl = circXmeans.cluster_points[i]
    plt.scatter(np.cos(cl),np.sin(cl), c=np.random.rand(3,))
plt.show()

circXmeans.labels