output cluster statistics

main.py

@@ -1,5 +1,6 @@
 from sklearn.cluster import DBSCAN
 from sklearn import metrics
+from collections import namedtuple
 import numpy as np
 import matplotlib.pyplot as plt
 from itertools import groupby
@@ -57,4 +58,17 @@ if __name__ == '__main__':
         xy = x[class_member_mask & ~core_samples_mask]
         plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                          markeredgecolor='k', markersize=6)
+
+        if k != -1:
+            xy = x[class_member_mask]
+            print("cluster %d:" % k)
+            center = np.array([np.average(xy[:, 0]), np.average(xy[:, 1])])
+            print("center = %f, %f" % (center[0], center[1]))
+            center_p = namedtuple("point", "lon lat")(center[0], center[1])
+            rad = 0
+            for loc in xy:
+                p = namedtuple("point", "lon lat")(loc[0], loc[1])
+                rad = max(rad, metric.spherical_distance(center_p, p))
+            rad *= 6400 * 1000
+            print("radius = %f m" % rad)
     plt.show()