colors = [plt.cm.Spectral(each)
for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
+ cluster_col = [1, 0, 0, 1]
if k == -1:
# Black used for noise.
col = [0, 0, 0, 1]
class_member_mask = (labels == k)
xy = X[class_member_mask & core_samples_mask]
- plt.plot(xy[:, 0], xy[:, 1], 'o',
+ plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(cluster_col),
markeredgecolor='k', markersize=10)
xy = X[class_member_mask & ~core_samples_mask]
else:
# Only print the frequency when this is a real cluster
plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) +
- " - Freq: " + str(labels.tolist().count(labels[count])), fontsize=10)
+ " : " + str(labels.tolist().count(labels[count])), fontsize=10)
count = count + 1