Implementing relaxed matching for layer 2 and layer 3.

[pingpong.git] / python_ml / plotting-dbscan-diff.py

diff --git a/python_ml/plotting-dbscan-diff.py b/python_ml/plotting-dbscan-diff.py
index ba89a152a3aebc0e83d232f3a337b2458bd72da6..15d7728d3bcdda3f61999036bb189f5e151609a9 100644 (file)
--- a/python_ml/plotting-dbscan-diff.py
+++ b/python_ml/plotting-dbscan-diff.py
@@ -65,6 +65,7 @@ unique_labels = set(labels)
 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]
@@ -73,7 +74,7 @@ for k, col in zip(unique_labels, colors):
 
        # print("Unique label: " + str(k) + " with freq: " + str(labels.tolist().count(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]