Scripts for VPN experiments.

[pingpong.git] / python_ml / plotting.py

diff --git a/python_ml/plotting.py b/python_ml/plotting.py
index 526bf8c4327ee811975e8aad0087e0f131347fcc..0089d021dfc629c1b31e0f25dad689d76826ba54 100644 (file)
--- a/python_ml/plotting.py
+++ b/python_ml/plotting.py
@@ -27,14 +27,14 @@ with open(path + filename, "r") as pairs:
 #print(pairsArr)
 X = np.array(pairsArr);
 
-clusters = 9
+clusters = 6
 
 # Plot the data points based on the clusters
 clusterer = KMeans(n_clusters=clusters, random_state=10)
 cluster_labels = clusterer.fit_predict(X)
 # 2nd Plot showing the actual clusters formed
 colors = cm.nipy_spectral(cluster_labels.astype(float) / clusters)
-ax2.scatter(X[:, 0], X[:, 1], marker='o', s=100, lw=0, alpha=0.3,
+ax2.scatter(X[:, 0], X[:, 1], marker='o', s=50, lw=0, alpha=0.3,
             c=colors, edgecolor='k')
 
 # Labeling the clusters
@@ -43,6 +43,7 @@ centers = clusterer.cluster_centers_
 for i, c in enumerate(centers):
        mark = '[' + str(int(c[0])) + ', ' + str(int(c[1])) + ']' + ', ' + str(clusterer.labels_.tolist().count(i))
        ax2.scatter(c[0], c[1], marker='$%s$' % mark, alpha=1, s=3000, edgecolor='k')
+       print('[' + str(int(c[0])) + ', ' + str(int(c[1])) + ']' + ', ' + str(clusterer.labels_.tolist().count(i)))
 
 ax2.set_title("The visualization of the clustered data.")
 ax2.set_xlabel("Feature space for the 1st feature")