Improving colors and looks of graph plots.

[pingpong.git] / python_ml / dlink_clustering.py

diff --git a/python_ml/dlink_clustering.py b/python_ml/dlink_clustering.py
index badd5b1e61593864080b1c59950f8926a0a7ac9b..82d445e597b9bab27f006201c997a0b3ec8fc77c 100644 (file)
--- a/python_ml/dlink_clustering.py
+++ b/python_ml/dlink_clustering.py
@@ -1,6 +1,45 @@
 from sklearn.cluster import KMeans
+import matplotlib.cm as cm
 import numpy as np
+import matplotlib.pyplot as plt
+
+# Create a subplot with 1 row and 2 columns
+fig, (ax2) = plt.subplots(1, 1)
+fig.set_size_inches(7, 7)
+
 X = np.array([[132, 192], [117, 960], [117, 962], [1343, 0], [117, 1109], [117, 1110], [117, 1111], [117, 1116], [117, 1117], [117, 1118], [117, 1119], [1015, 0], [117, 966]])
-kmeans = KMeans(n_clusters=5, random_state=0).fit(X)
-print(kmeans.labels_)
-print(kmeans.labels_.tolist().count(3))
+#kmeans = KMeans(n_clusters=5, random_state=0).fit(X)
+#print(kmeans.labels_)
+#print(kmeans.labels_.tolist().count(3))
+clusters = 5
+
+# 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,
+            c=colors, edgecolor='k')
+
+# Labeling the clusters
+centers = clusterer.cluster_centers_
+# Label with cluster centers and frequencies
+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')
+
+# Draw white circles at cluster centers
+#ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
+#            c="white", alpha=1, s=200, edgecolor='k')
+
+#for i, c in enumerate(centers):
+#    ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
+#                s=50, edgecolor='k')
+#for i, c in enumerate(centers):
+#      print(c[0], c[1])
+
+ax2.set_title("The visualization of the clustered data.")
+ax2.set_xlabel("Feature space for the 1st feature")
+ax2.set_ylabel("Feature space for the 2nd feature")
+plt.show()
+