# Read from file
# TODO: Just change the following path and filename
# when needed to read from a different file
-path = "/scratch/July-2018/Pairs/"
-device = "dlink-siren-on"
+path = "/scratch/July-2018/Pairs2/"
+device = "dlink-siren-device-off"
filename = device + ".txt"
+plt.ylim(0, 2000)
+plt.xlim(0, 2000)
# Number of triggers
trig = 50
# Compute DBSCAN
# eps = distances
# min_samples = minimum number of members of a cluster
-db = DBSCAN(eps=10, min_samples=trig - 5).fit(X)
+#db = DBSCAN(eps=20, min_samples=trig - 5).fit(X)
+# TODO: This is just for seeing more clusters
+db = DBSCAN(eps=20, min_samples=trig - 45).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
-print('Estimated number of clusters: %d' % n_clusters_)
+#print('Estimated number of clusters: %d' % n_clusters_)
import matplotlib.pyplot as plt
# Black removed and is used for noise instead.
unique_labels = set(labels)
-print("Labels: " + str(labels))
+#print("Labels: " + str(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]
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]
#if labels[count] != -1:
# If this is not a noise (i.e.,real data)
# plt.text(pair[0], pair[1], "Freq: " + str(labels.tolist().count(labels[count])), fontsize=10)
-
- plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) +
- "\nFreq: " + str(labels.tolist().count(labels[count])), fontsize=10)
+
+ if labels[count] == -1:
+ plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]), fontsize=10)
+ else:
+ # Only print the frequency when this is a real cluster
+ plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) +
+ " : " + str(labels.tolist().count(labels[count])), fontsize=10)
count = count + 1
-plt.title(device + ' - Estimated number of clusters: %d' % n_clusters_)
+plt.title(device + ' - Clusters: %d' % n_clusters_)
plt.show()