String path = "/Users/varmarken/temp/UCI IoT Project/experiments"; // Janus
// 1) D-Link July 26 experiment
-// final String inputPcapFile = path + "/2018-07/dlink/dlink.wlan1.local.pcap";
-// final String outputPcapFile = path + "/2018-07/dlink/dlink-processed.pcap";
-// final String triggerTimesFile = path + "/2018-07/dlink/dlink-july-26-2018.timestamps";
-// final String deviceIp = "192.168.1.199"; // .246 == phone; .199 == dlink plug?
+ final String inputPcapFile = path + "/2018-07/dlink/dlink.wlan1.local.pcap";
+ final String outputPcapFile = path + "/2018-07/dlink/dlink-processed.pcap";
+ final String triggerTimesFile = path + "/2018-07/dlink/dlink-july-26-2018.timestamps";
+ final String deviceIp = "192.168.1.246"; // .246 == phone; .199 == dlink plug?
// 2) TP-Link July 25 experiment
// final String inputPcapFile = path + "/2018-07/tplink/tplink.wlan1.local.pcap";
// final String inputPcapFile = path + "/2018-08/dlink-siren/dlink-siren.wlan1.local.pcap";
// final String outputPcapFile = path + "/2018-08/dlink-siren/dlink-siren-processed.pcap";
// final String triggerTimesFile = path + "/2018-08/dlink-siren/dlink-siren-aug-14-2018.timestamps";
-// final String deviceIp = "192.168.1.183"; // .246 == phone; .183 == siren
+// final String deviceIp = "192.168.1.246"; // .246 == phone; .183 == siren
// 14) Nest thermostat August 15 experiment
// final String inputPcapFile = path + "/2018-08/nest/nest.wlan1.local.pcap";
--- /dev/null
+from __future__ import print_function
+
+from sklearn.datasets import make_blobs
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_samples, silhouette_score
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import numpy as np
+
+print(__doc__)
+
+# Generating the sample data from make_blobs
+# This particular setting has one distinct cluster and 3 clusters placed close
+# together.
+'''X, y = make_blobs(n_samples=500,
+ n_features=2,
+ centers=4,
+ cluster_std=1,
+ center_box=(-10.0, 10.0),
+ shuffle=True,
+ random_state=1) # For reproducibility'''
+
+X = np.array([[132, 192], [117, 960], [117, 962], [1343, 0], [117, 1116], [117, 1117], [117, 1118], [117, 1119], [1015, 0], [117, 966]])
+
+range_n_clusters = [2, 3, 4, 5, 6]
+
+for n_clusters in range_n_clusters:
+ # Create a subplot with 1 row and 2 columns
+# fig, (ax1, ax2) = plt.subplots(1, 2)
+# fig.set_size_inches(18, 7)
+
+ # The 1st subplot is the silhouette plot
+ # The silhouette coefficient can range from -1, 1 but in this example all
+ # lie within [-0.1, 1]
+# ax1.set_xlim([-0.1, 1])
+ # The (n_clusters+1)*10 is for inserting blank space between silhouette
+ # plots of individual clusters, to demarcate them clearly.
+# ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
+
+ # Initialize the clusterer with n_clusters value and a random generator
+ # seed of 10 for reproducibility.
+# clusterer = KMeans(n_clusters=n_clusters, random_state=20)
+# cluster_labels = clusterer.fit_predict(X)
+
+ # The silhouette_score gives the average value for all the samples.
+ # This gives a perspective into the density and separation of the formed
+ # clusters
+ silhouette_avg = silhouette_score(X, cluster_labels)
+ print("For n_clusters =", n_clusters,
+ "The average silhouette_score is :", silhouette_avg)
+
+ # Compute the silhouette scores for each sample
+ sample_silhouette_values = silhouette_samples(X, cluster_labels)
+
+''' y_lower = 10
+ for i in range(n_clusters):
+ # Aggregate the silhouette scores for samples belonging to
+ # cluster i, and sort them
+ ith_cluster_silhouette_values = \
+ sample_silhouette_values[cluster_labels == i]
+
+ ith_cluster_silhouette_values.sort()
+
+ size_cluster_i = ith_cluster_silhouette_values.shape[0]
+ y_upper = y_lower + size_cluster_i
+
+ color = cm.nipy_spectral(float(i) / n_clusters)
+ ax1.fill_betweenx(np.arange(y_lower, y_upper),
+ 0, ith_cluster_silhouette_values,
+ facecolor=color, edgecolor=color, alpha=0.7)
+
+ # Label the silhouette plots with their cluster numbers at the middle
+ ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
+
+ # Compute the new y_lower for next plot
+ y_lower = y_upper + 10 # 10 for the 0 samples
+
+ ax1.set_title("The silhouette plot for the various clusters.")
+ ax1.set_xlabel("The silhouette coefficient values")
+ ax1.set_ylabel("Cluster label")
+
+ # The vertical line for average silhouette score of all the values
+ ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
+
+ ax1.set_yticks([]) # Clear the yaxis labels / ticks
+ ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
+
+ # 2nd Plot showing the actual clusters formed
+ colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
+ ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
+ c=colors, edgecolor='k')
+
+ # Labeling the clusters
+ centers = clusterer.cluster_centers_
+ # 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')
+
+ 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.suptitle(("Silhouette analysis for KMeans clustering on sample data "
+ "with n_clusters = %d" % n_clusters),
+ fontsize=14, fontweight='bold')
+
+ plt.show()
+
--- /dev/null
+from sklearn.cluster import KMeans
+import numpy as np
+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))
--- /dev/null
+from __future__ import print_function
+
+from sklearn.datasets import make_blobs
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_samples, silhouette_score
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import numpy as np
+
+print(__doc__)
+
+# Generating the sample data from make_blobs
+# This particular setting has one distinct cluster and 3 clusters placed close
+# together.
+'''X, y = make_blobs(n_samples=500,
+ n_features=2,
+ centers=4,
+ cluster_std=1,
+ center_box=(-10.0, 10.0),
+ shuffle=True,
+ random_state=1) # For reproducibility'''
+
+X = np.array([[132, 192], [117, 960], [117, 962], [1343, 0], [117, 1116], [117, 1117], [117, 1118], [117, 1119], [1015, 0], [117, 966]])
+
+range_n_clusters = [2, 3, 4, 5, 6]
+
+for n_clusters in range_n_clusters:
+ # Create a subplot with 1 row and 2 columns
+# fig, (ax1, ax2) = plt.subplots(1, 2)
+# fig.set_size_inches(18, 7)
+
+ # The 1st subplot is the silhouette plot
+ # The silhouette coefficient can range from -1, 1 but in this example all
+ # lie within [-0.1, 1]
+# ax1.set_xlim([-0.1, 1])
+ # The (n_clusters+1)*10 is for inserting blank space between silhouette
+ # plots of individual clusters, to demarcate them clearly.
+# ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
+
+ # Initialize the clusterer with n_clusters value and a random generator
+ # seed of 10 for reproducibility.
+# clusterer = KMeans(n_clusters=n_clusters, random_state=20)
+# cluster_labels = clusterer.fit_predict(X)
+
+ # The silhouette_score gives the average value for all the samples.
+ # This gives a perspective into the density and separation of the formed
+ # clusters
+ silhouette_avg = silhouette_score(X, cluster_labels)
+ print("For n_clusters =", n_clusters,
+ "The average silhouette_score is :", silhouette_avg)
+
+ # Compute the silhouette scores for each sample
+ sample_silhouette_values = silhouette_samples(X, cluster_labels)
+
+''' y_lower = 10
+ for i in range(n_clusters):
+ # Aggregate the silhouette scores for samples belonging to
+ # cluster i, and sort them
+ ith_cluster_silhouette_values = \
+ sample_silhouette_values[cluster_labels == i]
+
+ ith_cluster_silhouette_values.sort()
+
+ size_cluster_i = ith_cluster_silhouette_values.shape[0]
+ y_upper = y_lower + size_cluster_i
+
+ color = cm.nipy_spectral(float(i) / n_clusters)
+ ax1.fill_betweenx(np.arange(y_lower, y_upper),
+ 0, ith_cluster_silhouette_values,
+ facecolor=color, edgecolor=color, alpha=0.7)
+
+ # Label the silhouette plots with their cluster numbers at the middle
+ ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
+
+ # Compute the new y_lower for next plot
+ y_lower = y_upper + 10 # 10 for the 0 samples
+
+ ax1.set_title("The silhouette plot for the various clusters.")
+ ax1.set_xlabel("The silhouette coefficient values")
+ ax1.set_ylabel("Cluster label")
+
+ # The vertical line for average silhouette score of all the values
+ ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
+
+ ax1.set_yticks([]) # Clear the yaxis labels / ticks
+ ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
+
+ # 2nd Plot showing the actual clusters formed
+ colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
+ ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
+ c=colors, edgecolor='k')
+
+ # Labeling the clusters
+ centers = clusterer.cluster_centers_
+ # 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')
+
+ 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.suptitle(("Silhouette analysis for KMeans clustering on sample data "
+ "with n_clusters = %d" % n_clusters),
+ fontsize=14, fontweight='bold')
+
+ plt.show()
+
projects / pingpong.git / commitdiff