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Fix bugs of bluetooth feature extraction when number of unique bt_address is 2

pull/130/head
Meng Li 2021-04-05 14:09:50 +00:00
parent 8414977331
commit 68e12a2563
1 changed files with 16 additions and 15 deletions
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31
src/features/phone_bluetooth/doryab/main.py
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@ -73,21 +73,22 @@ def ownership_based_on_clustering(bt_frequency):
diff_k2 = [(X_array[xi] - centers_k2[labels_k2[xi]])**2 for xi in range(0, len(X_array))]
sum_dist_k2 = sum(diff_k2)
# K = 3, devices I own VS devices my partner/roommate owns (can also be other devices I own though) VS devices other people own
kmeans_k3 = KMeans(n_clusters=3, init=initial_k3, n_init = 1).fit(X)
labels_k3 = kmeans_k3.labels_
centers_k3 = [c[0] for c in kmeans_k3.cluster_centers_]
diff_k3 = [(X_array[xi] - centers_k3[labels_k3[xi]])**2 for xi in range(0, len(X_array))]
sum_dist_k3 = sum(diff_k3)
if sum_dist_k2 < sum_dist_k3: # K = 2 is better
labels = labels_k2
centers = centers_k2
numclust = 2
else:
labels = labels_k3
centers = centers_k3
numclust = 3
# By default, model with K = 2 is chosen
labels = labels_k2
centers = centers_k2
numclust = 2
if len(X_array) > 2:
# K = 3, devices I own VS devices my partner/roommate owns (can also be other devices I own though) VS devices other people own
kmeans_k3 = KMeans(n_clusters=3, init=initial_k3, n_init = 1).fit(X)
labels_k3 = kmeans_k3.labels_
centers_k3 = [c[0] for c in kmeans_k3.cluster_centers_]
diff_k3 = [(X_array[xi] - centers_k3[labels_k3[xi]])**2 for xi in range(0, len(X_array))]
sum_dist_k3 = sum(diff_k3)
# Model with K = 3 is chosen if sum of squared distances between clustered points and cluster centers is smaller or equal to what we get with K = 2
if sum_dist_k3 <= sum_dist_k2:
labels = labels_k3
centers = centers_k3
numclust = 3
maxcluster = np.where(labels == np.argmax(centers), 1, 0)
bt_frequency["own_device"] = maxcluster