ks = KShape(n_clusters=numCluster, n_init=1, random_state=0)
pred = ks.fit_predict(input_waves)
clustFile = h5py.File(
templatePath + str(numCluster) + "/" + str(numCluster) +
"_cluster_predictions_" + str(prefiltFreq[0]) + "-" +
str(prefiltFreq[1]) + "Hz.h5", "w")
clustFile.create_dataset("cluster_index", data=pred)
clustFile.create_dataset("centroids", data=ks.cluster_centers_)
clustFile.create_dataset("inertia", data=ks.inertia_)
clustFile.close()
modelFile = templatePath + str(numCluster) + "/" + str(
numCluster) + "_cluster_model_" + str(prefiltFreq[0]) + "-" + str(
prefiltFreq[1]) + "Hz.h5"
ks.to_hdf5(modelFile)
# load some variables
centroids = ks.cluster_centers_
# for each cluster, cross correlate, align and plot each event in the cluster in reference to the centroid
for c in range(numCluster):
# load centroid into dummy obspy event as master waveform for cross correlation
masterEvent = obspy.read(templatePath + "dummy_2Hz.h5")
masterEvent[0].data = centroids[c].ravel()
# make empty array for storage
clusterEvents = waves[pred == c]
clusterEvents_norm = input_waves[pred == c]
clusterEventsAligned = np.zeros(
# Keep first 3 classes
X_train = X_train[y_train < 4]
numpy.random.shuffle(X_train)
# Keep only 50 time series
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50])
sz = X_train.shape[1]
# Instantiate k-Shape model
ks = KShape(n_clusters=3, verbose=True, random_state=seed)
# Train
ks.fit(X_train)
# Save model
ks.to_hdf5('./ks_trained.hdf5')
# Load model
trained_ks = KShape.from_hdf5('./ks_trained.hdf5')
# Use loaded model to make predictions
y_pred = trained_ks.predict(X_train)
plt.figure()
for yi in range(3):
plt.subplot(3, 1, 1 + yi)
for xx in X_train[y_pred == yi]:
plt.plot(xx.ravel(), "k-", alpha=.2)
plt.plot(ks.cluster_centers_[yi].ravel(), "r-")
plt.xlim(0, sz)
plt.ylim(-4, 4)