class KMeans():
def __init__(self,
n_clusters,
algorithm='GlobalAlignmentKernelKMeans',
random_seed=0):
'''
initialize KMeans clustering model with specific kernel
hyperparameters:
n_clusters: number of clusters in Kmeans model
algorithm: which kernel to use for model, options
are 'GlobalAlignmentKernelKMeans' and 'TimeSeriesKMeans'
random_seed: random seed with which to initialize Kmeans
'''
try:
assert algorithm == 'GlobalAlignmentKernelKMeans' or algorithm == 'TimeSeriesKMeans'
except:
raise ValueError(
"algorithm must be one of \'GlobalAlignmentKernelKMeans\' or \'TimeSeriesKMeans\'"
)
self.n_clusters = n_clusters
self.random_seed = random_seed
self.algorithm = algorithm
self.km = None
def fit(self, train):
'''
fit KMeans clustering model on training data
parameters:
train : training time series
'''
if self.algorithm == 'TimeSeriesKMeans':
self.km = TimeSeriesKMeans(n_clusters=self.n_clusters,
n_init=20,
verbose=True,
random_state=self.random_seed)
else:
self.km = GlobalAlignmentKernelKMeans(
n_clusters=self.n_clusters,
sigma=sigma_gak(train),
n_init=20,
verbose=True,
random_state=self.random_seed)
return self.km.fit_predict(train)
def predict(self, test):
'''
clusters for time series in test data set
parameters:
test: test time series on which to predict clusters
returns: clusters for test data set
'''
return self.km.predict(test)
def test_gak_kmeans():
n, sz, d = 15, 10, 3
rng = np.random.RandomState(0)
time_series = rng.randn(n, sz, d)
gak_km = GlobalAlignmentKernelKMeans(n_clusters=3,
verbose=False,
max_iter=5,
random_state=rng).fit(time_series)
np.testing.assert_allclose(gak_km.labels_, gak_km.predict(time_series))
gak_km = GlobalAlignmentKernelKMeans(n_clusters=101,
verbose=False,
max_iter=5,
random_state=rng).fit(time_series)
assert gak_km._X_fit is None