def setUpClass(cls):
super(TestCluster, cls).setUpClass()
cls.dtraj_dir = tempfile.mkdtemp()
# generate Gaussian mixture
means = [
np.array([-3, 0]),
np.array([-1, 1]),
np.array([0, 0]),
np.array([1, -1]),
np.array([4, 2])
]
widths = [
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2])
]
# continuous trajectory
nsample = 1000
cls.T = len(means) * nsample
cls.X = np.zeros((cls.T, 2))
for i in range(len(means)):
cls.X[i * nsample:(i + 1) * nsample,
0] = widths[i][0] * np.random.randn() + means[i][0]
cls.X[i * nsample:(i + 1) * nsample,
1] = widths[i][1] * np.random.randn() + means[i][1]
# cluster in different ways
cls.km = coor.cluster_kmeans(data=cls.X, k=100)
cls.rs = coor.cluster_regspace(data=cls.X, dmin=0.5)
cls.rt = coor.cluster_uniform_time(data=cls.X, k=100)
cls.cl = [cls.km, cls.rs, cls.rt]
def setUpClass(cls):
super(TestCluster, cls).setUpClass()
cls.dtraj_dir = tempfile.mkdtemp()
# generate Gaussian mixture
means = [np.array([-3,0]),
np.array([-1,1]),
np.array([0,0]),
np.array([1,-1]),
np.array([4,2])]
widths = [np.array([0.3,2]),
np.array([0.3,2]),
np.array([0.3,2]),
np.array([0.3,2]),
np.array([0.3,2])]
# continuous trajectory
nsample = 1000
cls.T = len(means)*nsample
cls.X = np.zeros((cls.T, 2))
for i in range(len(means)):
cls.X[i*nsample:(i+1)*nsample,0] = widths[i][0] * np.random.randn() + means[i][0]
cls.X[i*nsample:(i+1)*nsample,1] = widths[i][1] * np.random.randn() + means[i][1]
# cluster in different ways
cls.km = coor.cluster_kmeans(data = cls.X, k = 100)
cls.rs = coor.cluster_regspace(data = cls.X, dmin=0.5)
cls.rt = coor.cluster_uniform_time(data = cls.X, k = 100)
cls.cl = [cls.km, cls.rs, cls.rt]