def __init__(
self, distribution, kernel, nu2=0.1, gamma=None, sample_discard=500, num_samples_Z=1000, stop_adapt=20000
):
Kameleon.__init__(self, distribution, kernel, Z=None, nu2=nu2, gamma=gamma)
assert stop_adapt > sample_discard
assert num_samples_Z > 0
self.sample_discard = sample_discard
self.num_samples_Z = num_samples_Z
self.stop_adapt = stop_adapt
def __init__(self, distribution, kernel, Z, nu2=0.1, gamma=0.1, num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0 : self.num_eigen]
self.eigvectors = u[:, 0 : self.num_eigen]
def __init__(self, distribution, kernel, nu2=0.1, gamma=None, \
sample_discard=500, num_samples_Z=1000, stop_adapt=20000):
Kameleon.__init__(self,
distribution,
kernel,
Z=None,
nu2=nu2,
gamma=gamma)
assert (stop_adapt > sample_discard)
assert (num_samples_Z > 0)
self.sample_discard = sample_discard
self.num_samples_Z = num_samples_Z
self.stop_adapt = stop_adapt
def __init__(self,
distribution,
kernel,
Z,
nu2=0.1,
gamma=0.1,
num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0:self.num_eigen]
self.eigvectors = u[:, 0:self.num_eigen]
