def __init__(self, input_dim, output_dim, active_dims=None):
Kernel.__init__(self, active_dims)
self.input_dim = input_dim
self.output_dim = output_dim
noise = np.random.random((output_dim))
self.noise = gpflow.Parameter(noise,
transform=gpflow.utilities.positive(),
name="noise")
def __init__(self, base_kern, branchPtTensor, b, fDebug=False):
''' branchPtTensor is tensor of branch points of size F X F X B where F the number of
functions and B the number of branching points '''
Kernel.__init__(self, input_dim=base_kern.input_dim + 1)
self.kern = base_kern
self.fm = branchPtTensor
self.fDebug = fDebug
assert isinstance(b, np.ndarray)
assert self.fm.shape[0] == self.fm.shape[1]
assert self.fm.shape[2] > 0
self.Bv = DataHolder(b)
def __init__(self,
input_dim,
variance=1.0,
frequency=np.array([1.0, 1.0]),
lengthscale=1.0,
correlation=0.0,
max_freq=1.0,
active_dims=None):
assert (input_dim == 1
) # the derivations are valid only for one dimensional input
Kernel.__init__(self, input_dim=input_dim, active_dims=active_dims)
self.variance = Param(variance, transforms.positive)
self.frequency = Param(frequency, transforms.Logistic(0.0, max_freq))
self.lengthscale = Param(lengthscale, transforms.positive)
correlation = np.clip(correlation, 1e-4,
1 - 1e-4) # clip for numerical reasons
self.correlation = Param(correlation, transforms.Logistic())
def __init__(self,
base_kern,
len_seqs,
len_windows,
num_features,
normalized=True):
Kernel.__init__(self, len_seqs * num_features)
self.len_seqs = len_seqs
self.len_windows = len_windows
self.base_kern = base_kern
self.num_features = num_features
self.normalized = normalized
self.variance = Parameter(1.0,
transform=transforms.positive,
dtype=settings.float_type)
if self.base_kern.input_dim != len_windows * num_features:
raise ValueError(
"Base_kern input dimensions must be consistent with window length."
)
def __init__(self, base_kern):
Kernel.__init__(self, input_dim=base_kern.input_dim + 1)
self.kern = base_kern
def __init__(self, input_dim, output_dim, active_dims=None, name=None):
Kernel.__init__(self, active_dims, name)
self.input_dim = input_dim
self.output_dim = output_dim