def pruneConvInput(tensorNP: tf.Tensor, rindexes: list = None) -> tf.Tensor:
#Prune input channels of everything
if not rindexes is None:
c = tensorNP[:, :, rindexes, :].copy()
else:
c = tensorNP.copy()
return c
def __init__(self,
data: tf.Tensor,
x_data_mean: tf.Tensor,
x_data_var: tf.Tensor,
kernel: Kernel,
num_inducing_variables: Optional[int] = None,
inducing_variable=None,
x_prior_mean=None,
x_prior_var=None):
"""
Initialise Bayesian GPLVM object. This method only works with a Gaussian likelihood.
:param data: data matrix, size N (number of points) x D (dimensions)
:param x_data_mean: initial latent positions, size N (number of points) x Q (latent dimensions).
:param x_data_var: variance of latent positions ([N, Q]), for the initialisation of the latent space.
:param kernel: kernel specification, by default Squared Exponential
:param num_inducing_variables: number of inducing points, M
:param inducing_variable: matrix of inducing points, size M (inducing points) x Q (latent dimensions). By default
random permutation of x_data_mean.
:param x_prior_mean: prior mean used in KL term of bound. By default 0. Same size as x_data_mean.
:param x_prior_var: pripor variance used in KL term of bound. By default 1.
"""
super().__init__(kernel, likelihoods.Gaussian())
self.data = data
assert x_data_var.ndim == 2
self.x_data_mean = Parameter(x_data_mean)
self.x_data_var = Parameter(x_data_var, transform=positive())
self.num_data, self.num_latent = x_data_mean.shape
self.output_dim = data.shape[-1]
assert np.all(x_data_mean.shape == x_data_var.shape)
assert x_data_mean.shape[0] == data.shape[0], 'X mean and Y must be same size.'
assert x_data_var.shape[0] == data.shape[0], 'X var and Y must be same size.'
if (inducing_variable is None) == (num_inducing_variables is None):
raise ValueError("BayesianGPLVM needs exactly one of `inducing_variable` and `num_inducing_variables`")
if inducing_variable is None:
# By default we initialize by subset of initial latent points
inducing_variable = np.random.permutation(x_data_mean.copy())[:num_inducing_variables]
self.inducing_variable = inducingpoint_wrapper(inducing_variable)
assert x_data_mean.shape[1] == self.num_latent
# deal with parameters for the prior mean variance of X
if x_prior_mean is None:
x_prior_mean = tf.zeros((self.num_data, self.num_latent), dtype=default_float())
if x_prior_var is None:
x_prior_var = tf.ones((self.num_data, self.num_latent))
self.x_prior_mean = tf.convert_to_tensor(np.atleast_1d(x_prior_mean), dtype=default_float())
self.x_prior_var = tf.convert_to_tensor(np.atleast_1d(x_prior_var), dtype=default_float())
assert self.x_prior_mean.shape[0] == self.num_data
assert self.x_prior_mean.shape[1] == self.num_latent
assert self.x_prior_var.shape[0] == self.num_data
assert self.x_prior_var.shape[1] == self.num_latent
