def __init__(self,
data: RegressionData,
kernel,
noise_variance: float = 1.0,
parallel=False,
max_parallel=10000):
self.noise_variance = Parameter(noise_variance, transform=positive())
ts, ys = data_input_to_tensor(data)
super().__init__(kernel, None, None, num_latent_gps=ys.shape[-1])
self.data = ts, ys
filter_spec = kernel.get_spec(ts.shape[0])
filter_ys_spec = tf.TensorSpec((ts.shape[0], 1),
config.default_float())
smoother_spec = kernel.get_spec(None)
smoother_ys_spec = tf.TensorSpec((None, 1), config.default_float())
if not parallel:
self._kf = tf.function(
partial(kf, return_loglikelihood=True, return_predicted=False),
input_signature=[filter_spec, filter_ys_spec])
self._kfs = tf.function(
kfs, input_signature=[smoother_spec, smoother_ys_spec])
else:
self._kf = tf.function(
partial(pkf,
return_loglikelihood=True,
max_parallel=ts.shape[0]),
input_signature=[filter_spec, filter_ys_spec])
self._kfs = tf.function(
partial(pkfs, max_parallel=max_parallel),
input_signature=[smoother_spec, smoother_ys_spec])
def predict_f(self,
Xnew: InputData,
full_cov: bool = False,
full_output_cov: bool = False) -> MeanAndVariance:
r"""
This method computes predictions at X \in R^{N \x D} input points
.. math::
p(F* | Y)
where F* are points on the GP at new data points, Y are noisy observations at training data points.
Note that full_cov => full_output_cov (regardless of the ordinate given for full_output_cov), to avoid ambiguity.
"""
full_output_cov = True if full_cov else full_output_cov
Xnew = tf.reshape(data_input_to_tensor(Xnew), (-1, self._M))
n = Xnew.shape[0]
f_mean, f_var = base_conditional(Kmn=self.kernel(self._X, Xnew),
Kmm=self.likelihood.add_to(self.KXX),
Knn=self.kernel(Xnew, Xnew),
f=self._Y - self._mean,
full_cov=True,
white=False)
f_mean += tf.reshape(self.mean_function(Xnew), f_mean.shape)
f_mean_shape = (self._L, n)
f_mean = tf.reshape(f_mean, f_mean_shape)
f_var = tf.reshape(f_var, f_mean_shape * 2)
if full_output_cov:
einsum = 'LNln -> LlNn'
else:
einsum = 'LNLn -> LNn'
f_var = tf.einsum(einsum, f_var)
if not full_cov:
f_var = tf.einsum('...NN->...N', f_var)
perm = tuple(reversed(range(tf.rank(f_var))))
return tf.transpose(f_mean), tf.transpose(f_var, perm)
def __init__(self,
variance,
lengthscales,
name='Kernel',
active_dims=None):
""" Kernel Constructor.
Args:
variance: An (L,L) symmetric, positive definite matrix for the signal variance.
lengthscales: An (L,M) matrix of positive definite lengthscales.
is_lengthscales_trainable: Whether the lengthscales of this kernel are trainable.
name: The name of this kernel.
active_dims: Which of the input dimensions are used. The default None means all of them.
"""
super(AnisotropicStationary, self).__init__(
active_dims=active_dims, name=name
) # Do not call gf.kernels.AnisotropicStationary.__init__()!
self.variance = Variance(value=np.atleast_2d(variance),
name=name + 'Variance')
self._L = self.variance.shape[0]
lengthscales = data_input_to_tensor(lengthscales)
lengthscales_shape = tuple(tf.shape(lengthscales).numpy())
self._M = 1 if lengthscales_shape in ((), (1, ), (1, 1), (
self._L, )) else lengthscales_shape[-1]
lengthscales = tf.reshape(
tf.broadcast_to(lengthscales, (self._L, self._M)),
(self._L, 1, self._M))
self.lengthscales = Parameter(lengthscales,
transform=positive(),
trainable=False,
name=name + 'Lengthscales')
self._validate_ard_active_dims(self.lengthscales[0, 0])
def test_data_input_to_tensor():
input1 = (1.0, (2.0, ))
output1 = data_input_to_tensor(input1)
assert output1[0].dtype == tf.float64
assert output1[1][0].dtype == tf.float64
input2 = (1.0, [2.0])
output2 = data_input_to_tensor(input2)
assert output2[0].dtype == tf.float64
assert output2[1][0].dtype == tf.float64
input3 = (1.0, (np.arange(3, dtype=np.float16), ) * 2)
output3 = data_input_to_tensor(input3)
assert output3[0].dtype == tf.float64
assert output3[1][0].dtype == tf.float16
assert output3[1][1].dtype == tf.float16
def _set_data(self):
from gpflow.models.util import ( # pylint:disable=import-outside-toplevel
data_input_to_tensor, )
for i, model in enumerate(self.models):
model.data = data_input_to_tensor((
self.design_space[self.sampled[:, i]],
self.y[self.sampled[:, i], i].reshape(-1, 1),
))
def __init__(
self,
data: RegressionData,
kernel: Kernel,
mean_function: Optional[MeanFunction] = None,
noise_variance: float = 1.0,
):
likelihood = gpflow.likelihoods.Gaussian(noise_variance)
_, Y_data = data
super().__init__(kernel,
likelihood,
mean_function,
num_latent_gps=Y_data.shape[-1])
self.data = data_input_to_tensor(data)
def run_one(seed, covariance_function, gp_model, n_training, n_pred):
t, ft, t_pred, ft_pred, y = get_data(seed, n_training, n_pred)
gp_dtype = gpf.config.default_float()
if gp_model is None:
model_name = ModelEnum(FLAGS.model)
gp_model = get_model(model_name, (t, y), FLAGS.noise_variance,
covariance_function, t.shape[0] + t_pred.shape[0])
else:
gp_model.data = data_input_to_tensor((t, y))
tensor_t_pred = tf.convert_to_tensor(t_pred, dtype=gp_dtype)
y_pred, _ = gp_model.predict_f(tensor_t_pred)
error = rmse(y_pred, ft_pred)
return error, gp_model
def __init__(
self,
data: RegressionData,
kernel: mf.kernels.MOStationary,
mean_function: Optional[mf.mean_functions.MOMeanFunction] = None,
noise_variance: float = 1.0):
"""
Args:
data: Tuple[InputData, OutputData], which determines L, M and N. Both InputData and OutputData must be of rank 2.
kernel: Must be well-formed, with an (L,L) variance and an (L,M) lengthscales matrix.
mean_function: Defaults to Zero.
noise_variance: Broadcast to (diagonal) (L,L) if necessary.
"""
self._X, self._Y = self.data = data_input_to_tensor(data)
if (rank := tf.rank(self._X)) != (required_rank := 2):
def __init__(
self,
value,
name: str = 'Variance',
cholesky_diagonal_lower_bound: float = CHOLESKY_DIAGONAL_LOWER_BOUND
):
""" Construct a non-diagonal covariance matrix. Mutable only through it's properties cholesky_diagonal and cholesky_lower_triangle.
Args:
value: A symmetric, positive definite matrix, expressed in tensorflow or numpy.
cholesky_diagonal_lower_bound: Lower bound on the diagonal of the Cholesky decomposition.
"""
super().__init__(name=name)
value = data_input_to_tensor(value)
self._shape = (value.shape[-1], value.shape[-1])
self._broadcast_shape = (value.shape[-1], 1, value.shape[-1], 1)
if value.shape != self._shape:
raise ValueError('Variance must have shape (L,L).')
cholesky = tf.linalg.cholesky(value)
self._cholesky_diagonal = tf.linalg.diag_part(cholesky)
if min(self._cholesky_diagonal) <= cholesky_diagonal_lower_bound:
raise ValueError(
f'The Cholesky diagonal of {name} must be strictly greater than {cholesky_diagonal_lower_bound}.'
)
self._cholesky_diagonal = Parameter(
self._cholesky_diagonal,
transform=positive(lower=cholesky_diagonal_lower_bound),
name=name + '.cholesky_diagonal')
mask = sum([
list(range(i * self._shape[0], i * (self._shape[0] + 1)))
for i in range(1, self._shape[0])
],
start=[])
self._cholesky_lower_triangle = Parameter(
tf.gather(tf.reshape(cholesky, [-1]), mask),
name=name + '.cholesky_lower_triangle')
self._row_lengths = tuple(range(self._shape[0]))
Args:
data: Tuple[InputData, OutputData], which determines L, M and N. Both InputData and OutputData must be of rank 2.
kernel: Must be well-formed, with an (L,L) variance and an (L,M) lengthscales matrix.
mean_function: Defaults to Zero.
noise_variance: Broadcast to (diagonal) (L,L) if necessary.
"""
self._X, self._Y = self.data = data_input_to_tensor(data)
if (rank := tf.rank(self._X)) != (required_rank := 2):
raise IndexError(
f'X should be of rank {required_rank} instead of {rank}.')
self._N, self._M = self._X.shape
self._L = self._Y.shape[-1]
if (shape := self._Y.shape) != (required_shape := (self._N, self._L)):
raise IndexError(
f'Y.shape should be {required_shape} instead of {shape}.')
self._Y = tf.reshape(
tf.transpose(self._Y),
[-1, 1]) # self_Y is now concatenated into an (LN,)-vector
if tf.shape(noise_variance).numpy != (self._L, self._L):
noise_variance = tf.broadcast_to(
data_input_to_tensor(noise_variance), (self._L, self._L))
noise_variance = tf.linalg.band_part(noise_variance, 0, 0)
likelihood = mf.likelihoods.MOGaussian(noise_variance)
if mean_function is None:
mean_function = mf.mean_functions.MOMeanFunction(self._L)
super().__init__(kernel, likelihood, mean_function, num_latent_gps=1)
self._mean = tf.reshape(self.mean_function(self._X), [-1, 1])
self._K_unit_variance = None if self.kernel.lengthscales.trainable else self.kernel.K_unit_variance(
self._X)
def __init__(
self,
data: OutputData,
split_space: bool,
Xp_mean: tf.Tensor,
Xp_var: tf.Tensor,
pi: tf.Tensor,
kernel_K: List[Kernel],
Zp: tf.Tensor,
Xs_mean=None,
Xs_var=None,
kernel_s=None,
Zs=None,
Xs_prior_mean=None,
Xs_prior_var=None,
Xp_prior_mean=None,
Xp_prior_var=None,
pi_prior=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: split_space, if true, have both shared and private space;
if false, only have private spaces (note: to recover GPLVM, set split_space=False and let K=1)
:param Xp_mean: mean latent positions in the private space [N, Qp] (Qp is the dimension of the private space)
:param Xp_var: variance of the latent positions in the private space [N, Qp]
:param pi: mixture responsibility of each category to each point [N, K] (K is the number of categories), i.e. q(c)
:param kernel_K: private space kernel, one for each category
:param Zp: inducing inputs of the private space [M, Qp]
:param num_inducing_variables: number of inducing points, M
:param Xs_mean: mean latent positions in the shared space [N, Qs] (Qs is the dimension of the shared space). i.e. mus in q(Xs) ~ N(Xs | mus, Ss)
:param Xs_var: variance of latent positions in shared space [N, Qs], i.e. Ss, assumed diagonal
:param kernel_s: shared space kernel
:param Zs: inducing inputs of the shared space [M, Qs] (M is the number of inducing points)
:param Xs_prior_mean: prior mean used in KL term of bound, [N, Qs]. By default 0. mean in p(Xs)
:param Xs_prior_var: prior variance used in KL term of bound, [N, Qs]. By default 1. variance in p(Xs)
:param Xp_prior_mean: prior mean used in KL term of bound, [N, Qp]. By default 0. mean in p(Xp)
:param Xp_prior_var: prior variance used in KL term of bound, [N, Qp]. By default 1. variance in p(Xp)
:param pi_prior: prior mixture weights used in KL term of the bound, [N, K]. By default uniform. p(c)
"""
# if don't want shared space, set shared space to none --> get a mixture of GPLVM
# if don't want private space, set shared space to none, set K = 1 and only include 1 kernel in `kernel_K` --> recover the original GPLVM
# TODO: think about how to do this with minibatch
# it's awkward since w/ minibatch the model usually doesn't store the data internally
# but for gplvm, you need to keep the q(xn) for all the n's
# so you need to know which ones to update for each minibatch, probably can be solved but not pretty
# using inference network / back constraints will solve this, since we will be keeping a global set of parameters
# rather than a set for each q(xn)
self.N, self.D = data.shape
self.Qp = Xp_mean.shape[1]
self.K = pi.shape[1]
self.split_space = split_space
assert Xp_var.ndim == 2
assert len(kernel_K) == self.K
assert np.all(Xp_mean.shape == Xp_var.shape)
assert Xp_mean.shape[0] == self.N, "Xp_mean and Y must be of same size"
assert pi.shape[0] == self.N, "pi and Y must be of the same size"
super().__init__()
self.likelihood = likelihoods.Gaussian()
self.kernel_K = kernel_K
self.data = data_input_to_tensor(data)
# the covariance of q(X) as a [N, Q] matrix, the assumption is that Sn's are diagonal
# i.e. the latent dimensions are uncorrelated
# otherwise would require a [N, Q, Q] matrix
self.Xp_mean = Parameter(Xp_mean)
self.Xp_var = Parameter(Xp_var, transform=positive())
self.pi = Parameter(pi, transform=tfp.bijectors.SoftmaxCentered())
self.Zp = inducingpoint_wrapper(Zp)
self.M = len(self.Zp)
# initialize the variational parameters for q(U), same way as in SVGP
# q_mu: List[K], mean of the inducing variables U [M, D], i.e m in q(U) ~ N(U | m, S),
# initialized as zeros
# q_sqrt: List[K], cholesky of the covariance matrix of the inducing variables [D, M, M]
# q_diag is false because natural gradient only works for full covariance
# initialized as all identities
# we need K sets of q(Uk), each approximating fs+fk
self.q_mu = []
self.q_sqrt = []
for k in range(self.K):
q_mu = np.zeros((self.M, self.D))
q_mu = Parameter(q_mu, dtype=default_float()) # [M, D]
self.q_mu.append(q_mu)
q_sqrt = [
np.eye(self.M, dtype=default_float()) for _ in range(self.D)
]
q_sqrt = np.array(q_sqrt)
q_sqrt = Parameter(q_sqrt, transform=triangular()) # [D, M, M]
self.q_sqrt.append(q_sqrt)
# deal with parameters for the prior
if Xp_prior_mean is None:
Xp_prior_mean = tf.zeros((self.N, self.Qp), dtype=default_float())
if Xp_prior_var is None:
Xp_prior_var = tf.ones((self.N, self.Qp))
if pi_prior is None:
pi_prior = tf.ones((self.N, self.K), dtype=default_float()) * 1/self.K
self.Xp_prior_mean = tf.convert_to_tensor(np.atleast_1d(Xp_prior_mean), dtype=default_float())
self.Xp_prior_var = tf.convert_to_tensor(np.atleast_1d(Xp_prior_var), dtype=default_float())
self.pi_prior = tf.convert_to_tensor(np.atleast_1d(pi_prior), dtype=default_float())
# if we have both shared space and private space, need to initialize the parameters for the shared space
if split_space:
assert Xs_mean is not None and Xs_var is not None and kernel_s is not None and Zs is not None, 'Xs_mean, Xs_var, kernel_s, Zs need to be initialize if `split_space=True`'
assert Xs_var.ndim == 2
assert np.all(Xs_mean.shape == Xs_var.shape)
assert Xs_mean.shape[0] == self.N, "Xs_mean and Y must be of same size"
self.Qs = Xs_mean.shape[1]
self.kernel_s = kernel_s
self.Xs_mean = Parameter(Xs_mean)
self.Xs_var = Parameter(Xs_var, transform=positive())
self.Zs = inducingpoint_wrapper(Zs)
if len(Zs) != len(Zp):
raise ValueError(
'`Zs` and `Zp` should have the same length'
)
if Xs_prior_mean is None:
Xs_prior_mean = tf.zeros((self.N, self.Qs), dtype=default_float())
if Xs_prior_var is None:
Xs_prior_var = tf.ones((self.N, self.Qs))
self.Xs_prior_mean = tf.convert_to_tensor(np.atleast_1d(Xs_prior_mean), dtype=default_float())
self.Xs_prior_var = tf.convert_to_tensor(np.atleast_1d(Xs_prior_var), dtype=default_float())
self.Fq = tf.zeros((self.N, self.K), dtype=default_float())
def __init__(
self,
data: OutputData,
kernel: Optional[Kernel] = None,
latent_dimensions: Optional[int] = 2,
num_inducing_variables: Optional[int] = None,
inducing_variable=None,
*,
mean_function=None,
q_diag: bool = False,
q_mu=None,
q_sqrt=None,
whiten: bool = False,
):
"""
- kernel, likelihood, inducing_variables, mean_function are appropriate
GPflow objects
- num_latent_gps is the number of latent processes to use, defaults to 2, as
the dimensionality reduction is at dimensions 2
- q_diag is a boolean. If True, the covariance is approximated by a
diagonal matrix.
- whiten is a boolean. If True, we use the whitened representation of
the inducing points.
- num_data is the total number of observations, defaults to X.shape[0]
(relevant when feeding in external minibatches)
"""
self.latent_dimensions = latent_dimensions
#grab data
self.data = data_input_to_tensor(data)
#define lat-space initialization
X_data_mean = pca_reduce(data, self.latent_dimensions)
num_data, num_latent_gps = data.shape
self.num_data = num_data
X_data_var = tf.ones((self.num_data, self.latent_dimensions),
dtype=default_float())
assert X_data_var.ndim == 2
#def kernel
if kernel is None:
kernel = gpflow.kernels.SquaredExponential()
#init Parameters latent
self.X_data_mean = Parameter(X_data_mean)
self.X_data_var = Parameter(X_data_var, transform=positive())
#init parameter inducing point
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
# Note that tf.random.shuffle returns a copy, it does not shuffle in-place
#maybe use k-means clustering
Z = tf.random.shuffle(X_data_mean)[:num_inducing_variables]
inducing_variable = InducingPoints(Z)
self.inducing_variable = inducingpoint_wrapper(inducing_variable)
#loss placeholder for analysis purpuse
self.loss_placeholder = defaultdict(
list, {k: []
for k in ("KL_x", "ELBO", "KL_u")})
# deal with parameters for the prior mean variance of X
X_prior_mean = tf.zeros((self.num_data, self.latent_dimensions),
dtype=default_float())
X_prior_var = tf.ones((self.num_data, self.latent_dimensions),
dtype=default_float())
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())
#sanity check
assert np.all(X_data_mean.shape == X_data_var.shape)
assert X_data_mean.shape[0] == self.data.shape[
0], "X mean and Y must be same size."
assert X_data_var.shape[0] == self.data.shape[
0], "X var and Y must be same size."
assert X_data_mean.shape[1] == self.latent_dimensions
assert self.X_prior_mean.shape[0] == self.num_data
assert self.X_prior_mean.shape[1] == self.latent_dimensions
assert self.X_prior_var.shape[0] == self.num_data
assert self.X_prior_var.shape[1] == self.latent_dimensions
# init the super class, accept args
super().__init__(kernel, likelihoods.Gaussian(variance=0.1),
mean_function, num_latent_gps)
self.q_diag = q_diag
self.whiten = whiten
#self.inducing_variable = inducingpoint_wrapper(inducing_variable)
# init variational parameters
num_inducing = self.inducing_variable.num_inducing
self._init_variational_parameters(num_inducing, q_mu, q_sqrt, q_diag)
def __init__(
self,
data: OutputData,
encoder,
kernel: Optional[Kernel] = 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: prior variance used in KL term of bound. By default 1.
"""
self.latent_dimensions = 2
#grab data
self.data = data_input_to_tensor(data)
num_data, num_latent_gps = data.shape
self.num_data = num_data
#def kernel
if kernel is None:
kernel = gpflow.kernels.SquaredExponential()
#init GPMODEL
super().__init__(kernel,
likelihoods.Gaussian(variance=0.1),
num_latent_gps=num_latent_gps)
#init parameter inducing point
if (inducing_variable is None):
raise ValueError(
"BayesianGPLVM needs exactly one of `inducing_variable` and `num_inducing_variables`"
)
else:
self.inducing_variable = inducingpoint_wrapper(inducing_variable)
#loss placeholder for analysis purpuse
self.loss_placeholder = defaultdict(list,
{k: []
for k in ("KL_x", "ELBO")})
# 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.latent_dimensions),
dtype=default_float())
if X_prior_var is None:
X_prior_var = tf.ones((self.num_data, self.latent_dimensions),
dtype=default_float())
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())
# Encoder
self.encoder = encoder
#sanity check
# assert np.all(X_data_mean.shape == X_data_var.shape)
# assert X_data_mean.shape[0] == self.data.shape[0], "X mean and Y must be same size."
# assert X_data_var.shape[0] == self.data.shape[0], "X var and Y must be same size."
# assert X_data_mean.shape[1] == self.latent_dimensions
assert self.X_prior_mean.shape[0] == self.num_data
assert self.X_prior_mean.shape[1] == self.latent_dimensions
assert self.X_prior_var.shape[0] == self.num_data
assert self.X_prior_var.shape[1] == self.latent_dimensions
def __init__(
self,
data: OutputData,
X_data_mean: Optional[tf.Tensor] = None,
X_data_var: Optional[tf.Tensor] = None,
kernel: Optional[Kernel] = None,
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: prior variance used in KL term of bound. By default 1.
"""
self.latent_dimensions = 2
#grab data
self.data = data_input_to_tensor(data)
#define lat-space initialization
if X_data_mean is None:
X_data_mean = pca_reduce(data, self.latent_dimensions)
num_data, num_latent_gps = X_data_mean.shape
self.num_data = num_data
if X_data_var is None:
X_data_var = tf.ones((self.num_data, self.latent_dimensions),
dtype=default_float())
assert X_data_var.ndim == 2
self.output_dim = self.data.shape[-1] #num_latent maybe
#def kernel
if kernel is None:
kernel = gpflow.kernels.SquaredExponential()
#init GPMODEL
super().__init__(kernel,
likelihoods.Gaussian(variance=0.1),
num_latent_gps=num_latent_gps)
#init Parameters latent
self.X_data_mean = Parameter(X_data_mean)
self.X_data_var = Parameter(X_data_var, transform=positive())
#init parameter inducing point
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
# Note that tf.random.shuffle returns a copy, it does not shuffle in-place
#maybe use k-means clustering
Z = tf.random.shuffle(X_data_mean)[:num_inducing_variables]
inducing_variable = InducingPoints(Z)
self.inducing_variable = inducingpoint_wrapper(inducing_variable)
#loss placeholder for analysis purpuse
self.loss_placeholder = defaultdict(list,
{k: []
for k in ("KL_x", "ELBO")})
# 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.latent_dimensions),
dtype=default_float())
if X_prior_var is None:
X_prior_var = tf.ones((self.num_data, self.latent_dimensions),
dtype=default_float())
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())
#sanity check
assert np.all(X_data_mean.shape == X_data_var.shape)
assert X_data_mean.shape[0] == self.data.shape[
0], "X mean and Y must be same size."
assert X_data_var.shape[0] == self.data.shape[
0], "X var and Y must be same size."
assert X_data_mean.shape[1] == self.latent_dimensions
assert self.X_prior_mean.shape[0] == self.num_data
assert self.X_prior_mean.shape[1] == self.latent_dimensions
assert self.X_prior_var.shape[0] == self.num_data
assert self.X_prior_var.shape[1] == self.latent_dimensions
