# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from .. import kern
from ..core import GP, Param
from ..likelihoods import Gaussian
[docs]class GPLVM(GP):
"""
Gaussian Process Latent Variable Model
"""
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm", Y_metadata=None, normalizer=False):
"""
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
:param normalizer:
normalize the outputs Y.
If normalizer is True, we will normalize using Standardize.
If normalizer is False (the default), no normalization will be done.
:type normalizer: bool
"""
if X is None:
from ..util.initialization import initialize_latent
X, fracs = initialize_latent(init, input_dim, Y)
else:
fracs = np.ones(input_dim)
if kernel is None:
kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM', Y_metadata=Y_metadata, normalizer=normalizer)
self.X = Param('latent_mean', X)
self.link_parameter(self.X, index=0)
[docs] def parameters_changed(self):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)