# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import sys
from .sparse_gp_regression import SparseGPRegression
from ..core import Param
[docs]class SparseGPLVM(SparseGPRegression):
"""
Sparse Gaussian Process Latent Variable Model
: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'
"""
def __init__(self, Y, input_dim, X=None, kernel=None, init='PCA', num_inducing=10):
if X is None:
from ..util.initialization import initialize_latent
X, fracs = initialize_latent(init, input_dim, Y)
X = Param('latent space', X)
super(SparseGPLVM, self).__init__(X, Y, kernel=kernel, num_inducing=num_inducing)
self.link_parameter(self.X, 0)
[docs] def parameters_changed(self):
super(SparseGPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X_diag(self.grad_dict['dL_dKdiag'], self.X)
self.X.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
[docs] def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=True, legend=True,
plot_limits=None,
aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_latent(self, labels, which_indices,
resolution, ax, marker, s,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)