Source code for GPy.models.sparse_gplvm

# 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)