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65 lines
2.7 KiB
Python
65 lines
2.7 KiB
Python
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# Copyright (c) 2012, James Hensman
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from ..core.sparse_gp_mpi import SparseGP_MPI
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from .. import likelihoods
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from .. import kern
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from ..inference.latent_function_inference.vardtc_md import VarDTC_MD
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from GPy.core.parameterization.variational import NormalPosterior
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class SparseGPRegressionMD(SparseGP_MPI):
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"""
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Sparse Gaussian Process Regression with Missing Data
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"""
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def __init__(self, X, Y, indexD, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None, individual_Y_noise=False, name='sparse_gp'):
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assert len(Y.shape)==1 or Y.shape[1]==1
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self.individual_Y_noise = individual_Y_noise
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self.indexD = indexD
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output_dim = int(np.max(indexD))+1
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num_data, input_dim = X.shape
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# kern defaults to rbf (plus white for stability)
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if kernel is None:
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kernel = kern.RBF(input_dim)# + kern.white(input_dim, variance=1e-3)
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# Z defaults to a subset of the data
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if Z is None:
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i = np.random.permutation(num_data)[:min(num_inducing, num_data)]
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Z = X.view(np.ndarray)[i].copy()
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else:
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assert Z.shape[1] == input_dim
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if individual_Y_noise:
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likelihood = likelihoods.Gaussian(variance=np.array([np.var(Y[indexD==d]) for d in range(output_dim)])*0.01)
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else:
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likelihood = likelihoods.Gaussian(variance=np.var(Y)*0.01)
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if not (X_variance is None):
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X = NormalPosterior(X,X_variance)
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infr = VarDTC_MD()
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SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
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self.output_dim = output_dim
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def parameters_changed(self):
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.indexD, self.output_dim, self.Y_metadata)
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self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'] if self.individual_Y_noise else self.grad_dict['dL_dthetaL'].sum())
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self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X)
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kerngrad = self.kern.gradient.copy()
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self.kern.update_gradients_full(self.grad_dict['dL_dKnm'], self.X, self.Z)
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kerngrad += self.kern.gradient
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self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None)
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self.kern.gradient += kerngrad
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#gradients wrt Z
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self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
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self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
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