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Add mean function functionality to dtc inference method

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This commit is contained in:
Mark Pullin 2017-11-13 21:15:38 +00:00
parent 754c67f71d
commit a24a9b3edc
5 changed files with 45 additions and 18 deletions
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5
GPy/models/sparse_gp_regression.py
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@ -30,7 +30,7 @@ class SparseGPRegression(SparseGP_MPI):
"""
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None, name='sparse_gp'):
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, mean_function=None, normalizer=None, mpi_comm=None, name='sparse_gp'):
num_data, input_dim = X.shape
# kern defaults to rbf (plus white for stability)
@ -55,7 +55,8 @@ class SparseGPRegression(SparseGP_MPI):
else:
infr = VarDTC()
SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, mean_function=mean_function,
inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
def parameters_changed(self):
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch