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Julien Bect 2020-06-24 06:39:40 +02:00 • committed by Neil Lawrence
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14
GPy/models/gp_multiout_regression_md.py
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@ -13,10 +13,18 @@ from ..util.linalg import tdot
from .sparse_gp_regression_md import SparseGPRegressionMD
class GPMultioutRegressionMD(SparseGP):
"""
Gaussian Process model for multi-output regression with missing data
"""Gaussian Process model for multi-output regression with missing data
This is an implementation of Latent Variable Multiple Output Gaussian Processes (LVMOGP) in [Dai_et_al_2017]_. This model targets at the use case, in which each output dimension is observed at a different set of inputs. The model takes a different data format: the inputs and outputs observations of all the output dimensions are stacked together correspondingly into two matrices. An extra array is used to indicate the index of output dimension for each data point. The output dimensions are indexed using integers from 0 to D-1 assuming there are D output dimensions.
This is an implementation of Latent Variable Multiple Output
Gaussian Processes (LVMOGP) in [Dai_et_al_2017]_. This model
targets at the use case, in which each output dimension is
observed at a different set of inputs. The model takes a different
data format: the inputs and outputs observations of all the output
dimensions are stacked together correspondingly into two
matrices. An extra array is used to indicate the index of output
dimension for each data point. The output dimensions are indexed
using integers from 0 to D-1 assuming there are D output
dimensions.
.. rubric:: References

15
GPy/models/sparse_gp_regression_md.py
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@ -10,10 +10,18 @@ from ..inference.latent_function_inference.vardtc_md import VarDTC_MD
from GPy.core.parameterization.variational import NormalPosterior
class SparseGPRegressionMD(SparseGP_MPI):
"""
Sparse Gaussian Process Regression with Missing Data
"""Sparse Gaussian Process Regression with Missing Data
This model targets at the use case, in which there are multiple output dimensions (different dimensions are assumed to be independent following the same GP prior) and each output dimension is observed at a different set of inputs. The model takes a different data format: the inputs and outputs observations of all the output dimensions are stacked together correspondingly into two matrices. An extra array is used to indicate the index of output dimension for each data point. The output dimensions are indexed using integers from 0 to D-1 assuming there are D output dimensions.
This model targets at the use case, in which there are multiple
output dimensions (different dimensions are assumed to be
independent following the same GP prior) and each output dimension
is observed at a different set of inputs. The model takes a
different data format: the inputs and outputs observations of all
the output dimensions are stacked together correspondingly into
two matrices. An extra array is used to indicate the index of
output dimension for each data point. The output dimensions are
indexed using integers from 0 to D-1 assuming there are D output
dimensions.
:param X: input observations.
:type X: numpy.ndarray
@ -29,6 +37,7 @@ class SparseGPRegressionMD(SparseGP_MPI):
:type num_inducing: (int, int)
:param boolean individual_Y_noise: whether individual output dimensions have their own noise variance or not, boolean
:param str name: the name of the model
"""
def __init__(self, X, Y, indexD, kernel=None, Z=None, num_inducing=10, normalizer=None, mpi_comm=None, individual_Y_noise=False, name='sparse_gp'):