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[normalizer] first commit for normalizer in GPy

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Conflicts:
	GPy/core/sparse_gp.py
	GPy/models/bayesian_gplvm.py
This commit is contained in:
mzwiessele 2014-08-27 12:05:13 -07:00
parent 7ed0e70a46
commit 7ec0e75c0e
6 changed files with 101 additions and 24 deletions
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21
GPy/models/bayesian_gplvm.py
View file

@ -25,9 +25,10 @@ class BayesianGPLVM(SparseGP):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', mpi_comm=None, **kwargs):
Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', mpi_comm=None, normalizer=None):
self.mpi_comm = mpi_comm
self.__IN_OPTIMIZATION__ = False
self.logger = logging.getLogger(self.__class__.__name__)
if X == None:
from ..util.initialization import initialize_latent
@ -49,7 +50,7 @@ class BayesianGPLVM(SparseGP):
if kernel is None:
self.logger.info("initializing kernel RBF")
kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) # + kern.white(input_dim)
kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) #+ kern.Bias(input_dim) + kern.White(input_dim)
if likelihood is None:
likelihood = Gaussian()
@ -71,11 +72,11 @@ class BayesianGPLVM(SparseGP):
inference_method = VarDTC()
if isinstance(inference_method,VarDTC_minibatch):
inference_method.mpi_comm = mpi_comm
if kernel.useGPU and isinstance(inference_method, VarDTC_GPU):
kernel.psicomp.GPU_direct = True
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, normalizer=normalizer)
self.logger.info("Adding X as parameter")
self.add_parameter(self.X, index=0)
@ -91,7 +92,7 @@ class BayesianGPLVM(SparseGP):
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
X.mean.gradient, X.variance.gradient = X_grad
def get_X_gradients(self, X):
"""Get the gradients of the posterior distribution of X in its specific form."""
return X.mean.gradient, X.variance.gradient
@ -218,22 +219,22 @@ class BayesianGPLVM(SparseGP):
del dc['N_list']
del dc['Y_local']
return dc
def __setstate__(self, state):
return super(BayesianGPLVM, self).__setstate__(state)
#=====================================================
# The MPI parallelization
# The MPI parallelization
# - can move to model at some point
#=====================================================
def _set_params_transformed(self, p):
if self.mpi_comm != None:
if self.__IN_OPTIMIZATION__ and self.mpi_comm.rank==0:
self.mpi_comm.Bcast(np.int32(1),root=0)
self.mpi_comm.Bcast(p, root=0)
super(BayesianGPLVM, self)._set_params_transformed(p)
def optimize(self, optimizer=None, start=None, **kwargs):
self.__IN_OPTIMIZATION__ = True
if self.mpi_comm==None:

9
GPy/models/gp_regression.py
View file

@ -15,17 +15,22 @@ class GPRegression(GP):
:param X: input observations
:param Y: observed values
:param kernel: a GPy kernel, defaults to rbf
:param Norm normalizer: [False]
Normalize Y with the norm given.
If normalizer is False, no normalization will be done
If it is None, we use GaussianNorm(alization)
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, Y, kernel=None, Y_metadata=None):
def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None):
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian()
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer)

8
GPy/models/sparse_gp_regression.py
View file

@ -30,7 +30,7 @@ class SparseGPRegression(SparseGP):
"""
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None):
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None):
num_data, input_dim = X.shape
# kern defaults to rbf (plus white for stability)
@ -49,7 +49,7 @@ class SparseGPRegression(SparseGP):
if not (X_variance is None):
X = NormalPosterior(X,X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer)
class SparseGPRegressionUncertainInput(SparseGP):
"""
@ -59,7 +59,7 @@ class SparseGPRegressionUncertainInput(SparseGP):
"""
def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10):
def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, normalizer=None):
"""
:param X: input observations
:type X: np.ndarray (num_data x input_dim)
@ -91,5 +91,5 @@ class SparseGPRegressionUncertainInput(SparseGP):
likelihood = likelihoods.Gaussian()
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC())
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC(), normalizer=normalizer)
self.ensure_default_constraints()