def __init__(self, X, Y, kernel, likelihood, mean_function=None, inference_method=None, name='gp', Y_metadata=None, normalizer=False):
super(GP, self).__init__(name)
assert X.ndim == 2
if isinstance(X, (ObsAr, VariationalPosterior)):
self.X = X.copy()
else: self.X = ObsAr(X)
self.num_data, self.input_dim = self.X.shape
assert Y.ndim == 2
logger.info("initializing Y")
if normalizer is True:
self.normalizer = MeanNorm()
elif normalizer is False:
self.normalizer = None
else:
self.normalizer = normalizer
if self.normalizer is not None:
self.normalizer.scale_by(Y)
self.Y_normalized = ObsAr(self.normalizer.normalize(Y))
self.Y = Y
elif isinstance(Y, np.ndarray):
self.Y = ObsAr(Y)
self.Y_normalized = self.Y
else:
self.Y = Y
if Y.shape[0] != self.num_data:
#There can be cases where we want inputs than outputs, for example if we have multiple latent
#function values
warnings.warn("There are more rows in your input data X, \
than in your output data Y, be VERY sure this is what you want")
_, self.output_dim = self.Y.shape
assert ((Y_metadata is None) or isinstance(Y_metadata, dict))
self.Y_metadata = Y_metadata
assert isinstance(kernel, kern.Kern)
#assert self.input_dim == kernel.input_dim
self.kern = kernel
assert isinstance(likelihood, likelihoods.Likelihood)
self.likelihood = likelihood
if self.kern._effective_input_dim != self.X.shape[1]:
warnings.warn("Your kernel has a different input dimension {} then the given X dimension {}. Be very sure this is what you want and you have not forgotten to set the right input dimenion in your kernel".format(self.kern._effective_input_dim, self.X.shape[1]))
#handle the mean function
self.mean_function = mean_function
if mean_function is not None:
assert isinstance(self.mean_function, Mapping)
assert mean_function.input_dim == self.input_dim
assert mean_function.output_dim == self.output_dim
self.link_parameter(mean_function)
#find a sensible inference method
logger.info("initializing inference method")
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian) or isinstance(likelihood, likelihoods.MixedNoise):
inference_method = exact_gaussian_inference.ExactGaussianInference()
else:
inference_method = expectation_propagation.EP()
print("defaulting to " + str(inference_method) + " for latent function inference")
self.inference_method = inference_method
logger.info("adding kernel and likelihood as parameters")
self.link_parameter(self.kern)
self.link_parameter(self.likelihood)
self.posterior = None
def __init__(self,
X,
Y,
kernel,
likelihood,
inference_method=None,
name='gp',
Y_metadata=None,
normalizer=False):
super(GP, self).__init__(name)
assert X.ndim == 2
if isinstance(X, (ObsAr, VariationalPosterior)):
self.X = X.copy()
else:
self.X = ObsAr(X)
self.num_data, self.input_dim = self.X.shape
assert Y.ndim == 2
logger.info("initializing Y")
if normalizer is True:
self.normalizer = MeanNorm()
elif normalizer is False:
self.normalizer = None
else:
self.normalizer = normalizer
if self.normalizer is not None:
self.normalizer.scale_by(Y)
self.Y_normalized = ObsAr(self.normalizer.normalize(Y))
self.Y = Y
else:
self.Y = ObsAr(Y)
self.Y_normalized = self.Y
assert Y.shape[0] == self.num_data
_, self.output_dim = self.Y.shape
#TODO: check the type of this is okay?
self.Y_metadata = Y_metadata
assert isinstance(kernel, kern.Kern)
#assert self.input_dim == kernel.input_dim
self.kern = kernel
assert isinstance(likelihood, likelihoods.Likelihood)
self.likelihood = likelihood
#find a sensible inference method
logger.info("initializing inference method")
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian) or isinstance(
likelihood, likelihoods.MixedNoise):
inference_method = exact_gaussian_inference.ExactGaussianInference(
)
else:
inference_method = expectation_propagation.EP()
print "defaulting to ", inference_method, "for latent function inference"
self.inference_method = inference_method
logger.info("adding kernel and likelihood as parameters")
self.link_parameter(self.kern)
self.link_parameter(self.likelihood)
