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Merge branch 'mean_functions' into devel

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James Hensman 2015-04-01 09:16:08 +01:00
commit 8c71d52b7f
27 changed files with 461 additions and 137 deletions
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20
GPy/core/gp.py
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@ -5,6 +5,7 @@ import numpy as np
import sys
from .. import kern
from model import Model
from mapping import Mapping
from parameterization import ObsAr
from .. import likelihoods
from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
@ -34,7 +35,7 @@ class GP(Model):
"""
def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp', Y_metadata=None, normalizer=False):
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
@ -75,6 +76,15 @@ class GP(Model):
assert isinstance(likelihood, likelihoods.Likelihood)
self.likelihood = likelihood
#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:
@ -153,9 +163,11 @@ class GP(Model):
This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call
this method yourself, there may be unexpected consequences.
"""
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.Y_metadata)
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.mean_function, self.Y_metadata)
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
if self.mean_function is not None:
self.mean_function.update_gradients(self.grad_dict['dL_dm'], self.X)
def log_likelihood(self):
"""
@ -192,6 +204,10 @@ class GP(Model):
#force mu to be a column vector
if len(mu.shape)==1: mu = mu[:,None]
#add the mean function in
if not self.mean_function is None:
mu += self.mean_function.f(_Xnew)
return mu, var
def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None):