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deleted kernpart, prod and add seem to work okay.

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James Hensman 2014-02-19 17:37:18 +00:00
parent 493506408c
commit 92d71384b7
16 changed files with 95 additions and 238 deletions
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26
GPy/core/gp.py
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@ -70,7 +70,7 @@ class GP(Model):
def log_likelihood(self):
return self._log_marginal_likelihood
def _raw_predict(self, _Xnew, which_parts='all', full_cov=False, stop=False):
def _raw_predict(self, _Xnew, full_cov=False):
"""
Internal helper function for making predictions, does not account
for normalization or likelihood
@ -80,29 +80,27 @@ class GP(Model):
diagonal of the covariance is returned.
"""
Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
Kx = self.kern.K(_Xnew, self.X).T
#LiKx, _ = dtrtrs(self.posterior.woodbury_chol, np.asfortranarray(Kx), lower=1)
WiKx = np.dot(self.posterior.woodbury_inv, Kx)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = self.kern.K(_Xnew, which_parts=which_parts)
Kxx = self.kern.K(_Xnew)
#var = Kxx - tdot(LiKx.T)
var = np.dot(Kx.T, WiKx)
else:
Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
Kxx = self.kern.Kdiag(_Xnew)
#var = Kxx - np.sum(LiKx*LiKx, 0)
var = Kxx - np.sum(WiKx*Kx, 0)
var = var.reshape(-1, 1)
return mu, var
def predict(self, Xnew, which_parts='all', full_cov=False, **likelihood_args):
def predict(self, Xnew, full_cov=False, **likelihood_args):
"""
Predict the function(s) at the new point(s) Xnew.
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param which_parts: specifies which outputs kernel(s) to use in prediction
:type which_parts: ('all', list of bools)
:param full_cov: whether to return the full covariance matrix, or just
the diagonal
:type full_cov: bool
@ -118,13 +116,13 @@ class GP(Model):
"""
#predict the latent function values
mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
mu, var = self._raw_predict(Xnew, full_cov=full_cov)
# now push through likelihood
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
return mean, var, _025pm, _975pm
def posterior_samples_f(self,X,size=10,which_parts='all',full_cov=True):
def posterior_samples_f(self,X,size=10, full_cov=True):
"""
Samples the posterior GP at the points X.
@ -132,13 +130,11 @@ class GP(Model):
:type X: np.ndarray, Nnew x self.input_dim.
:param size: the number of a posteriori samples.
:type size: int.
:param which_parts: which of the kernel functions to use (additively).
:type which_parts: 'all', or list of bools.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
m, v = self._raw_predict(X, which_parts=which_parts, full_cov=full_cov)
m, v = self._raw_predict(X, full_cov=full_cov)
v = v.reshape(m.size,-1) if len(v.shape)==3 else v
if not full_cov:
Ysim = np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T
@ -147,7 +143,7 @@ class GP(Model):
return Ysim
def posterior_samples(self,X,size=10,which_parts='all',full_cov=True,noise_model=None):
def posterior_samples(self,X,size=10, full_cov=True,noise_model=None):
"""
Samples the posterior GP at the points X.
@ -155,15 +151,13 @@ class GP(Model):
:type X: np.ndarray, Nnew x self.input_dim.
:param size: the number of a posteriori samples.
:type size: int.
:param which_parts: which of the kernel functions to use (additively).
:type which_parts: 'all', or list of bools.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
:param noise_model: for mixed noise likelihood, the noise model to use in the samples.
:type noise_model: integer.
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
Ysim = self.posterior_samples_f(X, size, which_parts=which_parts, full_cov=full_cov)
Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
if isinstance(self.likelihood, Gaussian):
noise_std = np.sqrt(self.likelihood._get_params())
Ysim += np.random.normal(0,noise_std,Ysim.shape)