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[core] updating system, security branching

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Max Zwiessele 2015-09-02 09:06:17 +01:00
parent 938cc49aed
commit 70a9a26d7e
15 changed files with 366 additions and 65 deletions
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26
GPy/core/sparse_gp.py
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@ -59,6 +59,8 @@ class SparseGP(GP):
logger.info("Adding Z as parameter")
self.link_parameter(self.Z, index=0)
self.posterior = None
self._predictive_variable = self.Z
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)
@ -114,10 +116,10 @@ class SparseGP(GP):
Make a prediction for the latent function values.
For certain inputs we give back a full_cov of shape NxN,
if there is missing data, each dimension has its own full_cov of shape NxNxD, and if full_cov is of,
if there is missing data, each dimension has its own full_cov of shape NxNxD, and if full_cov is of,
we take only the diagonal elements across N.
For uncertain inputs, the SparseGP bound produces a full covariance structure across D, so for full_cov we
For uncertain inputs, the SparseGP bound produces a full covariance structure across D, so for full_cov we
return a NxDxD matrix and in the not full_cov case, we return the diagonal elements across D (NxD).
This is for both with and without missing data. See for missing data SparseGP implementation py:class:'~GPy.models.sparse_gp_minibatch.SparseGPMiniBatch'.
"""
@ -125,7 +127,7 @@ class SparseGP(GP):
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self.Z, Xnew)
Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew)
@ -149,28 +151,28 @@ class SparseGP(GP):
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
else:
psi0_star = kern.psi0(self.Z, Xnew)
psi1_star = kern.psi1(self.Z, Xnew)
psi0_star = kern.psi0(self._predictive_variable, Xnew)
psi1_star = kern.psi1(self._predictive_variable, Xnew)
#psi2_star = kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
la = self.posterior.woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions?
if full_cov:
if full_cov:
var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1])
else:
else:
var = np.empty((Xnew.shape[0], la.shape[1]))
for i in range(Xnew.shape[0]):
_mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]]
psi2_star = kern.psi2(self.Z, NormalPosterior(_mu, _var))
psi2_star = kern.psi2(self._predictive_variable, NormalPosterior(_mu, _var))
tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
var_ = mdot(la.T, tmp, la)
p0 = psi0_star[i]
t = np.atleast_3d(self.posterior.woodbury_inv)
t2 = np.trace(t.T.dot(psi2_star), axis1=1, axis2=2)
if full_cov:
var_[di] += p0
var_[di] += -t2