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Merge branch 'devel' of github.com:/sheffieldml/GPy into devel

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This commit is contained in:
James Hensman 2015-02-25 16:10:38 +00:00
commit f1da8c0fec
5 changed files with 48 additions and 53 deletions
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1
GPy/core/gp.py
View file

@ -124,6 +124,7 @@ class GP(Model):
else:
self.X = ObsAr(X)
self.update_model(True)
self._trigger_params_changed()
def set_X(self,X):
"""

3
GPy/core/parameterization/parameter_core.py
View file

@ -1042,6 +1042,9 @@ class Parameterizable(OptimizationHandlable):
p = param_to_array(p)
d = f.create_dataset(n,p.shape,dtype=p.dtype)
d[:] = p
if hasattr(self, 'param_array'):
d = f.create_dataset('param_array',self.param_array.shape, dtype=self.param_array.dtype)
d[:] = self.param_array
f.close()
except:
raise 'Fails to write the parameters into a HDF5 file!'

52
GPy/core/sparse_gp.py
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@ -6,7 +6,8 @@ from gp import GP
from parameterization.param import Param
from ..inference.latent_function_inference import var_dtc
from .. import likelihoods
from parameterization.variational import VariationalPosterior
from parameterization.variational import VariationalPosterior, NormalPosterior
from ..util.linalg import mdot
import logging
from GPy.inference.latent_function_inference.posterior import Posterior
@ -102,7 +103,15 @@ class SparseGP(GP):
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values
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,
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
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.
"""
if kern is None: kern = self.kern
@ -121,15 +130,32 @@ class SparseGP(GP):
Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
else:
Kx = kern.psi1(self.Z, Xnew).T
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew.mean)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
else:
Kxx = kern.psi0(self.Z, Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
psi0_star = self.kern.psi0(self.Z, Xnew)
psi1_star = self.kern.psi1(self.Z, Xnew)
#psi2_star = self.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:
var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1])
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 = self.kern.psi2(self.Z, 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 = 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
var[i] = var_
else:
var[i] = np.diag(var_)+p0-t2
return mu, var

4
GPy/kern/_src/coregionalize.py
View file

@ -154,9 +154,9 @@ class Coregionalize(Kern):
def _gradient_reduce_numpy(self, dL_dK, index, index2):
index, index2 = index[:,0], index2[:,0]
dL_dK_small = np.zeros_like(self.B)
for i in range(k.output_dim):
for i in range(self.output_dim):
tmp1 = dL_dK[index==i]
for j in range(k.output_dim):
for j in range(self.output_dim):
dL_dK_small[j,i] = tmp1[:,index2==j].sum()
return dL_dK_small

41
GPy/models/sparse_gp_minibatch.py
View file

@ -3,6 +3,7 @@
import numpy as np
from ..core.parameterization.param import Param
from ..core.sparse_gp import SparseGP
from ..core.gp import GP
from ..inference.latent_function_inference import var_dtc
from .. import likelihoods
@ -16,14 +17,9 @@ from GPy.inference.optimization.stochastics import SparseGPStochastics,\
#SparseGPMissing
logger = logging.getLogger("sparse gp")
class SparseGPMiniBatch(GP):
class SparseGPMiniBatch(SparseGP):
"""
A general purpose Sparse GP model
'''
Created on 3 Nov 2014
@author: maxz
'''
A general purpose Sparse GP model, allowing missing data and stochastics across dimensions.
This model allows (approximate) inference using variational DTC or FITC
(Gaussian likelihoods) as well as non-conjugate sparse methods based on
@ -315,34 +311,3 @@ Created on 3 Nov 2014
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict, self.full_values, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)
self._outer_values_update(self.full_values)
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values
"""
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self.Z, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = var
else:
Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
else:
Kx = kern.psi1(self.Z, Xnew)
mu = np.dot(Kx, self.posterior.woodbury_vector)
if full_cov:
raise NotImplementedError, "TODO"
else:
Kxx = kern.psi0(self.Z, Xnew)
psi2 = kern.psi2(self.Z, Xnew)
var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
return mu, var