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sparse GP now working nicely

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James Hensman 2014-01-31 10:15:30 +00:00
parent e0dbfbe148
commit 9f40ab0f83
3 changed files with 24 additions and 12 deletions
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12
GPy/inference/latent_function_inference/posterior.py
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@ -14,7 +14,7 @@ class Posterior(object):
schemes and the model classes. schemes and the model classes.
""" """
def __init__(self, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None): def __init__(self, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None, woodbury_inv=None):
""" """
woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K
woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
@ -45,7 +45,9 @@ class Posterior(object):
#obligatory #obligatory
self._K = K self._K = K
if ((woodbury_chol is not None) and (woodbury_vector is not None) and (K is not None)) or ((mean is not None) and (cov is not None) and (K is not None)): if ((woodbury_chol is not None) and (woodbury_vector is not None))\
or ((woodbury_inv is not None) and (woodbury_vector is not None))\
or ((mean is not None) and (cov is not None)):
pass # we have sufficient to compute the posterior pass # we have sufficient to compute the posterior
else: else:
raise ValueError, "insufficient information to compute the posterior" raise ValueError, "insufficient information to compute the posterior"
@ -56,13 +58,16 @@ class Posterior(object):
self._woodbury_chol = woodbury_chol self._woodbury_chol = woodbury_chol
self._woodbury_vector = woodbury_vector self._woodbury_vector = woodbury_vector
#option 2.
self._woodbury_inv = woodbury_inv
#and woodbury vector
#option 2: #option 2:
self._mean = mean self._mean = mean
self._covariance = cov self._covariance = cov
#compute this lazily #compute this lazily
self._precision = None self._precision = None
self._woodbury_inv = None
@property @property
def mean(self): def mean(self):
@ -99,7 +104,6 @@ class Posterior(object):
symmetrify(self._woodbury_inv) symmetrify(self._woodbury_inv)
return self._woodbury_inv return self._woodbury_inv
@property @property
def woodbury_vector(self): def woodbury_vector(self):
if self._woodbury_vector is None: if self._woodbury_vector is None:

13
GPy/inference/latent_function_inference/varDTC.py
View file

@ -2,7 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
from posterior import Posterior from posterior import Posterior
from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dpotri, symmetrify
import numpy as np import numpy as np
log_2_pi = np.log(2*np.pi) log_2_pi = np.log(2*np.pi)
@ -163,7 +163,7 @@ class VarDTC(object):
else: else:
lik_1 = -0.5 * num_data * output_dim * (np.log(2.*np.pi) - np.log(beta)) - 0.5 * beta * trYYT lik_1 = -0.5 * num_data * output_dim * (np.log(2.*np.pi) - np.log(beta)) - 0.5 * beta * trYYT
lik_2 = -0.5 * output_dim * (np.sum(beta * psi0) - np.trace(A)) lik_2 = -0.5 * output_dim * (np.sum(beta * psi0) - np.trace(A))
lik_3 = -output_dim * (np.sum(np.log(np.diag(LB)))) # + 0.5 * num_inducing * np.log(sf2)) lik_3 = -output_dim * (np.sum(np.log(np.diag(LB))))
lik_4 = 0.5 * data_fit lik_4 = 0.5 * data_fit
log_marginal = lik_1 + lik_2 + lik_3 + lik_4 log_marginal = lik_1 + lik_2 + lik_3 + lik_4
@ -178,6 +178,7 @@ class VarDTC(object):
kern.update_gradients_sparse(X=X, Z=Z, **grad_dict) kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
#get sufficient things for posterior prediction #get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
if VVT_factor.shape[1] == Y.shape[1]: if VVT_factor.shape[1] == Y.shape[1]:
woodbury_vector = Cpsi1Vf # == Cpsi1V woodbury_vector = Cpsi1Vf # == Cpsi1V
else: else:
@ -185,11 +186,13 @@ class VarDTC(object):
tmp, _ = dtrtrs(Lm, np.asfortranarray(psi1V), lower=1, trans=0) tmp, _ = dtrtrs(Lm, np.asfortranarray(psi1V), lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1) tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1) woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
#TODO: totally wrong, fix. Bi, _ = dpotri(LB, lower=0)
woodbury_chol = np.eye(num_inducing) symmetrify(Bi)
woodbury_inv = backsub_both_sides(Lm, np.eye(num_inducing) - Bi)
#construct a posterior object #construct a posterior object
post = Posterior(woodbury_chol=woodbury_chol, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm) post = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
return post, log_marginal, grad_dict return post, log_marginal, grad_dict

11
GPy/plotting/matplot_dep/models_plots.py
View file

@ -5,6 +5,7 @@ import pylab as pb
import numpy as np import numpy as np
import Tango import Tango
from base_plots import gpplot, x_frame1D, x_frame2D from base_plots import gpplot, x_frame1D, x_frame2D
from ...util.misc import param_to_array
def plot_fit(model, plot_limits=None, which_data_rows='all', def plot_fit(model, plot_limits=None, which_data_rows='all',
@ -95,8 +96,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#add inducing inputs (if a sparse model is used) #add inducing inputs (if a sparse model is used)
if hasattr(model,"Z"): if hasattr(model,"Z"):
Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims] #Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12) Zu = param_to_array(model.Z[:,free_dims])
z_height = ax.get_ylim()[0]
ax.plot(Zu, np.zeros_like(Zu) + z_height, 'r|', mew=1.5, markersize=12)
#add error bars for uncertain (if input uncertainty is being modelled) #add error bars for uncertain (if input uncertainty is being modelled)
if hasattr(model,"has_uncertain_inputs"): if hasattr(model,"has_uncertain_inputs"):
@ -144,7 +148,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#add inducing inputs (if a sparse model is used) #add inducing inputs (if a sparse model is used)
if hasattr(model,"Z"): if hasattr(model,"Z"):
Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims] #Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = model.Z[:,free_dims]
ax.plot(Zu[:,free_dims[0]], Zu[:,free_dims[1]], 'wo') ax.plot(Zu[:,free_dims[0]], Zu[:,free_dims[1]], 'wo')
else: else: