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[more coverage] and predictive var fixes

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Max Zwiessele 2015-09-07 16:52:59 +01:00
parent ec7334846c
commit 929cf0a489
4 changed files with 32 additions and 14 deletions
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40
GPy/core/gp.py
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@ -108,9 +108,15 @@ class GP(Model):
# The predictive variable to be used to predict using the posterior object's
# woodbury_vector and woodbury_inv is defined as predictive_variable
# as long as the posterior has the right woodbury entries.
# It is the input variable used for the covariance between
# X_star and the posterior of the GP.
# This is usually just a link to self.X (full GP) or self.Z (sparse GP).
# Make sure to name this variable and the predict functions will "just work"
# as long as the posterior has the right woodbury entries.
# In maths the predictive variable is:
# K_{xx} - K_{xp}W_{pp}^{-1}K_{px}
# W_{pp} := \texttt{Woodbury inv}
# p := _predictive_variable
self._predictive_variable = self.X
@ -213,7 +219,7 @@ class GP(Model):
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:
elif self.posterior.woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
from ..util.linalg import mdot
for i in range(var.shape[2]):
@ -223,7 +229,7 @@ class GP(Model):
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
elif self.posterior.woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
@ -364,11 +370,15 @@ class GP(Model):
var_jac = dK2_dXdX - np.einsum('qim,miq->iq', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
return var_jac
if self.posterior.woodbury_inv.ndim == 3:
var_jac = []
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac.append(compute_cov_inner(self.posterior.woodbury_inv[:, :, d]))
var_jac = np.concatenate(var_jac)
if self.posterior.woodbury_inv.ndim == 3: # Missing data:
if full_cov:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[0],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = compute_cov_inner(self.posterior.woodbury_inv)
return mean_jac, var_jac
@ -391,10 +401,11 @@ class GP(Model):
mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
Sigma = np.zeros(mumuT.shape)
if var_jac.ndim == 3:
Sigma = np.einsum('iqd,ipd->iqp', var_jac, var_jac)
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = var_jac.sum(-1)
else:
Sigma = self.output_dim*np.einsum('iq,ip->iqp', var_jac, var_jac)
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = self.output_dim*var_jac
G = 0.
if mean:
G += mumuT
@ -412,8 +423,13 @@ class GP(Model):
"""
G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
from ..util.linalg import jitchol
return np.array([np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
#return np.array([np.sqrt(np.linalg.det(G[n, :, :])) for n in range(Xnew.shape[0])])
mag = np.empty(Xnew.shape[0])
for n in range(Xnew.shape[0]):
try:
mag[n] = np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :]))))))
except:
mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
return mag
def posterior_samples_f(self,X,size=10, full_cov=True):
"""