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merged num_data conflicts

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Max Zwiessele 2013-06-05 16:21:54 +01:00
commit b129e6a669
12 changed files with 67 additions and 66 deletions
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GPy/core/sparse_gp.py
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@ -13,13 +13,13 @@ class SparseGP(GPBase):
Variational sparse GP model
:param X: inputs
:type X: np.ndarray (N x input_dim)
:type X: np.ndarray (num_data x input_dim)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
:param kernel : the kernel (covariance function). See link kernels
:type kernel: a GPy.kern.kern instance
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x input_dim) | None
:type X_variance: np.ndarray (num_data x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
@ -69,7 +69,7 @@ class SparseGP(GPBase):
# The rather complex computations of self.A
if self.has_uncertain_inputs:
if self.likelihood.is_heteroscedastic:
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.num_data, 1, 1))).sum(0)
else:
psi2_beta = self.psi2.sum(0) * self.likelihood.precision
evals, evecs = linalg.eigh(psi2_beta)
@ -77,7 +77,7 @@ class SparseGP(GPBase):
tmp = evecs * np.sqrt(clipped_evals)
else:
if self.likelihood.is_heteroscedastic:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.num_data)))
else:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
@ -99,28 +99,28 @@ class SparseGP(GPBase):
# Compute dL_dKmm
tmp = tdot(self._LBi_Lmi_psi1V)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.input_dim * np.eye(self.num_inducing) + tmp)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.output_dim * np.eye(self.num_inducing) + tmp)
tmp = -0.5 * self.DBi_plus_BiPBi
tmp += -0.5 * self.B * self.input_dim
tmp += self.input_dim * np.eye(self.num_inducing)
tmp += -0.5 * self.B * self.output_dim
tmp += self.output_dim * np.eye(self.num_inducing)
self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
self.dL_dpsi0 = -0.5 * self.input_dim * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
self.dL_dpsi0 = -0.5 * self.output_dim * (self.likelihood.precision * np.ones([self.num_data, 1])).flatten()
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.input_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.output_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
if self.likelihood.is_heteroscedastic:
if self.has_uncertain_inputs:
self.dL_dpsi2 = self.likelihood.precision.flatten()[:, None, None] * dL_dpsi2_beta[None, :, :]
else:
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.num_data))
self.dL_dpsi2 = None
else:
dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
if self.has_uncertain_inputs:
# repeat for each of the N psi_2 matrices
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.num_data, axis=0)
else:
# subsume back into psi1 (==Kmn)
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
@ -135,17 +135,17 @@ class SparseGP(GPBase):
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
self.partial_for_likelihood = -0.5 * self.N * self.input_dim * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.input_dim * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood = -0.5 * self.num_data * self.output_dim * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.output_dim * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))