def _compute_bound(self, R, logdet=None, inv=None, gradient=False, terms=False):
"""
Rotate q(X) as X->RX: q(X)=N(R*mu, R*Cov*R')
Assume:
:math:`p(\mathbf{X}) = \prod^M_{m=1}
N(\mathbf{x}_m|0, \mathbf{\Lambda})`
Assume unit innovation noise covariance.
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check
# that.
# Assume constant mean and precision matrix over plates..
if inv is None:
invR = np.linalg.inv(R)
else:
invR = inv
if logdet is None:
logdetR = np.linalg.slogdet(R)[1]
else:
logdetR = logdet
# Transform moments of X and A:
Lambda_R_X0X0 = sum_to_plates(dot(self.Lambda, R, self.X0X0), (), plates_from=self.X_node.plates, ndim=2)
R_XnXn = dot(R, self.XnXn)
RA_XpXp_A = dot(R, self.A_XpXp_A)
sumr = np.sum(R, axis=0)
R_CovA_XpXp = sumr * self.CovA_XpXp
# Compute entropy H(X)
M = self.N * np.prod(self.X_node.plates) # total number of rotated vectors
logH_X = random.gaussian_entropy(-2 * M * logdetR, 0)
# Compute <log p(X)>
yy = tracedot(R_XnXn, R.T) + tracedot(Lambda_R_X0X0, R.T)
yz = tracedot(dot(R, self.A_XpXn), R.T) + tracedot(self.Lambda_mu_X0, R.T)
zz = tracedot(RA_XpXp_A, R.T) + np.einsum("...k,...k->...", R_CovA_XpXp, sumr)
logp_X = random.gaussian_logpdf(yy, yz, zz, 0, 0)
# Compute the bound
if terms:
bound = {self.X_node: logp_X + logH_X}
else:
bound = logp_X + logH_X
if not gradient:
return bound
# Compute dH(X)
dlogH_X = random.gaussian_entropy(-2 * M * invR.T, 0)
# Compute d<log p(X)>
dyy = 2 * (R_XnXn + Lambda_R_X0X0)
dyz = dot(R, self.A_XpXn + self.A_XpXn.T) + self.Lambda_mu_X0
dzz = 2 * (RA_XpXp_A + R_CovA_XpXp[None, :])
dlogp_X = random.gaussian_logpdf(dyy, dyz, dzz, 0, 0)
if terms:
d_bound = {self.X_node: dlogp_X + dlogH_X}
else:
d_bound = +dlogp_X + dlogH_X
return (bound, d_bound)
def _compute_bound(self, R, logdet=None, inv=None, Q=None, gradient=False):
"""
Rotate q(X) and q(alpha).
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check that.
#
# Transform the distributions and moments
#
D1 = self.D1
D2 = self.D2
N = self.N
D = D1 * D2
# Compute rotated second moment
if Q is not None:
X = np.reshape(self.X, (N,D1,D2))
CovX = np.reshape(self.CovX, (N,D1,D2,D1,D2))
# Rotate plates
sumQ = np.sum(Q, axis=0)
QX = np.einsum('ik,kab->iab', Q, X)
logdet_Q = np.sum(np.log(np.abs(sumQ)))
if self.axis == 'cols':
# Sum "rows"
#X = np.einsum('nkj->nj', X)
# Rotate "columns"
X_R = np.einsum('jk,nik->nij', R, X)
r_CovX_r = np.einsum('bk,bl,nakal->nab', R, R, CovX)
XX = (np.einsum('kai,kaj->aij', QX, QX)
+ np.einsum('d,daiaj->aij', sumQ**2, CovX))
else:
# Rotate "rows"
#print("OR THE BUG IS HERE...")
X_R = np.einsum('ik,nkj->nji', R, X)
r_CovX_r = np.einsum('ak,al,nkblb->nba', R, R, CovX)
XX = (np.einsum('kib,kjb->bij', QX, QX)
+ np.einsum('d,dibjb->bij', sumQ**2, CovX))
Q_X_R = np.einsum('nk,kij->nij', Q, X_R)
else:
# Reshape into matrix form
sh = (D1,D2,D1,D2)
XX = np.reshape(self.XX, sh)
if self.axis == 'cols':
XX = np.einsum('aiaj->aij', XX)
else:
XX = np.einsum('ibjb->bij', XX)
logdet_Q = 0
# Reshape vector to matrix
if self.axis == 'cols':
# Apply rotation on the right
R_XX = np.einsum('ik,akj->aij', R, XX)
r_XX_r = np.einsum('ik,il,akl->ai', R, R, XX)
if Q is not None:
# Debug stuff:
#print("debug for Q", np.shape(XX_R))
r_XQQX_r = np.einsum('ab->ab', r_XX_r) #r_XX_r #np.einsum('abab->b', XX_R)
# Compute q(alpha)
a_alpha = self.a
b_alpha = self.b0 + 0.5 * r_XX_r #np.einsum('ab->b', r_XX_r)
alpha_R = a_alpha / b_alpha
logalpha_R = -np.log(b_alpha) # + const
logdet_R = logdet
inv_R = inv
# Bug in here:
XX_dalpha = -np.einsum('ab,ab,abj->bj', alpha_R/b_alpha, r_XX_r, R_XX)
#XX = np.einsum('aiaj->ij', XX)
#XX_R = np.einsum('aiaj->ij', XX_R)
#print("THERE")
alpha_R_XX = np.einsum('ai,aij->ij', alpha_R, R_XX) # BUG IN HERE? In gradient
dalpha_R_XX = np.einsum('ai,aij->ij', alpha_R/b_alpha, R_XX)
invb_R_XX = np.einsum('ai,aij->ij', 1/b_alpha, R_XX)
#dalpha_RXXR = np.einsum('i,ii->i', alpha_R/b_alpha, XX_R)
ND = self.N * D1
else:
# Apply rotation on the left
R_XX = np.einsum('ik,bkj->bij', R, XX)
r_XX_r = np.einsum('ik,il,bkl->bi', R, R, XX)
if Q is not None:
# Debug stuff:
#print("debug for Q", np.shape(XX_R))
r_XQQX_r = np.einsum('bi->bi', r_XX_r) #np.einsum('abab->b', XX_R)
# Compute q(alpha)
a_alpha = self.a
b_alpha = self.b0 + 0.5 * r_XX_r #np.einsum('bi->b', r_XX_r)
#b_alpha = self.b0 + 0.5 * np.einsum('abab->b', XX_R)
alpha_R = a_alpha / b_alpha
logalpha_R = -np.log(b_alpha) # + const
logdet_R = logdet
inv_R = inv
#print("HERE IS THE BUG SOMEWHERE")
# Compute: <alpha>_* R <
#print(np.shape(alpha_R), np.shape(R), np.shape(XX), np.shape(R_XX), QX.shape, D1, D2)
alpha_R_XX = np.einsum('bi,bij->ij', alpha_R, R_XX)
dalpha_R_XX = np.einsum('bi,bij->ij', alpha_R/b_alpha, R_XX)
invb_R_XX = np.einsum('bi,bij->ij', 1/b_alpha, R_XX)
#dalpha_RXXR = np.einsum('b,ibib->i', alpha_R/b_alpha, XX_R)
XX_dalpha = -np.einsum('ba,ba,baj->aj', alpha_R/b_alpha, r_XX_r, R_XX)
#XX = np.einsum('ibjb->ij', XX)
#XX_R = np.nan * np.einsum('ibjb->ij', XX_R)
ND = self.N * D2
#
# Compute the cost
#
# Compute entropy H(X)
logH_X = utils.random.gaussian_entropy(-2*ND*logdet_R - 2*D*logdet_Q,
0)
# Compute entropy H(alpha)
logH_alpha = utils.random.gamma_entropy(0,
np.sum(np.log(b_alpha)),
0,
0,
0)
# Compute <log p(X|alpha)>
#logp_X = utils.random.gaussian_logpdf(np.einsum('ii,i', XX_R, alpha_R),
logp_X = utils.random.gaussian_logpdf(tracedot(alpha_R_XX, R.T),
0,
0,
N*np.sum(logalpha_R),
0)
# Compute <log p(alpha)>
logp_alpha = utils.random.gamma_logpdf(self.b0*np.sum(alpha_R),
np.sum(logalpha_R),
self.a0*np.sum(logalpha_R),
0,
0)
# Compute the bound
bound = (0
+ logp_X
+ logp_alpha
+ logH_X
+ logH_alpha
)
if not gradient:
return bound
#
# Compute the gradient with respect to R
#
# Compute dH(X)
dlogH_X = utils.random.gaussian_entropy(-2*ND*inv_R.T,
0)
# Compute dH(alpha)
d_log_b = invb_R_XX #np.einsum('i,ik,kj->ij', 1/b_alpha, R, XX)
dlogH_alpha = utils.random.gamma_entropy(0,
d_log_b,
0,
0,
0)
# Compute d<log p(X|alpha)>
d_log_alpha = -d_log_b
dXX_alpha = 2*alpha_R_XX #np.einsum('i,ik,kj->ij', alpha_R, R, XX)
#dalpha_xx = np.einsum('id,di', dalpha_R_XX, R.T)
#XX_dalpha = -np.einsum('i,ik,kj', dalpha_RXXR, R, XX) # BUG IS IN THIS TERM!!!!
#np.einsum('i,i,ii,ik,kj->ij', alpha_R, 1/b_alpha, XX_R, R, XX)
# TODO/FIXME: This gradient term seems to have a bug.
#
# DEBUG: If you set these gradient terms to zero, the gradient is more
# accurate..?!
#dXX_alpha = 0
#XX_dalpha = 0
dlogp_X = utils.random.gaussian_logpdf(dXX_alpha + XX_dalpha,
0,
0,
N*d_log_alpha,
0)
# Compute d<log p(alpha)>
d_alpha = -dalpha_R_XX #np.einsum('i,i,ik,kj->ij', alpha_R, 1/b_alpha, R, XX)
dlogp_alpha = utils.random.gamma_logpdf(self.b0*d_alpha,
d_log_alpha,
self.a0*d_log_alpha,
0,
0)
dR_bound = (0*dlogp_X
+ dlogp_X
+ dlogp_alpha
+ dlogH_X
+ dlogH_alpha
)
if Q is None:
return (bound, dR_bound)
#
# Compute the gradient with respect to Q (if Q given)
#
def d_helper(v):
return (np.einsum('iab,ab,jab->ij', Q_X_R, v, X_R)
+ np.einsum('n,ab,nab->n', sumQ, v, r_CovX_r))
# Compute dH(X)
dQ_logHX = utils.random.gaussian_entropy(-2*D/sumQ,
0)
# Compute dH(alpha)
d_log_b = d_helper(1/b_alpha)
dQ_logHalpha = utils.random.gamma_entropy(0,
d_log_b,
0,
0,
0)
# Compute d<log p(X|alpha)>
dXX_alpha = 2*d_helper(alpha_R)
XX_dalpha = -d_helper(r_XQQX_r*alpha_R/b_alpha)
d_log_alpha = -d_log_b
dQ_logpX = utils.random.gaussian_logpdf(dXX_alpha + XX_dalpha,
0,
0,
N*d_log_alpha,
0)
# Compute d<log p(alpha)>
d_alpha = -d_helper(alpha_R/b_alpha)
dQ_logpalpha = utils.random.gamma_logpdf(self.b0*d_alpha,
d_log_alpha,
self.a0*d_log_alpha,
0,
0)
dQ_bound = (0*dQ_logpX
+ dQ_logpX
+ dQ_logpalpha
+ dQ_logHX
+ dQ_logHalpha
)
return (bound, dR_bound, dQ_bound)
def _compute_bound(self, R, logdet=None, inv=None, Q=None, gradient=False):
"""
Rotate q(X) and q(alpha).
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check that.
#
# Transform the distributions and moments
#
D1 = self.D1
D2 = self.D2
N = self.N
D = D1 * D2
# Compute rotated second moment
if Q is not None:
X = np.reshape(self.X, (N, D1, D2))
CovX = np.reshape(self.CovX, (N, D1, D2, D1, D2))
# Rotate plates
sumQ = np.sum(Q, axis=0)
QX = np.einsum('ik,kab->iab', Q, X)
logdet_Q = np.sum(np.log(np.abs(sumQ)))
if self.axis == 'cols':
# Sum "rows"
#X = np.einsum('nkj->nj', X)
# Rotate "columns"
X_R = np.einsum('jk,nik->nij', R, X)
r_CovX_r = np.einsum('bk,bl,nakal->nab', R, R, CovX)
XX = (np.einsum('kai,kaj->aij', QX, QX) +
np.einsum('d,daiaj->aij', sumQ**2, CovX))
else:
# Rotate "rows"
#print("OR THE BUG IS HERE...")
X_R = np.einsum('ik,nkj->nji', R, X)
r_CovX_r = np.einsum('ak,al,nkblb->nba', R, R, CovX)
XX = (np.einsum('kib,kjb->bij', QX, QX) +
np.einsum('d,dibjb->bij', sumQ**2, CovX))
Q_X_R = np.einsum('nk,kij->nij', Q, X_R)
else:
# Reshape into matrix form
sh = (D1, D2, D1, D2)
XX = np.reshape(self.XX, sh)
if self.axis == 'cols':
XX = np.einsum('aiaj->aij', XX)
else:
XX = np.einsum('ibjb->bij', XX)
logdet_Q = 0
# Reshape vector to matrix
if self.axis == 'cols':
# Apply rotation on the right
R_XX = np.einsum('ik,akj->aij', R, XX)
r_XX_r = np.einsum('ik,il,akl->ai', R, R, XX)
if Q is not None:
# Debug stuff:
#print("debug for Q", np.shape(XX_R))
r_XQQX_r = np.einsum(
'ab->ab', r_XX_r) #r_XX_r #np.einsum('abab->b', XX_R)
# Compute q(alpha)
a_alpha = self.a
b_alpha = self.b0 + 0.5 * r_XX_r #np.einsum('ab->b', r_XX_r)
alpha_R = a_alpha / b_alpha
logalpha_R = -np.log(b_alpha) # + const
logdet_R = logdet
inv_R = inv
# Bug in here:
XX_dalpha = -np.einsum('ab,ab,abj->bj', alpha_R / b_alpha, r_XX_r,
R_XX)
#XX = np.einsum('aiaj->ij', XX)
#XX_R = np.einsum('aiaj->ij', XX_R)
#print("THERE")
alpha_R_XX = np.einsum('ai,aij->ij', alpha_R,
R_XX) # BUG IN HERE? In gradient
dalpha_R_XX = np.einsum('ai,aij->ij', alpha_R / b_alpha, R_XX)
invb_R_XX = np.einsum('ai,aij->ij', 1 / b_alpha, R_XX)
#dalpha_RXXR = np.einsum('i,ii->i', alpha_R/b_alpha, XX_R)
ND = self.N * D1
else:
# Apply rotation on the left
R_XX = np.einsum('ik,bkj->bij', R, XX)
r_XX_r = np.einsum('ik,il,bkl->bi', R, R, XX)
if Q is not None:
# Debug stuff:
#print("debug for Q", np.shape(XX_R))
r_XQQX_r = np.einsum('bi->bi',
r_XX_r) #np.einsum('abab->b', XX_R)
# Compute q(alpha)
a_alpha = self.a
b_alpha = self.b0 + 0.5 * r_XX_r #np.einsum('bi->b', r_XX_r)
#b_alpha = self.b0 + 0.5 * np.einsum('abab->b', XX_R)
alpha_R = a_alpha / b_alpha
logalpha_R = -np.log(b_alpha) # + const
logdet_R = logdet
inv_R = inv
#print("HERE IS THE BUG SOMEWHERE")
# Compute: <alpha>_* R <
#print(np.shape(alpha_R), np.shape(R), np.shape(XX), np.shape(R_XX), QX.shape, D1, D2)
alpha_R_XX = np.einsum('bi,bij->ij', alpha_R, R_XX)
dalpha_R_XX = np.einsum('bi,bij->ij', alpha_R / b_alpha, R_XX)
invb_R_XX = np.einsum('bi,bij->ij', 1 / b_alpha, R_XX)
#dalpha_RXXR = np.einsum('b,ibib->i', alpha_R/b_alpha, XX_R)
XX_dalpha = -np.einsum('ba,ba,baj->aj', alpha_R / b_alpha, r_XX_r,
R_XX)
#XX = np.einsum('ibjb->ij', XX)
#XX_R = np.nan * np.einsum('ibjb->ij', XX_R)
ND = self.N * D2
#
# Compute the cost
#
# Compute entropy H(X)
logH_X = utils.random.gaussian_entropy(
-2 * ND * logdet_R - 2 * D * logdet_Q, 0)
# Compute entropy H(alpha)
logH_alpha = utils.random.gamma_entropy(0, np.sum(np.log(b_alpha)), 0,
0, 0)
# Compute <log p(X|alpha)>
#logp_X = utils.random.gaussian_logpdf(np.einsum('ii,i', XX_R, alpha_R),
logp_X = utils.random.gaussian_logpdf(tracedot(alpha_R_XX, R.T), 0, 0,
N * np.sum(logalpha_R), 0)
# Compute <log p(alpha)>
logp_alpha = utils.random.gamma_logpdf(self.b0 * np.sum(alpha_R),
np.sum(logalpha_R),
self.a0 * np.sum(logalpha_R), 0,
0)
# Compute the bound
bound = (0 + logp_X + logp_alpha + logH_X + logH_alpha)
if not gradient:
return bound
#
# Compute the gradient with respect to R
#
# Compute dH(X)
dlogH_X = utils.random.gaussian_entropy(-2 * ND * inv_R.T, 0)
# Compute dH(alpha)
d_log_b = invb_R_XX #np.einsum('i,ik,kj->ij', 1/b_alpha, R, XX)
dlogH_alpha = utils.random.gamma_entropy(0, d_log_b, 0, 0, 0)
# Compute d<log p(X|alpha)>
d_log_alpha = -d_log_b
dXX_alpha = 2 * alpha_R_XX #np.einsum('i,ik,kj->ij', alpha_R, R, XX)
#dalpha_xx = np.einsum('id,di', dalpha_R_XX, R.T)
#XX_dalpha = -np.einsum('i,ik,kj', dalpha_RXXR, R, XX) # BUG IS IN THIS TERM!!!!
#np.einsum('i,i,ii,ik,kj->ij', alpha_R, 1/b_alpha, XX_R, R, XX)
# TODO/FIXME: This gradient term seems to have a bug.
#
# DEBUG: If you set these gradient terms to zero, the gradient is more
# accurate..?!
#dXX_alpha = 0
#XX_dalpha = 0
dlogp_X = utils.random.gaussian_logpdf(dXX_alpha + XX_dalpha, 0, 0,
N * d_log_alpha, 0)
# Compute d<log p(alpha)>
d_alpha = -dalpha_R_XX #np.einsum('i,i,ik,kj->ij', alpha_R, 1/b_alpha, R, XX)
dlogp_alpha = utils.random.gamma_logpdf(self.b0 * d_alpha, d_log_alpha,
self.a0 * d_log_alpha, 0, 0)
dR_bound = (0 * dlogp_X + dlogp_X + dlogp_alpha + dlogH_X +
dlogH_alpha)
if Q is None:
return (bound, dR_bound)
#
# Compute the gradient with respect to Q (if Q given)
#
def d_helper(v):
return (np.einsum('iab,ab,jab->ij', Q_X_R, v, X_R) +
np.einsum('n,ab,nab->n', sumQ, v, r_CovX_r))
# Compute dH(X)
dQ_logHX = utils.random.gaussian_entropy(-2 * D / sumQ, 0)
# Compute dH(alpha)
d_log_b = d_helper(1 / b_alpha)
dQ_logHalpha = utils.random.gamma_entropy(0, d_log_b, 0, 0, 0)
# Compute d<log p(X|alpha)>
dXX_alpha = 2 * d_helper(alpha_R)
XX_dalpha = -d_helper(r_XQQX_r * alpha_R / b_alpha)
d_log_alpha = -d_log_b
dQ_logpX = utils.random.gaussian_logpdf(dXX_alpha + XX_dalpha, 0, 0,
N * d_log_alpha, 0)
# Compute d<log p(alpha)>
d_alpha = -d_helper(alpha_R / b_alpha)
dQ_logpalpha = utils.random.gamma_logpdf(self.b0 * d_alpha,
d_log_alpha,
self.a0 * d_log_alpha, 0, 0)
dQ_bound = (0 * dQ_logpX + dQ_logpX + dQ_logpalpha + dQ_logHX +
dQ_logHalpha)
return (bound, dR_bound, dQ_bound)
def _compute_bound(self, R, logdet=None, inv=None, gradient=False):
"""
Rotate q(X) as X->RX: q(X)=N(R*mu, R*Cov*R')
Assume:
:math:`p(\mathbf{X}) = \prod^M_{m=1}
N(\mathbf{x}_m|0, \mathbf{\Lambda})`
Assume unit innovation noise covariance.
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check
# that.
# TODO/FIXME: Allow non-zero prior mean!
# Assume constant mean and precision matrix over plates..
invR = inv
logdetR = logdet
# Transform moments of X:
# Transform moments of A:
Lambda_R_X0X0 = dot(self.Lambda, R, self.X0X0)
R_XnXn = dot(R, self.XnXn)
RA_XpXp_A = dot(R, self.A_XpXp_A)
sumr = np.sum(R, axis=0)
R_CovA_XpXp = sumr * self.CovA_XpXp
## if not gradient:
## print("DEBUG TOO", dot(R_XnXn,R.T))
# Compute entropy H(X)
logH_X = utils.random.gaussian_entropy(-2*self.N*logdetR,
0)
# Compute <log p(X)>
yy = tracedot(R_XnXn, R.T) + tracedot(Lambda_R_X0X0, R.T)
yz = tracedot(dot(R,self.A_XpXn),R.T) + tracedot(self.Lambda_mu_X0, R.T)
zz = tracedot(RA_XpXp_A, R.T) + np.dot(R_CovA_XpXp, sumr) #RR_CovA_XpXp
logp_X = utils.random.gaussian_logpdf(yy,
yz,
zz,
0,
0)
# Compute dH(X)
dlogH_X = utils.random.gaussian_entropy(-2*self.N*invR.T,
0)
# Compute the bound
bound = (0
+ logp_X
+ logH_X
)
# TODO/FIXME: There might be a very small error in the gradient?
if gradient:
# Compute d<log p(X)>
dyy = 2 * (R_XnXn + Lambda_R_X0X0)
dyz = dot(R, self.A_XpXn + self.A_XpXn.T) + self.Lambda_mu_X0
dzz = 2 * (RA_XpXp_A + R_CovA_XpXp)
dlogp_X = utils.random.gaussian_logpdf(dyy,
dyz,
dzz,
0,
0)
d_bound = (0*dlogp_X
+ dlogp_X
+ dlogH_X
)
return (bound, d_bound)
else:
return bound
def _compute_bound(self, R, logdet=None, inv=None, gradient=False):
"""
Rotate q(X) as X->RX: q(X)=N(R*mu, R*Cov*R')
Assume:
:math:`p(\mathbf{X}) = \prod^M_{m=1}
N(\mathbf{x}_m|0, \mathbf{\Lambda})`
Assume unit innovation noise covariance.
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check
# that.
# TODO/FIXME: Allow non-zero prior mean!
# Assume constant mean and precision matrix over plates..
invR = inv
logdetR = logdet
# Transform moments of X:
# Transform moments of A:
Lambda_R_X0X0 = dot(self.Lambda, R, self.X0X0)
R_XnXn = dot(R, self.XnXn)
RA_XpXp_A = dot(R, self.A_XpXp_A)
sumr = np.sum(R, axis=0)
R_CovA_XpXp = sumr * self.CovA_XpXp
## if not gradient:
## print("DEBUG TOO", dot(R_XnXn,R.T))
# Compute entropy H(X)
logH_X = utils.random.gaussian_entropy(-2 * self.N * logdetR, 0)
# Compute <log p(X)>
yy = tracedot(R_XnXn, R.T) + tracedot(Lambda_R_X0X0, R.T)
yz = tracedot(dot(R, self.A_XpXn), R.T) + tracedot(
self.Lambda_mu_X0, R.T)
zz = tracedot(RA_XpXp_A, R.T) + np.dot(R_CovA_XpXp,
sumr) #RR_CovA_XpXp
logp_X = utils.random.gaussian_logpdf(yy, yz, zz, 0, 0)
# Compute dH(X)
dlogH_X = utils.random.gaussian_entropy(-2 * self.N * invR.T, 0)
# Compute the bound
bound = (0 + logp_X + logH_X)
# TODO/FIXME: There might be a very small error in the gradient?
if gradient:
# Compute d<log p(X)>
dyy = 2 * (R_XnXn + Lambda_R_X0X0)
dyz = dot(R, self.A_XpXn + self.A_XpXn.T) + self.Lambda_mu_X0
dzz = 2 * (RA_XpXp_A + R_CovA_XpXp)
dlogp_X = utils.random.gaussian_logpdf(dyy, dyz, dzz, 0, 0)
d_bound = (0 * dlogp_X + dlogp_X + dlogH_X)
return (bound, d_bound)
else:
return bound
