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

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This commit is contained in:
Zhenwen Dai 2015-09-04 12:33:49 +01:00
commit 296e4b078d
1 changed files with 41 additions and 6 deletions
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47
GPy/kern/_src/add.py
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@ -128,6 +128,41 @@ class Add(CombinationKernel):
raise NotImplementedError("psi2 cannot be computed for this kernel") raise NotImplementedError("psi2 cannot be computed for this kernel")
return psi2 return psi2
@Cache_this(limit=2, force_kwargs=['which_parts'])
def psi2n(self, Z, variational_posterior):
psi2 = reduce(np.add, (p.psi2n(Z, variational_posterior) for p in self.parts))
#return psi2
# compute the "cross" terms
from .static import White, Bias
from .rbf import RBF
#from rbf_inv import RBFInv
from .linear import Linear
#ffrom fixed import Fixed
for p1, p2 in itertools.combinations(self.parts, 2):
# i1, i2 = p1.active_dims, p2.active_dims
# white doesn;t combine with anything
if isinstance(p1, White) or isinstance(p2, White):
pass
# rbf X bias
#elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
elif isinstance(p1, Bias) and isinstance(p2, (RBF, Linear)):
tmp = p2.psi1(Z, variational_posterior).sum(axis=0)
psi2 += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
#elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
elif isinstance(p2, Bias) and isinstance(p1, (RBF, Linear)):
tmp = p1.psi1(Z, variational_posterior).sum(axis=0)
psi2 += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
elif isinstance(p2, (RBF, Linear)) and isinstance(p1, (RBF, Linear)):
assert np.intersect1d(p1.active_dims, p2.active_dims).size == 0, "only non overlapping kernel dimensions allowed so far"
tmp1 = p1.psi1(Z, variational_posterior)
tmp2 = p2.psi1(Z, variational_posterior)
psi2 += np.einsum('nm,no->nmo',tmp1,tmp2)+np.einsum('nm,no->nmo',tmp2,tmp1)
#(tmp1[:, :, None] * tmp2[:, None, :]) + (tmp2[:, :, None] * tmp1[:, None, :])
else:
raise NotImplementedError("psi2 cannot be computed for this kernel")
return psi2
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from .static import White, Bias from .static import White, Bias
for p1 in self.parts: for p1 in self.parts:
@ -139,9 +174,9 @@ class Add(CombinationKernel):
if isinstance(p2, White): if isinstance(p2, White):
continue continue
elif isinstance(p2, Bias): elif isinstance(p2, Bias):
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.variance * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
else:# np.setdiff1d(p1.active_dims, ar2, assume_unique): # TODO: Careful, not correct for overlapping active_dims else:# np.setdiff1d(p1.active_dims, ar2, assume_unique): # TODO: Careful, not correct for overlapping active_dims
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.psi1(Z, variational_posterior) * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior) p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
def gradients_Z_expectations(self, dL_psi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): def gradients_Z_expectations(self, dL_psi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
@ -156,9 +191,9 @@ class Add(CombinationKernel):
if isinstance(p2, White): if isinstance(p2, White):
continue continue
elif isinstance(p2, Bias): elif isinstance(p2, Bias):
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.variance * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
else: else:
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.psi1(Z, variational_posterior) * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
target += p1.gradients_Z_expectations(dL_psi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior) target += p1.gradients_Z_expectations(dL_psi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
return target return target
@ -174,9 +209,9 @@ class Add(CombinationKernel):
if isinstance(p2, White): if isinstance(p2, White):
continue continue
elif isinstance(p2, Bias): elif isinstance(p2, Bias):
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.variance * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
else: else:
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.psi1(Z, variational_posterior) * 2. eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
grads = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior) grads = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
[np.add(target_grads[i],grads[i],target_grads[i]) for i in range(len(grads))] [np.add(target_grads[i],grads[i],target_grads[i]) for i in range(len(grads))]
return target_grads return target_grads