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slicing now returns the right shape, when computing derivative wrt X or Z

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Max Zwiessele 2014-03-17 15:43:09 +00:00
parent f88b4c92e8
commit 62d594d977
3 changed files with 50 additions and 18 deletions
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14
GPy/kern/_src/add.py
View file

@ -58,7 +58,13 @@ class Add(CombinationKernel):
:type X2: np.ndarray (num_inducing x input_dim)"""
target = np.zeros(X.shape)
[target.__setitem__([Ellipsis, p.active_dims], target[:, p.active_dims]+p.gradients_X(dL_dK, X, X2)) for p in self.parts]
[target.__iadd__(p.gradients_X(dL_dK, X, X2)) for p in self.parts]
return target
def gradients_X_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape)
[target.__iadd__(p.gradients_X_diag(dL_dKdiag, X)) for p in self.parts]
#[target.__setitem__([Ellipsis, p.active_dims], target[:, p.active_dims]+p.gradients_X(dL_dK, X, X2)) for p in self.parts]
return target
def psi0(self, Z, variational_posterior):
@ -131,7 +137,7 @@ class Add(CombinationKernel):
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
else:
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
target[:, p1.active_dims] += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
target += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
return target
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
@ -151,8 +157,8 @@ class Add(CombinationKernel):
else:
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
target_mu[:, p1.active_dims] += a
target_S[:, p1.active_dims] += b
target_mu += a
target_S += b
return target_mu, target_S
def _getstate(self):

46
GPy/kern/_src/kernel_slice_operations.py
View file

@ -4,6 +4,7 @@ Created on 11 Mar 2014
@author: maxz
'''
from ...core.parameterization.parameterized import ParametersChangedMeta
import numpy as np
class KernCallsViaSlicerMeta(ParametersChangedMeta):
def __call__(self, *args, **kw):
@ -12,18 +13,18 @@ class KernCallsViaSlicerMeta(ParametersChangedMeta):
instance.Kdiag = _slice_wrapper(instance, instance.Kdiag, diag=True)
instance.update_gradients_full = _slice_wrapper(instance, instance.update_gradients_full, diag=False, derivative=True)
instance.update_gradients_diag = _slice_wrapper(instance, instance.update_gradients_diag, diag=True, derivative=True)
instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, diag=False, derivative=True)
instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, diag=True, derivative=True)
instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, diag=False, derivative=True, ret_X=True)
instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, diag=True, derivative=True, ret_X=True)
instance.psi0 = _slice_wrapper(instance, instance.psi0, diag=False, derivative=False)
instance.psi1 = _slice_wrapper(instance, instance.psi1, diag=False, derivative=False)
instance.psi2 = _slice_wrapper(instance, instance.psi2, diag=False, derivative=False)
instance.update_gradients_expectations = _slice_wrapper(instance, instance.update_gradients_expectations, derivative=True, psi_stat=True)
instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, derivative=True, psi_stat_Z=True)
instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, derivative=True, psi_stat=True)
instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, derivative=True, psi_stat_Z=True, ret_X=True)
instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, derivative=True, psi_stat=True, ret_X=True)
instance.parameters_changed()
return instance
def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False, psi_stat_Z=False):
def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False, psi_stat_Z=False, ret_X=False):
"""
This method wraps the functions in kernel to make sure all kernels allways see their respective input dimension.
The different switches are:
@ -34,11 +35,16 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
"""
if derivative:
if diag:
def x_slice_wrapper(dL_dK, X):
def x_slice_wrapper(dL_dKdiag, X):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced:
ret = np.zeros(X.shape)
X = kern._slice_X(X) if not kern._sliced_X else X
# if the return value is of shape X.shape, we need to make sure to return the right shape
kern._sliced_X += 1
try:
ret = operation(dL_dK, X)
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dKdiag, X)
else: ret = operation(dL_dKdiag, X)
except:
raise
finally:
@ -46,10 +52,22 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
return ret
elif psi_stat:
def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced:
ret1, ret2 = np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
kern._sliced_X += 1
# if the return value is of shape X.shape, we need to make sure to return the right shape
try:
ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
if ret_X_not_sliced:
ret = list(operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
r2 = ret[:2]
ret[0] = ret1
ret[1] = ret2
ret[0][:, kern.active_dims] = r2[0]
ret[1][:, kern.active_dims] = r2[1]
del r2
else: ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
@ -57,10 +75,14 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
return ret
elif psi_stat_Z:
def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced: ret = np.zeros(Z.shape)
Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
kern._sliced_X += 1
try:
ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
if ret_X_not_sliced:
ret[:, kern.active_dims] = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
else: ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
@ -68,10 +90,14 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
return ret
else:
def x_slice_wrapper(dL_dK, X, X2=None):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced:
ret = np.zeros(X.shape)
X, X2 = kern._slice_X(X) if not kern._sliced_X else X, kern._slice_X(X2) if X2 is not None and not kern._sliced_X else X2
kern._sliced_X += 1
try:
ret = operation(dL_dK, X, X2)
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dK, X, X2)
else: ret = operation(dL_dK, X, X2)
except:
raise
finally:

8
GPy/kern/_src/prod.py
View file

@ -51,15 +51,15 @@ class Prod(CombinationKernel):
def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape)
for k1,k2 in itertools.combinations(self.parts, 2):
target[:,k1.active_dims] += k1.gradients_X(dL_dK*k2.K(X, X2), X, X2)
target[:,k2.active_dims] += k2.gradients_X(dL_dK*k1.K(X, X2), X, X2)
target += k1.gradients_X(dL_dK*k2.K(X, X2), X, X2)
target += k2.gradients_X(dL_dK*k1.K(X, X2), X, X2)
return target
def gradients_X_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape)
for k1,k2 in itertools.combinations(self.parts, 2):
target[:,k1.active_dims] += k1.gradients_X(dL_dKdiag*k2.Kdiag(X), X)
target[:,k2.active_dims] += k2.gradients_X(dL_dKdiag*k1.Kdiag(X), X)
target += k1.gradients_X(dL_dKdiag*k2.Kdiag(X), X)
target += k2.gradients_X(dL_dKdiag*k1.Kdiag(X), X)
return target