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slice operations now bound functions, not added after the fact

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mzwiessele 2014-03-26 14:59:38 +00:00
parent ebb919bb8b
commit a126f288d2
1 changed files with 37 additions and 35 deletions
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72
GPy/kern/_src/kernel_slice_operations.py
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@ -6,24 +6,26 @@ Created on 11 Mar 2014
from ...core.parameterization.parameterized import ParametersChangedMeta
import numpy as np
def put_clean(dct, name, *args, **kw):
if name in dct:
dct['_clean_{}'.format(name)] = dct[name]
dct[name] = _slice_wrapper(None, dct[name], *args, **kw)
class KernCallsViaSlicerMeta(ParametersChangedMeta):
def __call__(self, *args, **kw):
instance = super(ParametersChangedMeta, self).__call__(*args, **kw)
instance.K = _slice_wrapper(instance, instance.K)
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, 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, 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 __new__(cls, name, bases, dct):
put_clean(dct, 'K')
put_clean(dct, 'Kdiag', diag=True)
put_clean(dct, 'update_gradients_full', diag=False, derivative=True)
put_clean(dct, 'gradients_X', diag=False, derivative=True, ret_X=True)
put_clean(dct, 'gradients_X_diag', diag=True, derivative=True, ret_X=True)
put_clean(dct, 'psi0', diag=False, derivative=False)
put_clean(dct, 'psi1', diag=False, derivative=False)
put_clean(dct, 'psi2', diag=False, derivative=False)
put_clean(dct, 'update_gradients_expectations', derivative=True, psi_stat=True)
put_clean(dct, 'gradients_Z_expectations', derivative=True, psi_stat_Z=True, ret_X=True)
put_clean(dct, 'gradients_qX_expectations', derivative=True, psi_stat=True, ret_X=True)
return super(KernCallsViaSlicerMeta, cls).__new__(cls, name, bases, dct)
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.
@ -35,7 +37,7 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
"""
if derivative:
if diag:
def x_slice_wrapper(dL_dKdiag, X):
def x_slice_wrapper(kern, dL_dKdiag, X):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced:
ret = np.zeros(X.shape)
@ -43,15 +45,15 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
# if the return value is of shape X.shape, we need to make sure to return the right shape
kern._sliced_X += 1
try:
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dKdiag, X)
else: ret = operation(dL_dKdiag, X)
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(kern, dL_dKdiag, X)
else: ret = operation(kern, dL_dKdiag, X)
except:
raise
finally:
kern._sliced_X -= 1
return ret
elif psi_stat:
def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
def x_slice_wrapper(kern, 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)
@ -60,44 +62,44 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
# if the return value is of shape X.shape, we need to make sure to return the right shape
try:
if ret_X_not_sliced:
ret = list(operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
ret = list(operation(kern, 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)
else: ret = operation(kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
kern._sliced_X -= 1
return ret
elif psi_stat_Z:
def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
def x_slice_wrapper(kern, 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:
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)
ret[:, kern.active_dims] = operation(kern, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
else: ret = operation(kern, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
kern._sliced_X -= 1
return ret
else:
def x_slice_wrapper(dL_dK, X, X2=None):
def x_slice_wrapper(kern, 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:
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dK, X, X2)
else: ret = operation(dL_dK, X, X2)
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(kern, dL_dK, X, X2)
else: ret = operation(kern, dL_dK, X, X2)
except:
raise
finally:
@ -105,30 +107,30 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
return ret
else:
if diag:
def x_slice_wrapper(X, *args, **kw):
def x_slice_wrapper(kern, X, *args, **kw):
X = kern._slice_X(X) if not kern._sliced_X else X
kern._sliced_X += 1
try:
ret = operation(X, *args, **kw)
ret = operation(kern, X, *args, **kw)
except:
raise
finally:
kern._sliced_X -= 1
return ret
else:
def x_slice_wrapper(X, X2=None, *args, **kw):
def x_slice_wrapper(kern, X, X2=None, *args, **kw):
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(X, X2, *args, **kw)
ret = operation(kern, X, X2, *args, **kw)
except: raise
finally:
kern._sliced_X -= 1
return ret
x_slice_wrapper._operation = operation
x_slice_wrapper.__name__ = ("slicer("+str(operation)
+(","+str(bool(diag)) if diag else'')
+(','+str(bool(derivative)) if derivative else '')
+(","+str('diag') if diag else'')
+(','+str('derivative') if derivative else '')
+')')
x_slice_wrapper.__doc__ = "**sliced**\n" + (operation.__doc__ or "")
return x_slice_wrapper