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kern merge commencing
This commit is contained in:
Max Zwiessele
parent
1294c24a28
commit
f8ff2c7df2
1 changed files with 111 additions and 120 deletions
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@ -5,130 +5,121 @@ Created on 11 Mar 2014
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'''
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from ...core.parameterization.parameterized import ParametersChangedMeta
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import numpy as np
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import functools
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class KernCallsViaSlicerMeta(ParametersChangedMeta):
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def __call__(self, *args, **kw):
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instance = super(ParametersChangedMeta, self).__call__(*args, **kw)
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instance.K = _slice_wrapper(instance, instance.K)
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instance.Kdiag = _slice_wrapper(instance, instance.Kdiag, diag=True)
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instance.update_gradients_full = _slice_wrapper(instance, instance.update_gradients_full, diag=False, derivative=True)
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instance.update_gradients_diag = _slice_wrapper(instance, instance.update_gradients_diag, diag=True, derivative=True)
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instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, diag=False, derivative=True, ret_X=True)
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instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, diag=True, derivative=True, ret_X=True)
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instance.psi0 = _slice_wrapper(instance, instance.psi0, diag=False, derivative=False)
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instance.psi1 = _slice_wrapper(instance, instance.psi1, diag=False, derivative=False)
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instance.psi2 = _slice_wrapper(instance, instance.psi2, diag=False, derivative=False)
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instance.update_gradients_expectations = _slice_wrapper(instance, instance.update_gradients_expectations, derivative=True, psi_stat=True)
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instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, derivative=True, psi_stat_Z=True, ret_X=True)
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instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, derivative=True, psi_stat=True, ret_X=True)
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instance.K = _Slice_wrapper(instance, instance.K)
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instance.Kdiag = _Slice_wrapper_diag(instance, instance.Kdiag)
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instance.update_gradients_full = _Slice_wrapper_derivative(instance, instance.update_gradients_full)
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instance.update_gradients_diag = _Slice_wrapper_diag_derivative(instance, instance.update_gradients_diag)
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instance.gradients_X = _Slice_wrapper_grad_X(instance, instance.gradients_X)
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instance.gradients_X_diag = _Slice_wrapper_grad_X_diag(instance, instance.gradients_X_diag)
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instance.psi0 = _Slice_wrapper(instance, instance.psi0)
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instance.psi1 = _Slice_wrapper(instance, instance.psi1)
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instance.psi2 = _Slice_wrapper(instance, instance.psi2)
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instance.update_gradients_expectations = _Slice_wrapper_psi_stat_derivative_no_ret(instance, instance.update_gradients_expectations)
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instance.gradients_Z_expectations = _Slice_wrapper_psi_stat_derivative_Z(instance, instance.gradients_Z_expectations)
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instance.gradients_qX_expectations = _Slice_wrapper_psi_stat_derivative(instance, instance.gradients_qX_expectations)
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instance.parameters_changed()
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return instance
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def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False, psi_stat_Z=False, ret_X=False):
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"""
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This method wraps the functions in kernel to make sure all kernels allways see their respective input dimension.
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The different switches are:
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diag: if X2 exists
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derivative: if first arg is dL_dK
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psi_stat: if first 3 args are dL_dpsi0..2
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psi_stat_Z: if first 2 args are dL_dpsi1..2
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"""
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if derivative:
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if diag:
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def x_slice_wrapper(dL_dKdiag, X):
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ret_X_not_sliced = ret_X and kern._sliced_X == 0
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if ret_X_not_sliced:
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ret = np.zeros(X.shape)
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X = kern._slice_X(X) if not kern._sliced_X else X
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# if the return value is of shape X.shape, we need to make sure to return the right shape
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kern._sliced_X += 1
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try:
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if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dKdiag, X)
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else: ret = operation(dL_dKdiag, X)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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elif psi_stat:
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def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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ret_X_not_sliced = ret_X and kern._sliced_X == 0
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if ret_X_not_sliced:
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ret1, ret2 = np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
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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
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kern._sliced_X += 1
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# if the return value is of shape X.shape, we need to make sure to return the right shape
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try:
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if ret_X_not_sliced:
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ret = list(operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
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r2 = ret[:2]
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ret[0] = ret1
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ret[1] = ret2
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ret[0][:, kern.active_dims] = r2[0]
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ret[1][:, kern.active_dims] = r2[1]
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del r2
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else: ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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elif psi_stat_Z:
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def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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ret_X_not_sliced = ret_X and kern._sliced_X == 0
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if ret_X_not_sliced: ret = np.zeros(Z.shape)
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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
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kern._sliced_X += 1
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try:
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if ret_X_not_sliced:
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ret[:, kern.active_dims] = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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else: ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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def x_slice_wrapper(dL_dK, X, X2=None):
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ret_X_not_sliced = ret_X and kern._sliced_X == 0
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if ret_X_not_sliced:
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ret = np.zeros(X.shape)
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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
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kern._sliced_X += 1
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try:
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if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dK, X, X2)
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else: ret = operation(dL_dK, X, X2)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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if diag:
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def x_slice_wrapper(X, *args, **kw):
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X = kern._slice_X(X) if not kern._sliced_X else X
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kern._sliced_X += 1
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try:
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ret = operation(X, *args, **kw)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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def x_slice_wrapper(X, X2=None, *args, **kw):
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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
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kern._sliced_X += 1
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try:
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ret = operation(X, X2, *args, **kw)
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except: raise
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finally:
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kern._sliced_X -= 1
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return ret
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x_slice_wrapper._operation = operation
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x_slice_wrapper.__name__ = ("slicer("+str(operation)
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+(","+str(bool(diag)) if diag else'')
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+(','+str(bool(derivative)) if derivative else '')
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+')')
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x_slice_wrapper.__doc__ = "**sliced**\n" + (operation.__doc__ or "")
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return x_slice_wrapper
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class _Slice_wrap(object):
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def __init__(self, instance, f):
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self.k = instance
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self.f = f
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def copy_to(self, new_instance):
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return self.__class__(new_instance, self.f)
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def _slice_X(self, X):
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return self.k._slice_X(X) if not self.k._sliced_X else X
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def _slice_X_X2(self, X, X2):
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return self.k._slice_X(X) if not self.k._sliced_X else X, self.k._slice_X(X2) if X2 is not None and not self.k._sliced_X else X2
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def __enter__(self):
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self.k._sliced_X += 1
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return self
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def __exit__(self, *a):
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self.k._sliced_X -= 1
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class _Slice_wrapper(_Slice_wrap):
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def __call__(self, X, X2 = None, *a, **kw):
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X, X2 = self._slice_X_X2(X, X2)
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with self:
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ret = self.f(X, X2, *a, **kw)
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return ret
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class _Slice_wrapper_diag(_Slice_wrap):
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def __call__(self, X, *a, **kw):
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X = self._slice_X(X)
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with self:
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ret = self.f(X, *a, **kw)
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return ret
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class _Slice_wrapper_derivative(_Slice_wrap):
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def __call__(self, dL_dK, X, X2=None):
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self._slice_X(X)
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with self:
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ret = self.f(dL_dK, X, X2)
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return ret
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class _Slice_wrapper_diag_derivative(_Slice_wrap):
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def __call__(self, dL_dKdiag, X):
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X = self._slice_X(X)
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with self:
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ret = self.f(dL_dKdiag, X)
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return ret
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class _Slice_wrapper_grad_X(_Slice_wrap):
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def __call__(self, dL_dK, X, X2=None):
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ret = np.zeros(X.shape)
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X, X2 = self._slice_X_X2(X, X2)
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with self:
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ret[:, self.k.active_dims] = self.f(dL_dK, X, X2)
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return ret
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class _Slice_wrapper_grad_X_diag(_Slice_wrap):
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def __call__(self, dL_dKdiag, X):
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ret = np.zeros(X.shape)
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X = self._slice_X(X)
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with self:
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ret[:, self.k.active_dims] = self.f(dL_dKdiag, X)
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return ret
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class _Slice_wrapper_psi_stat_derivative_no_ret(_Slice_wrap):
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def __call__(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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Z, variational_posterior = self._slice_X_X2(Z, variational_posterior)
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with self:
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ret = self.f(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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return ret
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class _Slice_wrapper_psi_stat_derivative(_Slice_wrap):
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def __call__(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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ret1, ret2 = np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
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Z, variational_posterior = self._slice_X_X2(Z, variational_posterior)
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with self:
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ret = list(self.f(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
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r2 = ret[:2]
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ret[0] = ret1
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ret[1] = ret2
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ret[0][:, self.k.active_dims] = r2[0]
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ret[1][:, self.k.active_dims] = r2[1]
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del r2
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return ret
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class _Slice_wrapper_psi_stat_derivative_Z(_Slice_wrap):
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def __call__(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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ret1, ret2 = np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
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Z, variational_posterior = self._slice_X_X2(Z, variational_posterior)
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with self:
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ret = list(self.f(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
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r2 = ret[:2]
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ret[0] = ret1
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ret[1] = ret2
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ret[0][:, self.k.active_dims] = r2[0]
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ret[1][:, self.k.active_dims] = r2[1]
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del r2
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return ret
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