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[kern psi2] added flag for returning psi2 in N, not used yet, see #139

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Max Zwiessele 2014-10-10 11:06:03 +01:00
parent 829e40b25c
commit c128c6f948
3 changed files with 22 additions and 8 deletions
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17
GPy/kern/_src/kern.py
View file

@ -6,6 +6,7 @@ import numpy as np
from ...core.parameterization.parameterized import Parameterized
from kernel_slice_operations import KernCallsViaSlicerMeta
from ...util.caching import Cache_this
from GPy.core.parameterization.observable_array import ObsAr
@ -54,6 +55,20 @@ class Kern(Parameterized):
self._sliced_X = 0
self.useGPU = self._support_GPU and useGPU
self._return_psi2_n_flag = ObsAr(np.zeros(1)).astype(bool)
@property
def return_psi2_n(self):
"""
Flag whether to pass back psi2 as NxMxM or MxM, by summing out N.
"""
return self._return_psi2_n_flag[0]
@return_psi2_n.setter
def return_psi2_n(self, val):
def visit(self):
if isinstance(self, Kern):
self._return_psi2_n_flag[0]=val
self.traverse(visit)
@Cache_this(limit=20)
def _slice_X(self, X):
@ -162,7 +177,7 @@ class Kern(Parameterized):
def __mul__(self, other):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)
def __imul__(self, other):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)

11
GPy/kern/_src/psi_comp/__init__.py
View file

@ -10,7 +10,6 @@ import sslinear_psi_comp
import linear_psi_comp
class PSICOMP_RBF(Pickleable):
@Cache_this(limit=2, ignore_args=(0,))
def psicomputations(self, variance, lengthscale, Z, variational_posterior):
if isinstance(variational_posterior, variational.NormalPosterior):
@ -19,7 +18,7 @@ class PSICOMP_RBF(Pickleable):
return ssrbf_psi_comp.psicomputations(variance, lengthscale, Z, variational_posterior)
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
@Cache_this(limit=2, ignore_args=(0,1,2,3))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
if isinstance(variational_posterior, variational.NormalPosterior):
@ -28,10 +27,10 @@ class PSICOMP_RBF(Pickleable):
return ssrbf_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior)
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
def _setup_observers(self):
pass
class PSICOMP_Linear(Pickleable):
@Cache_this(limit=2, ignore_args=(0,))
@ -42,7 +41,7 @@ class PSICOMP_Linear(Pickleable):
return sslinear_psi_comp.psicomputations(variance, Z, variational_posterior)
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
@Cache_this(limit=2, ignore_args=(0,1,2,3))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, Z, variational_posterior):
if isinstance(variational_posterior, variational.NormalPosterior):
@ -51,6 +50,6 @@ class PSICOMP_Linear(Pickleable):
return sslinear_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, Z, variational_posterior)
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
def _setup_observers(self):
pass

2
GPy/kern/_src/psi_comp/rbf_psi_comp.py
View file

@ -139,7 +139,7 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S):
denom2 = np.square(denom)
_psi2 = _psi2computations(variance, lengthscale, Z, mu, S) # NxMxM
Lpsi2 = dL_dpsi2[None,:,:]*_psi2
Lpsi2 = dL_dpsi2*_psi2 # dL_dpsi2 is MxM, using broadcast to multiply N out
Lpsi2sum = np.einsum('nmo->n',Lpsi2) #N
Lpsi2Z = np.einsum('nmo,oq->nq',Lpsi2,Z) #NxQ
Lpsi2Z2 = np.einsum('nmo,oq,oq->nq',Lpsi2,Z,Z) #NxQ