def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
self.psicomp = PSICOMP_RBF()
if self.useGPU:
self.psicomp = PSICOMP_RBF_GPU()
else:
self.psicomp = PSICOMP_RBF()
class RBF(Stationary):
"""
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
.. math::
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
"""
_support_GPU = True
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
self.psicomp = PSICOMP_RBF()
if self.useGPU:
self.psicomp = PSICOMP_RBF_GPU()
else:
self.psicomp = PSICOMP_RBF()
def K_of_r(self, r):
return self.variance * np.exp(-0.5 * r**2)
def dK_dr(self, r):
return -r*self.K_of_r(r)
def __getstate__(self):
dc = super(RBF, self).__getstate__()
if self.useGPU:
dc['psicomp'] = PSICOMP_RBF()
return dc
def __setstate__(self, state):
return super(RBF, self).__setstate__(state)
def spectrum(self, omega):
assert self.input_dim == 1 #TODO: higher dim spectra?
return self.variance*np.sqrt(2*np.pi)*self.lengthscale*np.exp(-self.lengthscale*2*omega**2/2)
#---------------------------------------#
# PSI statistics #
#---------------------------------------#
def psi0(self, Z, variational_posterior):
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[0]
def psi1(self, Z, variational_posterior):
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[1]
def psi2(self, Z, variational_posterior):
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[2]
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
dL_dvar, dL_dlengscale = self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)[:2]
self.variance.gradient = dL_dvar
self.lengthscale.gradient = dL_dlengscale
def gradients_Z_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)[2]
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)[3:]
def __getstate__(self):
dc = super(RBF, self).__getstate__()
if self.useGPU:
dc['psicomp'] = PSICOMP_RBF()
return dc
