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77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from .stationary import Stationary
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from .psi_comp import PSICOMP_RBF
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from .psi_comp.rbf_psi_gpucomp import PSICOMP_RBF_GPU
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from ...util.config import *
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class RBF(Stationary):
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"""
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Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
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.. math::
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k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
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"""
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_support_GPU = True
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def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
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super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
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if self.useGPU:
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self.psicomp = PSICOMP_RBF_GPU()
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else:
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self.psicomp = PSICOMP_RBF()
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def K_of_r(self, r):
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return self.variance * np.exp(-0.5 * r**2)
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def dK_dr(self, r):
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return -r*self.K_of_r(r)
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def dK2_drdr(self, r):
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return (r**2-1)*self.K_of_r(r)
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def __getstate__(self):
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dc = super(RBF, self).__getstate__()
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if self.useGPU:
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dc['psicomp'] = PSICOMP_RBF()
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dc['useGPU'] = False
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return dc
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def __setstate__(self, state):
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return super(RBF, self).__setstate__(state)
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def spectrum(self, omega):
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assert self.input_dim == 1 #TODO: higher dim spectra?
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return self.variance*np.sqrt(2*np.pi)*self.lengthscale*np.exp(-self.lengthscale*2*omega**2/2)
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#---------------------------------------#
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# PSI statistics #
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#---------------------------------------#
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def psi0(self, Z, variational_posterior):
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return self.psicomp.psicomputations(self, Z, variational_posterior)[0]
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def psi1(self, Z, variational_posterior):
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return self.psicomp.psicomputations(self, Z, variational_posterior)[1]
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def psi2(self, Z, variational_posterior):
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return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=False)[2]
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def psi2n(self, Z, variational_posterior):
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return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=True)[2]
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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dL_dvar, dL_dlengscale = self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[:2]
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self.variance.gradient = dL_dvar
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self.lengthscale.gradient = dL_dlengscale
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def gradients_Z_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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return self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[2]
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def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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return self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[3:]
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