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Julien Bect 2020-06-24 16:09:02 +02:00 • committed by Neil Lawrence
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18
GPy/kern/__init__.py
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@ -3,9 +3,20 @@
Introduction
^^^^^^^^^^^^
In terms of Gaussian Processes, a kernel is a function that specifies the degree of similarity between variables given their relative positions in parameter space. If known variables *x* and *x'* are close together then observed variables *y* and *y'* may also be similar, depending on the kernel function and its parameters. *Note: this may be too simple a definition for the broad range of kernels available in :py:class:`GPy`.*
In terms of Gaussian Processes, a kernel is a function that specifies
the degree of similarity between variables given their relative
positions in parameter space. If known variables *x* and *x'* are
close together then observed variables *y* and *y'* may also be
similar, depending on the kernel function and its parameters. *Note:
this may be too simple a definition for the broad range of kernels
available in :py:class:`GPy`.*
:py:class:`GPy.kern.src.kern.Kern` is a generic kernel object inherited by more specific, end-user kernels used in models. It provides methods that specific kernels should generally have such as :py:class:`GPy.kern.src.kern.Kern.K` to compute the value of the kernel, :py:class:`GPy.kern.src.kern.Kern.add` to combine kernels and numerous functions providing information on kernel gradients.
:py:class:`GPy.kern.src.kern.Kern` is a generic kernel object
inherited by more specific, end-user kernels used in models. It
provides methods that specific kernels should generally have such as
:py:class:`GPy.kern.src.kern.Kern.K` to compute the value of the
kernel, :py:class:`GPy.kern.src.kern.Kern.add` to combine kernels and
numerous functions providing information on kernel gradients.
There are several inherited types of kernel that provide a basis for specific end user kernels:
@ -20,7 +31,6 @@ e.g. the archetype :py:class:`GPy.kern.RBF` does not inherit directly from :py:c
"""
from .src.kern import Kern
from .src.add import Add
from .src.prod import Prod
@ -61,4 +71,4 @@ from .src.sde_stationary import sde_RBF,sde_Exponential,sde_RatQuad
from .src.sde_brownian import sde_Brownian
from .src.multioutput_kern import MultioutputKern
from .src.multioutput_derivative_kern import MultioutputDerivativeKern
from .src.diff_kern import DiffKern
from .src.diff_kern import DiffKern

35
GPy/kern/src/psi_comp/ssrbf_psi_gpucomp.py
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@ -448,12 +448,34 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
dL_dpsi0_sum = dL_dpsi0.sum()
self.reset_derivative()
# t=self.g_psi1compDer(dvar_gpu,dl_gpu,dZ_gpu,dmu_gpu,dS_gpu,dL_dpsi1_gpu,psi1_gpu, np.float64(variance),l_gpu,Z_gpu,mu_gpu,S_gpu, np.int32(N), np.int32(M), np.int32(Q), block=(self.threadnum,1,1), grid=(self.blocknum,1),time_kernel=True)
# t=self.g_psi1compDer(dvar_gpu,dl_gpu,dZ_gpu,dmu_gpu,dS_gpu,dL_dpsi1_gpu,psi1_gpu,
# np.float64(variance),l_gpu,Z_gpu,mu_gpu,S_gpu, np.int32(N),
# np.int32(M), np.int32(Q), block=(self.threadnum,1,1),
# grid=(self.blocknum,1),time_kernel=True)
# print 'g_psi1compDer '+str(t)
# t=self.g_psi2compDer(dvar_gpu,dl_gpu,dZ_gpu,dmu_gpu,dS_gpu,dL_dpsi2_gpu,psi2n_gpu, np.float64(variance),l_gpu,Z_gpu,mu_gpu,S_gpu, np.int32(N), np.int32(M), np.int32(Q), block=(self.threadnum,1,1), grid=(self.blocknum,1),time_kernel=True)
# t=self.g_psi2compDer(dvar_gpu,dl_gpu,dZ_gpu,dmu_gpu,dS_gpu,dL_dpsi2_gpu,psi2n_gpu,
# np.float64(variance),l_gpu,Z_gpu,mu_gpu,S_gpu, np.int32(N),
# np.int32(M), np.int32(Q), block=(self.threadnum,1,1),
# grid=(self.blocknum,1),time_kernel=True)
# print 'g_psi2compDer '+str(t)
self.g_psi1compDer.prepared_call((self.blocknum,1),(self.threadnum,1,1),dvar_gpu.gpudata,dl_gpu.gpudata,dZ_gpu.gpudata,dmu_gpu.gpudata,dS_gpu.gpudata,dgamma_gpu.gpudata,dL_dpsi1_gpu.gpudata,psi1_gpu.gpudata, log_denom1_gpu.gpudata, log_gamma_gpu.gpudata, log_gamma1_gpu.gpudata, np.float64(variance),l_gpu.gpudata,Z_gpu.gpudata,mu_gpu.gpudata,S_gpu.gpudata,gamma_gpu.gpudata,np.int32(N), np.int32(M), np.int32(Q))
self.g_psi2compDer.prepared_call((self.blocknum,1),(self.threadnum,1,1),dvar_gpu.gpudata,dl_gpu.gpudata,dZ_gpu.gpudata,dmu_gpu.gpudata,dS_gpu.gpudata,dgamma_gpu.gpudata,dL_dpsi2_gpu.gpudata,psi2n_gpu.gpudata, log_denom2_gpu.gpudata, log_gamma_gpu.gpudata, log_gamma1_gpu.gpudata, np.float64(variance),l_gpu.gpudata,Z_gpu.gpudata,mu_gpu.gpudata,S_gpu.gpudata,gamma_gpu.gpudata,np.int32(N), np.int32(M), np.int32(Q))
self.g_psi1compDer.prepared_call((self.blocknum,1), (self.threadnum,1,1),
dvar_gpu.gpudata, dl_gpu.gpudata, dZ_gpu.gpudata,
dmu_gpu.gpudata, dS_gpu.gpudata, dgamma_gpu.gpudata,
dL_dpsi1_gpu.gpudata, psi1_gpu.gpudata,
log_denom1_gpu.gpudata, log_gamma_gpu.gpudata,
log_gamma1_gpu.gpudata, np.float64(variance),
l_gpu.gpudata, Z_gpu.gpudata, mu_gpu.gpudata,
S_gpu.gpudata, gamma_gpu.gpudata, np.int32(N),
np.int32(M), np.int32(Q))
self.g_psi2compDer.prepared_call((self.blocknum,1), (self.threadnum,1,1),
dvar_gpu.gpudata, dl_gpu.gpudata, dZ_gpu.gpudata,
dmu_gpu.gpudata, dS_gpu.gpudata, dgamma_gpu.gpudata,
dL_dpsi2_gpu.gpudata, psi2n_gpu.gpudata,
log_denom2_gpu.gpudata, log_gamma_gpu.gpudata,
log_gamma1_gpu.gpudata, np.float64(variance),
l_gpu.gpudata, Z_gpu.gpudata, mu_gpu.gpudata,
S_gpu.gpudata, gamma_gpu.gpudata, np.int32(N),
np.int32(M), np.int32(Q))
dL_dvar = dL_dpsi0_sum + gpuarray.sum(dvar_gpu).get()
sum_axis(grad_mu_gpu,dmu_gpu,N*Q,self.blocknum)
@ -468,7 +490,6 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
dL_dlengscale = grad_l_gpu.get()
else:
dL_dlengscale = gpuarray.sum(dl_gpu).get()
return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS, dL_dgamma
return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS, dL_dgamma