mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-03 16:52:39 +02:00
111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
from kernpart import kernpart
|
|
import numpy as np
|
|
import hashlib
|
|
|
|
class prod(kernpart):
|
|
"""
|
|
Computes the product of 2 kernels
|
|
|
|
:param k1, k2: the kernels to multiply
|
|
:type k1, k2: kernpart
|
|
:param tensor: The kernels are either multiply as functions defined on the same input space (default) or on the product of the input spaces
|
|
:type tensor: Boolean
|
|
:rtype: kernel object
|
|
|
|
"""
|
|
def __init__(self,k1,k2,tensor=False):
|
|
self.Nparam = k1.Nparam + k2.Nparam
|
|
self.name = k1.name + '<times>' + k2.name
|
|
self.k1 = k1
|
|
self.k2 = k2
|
|
if tensor:
|
|
self.D = k1.D + k2.D
|
|
self.slice1 = slice(0,self.k1.D)
|
|
self.slice2 = slice(self.k1.D,self.k1.D+self.k2.D)
|
|
else:
|
|
assert k1.D == k2.D, "Error: The input spaces of the kernels to sum don't have the same dimension."
|
|
self.D = k1.D
|
|
self.slice1 = slice(0,self.D)
|
|
self.slice2 = slice(0,self.D)
|
|
|
|
self._X, self._X2, self._params = np.empty(shape=(3,1))
|
|
self._set_params(np.hstack((k1._get_params(),k2._get_params())))
|
|
|
|
def _get_params(self):
|
|
"""return the value of the parameters."""
|
|
return np.hstack((self.k1._get_params(), self.k2._get_params()))
|
|
|
|
def _set_params(self,x):
|
|
"""set the value of the parameters."""
|
|
self.k1._set_params(x[:self.k1.Nparam])
|
|
self.k2._set_params(x[self.k1.Nparam:])
|
|
|
|
def _get_param_names(self):
|
|
"""return parameter names."""
|
|
return [self.k1.name + '_' + param_name for param_name in self.k1._get_param_names()] + [self.k2.name + '_' + param_name for param_name in self.k2._get_param_names()]
|
|
|
|
def K(self,X,X2,target):
|
|
self._K_computations(X,X2)
|
|
target += self._K1 * self._K2
|
|
|
|
def dK_dtheta(self,dL_dK,X,X2,target):
|
|
"""derivative of the covariance matrix with respect to the parameters."""
|
|
self._K_computations(X,X2)
|
|
if X2 is None:
|
|
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.Nparam])
|
|
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.Nparam:])
|
|
else:
|
|
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.Nparam])
|
|
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.Nparam:])
|
|
|
|
def Kdiag(self,X,target):
|
|
"""Compute the diagonal of the covariance matrix associated to X."""
|
|
target1 = np.zeros(X.shape[0])
|
|
target2 = np.zeros(X.shape[0])
|
|
self.k1.Kdiag(X[:,self.slice1],target1)
|
|
self.k2.Kdiag(X[:,self.slice2],target2)
|
|
target += target1 * target2
|
|
|
|
def dKdiag_dtheta(self,dL_dKdiag,X,target):
|
|
K1 = np.zeros(X.shape[0])
|
|
K2 = np.zeros(X.shape[0])
|
|
self.k1.Kdiag(X[:,self.slice1],K1)
|
|
self.k2.Kdiag(X[:,self.slice2],K2)
|
|
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.Nparam])
|
|
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.Nparam:])
|
|
|
|
def dK_dX(self,dL_dK,X,X2,target):
|
|
"""derivative of the covariance matrix with respect to X."""
|
|
self._K_computations(X,X2)
|
|
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target)
|
|
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target)
|
|
|
|
def dKdiag_dX(self, dL_dKdiag, X, target):
|
|
K1 = np.zeros(X.shape[0])
|
|
K2 = np.zeros(X.shape[0])
|
|
self.k1.Kdiag(X[:,self.slice1],K1)
|
|
self.k2.Kdiag(X[:,self.slice2],K2)
|
|
|
|
self.k1.dK_dX(dL_dKdiag*K2, X[:,self.slice1], target)
|
|
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.slice2], target)
|
|
|
|
def _K_computations(self,X,X2):
|
|
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
|
|
self._X = X.copy()
|
|
self._params == self._get_params().copy()
|
|
if X2 is None:
|
|
self._X2 = None
|
|
self._K1 = np.zeros((X.shape[0],X.shape[0]))
|
|
self._K2 = np.zeros((X.shape[0],X.shape[0]))
|
|
self.k1.K(X[:,self.slice1],None,self._K1)
|
|
self.k2.K(X[:,self.slice2],None,self._K2)
|
|
else:
|
|
self._X2 = X2.copy()
|
|
self._K1 = np.zeros((X.shape[0],X2.shape[0]))
|
|
self._K2 = np.zeros((X.shape[0],X2.shape[0]))
|
|
self.k1.K(X[:,self.slice1],X2[:,self.slice1],self._K1)
|
|
self.k2.K(X[:,self.slice2],X2[:,self.slice2],self._K2)
|
|
|