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93 lines
3.5 KiB
Python
93 lines
3.5 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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from kernpart import kernpart
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import numpy as np
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import hashlib
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#from scipy import integrate # This may not be necessary (Nicolas, 20th Feb)
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class product_orthogonal(kernpart):
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"""
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Computes the product of 2 kernels
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:param k1, k2: the kernels to multiply
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:type k1, k2: kernpart
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:rtype: kernel object
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"""
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def __init__(self,k1,k2):
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self.D = k1.D + k2.D
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self.Nparam = k1.Nparam + k2.Nparam
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self.name = k1.name + '<times>' + k2.name
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self.k1 = k1
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self.k2 = k2
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self._set_params(np.hstack((k1._get_params(),k2._get_params())))
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def _get_params(self):
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"""return the value of the parameters."""
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return self.params
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def _set_params(self,x):
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"""set the value of the parameters."""
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self.k1._set_params(x[:self.k1.Nparam])
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self.k2._set_params(x[self.k1.Nparam:])
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self.params = x
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def _get_param_names(self):
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"""return parameter names."""
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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()]
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def K(self,X,X2,target):
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"""Compute the covariance matrix between X and X2."""
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if X2 is None: X2 = X
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target1 = np.zeros((X.shape[0],X2.shape[0]))
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target2 = np.zeros((X.shape[0],X2.shape[0]))
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self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],target1)
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self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
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target += target1 * target2
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def dK_dtheta(self,partial,X,X2,target):
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"""derivative of the covariance matrix with respect to the parameters."""
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if X2 is None: X2 = X
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K1 = np.zeros((X.shape[0],X2.shape[0]))
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K2 = np.zeros((X.shape[0],X2.shape[0]))
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self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
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self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
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k1_target = np.zeros(self.k1.Nparam)
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k2_target = np.zeros(self.k2.Nparam)
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self.k1.dK_dtheta(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], k1_target)
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self.k2.dK_dtheta(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], k2_target)
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target[:self.k1.Nparam] += k1_target
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target[self.k1.Nparam:] += k2_target
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def Kdiag(self,X,target):
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"""Compute the diagonal of the covariance matrix associated to X."""
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target1 = np.zeros((X.shape[0],))
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target2 = np.zeros((X.shape[0],))
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self.k1.Kdiag(X[:,:self.k1.D],target1)
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self.k2.Kdiag(X[:,self.k1.D:],target2)
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target += target1 * target2
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def dKdiag_dtheta(self,partial,X,target):
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K1 = np.zeros(X.shape[0])
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K2 = np.zeros(X.shape[0])
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self.k1.Kdiag(X[:,:self.k1.D],K1)
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self.k2.Kdiag(X[:,self.k1.D:],K2)
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self.k1.dKdiag_dtheta(partial*K2,X[:,:self.k1.D],target[:self.k1.Nparam])
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self.k2.dKdiag_dtheta(partial*K1,X[:,self.k1.D:],target[self.k1.Nparam:])
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def dK_dX(self,partial,X,X2,target):
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"""derivative of the covariance matrix with respect to X."""
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if X2 is None: X2 = X
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K1 = np.zeros((X.shape[0],X2.shape[0]))
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K2 = np.zeros((X.shape[0],X2.shape[0]))
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self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
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self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
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self.k1.dK_dX(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
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self.k2.dK_dX(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
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def dKdiag_dX(self,X,target):
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pass
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