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101 lines
4.5 KiB
Python
101 lines
4.5 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 prod_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.input_dim = k1.input_dim + k2.input_dim
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self.num_params = k1.num_params + k2.num_params
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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._X, self._X2, self._params = np.empty(shape=(3,1))
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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 np.hstack((self.k1._get_params(), self.k2._get_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.num_params])
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self.k2._set_params(x[self.k1.num_params:])
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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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self._K_computations(X,X2)
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target += self._K1 * self._K2
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def _param_grad_helper(self,dL_dK,X,X2,target):
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"""derivative of the covariance matrix with respect to the parameters."""
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self._K_computations(X,X2)
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if X2 is None:
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self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
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self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
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else:
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self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
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self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
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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.input_dim],target1)
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self.k2.Kdiag(X[:,self.k1.input_dim:],target2)
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target += target1 * target2
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def dKdiag_dtheta(self,dL_dKdiag,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.input_dim],K1)
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self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
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self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.input_dim],target[:self.k1.num_params])
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self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.input_dim:],target[self.k1.num_params:])
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def gradients_X(self,dL_dK,X,X2,target):
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"""derivative of the covariance matrix with respect to X."""
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self._K_computations(X,X2)
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self.k1.gradients_X(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
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self.k2.gradients_X(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
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def dKdiag_dX(self, dL_dKdiag, 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[:,0:self.k1.input_dim],K1)
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self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
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self.k1.gradients_X(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
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self.k2.gradients_X(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
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def _K_computations(self,X,X2):
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if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
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self._X = X.copy()
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self._params == self._get_params().copy()
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if X2 is None:
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self._X2 = None
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self._K1 = np.zeros((X.shape[0],X.shape[0]))
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self._K2 = np.zeros((X.shape[0],X.shape[0]))
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self.k1.K(X[:,:self.k1.input_dim],None,self._K1)
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self.k2.K(X[:,self.k1.input_dim:],None,self._K2)
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else:
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self._X2 = X2.copy()
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self._K1 = np.zeros((X.shape[0],X2.shape[0]))
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self._K2 = np.zeros((X.shape[0],X2.shape[0]))
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self.k1.K(X[:,:self.k1.input_dim],X2[:,:self.k1.input_dim],self._K1)
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self.k2.K(X[:,self.k1.input_dim:],X2[:,self.k1.input_dim:],self._K2)
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