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89 lines
2.3 KiB
Python
89 lines
2.3 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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class bias(kernpart):
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def __init__(self,input_dim,variance=1.):
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"""
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:param input_dim: the number of input dimensions
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:type input_dim: int
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:param variance: the variance of the kernel
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:type variance: float
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"""
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self.input_dim = input_dim
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self.Nparam = 1
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self.name = 'bias'
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self._set_params(np.array([variance]).flatten())
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def _get_params(self):
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return self.variance
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def _set_params(self,x):
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assert x.shape==(1,)
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self.variance = x
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def _get_param_names(self):
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return ['variance']
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def K(self,X,X2,target):
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target += self.variance
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def Kdiag(self,X,target):
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target += self.variance
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def dK_dtheta(self,dL_dKdiag,X,X2,target):
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target += dL_dKdiag.sum()
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def dKdiag_dtheta(self,dL_dKdiag,X,target):
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target += dL_dKdiag.sum()
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def dK_dX(self, dL_dK,X, X2, target):
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pass
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def dKdiag_dX(self,dL_dKdiag,X,target):
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pass
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#---------------------------------------#
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# PSI statistics #
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#---------------------------------------#
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def psi0(self, Z, mu, S, target):
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target += self.variance
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def psi1(self, Z, mu, S, target):
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self._psi1 = self.variance
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target += self._psi1
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def psi2(self, Z, mu, S, target):
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target += self.variance**2
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def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
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target += dL_dpsi0.sum()
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def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
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target += dL_dpsi1.sum()
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def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
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target += 2.*self.variance*dL_dpsi2.sum()
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def dpsi0_dZ(self, dL_dpsi0, Z, mu, S, target):
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pass
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def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
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pass
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def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
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pass
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def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
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pass
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def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
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pass
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def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
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pass
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