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91 lines
3.2 KiB
Python
91 lines
3.2 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 kern import Kern
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import numpy as np
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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import numpy as np
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class Static(Kern):
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def __init__(self, input_dim, variance, name):
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super(Static, self).__init__(input_dim, name)
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self.variance = Param('variance', variance, Logexp())
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self.add_parameters(self.variance)
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def Kdiag(self, X):
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ret = np.empty((X.shape[0],), dtype=np.float64)
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ret[:] = self.variance
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return ret
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def gradients_X(self, dL_dK, X, X2=None):
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return np.zeros(X.shape)
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def gradients_X_diag(self, dL_dKdiag, X):
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return np.zeros(X.shape)
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def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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return np.zeros(Z.shape)
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def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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return np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
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def psi0(self, Z, variational_posterior):
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return self.Kdiag(variational_posterior.mean)
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def psi1(self, Z, variational_posterior):
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return self.K(variational_posterior.mean, Z)
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def psi2(self, Z, variational_posterior):
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K = self.K(variational_posterior.mean, Z)
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return K[:,:,None]*K[:,None,:] # NB. more efficient implementations on inherriting classes
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class White(Static):
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def __init__(self, input_dim, variance=1., name='white'):
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super(White, self).__init__(input_dim, variance, name)
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def K(self, X, X2=None):
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if X2 is None:
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return np.eye(X.shape[0])*self.variance
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else:
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return np.zeros((X.shape[0], X2.shape[0]))
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def psi2(self, Z, variational_posterior):
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return np.zeros((variational_posterior.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
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def update_gradients_full(self, dL_dK, X):
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self.variance.gradient = np.trace(dL_dK)
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def update_gradients_diag(self, dL_dKdiag, X):
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self.variance.gradient = dL_dKdiag.sum()
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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self.variance.gradient = dL_dpsi0.sum()
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class Bias(Static):
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def __init__(self, input_dim, variance=1., name='bias'):
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super(Bias, self).__init__(input_dim, variance, name)
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def K(self, X, X2=None):
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shape = (X.shape[0], X.shape[0] if X2 is None else X2.shape[0])
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ret = np.empty(shape, dtype=np.float64)
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ret[:] = self.variance
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return ret
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def update_gradients_full(self, dL_dK, X, X2=None):
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self.variance.gradient = dL_dK.sum()
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def update_gradients_diag(self, dL_dKdiag, X):
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self.variance.gradient = dL_dK.sum()
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def psi2(self, Z, variational_posterior):
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ret = np.empty((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
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ret[:] = self.variance**2
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return ret
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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self.variance.gradient = dL_dpsi0.sum() + dL_dpsi1.sum() + 2.*self.variance*dL_dpsi2.sum()
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