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41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
# Copyright (c) 2014, James Hensman
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from kern import Kern
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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class Poly(Kern):
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"""
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Polynomial kernel
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"""
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def __init__(self, input_dim, variance=1., order=3., active_dims=None, name='poly'):
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super(Poly, self).__init__(input_dim, active_dims, name)
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self.variance = Param('variance', variance, Logexp())
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self.link_parameter(self.variance)
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self.order=order
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def K(self, X, X2=None):
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return (self._dot_product(X, X2) + 1.)**self.order * self.variance
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def _dot_product(self, X, X2=None):
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if X2 is None:
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return np.dot(X, X.T)
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else:
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return np.dot(X, X2.T)
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def Kdiag(self, X):
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return self.variance*(np.square(X).sum(1) + 1.)**self.order
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def update_gradients_full(self, dL_dK, X, X2=None):
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self.variance.gradient = np.sum(dL_dK * (self._dot_product(X, X2) + 1.)**self.order)
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def update_gradients_diag(self, dL_dKdiag, X):
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raise NotImplementedError
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def gradients_X(self, dL_dK, X, X2=None):
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raise NotImplementedError
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def gradients_X_diag(self, dL_dKdiag, X):
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raise NotImplementedError
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