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65 lines
1.7 KiB
Python
65 lines
1.7 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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def theta(x):
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"""Heavisdie step function"""
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return np.where(x>=0.,1.,0.)
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class Brownian(Kernpart):
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"""
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Brownian Motion kernel.
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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:
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:type variance: float
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"""
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def __init__(self,input_dim,variance=1.):
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self.input_dim = input_dim
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assert self.input_dim==1, "Brownian motion in 1D only"
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self.num_params = 1
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self.name = 'Brownian'
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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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if X2 is None:
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X2 = X
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target += self.variance*np.fmin(X,X2.T)
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def Kdiag(self,X,target):
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target += self.variance*X.flatten()
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def _param_grad_helper(self,dL_dK,X,X2,target):
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if X2 is None:
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X2 = X
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target += np.sum(np.fmin(X,X2.T)*dL_dK)
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def dKdiag_dtheta(self,dL_dKdiag,X,target):
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target += np.dot(X.flatten(), dL_dKdiag)
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def gradients_X(self,dL_dK,X,X2,target):
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raise NotImplementedError, "TODO"
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#target += self.variance
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#target -= self.variance*theta(X-X2.T)
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#if X.shape==X2.shape:
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#if np.all(X==X2):
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#np.add(target[:,:,0],self.variance*np.diag(X2.flatten()-X.flatten()),target[:,:,0])
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def dKdiag_dX(self,dL_dKdiag,X,target):
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target += self.variance*dL_dKdiag[:,None]
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