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61 lines
1.8 KiB
Python
61 lines
1.8 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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from ...core.parameterization import Param
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def theta(x):
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"""Heaviside step function"""
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return np.where(x>=0.,1.,0.)
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class Spline(Kernpart):
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"""
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Spline kernel
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:param input_dim: the number of input dimensions (fixed to 1 right now TODO)
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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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def __init__(self,input_dim,variance=1.,lengthscale=1.):
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self.input_dim = input_dim
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assert self.input_dim==1
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self.num_params = 1
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self.name = 'spline'
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self.variance = Param('variance', variance)
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self.lengthscale = Param('lengthscale', lengthscale)
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self.add_parameters(self.variance, self.lengthscale)
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# def _get_params(self):
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# return self.variance
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#
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# def _set_params(self,x):
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# self.variance = x
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#
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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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assert np.all(X>0), "Spline covariance is for +ve domain only. TODO: symmetrise"
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assert np.all(X2>0), "Spline covariance is for +ve domain only. TODO: symmetrise"
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t = X
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s = X2.T
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s_t = s-t # broadcasted subtraction
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target += self.variance*(0.5*(t*s**2) - s**3/6. + (s_t)**3*theta(s_t)/6.)
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def Kdiag(self,X,target):
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target += self.variance*X.flatten()**3/3.
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def _param_grad_helper(self,X,X2,target):
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target += 0.5*(t*s**2) - s**3/6. + (s_t)**3*theta(s_t)/6.
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def dKdiag_dtheta(self,X,target):
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target += X.flatten()**3/3.
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def dKdiag_dX(self,X,target):
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target += self.variance*X**2
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