mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-07 11:02:38 +02:00
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
from .kern import Kern
|
|
from ...core.parameterization import Param
|
|
from ...core.parameterization.transformations import Logexp
|
|
import numpy as np
|
|
|
|
class Brownian(Kern):
|
|
"""
|
|
Brownian motion in 1D only.
|
|
|
|
Negative times are treated as a separate (backwards!) Brownian motion.
|
|
|
|
:param input_dim: the number of input dimensions
|
|
:type input_dim: int
|
|
:param variance:
|
|
:type variance: float
|
|
"""
|
|
def __init__(self, input_dim=1, variance=1., active_dims=None, name='Brownian'):
|
|
assert input_dim==1, "Brownian motion in 1D only"
|
|
super(Brownian, self).__init__(input_dim, active_dims, name)
|
|
|
|
self.variance = Param('variance', variance, Logexp())
|
|
self.link_parameters(self.variance)
|
|
|
|
def K(self,X,X2=None):
|
|
if X2 is None:
|
|
X2 = X
|
|
return self.variance*np.where(np.sign(X)==np.sign(X2.T),np.fmin(np.abs(X),np.abs(X2.T)), 0.)
|
|
|
|
def Kdiag(self,X):
|
|
return self.variance*np.abs(X.flatten())
|
|
|
|
def update_gradients_full(self, dL_dK, X, X2=None):
|
|
if X2 is None:
|
|
X2 = X
|
|
self.variance.gradient = np.sum(dL_dK * np.where(np.sign(X)==np.sign(X2.T),np.fmin(np.abs(X),np.abs(X2.T)), 0.))
|
|
|
|
#def update_gradients_diag(self, dL_dKdiag, X):
|
|
#self.variance.gradient = np.dot(np.abs(X.flatten()), dL_dKdiag)
|
|
|
|
#def gradients_X(self, dL_dK, X, X2=None):
|
|
#if X2 is None:
|
|
#return np.sum(self.variance*dL_dK*np.abs(X),1)[:,None]
|
|
#else:
|
|
#return np.sum(np.where(np.logical_and(np.abs(X)<np.abs(X2.T), np.sign(X)==np.sign(X2)), self.variance*dL_dK,0.),1)[:,None]
|
|
|
|
|
|
|