# 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 paramz.transformations import Logexp
import numpy as np
[docs]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)
[docs] def to_dict(self):
"""
Convert the object into a json serializable dictionary.
Note: It uses the private method _save_to_input_dict of the parent.
:return dict: json serializable dictionary containing the needed information to instantiate the object
"""
input_dict = super(RBF, self)._save_to_input_dict()
input_dict["class"] = "GPy.kern.Brownian"
return input_dict
[docs] 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.)
[docs] def Kdiag(self,X):
return self.variance*np.abs(X.flatten())
[docs] 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]