Source code for GPy.kern.src.brownian

# 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 =, 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]