old_contents: # 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
  
  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]
  
  
  
  
new_contents: # 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
  
  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 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
  
      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]
  
  
  
  
subject: Update brownian.py