class_docstr: Base class for proposing new values of position and momentum by simulating Hamiltonian Dynamics.
  
  Classes inheriting this base class can be passed as an argument for
  simulate_dynamics in inference algorithms.
  
  Parameters
  ----------
  model : An instance of pgmpy.models
      Model for which DiscretizeTime object is initialized
  
  position : A 1d array like object (numpy.ndarray or list)
      Vector representing values of parameter position( or X)
  
  momentum: A 1d array like object (numpy.ndarray or list)
      Vector representing the proposed value for momentum (velocity)
  
  stepsize: Float
      stepsize for the simulating dynamics
  
  grad_log_pdf : A subclass of pgmpy.inference.continuous.BaseGradLogPDF
      A class for finding gradient log and log of distribution
  
  grad_log_position: A 1d array like object, defaults to None
      Vector representing gradient log at given position
      If None, then will be calculated
  
  Examples
  --------
  >>> from pgmpy.sampling import BaseSimulateHamiltonianDynamics
  >>> from pgmpy.factors.continuous import GaussianDistribution
  >>> from pgmpy.sampling import GradLogPDFGaussian
  >>> import numpy as np
  >>> # Class should initialize self.new_position, self.new_momentum and self.new_grad_logp
  >>> # self.new_grad_logp represents gradient log at new proposed value of position
  >>> class ModifiedEuler(BaseSimulateHamiltonianDynamics):
  ...     def __init__(self, model, position, momentum, stepsize, grad_log_pdf, grad_log_position=None):
  ...         BaseSimulateHamiltonianDynamics.__init__(self, model, position, momentum,
  ...                                                  stepsize, grad_log_pdf, grad_log_position)
  ...         self.new_position, self.new_momentum, self.new_grad_logp = self._get_proposed_values()
  ...     def _get_proposed_values(self):
  ...         momentum_bar = self.momentum + self.stepsize * self.grad_log_position
  ...         position_bar = self.position + self.stepsize * momentum_bar
  ...         grad_log_position, _ = self.grad_log_pdf(position_bar, self.model).get_gradient_log_pdf()
  ...         return position_bar, momentum_bar, grad_log_position
  >>> pos = np.array([1, 2])
  >>> momentum = np.array([0, 0])
  >>> mean = np.array([0, 0])
  >>> covariance = np.eye(2)
  >>> model = GaussianDistribution(['x', 'y'], mean, covariance)
  >>> new_pos, new_momentum, new_grad = ModifiedEuler(model, pos, momentum,
  ...                                                 0.25, GradLogPDFGaussian).get_proposed_values()
  >>> new_pos
  array([0.9375, 1.875])
  >>> new_momentum
  array([-0.25, -0.5])
  >>> new_grad
  array([-0.9375, -1.875])
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docstr: Returns the new proposed values of position and momentum
  
  Returns
  -------
  Returns a tuple of following type (in order)
  
  numpy.array: A 1d numpy.array representing new proposed value of position
  
  numpy.array: A 1d numpy.array representing new proposed value of momentum
  
  numpy.array: A 1d numpy.array representing gradient of log distribution at new proposed value of position
  
  Example
  -------
  >>> # Using implementation of ModifiedEuler
  >>> from pgmpy.inference.continuous import ModifiedEuler, GradLogPDFGaussian as GLPG
  >>> from pgmpy.factors import GaussianDistribution
  >>> import numpy as np
  >>> pos = np.array([3, 4])
  >>> momentum = np.array([1, 1])
  >>> mean = np.array([-1, 1])
  >>> covariance = 3*np.eye(2)
  >>> model = GaussianDistribution(['x', 'y'], mean, covariance)
  >>> new_pos, new_momentum, new_grad = ModifiedEuler(model, pos, momentum, 0.70, GLPG).get_proposed_values()
  >>> new_pos
  array([ 3.04666667,  4.21      ])
  >>> new_momentum
  array([ 0.06666667,  0.3       ])
  >>> new_grad
  array([-1.34888889, -1.07      ])
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code: def get_proposed_values(self):
      """
      Returns the new proposed values of position and momentum
  
      Returns
      -------
      Returns a tuple of following type (in order)
  
      numpy.array: A 1d numpy.array representing new proposed value of position
  
      numpy.array: A 1d numpy.array representing new proposed value of momentum
  
      numpy.array: A 1d numpy.array representing gradient of log distribution at new proposed value of position
  
      Example
      -------
      >>> # Using implementation of ModifiedEuler
      >>> from pgmpy.inference.continuous import ModifiedEuler, GradLogPDFGaussian as GLPG
      >>> from pgmpy.factors import GaussianDistribution
      >>> import numpy as np
      >>> pos = np.array([3, 4])
      >>> momentum = np.array([1, 1])
      >>> mean = np.array([-1, 1])
      >>> covariance = 3*np.eye(2)
      >>> model = GaussianDistribution(['x', 'y'], mean, covariance)
      >>> new_pos, new_momentum, new_grad = ModifiedEuler(model, pos, momentum, 0.70, GLPG).get_proposed_values()
      >>> new_pos
      array([ 3.04666667,  4.21      ])
      >>> new_momentum
      array([ 0.06666667,  0.3       ])
      >>> new_grad
      array([-1.34888889, -1.07      ])
      """
      return self.new_position, self.new_momentum, self.new_grad_logp
  
imports: import itertools
  import math
  import os
  import networkx
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from pgmpy.inference import Inference
  from pgmpy.utils import _check_1d_array_object, _check_length_equal, compat_fns
label_desc: Use image node_id 2 for calling the BaseSimulateHamiltonianDynamics obj's underlying member method code with example usage: obj.get_proposed_values() and returns: self,  self,  self