class_docstr: Initialise a PomdpXWriter Object
  
  Parameters
  ----------
      model: A Bayesian of Markov Model
          The model to write
  
  encoding: String(optional)
      Encoding for text data
  
  prettyprint: Bool(optional)
      Indentation in output XML if true
class_docstr_tok: ["Initialise","a","PomdpXWriter","Object","Parameters","--","--","--","--","--","model",":","A","Bayesian","of","Markov","Model","The","model","to","write","encoding",":","String","(","optional",")","Encoding","for","text","data","prettyprint",":","Bool","(","optional",")","Indentation","in","output","XML","if","true"]
code: def __init__(self, model_data, encoding="utf-8", prettyprint=True):
      self.model = model_data
  
      self.encoding = encoding
      self.prettyprint = prettyprint
  
      self.xml = etree.Element("pomdpx", attrib={"version": "1.0"})
      self.description = etree.SubElement(self.xml, "Description")
      self.discount = etree.SubElement(self.xml, "Discount")
      self.variable = etree.SubElement(self.xml, "Variable")
      self.initial_belief = etree.SubElement(
          self.xml, "InitialStateBelief"
      )
      self.transition_function = etree.SubElement(
          self.xml, "StateTransitionFunction"
      )
      self.observation_function = etree.SubElement(
          self.xml, "ObsFunction"
      )
      self.reward_function = etree.SubElement(
          self.xml, "RewardFunction"
      )
  
imports: import xml.etree.ElementTree
  from collections import defaultdict
label_desc: Use image node_id 1 to create a new PomdpXWriter object from inherited base classes: object with example: obj = PomdpXWriter(model_data, encoding, prettyprint)