class_docstr: Base class for independencies.
  independencies class represents a set of Conditional Independence
  assertions (eg: "X is independent of Y given Z" where X, Y and Z
  are random variables) or Independence assertions (eg: "X is
  independent of Y" where X and Y are random variables).
  Initialize the independencies Class with Conditional Independence
  assertions or Independence assertions.
  
  Parameters
  ----------
  assertions: Lists or Tuples
          Each assertion is a list or tuple of the form: [event1,
          event2 and event3]
          eg: assertion ['X', 'Y', 'Z'] would be X is independent
          of Y given Z.
  
  Examples
  --------
  Creating an independencies object with one independence assertion:
  Random Variable X is independent of Y
  
  >>> independencies = independencies(['X', 'Y'])
  
  Creating an independencies object with three conditional
  independence assertions:
  First assertion is Random Variable X is independent of Y given Z.
  
  >>> independencies = independencies(['X', 'Y', 'Z'],
  ...             ['a', ['b', 'c'], 'd'],
  ...             ['l', ['m', 'n'], 'o'])
  
  Public Methods
  --------------
  add_assertions
  get_assertions
  get_factorized_product
  closure
  entails
  is_equivalent
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docstr: Returns True if the two Independencies-objects are equivalent, otherwise False.
  (i.e. any Bayesian Network that satisfies the one set
  of conditional independencies also satisfies the other).
  
  Might be very slow if more than six variables are involved.
  
  Parameters
  ----------
  other: Independencies()-object
  
  Examples
  --------
  >>> from pgmpy.independencies import Independencies
  >>> ind1 = Independencies(['X', ['Y', 'W'], 'Z'])
  >>> ind2 = Independencies(['X', 'Y', 'Z'], ['X', 'W', 'Z'])
  >>> ind3 = Independencies(['X', 'Y', 'Z'], ['X', 'W', 'Z'], ['X', 'Y', ['W','Z']])
  >>> ind1.is_equivalent(ind2)
  False
  >>> ind1.is_equivalent(ind3)
  True
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code: def is_equivalent(self, other):
      """
      Returns True if the two Independencies-objects are equivalent, otherwise False.
      (i.e. any Bayesian Network that satisfies the one set
      of conditional independencies also satisfies the other).
  
      Might be very slow if more than six variables are involved.
  
      Parameters
      ----------
      other: Independencies()-object
  
      Examples
      --------
      >>> from pgmpy.independencies import Independencies
      >>> ind1 = Independencies(['X', ['Y', 'W'], 'Z'])
      >>> ind2 = Independencies(['X', 'Y', 'Z'], ['X', 'W', 'Z'])
      >>> ind3 = Independencies(['X', 'Y', 'Z'], ['X', 'W', 'Z'], ['X', 'Y', ['W','Z']])
      >>> ind1.is_equivalent(ind2)
      False
      >>> ind1.is_equivalent(ind3)
      True
      """
      return self.entails(other) and other.entails(self)
  
imports: import itertools
label_desc: Use image node_id 11 for calling the Independencies obj's underlying member method code with example usage: obj.is_equivalent(other) and returns: unknown