def __init__(self, variables, cardinality, inhibitor_probability):
        # TODO: Accept values of each state so that it could be
        # put into F to compute the final state values of the output
        """
        Init method for NoisyOrModel.

        Parameters
        ----------
        variables: list, tuple, dict (array like)
            array containing names of the variables.

        cardinality: list, tuple, dict (array like)
            array containing integers representing the cardinality
            of the variables.

        inhibitor_probability: list, tuple, dict (array_like)
            array containing the inhibitor probabilities of each variable.

        Examples
        --------
        >>> from pgmpy.models import NoisyOrModel
        >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4],
        ...                                                      [0.2, 0.4, 0.7],
        ...                                                      [0.1, 0.4]])
        """
        self.variables = np.array([])
        self.cardinality = np.array([], dtype=np.int)
        self.inhibitor_probability = []
        self.add_variables(variables, cardinality, inhibitor_probability)
def __init__ ( self , variables , cardinality , inhibitor_probability ) : self . variables = np . array ( [ ] ) self . cardinality = np . array ( [ ] , dtype = np . int ) self . inhibitor_probability = [ ] self . add_variables ( variables , cardinality , inhibitor_probability )
Init method for NoisyOrModel.

        Parameters
        ----------
        variables: list, tuple, dict (array like)
            array containing names of the variables.

        cardinality: list, tuple, dict (array like)
            array containing integers representing the cardinality
            of the variables.

        inhibitor_probability: list, tuple, dict (array_like)
            array containing the inhibitor probabilities of each variable.

        Examples
        --------
        >>> from pgmpy.models import NoisyOrModel
        >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4],
        ...                                                      [0.2, 0.4, 0.7],
        ...                                                      [0.1, 0.4]])