def __init__(self, verbose=False):
        """creates an instance of an Intercept term

        Parameters
        ----------

        Attributes
        ----------
        n_coefs : int
            Number of coefficients contributed by the term to the model

        istensor : bool
            whether the term is a tensor product of sub-terms

        isintercept : bool
            whether the term is an intercept

        hasconstraint : bool
            whether the term has any constraints

        info : dict
            contains dict with the sufficient information to duplicate the term
        """
        self._name = 'intercept_term'
        self._minimal_name = 'intercept'

        super(Intercept, self).__init__(feature=None, fit_linear=False, fit_splines=False, lam=None, penalties=None, constraints=None, verbose=verbose)

        self._exclude += ['fit_splines', 'fit_linear', 'lam', 'penalties', 'constraints', 'feature', 'dtype']
        self._args = []
def __init__ ( self , verbose = False ) : self . _name = 'intercept_term' self . _minimal_name = 'intercept' super ( Intercept , self ) . __init__ ( feature = None , fit_linear = False , fit_splines = False , lam = None , penalties = None , constraints = None , verbose = verbose ) self . _exclude += [ 'fit_splines' , 'fit_linear' , 'lam' , 'penalties' , 'constraints' , 'feature' , 'dtype' ] self . _args = [ ]
creates an instance of an Intercept term

        Parameters
        ----------

        Attributes
        ----------
        n_coefs : int
            Number of coefficients contributed by the term to the model

        istensor : bool
            whether the term is a tensor product of sub-terms

        isintercept : bool
            whether the term is an intercept

        hasconstraint : bool
            whether the term has any constraints

        info : dict
            contains dict with the sufficient information to duplicate the term