copulas/univariate/base.py
def fit(self, X, *args, **kwargs):
        """Fit scipy model to an array of values.

        Args:
            X(`np.ndarray` or `pd.DataFrame`):  Datapoints to be estimated from. Must be 1-d

        Returns:
            None
        """

        self.constant_value = self._get_constant_value(X)

        if self.constant_value is None:
            if self.unfittable_model:
                self.model = getattr(scipy.stats, self.model_class)(*args, **kwargs)
            else:
                self.model = getattr(scipy.stats, self.model_class)(X, *args, **kwargs)

            for name in self.METHOD_NAMES:
                attribute = getattr(self.__class__, name)
                if isinstance(attribute, str):
                    setattr(self, name, getattr(self.model, attribute))

                elif attribute is None:
                    setattr(self, name, missing_method_scipy_wrapper(lambda x: x))

        else:
            self._replace_constant_methods()

        self.fitted = True
Fit scipy model to an array of values.

        Args:
            X(`np.ndarray` or `pd.DataFrame`):  Datapoints to be estimated from. Must be 1-d

        Returns:
            None
        
Fit scipy model to an array of values.
Fit scipy model to an array of values .
def fit(self, X, *args, **kwargs):
        self.constant_value = self._get_constant_value(X)
        if self.constant_value is None:
            if self.unfittable_model:
                self.model = getattr(scipy.stats, self.model_class)(*args, **kwargs)
            else:
                self.model = getattr(scipy.stats, self.model_class)(X, *args, **kwargs)
            for name in self.METHOD_NAMES:
                attribute = getattr(self.__class__, name)
                if isinstance(attribute, str):
                    setattr(self, name, getattr(self.model, attribute))
                elif attribute is None:
                    setattr(self, name, missing_method_scipy_wrapper(lambda x: x))
        else:
            self._replace_constant_methods()
        self.fitted = True
def fit ( self , X , * args , ** kwargs ) : self . constant_value = self . _get_constant_value ( X ) if self . constant_value is None : if self . unfittable_model : self . model = getattr ( scipy . stats , self . model_class ) ( * args , ** kwargs ) else : self . model = getattr ( scipy . stats , self . model_class ) ( X , * args , ** kwargs ) for name in self . METHOD_NAMES : attribute = getattr ( self . __class__ , name ) if isinstance ( attribute , str ) : setattr ( self , name , getattr ( self . model , attribute ) ) elif attribute is None : setattr ( self , name , missing_method_scipy_wrapper ( lambda x : x ) ) else : self . _replace_constant_methods ( ) self . fitted = True
Fit scipy model to an array of values.
Fit scipy model to an array of values .
"""Fit scipy model to an array of values.

        Args:
            X(`np.ndarray` or `pd.DataFrame`):  Datapoints to be estimated from. Must be 1-d

        Returns:
            None
        """
None
self
X
Datapoints to be estimated from. Must be 1-d
Datapoints to be estimated from . Must be 1 - d