def fit(self, X, y=None, **kwargs):
        """Determine transformation parameters from data in X.

        Subsequent calls to `transform(Y)` compute the pairwise
        distance to `X`.
        Parameters of the clinical kernel are only updated
        if `fit_once` is `False`, otherwise you have to
        explicitly call `prepare()` once.

        Parameters
        ----------
        X: pandas.DataFrame, shape = (n_samples, n_features)
            Data to estimate parameters from.

        y : None
            Argument is ignored (included for compatibility reasons).

        kwargs : dict
            Argument is ignored (included for compatibility reasons).

        Returns
        -------
        self : object
            Returns the instance itself.
        """
        if X.ndim != 2:
            raise ValueError("expected 2d array, but got %d" % X.ndim)

        if self.fit_once:
            self.X_fit_ = X
        else:
            self._prepare_by_column_dtype(X)

        return self
def fit ( self , X , y = None , ** kwargs ) : if X . ndim != 2 : raise ValueError ( "expected 2d array, but got %d" % X . ndim ) if self . fit_once : self . X_fit_ = X else : self . _prepare_by_column_dtype ( X ) return self
Determine transformation parameters from data in X.

        Subsequent calls to `transform(Y)` compute the pairwise
        distance to `X`.
        Parameters of the clinical kernel are only updated
        if `fit_once` is `False`, otherwise you have to
        explicitly call `prepare()` once.

        Parameters
        ----------
        X: pandas.DataFrame, shape = (n_samples, n_features)
            Data to estimate parameters from.

        y : None
            Argument is ignored (included for compatibility reasons).

        kwargs : dict
            Argument is ignored (included for compatibility reasons).

        Returns
        -------
        self : object
            Returns the instance itself.