(self, X)
return np.sqrt(X)
A reference implementation of a transform function.

        Parameters
        ----------
        X : {array-like, sparse-matrix}, shape (n_samples, n_features)
            The input samples.

        Returns
        -------
        X_transformed : array, shape (n_samples, n_features)
            The array containing the element-wise square roots of the values
            in ``X``.
A reference implementation of a transform function.
A reference implementation of a transform function .
def transform(self, X):
        """ A reference implementation of a transform function.

        Parameters
        ----------
        X : {array-like, sparse-matrix}, shape (n_samples, n_features)
            The input samples.

        Returns
        -------
        X_transformed : array, shape (n_samples, n_features)
            The array containing the element-wise square roots of the values
            in ``X``.
        """
        # Check is fit had been called
        check_is_fitted(self, 'n_features_')

        # Input validation
        X = check_array(X, accept_sparse=True)

        # Check that the input is of the same shape as the one passed
        # during fit.
        if X.shape[1] != self.n_features_:
            raise ValueError('Shape of input is different from what was seen'
                             'in `fit`')
        return np.sqrt(X)
def transform ( self , X ) : # Check is fit had been called check_is_fitted ( self , 'n_features_' ) # Input validation X = check_array ( X , accept_sparse = True ) # Check that the input is of the same shape as the one passed # during fit. if X . shape [ 1 ] != self . n_features_ : raise ValueError ( 'Shape of input is different from what was seen' 'in `fit`' ) return np . sqrt ( X )