splearn/pipeline.py
def fit_transform(self, Z, **fit_params):
        """TODO: rewrite docstring
        Fit all transformers using X, transform the data and concatenate
        results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        return self.fit(Z, **fit_params).transform(Z)
def fit_transform(self, Z, **fit_params):
        """TODO: rewrite docstring
        Fit all transformers using X, transform the data and concatenate
        results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        return self.fit(Z, **fit_params).transform(Z)
def fit_transform ( self , Z , * * fit_params ) : return self . fit ( Z , * * fit_params ) . transform ( Z )
TODO: rewrite docstring
        Fit all transformers using X, transform the data and concatenate
        results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
TODO : rewrite docstring Fit all transformers using X transform the data and concatenate results . Parameters ---------- X : array - like or sparse matrix shape ( n_samples n_features ) Input data to be transformed . Returns ------- X_t : array - like or sparse matrix shape ( n_samples sum_n_components ) hstack of results of transformers . sum_n_components is the sum of n_components ( output dimension ) over transformers .