splearn/decomposition/truncated_svd.py
def transform(self, Z):
        """Perform dimensionality reduction on X.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            New data.

        Returns
        -------
        X_new : array, shape (n_samples, n_components)
            Reduced version of X. This will always be a dense array.
        """
        X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
        check_rdd(X, (sp.spmatrix, np.ndarray))

        mapper = self.broadcast(
            super(SparkTruncatedSVD, self).transform, Z.context)
        return Z.transform(mapper, column='X', dtype=np.ndarray)
def transform(self, Z):
        X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
        check_rdd(X, (sp.spmatrix, np.ndarray))

        mapper = self.broadcast(
            super(SparkTruncatedSVD, self).transform, Z.context)
        return Z.transform(mapper, column='X', dtype=np.ndarray)
def transform ( self , Z ) : X = Z [ : , 'X' ] if isinstance ( Z , DictRDD ) else Z check_rdd ( X , ( sp . spmatrix , np . ndarray ) ) mapper = self . broadcast ( super ( SparkTruncatedSVD , self ) . transform , Z . context ) return Z . transform ( mapper , column = 'X' , dtype = np . ndarray )
Perform dimensionality reduction on X.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            New data.

        Returns
        -------
        X_new : array, shape (n_samples, n_components)
            Reduced version of X. This will always be a dense array.
Perform dimensionality reduction on X .