def transform(self, X):
        check_is_fitted(self, 'n_components_')

        W, _, n_iter_ = non_negative_factorization(
            X=X, W=None, H=self.components_, n_components=self.n_components_,
            init=self.init, update_H=False, solver=self.solver,
            tol=self.tol, max_iter=self.max_iter, alpha=self.alpha,
            l1_ratio=self.l1_ratio, regularization='both',
            random_state=self.random_state, verbose=self.verbose,
            shuffle=self.shuffle,
            nls_max_iter=self.nls_max_iter, sparseness=self.sparseness,
            beta=self.beta, eta=self.eta)

        self.n_iter_ = n_iter_
        return W
Transform the data X according to the fitted NMF model
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/decomposition/nmf.py/NMF.transform