48e5569ebc13b3d5fc42e3efebcddd75a96ff241,tensorly/backend/core.py,Backend,partial_svd,#Backend#Any#Any#Any#,726

Before Change

        else:
            // We can perform a partial SVD
            // construct np.random.RandomState for sampling a starting vector
            if random_state is None:
                // if random_state is not specified, do not initialize a starting vector
                v0 = None
            elif isinstance(random_state, int):
                rns = np.random.RandomState(random_state)
                // initilize with [-1, 1] as in ARPACK
                v0 = rns.uniform(-1, 1, min_dim)
            elif isinstance(random_state, np.random.RandomState):
                // initilize with [-1, 1] as in ARPACK
                v0 = random_state.uniform(-1, 1, min_dim)

            // First choose whether to use X * X.T or X.T *X
            if dim_1 < dim_2:
                S, U = scipy.sparse.linalg.eigsh(
                    np.dot(matrix, matrix.T.conj()), k=n_eigenvecs, which="LM", v0=v0
                )

After Change

            U, S, V = U[:, :n_eigenvecs], S[:n_eigenvecs], V[:n_eigenvecs, :]
        else:
            // We can perform a partial SVD
            rng = self.check_random_state(random_state)
            // initilize with [-1, 1] as in ARPACK
            v0 = rng.uniform(-1, 1, min_dim)

            // First choose whether to use X * X.T or X.T *X

Instances