lin_alg_fun.py 文件源码

def svd_wrapper(X, rank = None):
    """
    Computes the (possibly partial) SVD of a matrix. Handles the case where
    X is either dense or sparse.

    Parameters
    ----------
    X: either dense or sparse
    rank: rank of the desired SVD (required for sparse matrices)

    Output
    ------
    U, D, V
    the columns of U are the left singular vectors
    the COLUMNS of V are the left singular vectors

    """
    if isinstance(X, LinearOperator):
        scipy_svds = svds(convert2scipy(X), rank)
        U, D, V = fix_scipy_svds(scipy_svds)
        V = V.T

    elif issparse(X):
        scipy_svds = svds(X, rank)
        U, D, V = fix_scipy_svds(scipy_svds)
        V = V.T

    else:
        # TODO: implement partial SVD
        U, D, V = full_svd(X, full_matrices=False)
        V = V.T

        if rank:
            U = U[:, :rank]
            D = D[:rank]
            V = V[:, :rank]

    return U, D, V