extmath.py 文件源码

def truncated_svd(A, k):
    """Compute the truncated SVD of the matrix `A` i.e. the `k` largest
    singular values as well as the corresponding singular vectors. It might
    return less singular values/vectors, if one dimension of `A` is smaller
    than `k`.

    In the background it performs a full SVD. Therefore, it might be
    inefficient when `k` is much smaller than the dimensions of `A`.

    :param A: A real or complex matrix
    :param k: Number of singular values/vectors to compute
    :returns: u, s, v, where
        u: left-singular vectors
        s: singular values in descending order
        v: right-singular vectors

    """
    u, s, v = np.linalg.svd(A)
    k_prime = min(k, len(s))
    return u[:, :k_prime], s[:k_prime], v[:k_prime]


####################
#  Randomized SVD  #
####################