Question

我实现了一个基于gmpy2.mpq的高斯估计函数。分析后，它被证明是我的程序的瓶颈。我试过Cython来优化它。

Cython太棒了，速度已经增加了两倍左右，但是我试图通过静态输入来加快速度。

当我这样做时，有两个问题：

gmpy2.mpq是一个函数而不是一个类型。如何静态输入呢？
如何静态输入给定类型的列表？

如果列表有更快的替代方案，那将会很棒。

我已经附上了代码，仅供参考。

def gauss_estimate(a, b):
    """Gauss estimation for integers

    :param a:a n*m 2d sequence of integers, where n>=m
    :param b:an n-length 1d sequence of integers
    :returns: a m-length 1d sequence of mpq that a@x=b, gmpy2.mpq is a fast
        implementing of fraction
    :raises: a SingularError if the rank of a is smaller than m
    :raises: a NoSolutionError if the rank of a is larger than m
    """
    cdef int n, m, i, j
    cdef list aa, bb
    if isinstance(a, ndarray):
        a = a.tolist()
    if isinstance(b, ndarray):
        b = b.tolist()
    aa = [[mpq(aii) for aii in ai] for ai in a]
    n = len(aa)
    m = len(aa[0])
    if n < m:
        raise ValueError('Wrong shape of a')
    for ai in aa:
        if len(ai) != m:
            raise ValueError('Wrong shape of ai')
    bb = [mpq(bi) for bi in b]
    if len(bb) != n:
        raise ValueError('Wrong shape of b')
    for i in range(m):
        if aa[i][i] == 0:
            for j in range(i, n):
                if aa[j][i] != 0:
                    aa[i], aa[j] = aa[j], aa[i]
                    bb[i], bb[j] = bb[j], bb[i]
                    break
            else:
                raise SingularError('The rank of a is smaller than m')
        bb[i] /= aa[i][i]
        for j in reversed(range(i, m)):
            aa[i][j] /= aa[i][i]
        for j in range(i+1, n):
            bb[j] -= aa[j][i] * bb[i]
            for k in reversed(range(i, m)):
                aa[j][k] -= aa[j][i] * aa[i][k]
            assert aa[j][i] == 0
    for i in range(m, n):
        if bb[i] != 0:
            raise NoSolutionError('No solution found')
    for i in reversed(range(m)):
        for j in range(0, i):
            bb[j] -= bb[i] * aa[j][i]
    for i in range(m):
        assert aa[i][i] == 1
    return tuple(bb[:m])

如何在Cython中静态输入gmpy2.mpq列表？

0 个答案: