生成sinkhorn算法代码

抱歉，作为AI语言模型，我无法直接生成代码。不过，以下是Python中使用sinkhorn算法的示例代码，供您参考：

import numpy as np

def sinkhorn(K, r, c, reg, numItermax=1000, stopThr=1e-9):
    """
    Solves the entropic regularization optimal transport problem and returns the OT matrix
    The function solves the following optimization problem:
    .. math::
        \gamma = arg\min_\gamma <\gamma,K>_F + reg\cdot\Omega(\gamma)
        s.t. \gamma 1 = \alpha
             \gamma^T 1= \beta
             \gamma\geq 0
    where :
    - K: loss matrix NxM
    - r: row margins (sums) len(N)
    - c: column margins (sums) len(M)
    - reg: regularization term >0
    - gamma: transport plan NxM
    - <.,.>_F : Frobenius dot product, ie <A,B>_F = \sum_i\sum_j A_{i,j}B_{i,j}
    - \Omega : entropy, ie \Omega(\gamma)=\sum_{i,j} \gamma_{i,j}(\log(\gamma_{i,j})-1)
    The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_
    Parameters
    ----------
    K : ndarray, shape (n_samples, n_features)
        Ground distance matrix between the two point sets.
    r : ndarray, shape (n_samples,)
        Marginal of the first point set.
    c : ndarray, shape (n_samples,)
        Marginal of the second point set.
    reg : float
        Regularization term. The higher reg, the more sparse the solution.
    numItermax : int, optional
        Max number of iterations
    stopThr : float, optional
        Stop threshol on error (>0)
    Returns
    -------
    gamma : ndarray, shape (n_samples, n_features)
        Optimal transportation matrix for the given parameters
    """
    assert len(r) == K.shape[0], "nb of rows in K and len(r) do not match"
    assert len(c) == K.shape[1], "nb of cols in K and len(c) do not match"
    r = np.asarray(r, dtype=np.float64)
    c = np.asarray(c, dtype=np.float64)
    K = np.asarray(K, dtype=np.float64)
    # init data
    Nini = len(r)
    Nfin = len(c)
    u = np.ones(Nini) / Nini
    v = np.ones(Nfin) / Nfin
    # print(reg)
    Kp = (np.exp(-reg * K))
    Ktransposepy = np.dot(Kp.transpose(), y)
    cpt = 0
    err = 1
    while (err > stopThr and cpt < numItermax):
        uprev = u
        vprev = v
        KtransposeU = np.dot(Kp, u)
        v = c / KtransposeU
        u = r / Ktransposepy
        if (np.abs(u).max() > 1e9 or np.abs(v).max() > 1e9):
            # to avoid numerical problem
            # (we need to homogenize the matrix),
            # better to restart
            print('Warning: numerically unstable')
            reg = reg * 10
            u = np.ones(Nini) / Nini
            v = np.ones(Nfin) / Nfin
            Kp = (np.exp(-reg * K))
        if cpt % 10 == 0:
            # we can speed up the process by checking for the error only all
            # the 10th iterations
            transp = u.reshape((Nini, 1)) * Kp * v.reshape((1, Nfin))
            err = np.linalg.norm(np.sum(transp, axis=1) - r) + \
                  np.linalg.norm(np.sum(transp, axis=0) - c)
            # print("err=", err, "stab. coef=", np.sum(Kp * np.log(Kp / K)))
        cpt += 1
    return u.reshape((Nini, 1)) * Kp * v.reshape((1, Nfin))

这里使用了numpy库和一些数学运算符和函数，包括矩阵乘法和范数计算。具体实现可根据您的需求进行修改。