如果输入类型是“距离”，那么可以有一种情况，即矩阵X可以包含inf，它基本上表示图形的断开点，这是一种完全有效的情况，但“B = -0.5 * H @ D**2@H”在这种情况下抛出错误。

def cmds(X, n_dim, input_type='raw'):
    """
    Classical(linear) multidimensional scaling (MDS)
    
    Parameters
    ----------
    X: (d, n) array or (n,n) array
        input data. The data are placed in column-major order. 
        That is, samples are placed in the matrix (X) as column vectors
        d: dimension of points
        n: number of points
        
    n_dim: dimension of target space
    
    input_type: it indicates whether data are raw or distance
        - raw: raw data. (n,d) array. 
        - distance: precomputed distances between the data. (n,n) array.
    Returns
    -------
    Y: (n_dim, n) array. projected embeddings.
    evals: (n_dim) eigen values
    evecs: corresponding eigen vectors in column vectors
    """

    if input_type == 'distance':
        D = X
    elif input_type == 'raw':
        Xt = X.T
        D = euclidean_distances(Xt,Xt)
        
    # Centering matrix
    H = np.eye(D.shape[0]) - np.ones(D.shape) / D.shape[0]

    # Double-center the distance matrix
    B = -0.5 * H @ D**2 @ H

    # Eigen decomposition
    evals, evecs = np.linalg.eigh(B)

    # Sorting eigenvalues and eigenvectors in decreasing order
    sort_indices = np.argsort(evals)[::-1]
    evals = evals[sort_indices]
    evecs = evecs[:, sort_indices]

    # Selecting top n_dim eigenvectors
    evecs = evecs[:, :n_dim]

    # Projecting data to the new space
    Y = np.sqrt(np.diag(evals[:n_dim])) @ evecs.T

    return Y, evals, evecs

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由于这是一个完全有效的情况下，如果两个点不连接，它们之间的距离将被设置为inf。但代码将失败时，计算中心矩阵和特征值。请帮助我在这里处理这种情况