R语言 报错Loop 0 is not valid: Edge 3 is degenerate (duplicate vertex)

这个错误通常是由于图形中存在重复的顶点或边而导致的。你需要检查你的数据和代码，找出哪些点或边出现了重复。你可以尝试使用一些数据清洗的方法来去除重复的数据，或者使用一些图形处理库来处理这些问题。在R语言中，可以使用igraph等库来进行图形处理，也可以使用dplyr等库进行数据清洗。同时，你也可以在搜索引擎上搜索这个错误信息，寻找更多的解决方法。

相关问题

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中，用Spyder查看，有报错，可能是缩进有问题，无法被调用，根据这个问题，给出解决办法，给出改正后的完整代码

好的，根据你提供的代码，我发现其中存在一些缩进错误，导致代码无法运行。下面是修改后的完整代码，请注意缩进：

import numpy as np
from scipy.sparse.linalg import eigsh, LinearOperator
from scipy.sparse import isspmatrix, is_pydata_spmatrix


class SVDRecommender:
    def __init__(self, k=50, ncv=None, tol=0, which='LM', v0=None,
                 maxiter=None, return_singular_vectors=True, solver='arpack'):
        self.k = k
        self.ncv = ncv
        self.tol = tol
        self.which = which
        self.v0 = v0
        self.maxiter = maxiter
        self.return_singular_vectors = return_singular_vectors
        self.solver = solver

    def svds(self, A):
        if self.which == 'LM':
            largest = True
        elif self.which == 'SM':
            largest = False
        else:
            raise ValueError("which must be either 'LM' or 'SM'.")

        if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)):
            A = np.asarray(A)
            n, m = A.shape

        if self.k <= 0 or self.k >= min(n, m):
            raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k)

        if isinstance(A, LinearOperator):
            if n > m:
                X_dot = A.matvec
                X_matmat = A.matmat
                XH_dot = A.rmatvec
                XH_mat = A.rmatmat
            else:
                X_dot = A.rmatvec
                X_matmat = A.rmatmat
                XH_dot = A.matvec
                XH_mat = A.matmat

            dtype = getattr(A, 'dtype', None)

            if dtype is None:
                dtype = A.dot(np.zeros([m, 1])).dtype

        else:
            if n > m:
                X_dot = X_matmat = A.dot
                XH_dot = XH_mat = _herm(A).dot
            else:
                XH_dot = XH_mat = A.dot
                X_dot = X_matmat = _herm(A).dot

        def matvec_XH_X(x):
            return XH_dot(X_dot(x))

        def matmat_XH_X(x):
            return XH_mat(X_matmat(x))

        XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape)))

        # Get a low rank approximation of the implicitly defined gramian matrix.
        eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv,
                                which=self.which, v0=self.v0)

        # Gramian matrix has real non-negative eigenvalues.
        eigvals = np.maximum(eigvals.real, 0)

        # Use complex detection of small eigenvalues from pinvh.
        t = eigvec.dtype.char.lower()
        factor = {'f': 1E3, 'd': 1E6}
        cond = factor[t] * np.finfo(t).eps
        cutoff = cond * np.max(eigvals)

        # Get a mask indicating which eigenpairs are not degenerate tiny,
        # and create a reordering array for thresholded singular values.
        above_cutoff = (eigvals > cutoff)
        nlarge = above_cutoff.sum()
        nsmall = self.k - nlarge
        slarge = np.sqrt(eigvals[above_cutoff])
        s = np.zeros_like(eigvals)
        s[:nlarge] = slarge

        if not self.return_singular_vectors:
            return np.sort(s)

        if n > m:
            vlarge = eigvec[:, above_cutoff]
            ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None
            vhlarge = _herm(vlarge)
        else:
            ularge = eigvec[:, above_cutoff]
            vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None

        u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None
        vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None

        indexes_sorted = np.argsort(s)
        s = s[indexes_sorted]

        if u is not None:
            u = u[:, indexes_sorted]

        if vh is not None:
            vh = vh[indexes_sorted]

        return u, s, vh


def _augmented_orthonormal_cols(U, n):
    if U.shape[0] <= n:
        return U

    Q, R = np.linalg.qr(U)
    return Q[:, :n]


def _augmented_orthonormal_rows(V, n):
    if V.shape[1] <= n:
        return V

    Q, R = np.linalg.qr(V.T)
    return Q[:, :n].T


def _herm(x):
    return np.conjugate(x.T)

ConvergenceWarning: Affinity propagation did not converge, this model may return degenerate cluster centers and labels. warnings.warn(

这是一个警告信息，提示你使用的 Affinity Propagation 聚类算法没有达到收敛状态，可能会导致聚类中心和标签退化。

Affinity Propagation 是一种基于图模型的聚类算法，它通过消息传递的方式不断更新各个数据点之间的相似度和聚类中心，直到达到收敛状态。但是在某些情况下，算法可能无法收敛，导致聚类中心和标签的结果不可靠。

出现这个警告信息可能是由于数据量太大、数据维度过高或者数据相似度矩阵不合理等原因导致的。如果你的数据集很大，可以考虑使用其他聚类算法，如 k-means、DBSCAN 等；如果数据维度过高，可以考虑对数据进行降维处理；如果数据相似度矩阵不合理，可以重新选择相似度度量方法。

总之，当你使用 Affinity Propagation 聚类算法时，如果出现这个警告信息，需要仔细检查数据集和参数设置，确保聚类结果的准确性和可靠性。