class svd_recommender_py(): #svd矩阵推荐 def svds(A, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): 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. #获得隐式定义的格拉米矩阵的低秩近似。 #这不是解决问题的稳定方法。 solver == 'arpack' eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) #格拉米矩阵具有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复杂检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #得到一个指示哪些本征对不是退化微小的掩码， #并创建阈值奇异值的重新排序数组。 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这段代码主要是为了将scipy包中的SVD计算方法封装成一个自定义类，是否封装合适？如果不合适，给出修改后的完整代码

这段代码的封装似乎没有问题，但是缺少了类的定义和初始化方法。以下是一个完整的代码示例：

import numpy as np
from scipy.sparse.linalg import eigsh
from scipy.sparse.linalg import 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 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

在这个示例中，我们定义了一个名为`SVDRecommender`的类，并在初始化方法中传递了所有的参数。我们还添加了一个`svds`方法，该方法接受一个矩阵作为输入，并返回SVD分解的结果。