# Path: sklearn/utils/validation.py
# def as_float_array(X, copy=True, force_all_finite=True):
#     """Converts an array-like to an array of floats
# 
#     The new dtype will be np.float32 or np.float64, depending on the original
#     type. The function can create a copy or modify the argument depending
#     on the argument copy.
# 
#     Parameters
#     ----------
#     X : {array-like, sparse matrix}
# 
#     copy : bool, optional
#         If True, a copy of X will be created. If False, a copy may still be
#         returned if X's dtype is not a floating point type.
# 
#     force_all_finite : boolean (default=True)
#         Whether to raise an error on np.inf and np.nan in X.
# 
#     Returns
#     -------
#     XT : {array, sparse matrix}
#         An array of type np.float
#     """
#     if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
#                                     and not sp.issparse(X)):
#         return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
#                            copy=copy, force_all_finite=force_all_finite,
#                            ensure_2d=False)
#     elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
#         return X.copy() if copy else X
#     elif X.dtype in [np.float32, np.float64]:  # is numpy array
#         return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
#     else:
#         return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
# 
# def check_random_state(seed):
#     """Turn seed into a np.random.RandomState instance
# 
#     If seed is None, return the RandomState singleton used by np.random.
#     If seed is an int, return a new RandomState instance seeded with seed.
#     If seed is already a RandomState instance, return it.
#     Otherwise raise ValueError.
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (numbers.Integral, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# Path: sklearn/utils/validation.py
# def check_array(array, accept_sparse=None, dtype=None, order=None, copy=False,
#                 force_all_finite=True, ensure_2d=True, allow_nd=False):
#     """Input validation on an array, list, sparse matrix or similar.
# 
#     By default, the input is converted to an at least 2nd numpy array.
# 
#     Parameters
#     ----------
#     array : object
#         Input object to check / convert.
# 
#     accept_sparse : string, list of string or None (default=None)
#         String[s] representing allowed sparse matrix formats, such as 'csc',
#         'csr', etc.  None means that sparse matrix input will raise an error.
#         If the input is sparse but not in the allowed format, it will be
#         converted to the first listed format.
# 
#     dtype : string, type or None (default=none)
#         Data type of result. If None, the dtype of the input is preserved.
# 
#     order : 'F', 'C' or None (default=None)
#         Whether an array will be forced to be fortran or c-style.
# 
#     copy : boolean (default=False)
#         Whether a forced copy will be triggered. If copy=False, a copy might
#         be triggered by a conversion.
# 
#     force_all_finite : boolean (default=True)
#         Whether to raise an error on np.inf and np.nan in X.
# 
#     ensure_2d : boolean (default=True)
#         Whether to make X at least 2d.
# 
#     allow_nd : boolean (default=False)
#         Whether to allow X.ndim > 2.
# 
#     Returns
#     -------
#     X_converted : object
#         The converted and validated X.
#     """
#     if isinstance(accept_sparse, str):
#         accept_sparse = [accept_sparse]
# 
#     if sp.issparse(array):
#         array = _ensure_sparse_format(array, accept_sparse, dtype, order,
#                                       copy, force_all_finite)
#     else:
#         if ensure_2d:
#             array = np.atleast_2d(array)
#         array = np.array(array, dtype=dtype, order=order, copy=copy)
#         if not allow_nd and array.ndim >= 3:
#             raise ValueError("Found array with dim %d. Expected <= 2" %
#                              array.ndim)
#         if force_all_finite:
#             _assert_all_finite(array)
# 
#     return array


import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_random_state, as_float_array
from ..utils.validation import check_array
from ..utils.extmath import norm
from ..metrics.pairwise import pairwise_kernels
from ..neighbors import kneighbors_graph
from ..manifold import spectral_embedding
from .k_means_ import k_means
    from scipy.sparse import csc_matrix
    from scipy.linalg import LinAlgError
      http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf
    """

    def __init__(self, n_clusters=8, eigen_solver=None, random_state=None,
                 n_init=10, gamma=1., affinity='rbf', n_neighbors=10,
                 eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1,
                 kernel_params=None):
        self.n_clusters = n_clusters
        self.eigen_solver = eigen_solver
        self.random_state = random_state
        self.n_init = n_init
        self.gamma = gamma
        self.affinity = affinity
        self.n_neighbors = n_neighbors
        self.eigen_tol = eigen_tol
        self.assign_labels = assign_labels
        self.degree = degree
        self.coef0 = coef0
        self.kernel_params = kernel_params

    def fit(self, X, y=None):
        """Creates an affinity matrix for X using the selected affinity,
        then applies spectral clustering to this affinity matrix.

        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            OR, if affinity==`precomputed`, a precomputed affinity
            matrix of shape (n_samples, n_samples)
        """

# Path: sklearn/utils/validation.py
# def as_float_array(X, copy=True, force_all_finite=True):
#     """Converts an array-like to an array of floats
# 
#     The new dtype will be np.float32 or np.float64, depending on the original
#     type. The function can create a copy or modify the argument depending
#     on the argument copy.
# 
#     Parameters
#     ----------
#     X : {array-like, sparse matrix}
# 
#     copy : bool, optional
#         If True, a copy of X will be created. If False, a copy may still be
#         returned if X's dtype is not a floating point type.
# 
#     force_all_finite : boolean (default=True)
#         Whether to raise an error on np.inf and np.nan in X.
# 
#     Returns
#     -------
#     XT : {array, sparse matrix}
#         An array of type np.float
#     """
#     if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
#                                     and not sp.issparse(X)):
#         return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
#                            copy=copy, force_all_finite=force_all_finite,
#                            ensure_2d=False)
#     elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
#         return X.copy() if copy else X
#     elif X.dtype in [np.float32, np.float64]:  # is numpy array
#         return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
#     else:
#         return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
# 
# def check_random_state(seed):
#     """Turn seed into a np.random.RandomState instance
# 
#     If seed is None, return the RandomState singleton used by np.random.
#     If seed is an int, return a new RandomState instance seeded with seed.
#     If seed is already a RandomState instance, return it.
#     Otherwise raise ValueError.
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (numbers.Integral, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# Path: sklearn/utils/validation.py
# def check_array(array, accept_sparse=None, dtype=None, order=None, copy=False,
#                 force_all_finite=True, ensure_2d=True, allow_nd=False):
#     """Input validation on an array, list, sparse matrix or similar.
# 
#     By default, the input is converted to an at least 2nd numpy array.
# 
#     Parameters
#     ----------
#     array : object
#         Input object to check / convert.
# 
#     accept_sparse : string, list of string or None (default=None)
#         String[s] representing allowed sparse matrix formats, such as 'csc',
#         'csr', etc.  None means that sparse matrix input will raise an error.
#         If the input is sparse but not in the allowed format, it will be
#         converted to the first listed format.
# 
#     dtype : string, type or None (default=none)
#         Data type of result. If None, the dtype of the input is preserved.
# 
#     order : 'F', 'C' or None (default=None)
#         Whether an array will be forced to be fortran or c-style.
# 
#     copy : boolean (default=False)
#         Whether a forced copy will be triggered. If copy=False, a copy might
#         be triggered by a conversion.
# 
#     force_all_finite : boolean (default=True)
#         Whether to raise an error on np.inf and np.nan in X.
# 
#     ensure_2d : boolean (default=True)
#         Whether to make X at least 2d.
# 
#     allow_nd : boolean (default=False)
#         Whether to allow X.ndim > 2.
# 
#     Returns
#     -------
#     X_converted : object
#         The converted and validated X.
#     """
#     if isinstance(accept_sparse, str):
#         accept_sparse = [accept_sparse]
# 
#     if sp.issparse(array):
#         array = _ensure_sparse_format(array, accept_sparse, dtype, order,
#                                       copy, force_all_finite)
#     else:
#         if ensure_2d:
#             array = np.atleast_2d(array)
#         array = np.array(array, dtype=dtype, order=order, copy=copy)
#         if not allow_nd and array.ndim >= 3:
#             raise ValueError("Found array with dim %d. Expected <= 2" %
#                              array.ndim)
#         if force_all_finite:
#             _assert_all_finite(array)
# 
#     return array


# Path: sklearn/cluster/spectral.py
import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_random_state, as_float_array
from ..utils.validation import check_array
from ..utils.extmath import norm
from ..metrics.pairwise import pairwise_kernels
from ..neighbors import kneighbors_graph
from ..manifold import spectral_embedding
from .k_means_ import k_means
    from scipy.sparse import csc_matrix
    from scipy.linalg import LinAlgError

      http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf
    """

    def __init__(self, n_clusters=8, eigen_solver=None, random_state=None,
                 n_init=10, gamma=1., affinity='rbf', n_neighbors=10,
                 eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1,
                 kernel_params=None):
        self.n_clusters = n_clusters
        self.eigen_solver = eigen_solver
        self.random_state = random_state
        self.n_init = n_init
        self.gamma = gamma
        self.affinity = affinity
        self.n_neighbors = n_neighbors
        self.eigen_tol = eigen_tol
        self.assign_labels = assign_labels
        self.degree = degree
        self.coef0 = coef0
        self.kernel_params = kernel_params

    def fit(self, X, y=None):
        """Creates an affinity matrix for X using the selected affinity,
        then applies spectral clustering to this affinity matrix.

        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            OR, if affinity==`precomputed`, a precomputed affinity
            matrix of shape (n_samples, n_samples)
        """

        X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])