# 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
    -------
    labels : array of integers, shape: n_samples
        The labels of the clusters.

    References
    ----------

    - Multiclass spectral clustering, 2003
      Stella X. Yu, Jianbo Shi
      http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf

    Notes
    -----

    The eigenvector embedding is used to iteratively search for the
    closest discrete partition.  First, the eigenvector embedding is
    normalized to the space of partition matrices. An optimal discrete
    partition matrix closest to this normalized embedding multiplied by
    an initial rotation is calculated.  Fixing this discrete partition
    matrix, an optimal rotation matrix is calculated.  These two
    calculations are performed until convergence.  The discrete partition
    matrix is returned as the clustering solution.  Used in spectral
    clustering, this method tends to be faster and more robust to random
    initialization than k-means.

    """


    random_state = check_random_state(random_state)


# 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

    -------
    labels : array of integers, shape: n_samples
        The labels of the clusters.

    References
    ----------

    - Multiclass spectral clustering, 2003
      Stella X. Yu, Jianbo Shi
      http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf

    Notes
    -----

    The eigenvector embedding is used to iteratively search for the
    closest discrete partition.  First, the eigenvector embedding is
    normalized to the space of partition matrices. An optimal discrete
    partition matrix closest to this normalized embedding multiplied by
    an initial rotation is calculated.  Fixing this discrete partition
    matrix, an optimal rotation matrix is calculated.  These two
    calculations are performed until convergence.  The discrete partition
    matrix is returned as the clustering solution.  Used in spectral
    clustering, this method tends to be faster and more robust to random
    initialization than k-means.

    """


    random_state = check_random_state(random_state)


    vectors = as_float_array(vectors, copy=copy)