# Path: sklearn/utils/validation.py
# 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)
# 
# 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
# 
# def check_consistent_length(*arrays):
#     """Check that all arrays have consistent first dimensions.
# 
#     Checks whether all objects in arrays have the same shape or length.
# 
#     Parameters
#     ----------
#     *arrays : list or tuple of input objects.
#         Objects that will be checked for consistent length.
#     """
# 
#     uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
#     if len(uniques) > 1:
#         raise ValueError("Found arrays with inconsistent numbers of samples: %s"
#                          % str(uniques))
# 
# Path: sklearn/utils/validation.py
# def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
#     """Perform is_fitted validation for estimator.
# 
#     Checks if the estimator is fitted by verifying the presence of 
#     "all_or_any" of the passed attributes and raises a NotFittedError with the
#     given message.
# 
#     Parameters
#     ----------
#     estimator : estimator instance.
#         estimator instance for which the check is performed.
# 
#     attributes : attribute name(s) given as string or a list/tuple of strings
#         Eg. : ["coef_", "estimator_", ...], "coef_"
# 
#     msg : string
#         The default error message is, "This %(name)s instance is not fitted
#         yet. Call 'fit' with appropriate arguments before using this method."
# 
#         For custom messages if "%(name)s" is present in the message string,
#         it is substituted for the estimator name.
# 
#         Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
# 
#     all_or_any : callable, {all, any}, default all
#         Specify whether all or any of the given attributes must exist.
#     """
#     if msg is None:
#         msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
#                "appropriate arguments before using this method.")
# 
#     if not hasattr(estimator, 'fit'):
#         raise ValueError("%s is not an estimator instance." % (estimator))
# 
#     if not isinstance(attributes, (list, tuple)):
#         attributes = [attributes]
# 
#     if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
#         raise NotFittedError(msg % {'name' : type(estimator).__name__})


import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import manhattan_distances
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.validation import  check_is_fitted
from . import regression_models as regression
from . import correlation_models as correlation
            An array containing the autocorrelation parameters at which the
            Gaussian Process model parameters should be determined.
            Default uses the built-in autocorrelation parameters
            (ie ``theta = self.theta_``).

        Returns
        -------
        reduced_likelihood_function_value : double
            The value of the reduced likelihood function associated to the
            given autocorrelation parameters theta.

        par : dict
            A dictionary containing the requested Gaussian Process model
            parameters:

                sigma2
                        Gaussian Process variance.
                beta
                        Generalized least-squares regression weights for
                        Universal Kriging or given beta0 for Ordinary
                        Kriging.
                gamma
                        Gaussian Process weights.
                C
                        Cholesky decomposition of the correlation matrix [R].
                Ft
                        Solution of the linear equation system : [R] x Ft = F
                G
                        QR decomposition of the matrix Ft.
        """

# Path: sklearn/utils/validation.py
# 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)
# 
# 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
# 
# def check_consistent_length(*arrays):
#     """Check that all arrays have consistent first dimensions.
# 
#     Checks whether all objects in arrays have the same shape or length.
# 
#     Parameters
#     ----------
#     *arrays : list or tuple of input objects.
#         Objects that will be checked for consistent length.
#     """
# 
#     uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
#     if len(uniques) > 1:
#         raise ValueError("Found arrays with inconsistent numbers of samples: %s"
#                          % str(uniques))
# 
# Path: sklearn/utils/validation.py
# def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
#     """Perform is_fitted validation for estimator.
# 
#     Checks if the estimator is fitted by verifying the presence of 
#     "all_or_any" of the passed attributes and raises a NotFittedError with the
#     given message.
# 
#     Parameters
#     ----------
#     estimator : estimator instance.
#         estimator instance for which the check is performed.
# 
#     attributes : attribute name(s) given as string or a list/tuple of strings
#         Eg. : ["coef_", "estimator_", ...], "coef_"
# 
#     msg : string
#         The default error message is, "This %(name)s instance is not fitted
#         yet. Call 'fit' with appropriate arguments before using this method."
# 
#         For custom messages if "%(name)s" is present in the message string,
#         it is substituted for the estimator name.
# 
#         Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
# 
#     all_or_any : callable, {all, any}, default all
#         Specify whether all or any of the given attributes must exist.
#     """
#     if msg is None:
#         msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
#                "appropriate arguments before using this method.")
# 
#     if not hasattr(estimator, 'fit'):
#         raise ValueError("%s is not an estimator instance." % (estimator))
# 
#     if not isinstance(attributes, (list, tuple)):
#         attributes = [attributes]
# 
#     if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
#         raise NotFittedError(msg % {'name' : type(estimator).__name__})


# Path: sklearn/gaussian_process/gaussian_process.py
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import manhattan_distances
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.validation import  check_is_fitted
from . import regression_models as regression
from . import correlation_models as correlation

            An array containing the autocorrelation parameters at which the
            Gaussian Process model parameters should be determined.
            Default uses the built-in autocorrelation parameters
            (ie ``theta = self.theta_``).

        Returns
        -------
        reduced_likelihood_function_value : double
            The value of the reduced likelihood function associated to the
            given autocorrelation parameters theta.

        par : dict
            A dictionary containing the requested Gaussian Process model
            parameters:

                sigma2
                        Gaussian Process variance.
                beta
                        Generalized least-squares regression weights for
                        Universal Kriging or given beta0 for Ordinary
                        Kriging.
                gamma
                        Gaussian Process weights.
                C
                        Cholesky decomposition of the correlation matrix [R].
                Ft
                        Solution of the linear equation system : [R] x Ft = F
                G
                        QR decomposition of the matrix Ft.
        """

        check_is_fitted(self, "X")