# Path: pygp/likelihoods/_base.py
# class RealLikelihood(Likelihood):
#     """
#     Likelihood model with real-valued outputs.
#     """
#     def transform(self, y):
#         return np.array(y, ndmin=1, dtype=float, copy=False)
# 
# Path: pygp/utils/models.py
# def printable(cls):
#     """
#     Decorator which marks classes as being able to be pretty-printed as a
#     function of their hyperparameters. This decorator defines a __repr__ method
#     for the given class which uses the class's `get_hyper` and `_params`
#     methods to print it.
#     """
#     def _repr(obj):
#         """Represent the object as a function of its hyperparameters."""
#         hyper = obj.get_hyper()
#         substrings = []
#         for key, block, log in get_params(obj):
#             val = hyper[block]
#             val = val[0] if (len(val) == 1) else val
#             val = np.exp(val) if log else val
#             substrings += ['%s=%s' % (key, val)]
#         return obj.__class__.__name__ + '(' + ', '.join(substrings) + ')'
#     cls.__repr__ = _repr
#     return cls


import numpy as np
from mwhutils.random import rstate
from ._base import RealLikelihood
from ..utils.models import printable
"""
Implementation of the Gaussian likelihood model.
"""

# future imports
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function

# global imports

# local imports

# exported symbols
__all__ = ['Gaussian']


@printable

# Path: pygp/likelihoods/_base.py
# class RealLikelihood(Likelihood):
#     """
#     Likelihood model with real-valued outputs.
#     """
#     def transform(self, y):
#         return np.array(y, ndmin=1, dtype=float, copy=False)
# 
# Path: pygp/utils/models.py
# def printable(cls):
#     """
#     Decorator which marks classes as being able to be pretty-printed as a
#     function of their hyperparameters. This decorator defines a __repr__ method
#     for the given class which uses the class's `get_hyper` and `_params`
#     methods to print it.
#     """
#     def _repr(obj):
#         """Represent the object as a function of its hyperparameters."""
#         hyper = obj.get_hyper()
#         substrings = []
#         for key, block, log in get_params(obj):
#             val = hyper[block]
#             val = val[0] if (len(val) == 1) else val
#             val = np.exp(val) if log else val
#             substrings += ['%s=%s' % (key, val)]
#         return obj.__class__.__name__ + '(' + ', '.join(substrings) + ')'
#     cls.__repr__ = _repr
#     return cls


# Path: pygp/likelihoods/gaussian.py
import numpy as np
from mwhutils.random import rstate
from ._base import RealLikelihood
from ..utils.models import printable

"""
Implementation of the Gaussian likelihood model.
"""

# future imports
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function

# global imports

# local imports

# exported symbols
__all__ = ['Gaussian']


@printable

class Gaussian(RealLikelihood):