parameters: (self, covariance_function, historical_data)
docstring: Construct a GaussianProcess object that knows how to call C++ for evaluation of member functions.
  
          TODO(GH-56): Have a way to set private RNG state for self.sample_point_from_gp()
  
          :param covariance_function: covariance object encoding assumptions about the GP's behavior on our data
          :type covariance_function: :class:`moe.optimal_learning.python.interfaces.covariance_interface.CovarianceInterface` subclass
          :param historical_data: object specifying the already-sampled points, the objective value at those points, and the noise variance associated with each observation
          :type historical_data: :class:`moe.optimal_learning.python.data_containers.HistoricalData` object
docstring_summary: Construct a GaussianProcess object that knows how to call C++ for evaluation of member functions.
docstring_tokens: Construct a GaussianProcess object that knows how to call C ++ for evaluation of member functions .
function: def __init__(self, covariance_function, historical_data):
          """Construct a GaussianProcess object that knows how to call C++ for evaluation of member functions.
  
          TODO(GH-56): Have a way to set private RNG state for self.sample_point_from_gp()
  
          :param covariance_function: covariance object encoding assumptions about the GP's behavior on our data
          :type covariance_function: :class:`moe.optimal_learning.python.interfaces.covariance_interface.CovarianceInterface` subclass
          :param historical_data: object specifying the already-sampled points, the objective value at those points, and the noise variance associated with each observation
          :type historical_data: :class:`moe.optimal_learning.python.data_containers.HistoricalData` object
  
          """
          self._covariance = copy.deepcopy(covariance_function)
          self._historical_data = copy.deepcopy(historical_data)
          self._build_precomputed_data()
function_tokens: def __init__ ( self , covariance_function , historical_data ) : self . _covariance = copy . deepcopy ( covariance_function ) self . _historical_data = copy . deepcopy ( historical_data ) self . _build_precomputed_data ( )