# Path: gptwosample/data/data_base.py
# def get_training_data_structure(x1, x2, y1, y2):
# def get_model_structure(individual=None, common=None):
# def has_model_structure(structure):
#     def __init__(self, *args, **kwargs):
# class DataStructureError(TypeError):


from gptwosample.data.data_base import input_id, output_id, individual_id, \
    common_id, has_model_structure, get_model_structure, DataStructureError
from pygp.gp.gp_base import GP
from pygp.gp.composite import GroupGP
from pygp.optimize.optimize_base import opt_hyper
from pygp.plot.gpr_plot import plot_sausage, plot_training_data
from copy import deepcopy
from matplotlib import cm
from pygp.likelihood.likelihood_base import GaussLikISO
import scipy
import numpy
        import pylab

    def _invalidate_cache(self):
        # self._learned_hyperparameters = dict([name,None for name in self._models.keys()])
        self._model_likelihoods = get_model_structure()
        self._learned_hyperparameters = get_model_structure()
        self._interpolation_interval_cache = None
        self._predicted_mean_variance = None

class TwoSampleShare(TwoSampleBase):
    """
    This class provides comparison of two Timeline Groups to each other.

    see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase` for detailed description of provided methods.
    """
    def __init__(self, covar, *args, **kwargs):
        """
        see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase`
        """
        if not kwargs.has_key('initial_hyperparameters'):
            kwargs['initial_hyperparameters'] = \
                get_model_structure(individual={'covar':numpy.zeros(covar.get_number_of_parameters())},
                                    common={'covar':numpy.zeros(covar.get_number_of_parameters())})
        super(TwoSampleShare, self).__init__(*args, **kwargs)
        gpr1 = GP(deepcopy(covar), likelihood=GaussLikISO())
        gpr2 = GP(deepcopy(covar), likelihood=GaussLikISO())
        # individual = GroupGP([gpr1,gpr2])
        # common = GP(covar)
        # self.covar = covar
        # set models for this TwoSampleBase Test
        self._models = {individual_id:GroupGP([gpr1, gpr2]),

# Path: gptwosample/data/data_base.py
# def get_training_data_structure(x1, x2, y1, y2):
# def get_model_structure(individual=None, common=None):
# def has_model_structure(structure):
#     def __init__(self, *args, **kwargs):
# class DataStructureError(TypeError):


# Path: gptwosample/twosample/twosample_base.py
from gptwosample.data.data_base import input_id, output_id, individual_id, \
    common_id, has_model_structure, get_model_structure, DataStructureError
from pygp.gp.gp_base import GP
from pygp.gp.composite import GroupGP
from pygp.optimize.optimize_base import opt_hyper
from pygp.plot.gpr_plot import plot_sausage, plot_training_data
from copy import deepcopy
from matplotlib import cm
from pygp.likelihood.likelihood_base import GaussLikISO
import scipy
import numpy
        import pylab


    def _invalidate_cache(self):
        # self._learned_hyperparameters = dict([name,None for name in self._models.keys()])
        self._model_likelihoods = get_model_structure()
        self._learned_hyperparameters = get_model_structure()
        self._interpolation_interval_cache = None
        self._predicted_mean_variance = None

class TwoSampleShare(TwoSampleBase):
    """
    This class provides comparison of two Timeline Groups to each other.

    see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase` for detailed description of provided methods.
    """
    def __init__(self, covar, *args, **kwargs):
        """
        see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase`
        """
        if not kwargs.has_key('initial_hyperparameters'):
            kwargs['initial_hyperparameters'] = \
                get_model_structure(individual={'covar':numpy.zeros(covar.get_number_of_parameters())},
                                    common={'covar':numpy.zeros(covar.get_number_of_parameters())})
        super(TwoSampleShare, self).__init__(*args, **kwargs)
        gpr1 = GP(deepcopy(covar), likelihood=GaussLikISO())
        gpr2 = GP(deepcopy(covar), likelihood=GaussLikISO())
        # individual = GroupGP([gpr1,gpr2])
        # common = GP(covar)
        # self.covar = covar
        # set models for this TwoSampleBase Test
        self._models = {individual_id:GroupGP([gpr1, gpr2]),

                        common_id:GP(deepcopy(covar), likelihood=GaussLikISO())}