# 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
                Specifies whether or not to optimize the hyperparameters for the given data

            priors : {'covar': priors for covar, ...}
                Default: None; The prior beliefs you provide for the hyperparamaters of the covariance function.


        """

        self._learn_hyperparameters = learn_hyperparameters
        if has_model_structure(priors):
            self._priors = priors
        else:
            self._priors = get_model_structure(priors, priors)

        self._models = dict()

        if initial_hyperparameters is None and priors is not None:
            self._initial_hyperparameters = get_model_structure({}, {});
            for name, prior in self._priors.iteritems():
                if prior.has_key('covar'):
                    # logarithmize the right hyperparameters for the covariance
                    logtheta = scipy.array([p[1][0] * p[1][1] for p in prior['covar']], dtype='float')
                    # out since version 1.0.0
                    # logtheta[covar.get_Iexp(logtheta)] = SP.log(logtheta[covar.get_Iexp(logtheta)])
                    self._initial_hyperparameters[name]['covar'] = logtheta
        elif has_model_structure(initial_hyperparameters):
            self._initial_hyperparameters = initial_hyperparameters
        elif initial_hyperparameters is not None:
            self._initial_hyperparameters = get_model_structure(initial_hyperparameters, initial_hyperparameters)
        else:

# 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

                Specifies whether or not to optimize the hyperparameters for the given data

            priors : {'covar': priors for covar, ...}
                Default: None; The prior beliefs you provide for the hyperparamaters of the covariance function.


        """

        self._learn_hyperparameters = learn_hyperparameters
        if has_model_structure(priors):
            self._priors = priors
        else:
            self._priors = get_model_structure(priors, priors)

        self._models = dict()

        if initial_hyperparameters is None and priors is not None:
            self._initial_hyperparameters = get_model_structure({}, {});
            for name, prior in self._priors.iteritems():
                if prior.has_key('covar'):
                    # logarithmize the right hyperparameters for the covariance
                    logtheta = scipy.array([p[1][0] * p[1][1] for p in prior['covar']], dtype='float')
                    # out since version 1.0.0
                    # logtheta[covar.get_Iexp(logtheta)] = SP.log(logtheta[covar.get_Iexp(logtheta)])
                    self._initial_hyperparameters[name]['covar'] = logtheta
        elif has_model_structure(initial_hyperparameters):
            self._initial_hyperparameters = initial_hyperparameters
        elif initial_hyperparameters is not None:
            self._initial_hyperparameters = get_model_structure(initial_hyperparameters, initial_hyperparameters)
        else:

            self._initial_hyperparameters = get_model_structure({})