# 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 set_data_by_xy_data(self, x1, x2, y1, y2):
        #not_missing = (numpy.isfinite(x1) * numpy.isfinite(x2) * numpy.isfinite(y1) * numpy.isfinite(y2)).flatten()
        #x1, x2 = x1[not_missing], x2[not_missing]
        #y1, y2 = y1[not_missing], y2[not_missing]

        X = numpy.array([x1, x2]); Y = numpy.array([y1, y2])
        # set individual model's data
        self._models[individual_id].setData(X, Y)
        # set common model's data
        self._models[common_id].setData(scipy.concatenate(X), scipy.concatenate(Y))

    def set_data(self, training_data):
        """
        Set the data of prediction.

        **Parameters:**

        training_data : dict traning_data
            The training data to learn from. Input are time-values and
            output are expression-values of e.g. a timeseries.

            Training data training_data has following structure::

                {'input' : {'group 1':[double] ... 'group n':[double]},
                 'output' : {'group 1':[double] ... 'group n':[double]}}

        """
        try:
            self.set_data_by_xy_data(training_data[input_id]['group_1'],

# 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 set_data_by_xy_data(self, x1, x2, y1, y2):
        #not_missing = (numpy.isfinite(x1) * numpy.isfinite(x2) * numpy.isfinite(y1) * numpy.isfinite(y2)).flatten()
        #x1, x2 = x1[not_missing], x2[not_missing]
        #y1, y2 = y1[not_missing], y2[not_missing]

        X = numpy.array([x1, x2]); Y = numpy.array([y1, y2])
        # set individual model's data
        self._models[individual_id].setData(X, Y)
        # set common model's data
        self._models[common_id].setData(scipy.concatenate(X), scipy.concatenate(Y))

    def set_data(self, training_data):
        """
        Set the data of prediction.

        **Parameters:**

        training_data : dict traning_data
            The training data to learn from. Input are time-values and
            output are expression-values of e.g. a timeseries.

            Training data training_data has following structure::

                {'input' : {'group 1':[double] ... 'group n':[double]},
                 'output' : {'group 1':[double] ... 'group n':[double]}}

        """
        try:
            self.set_data_by_xy_data(training_data[input_id]['group_1'],

                                     training_data[input_id]['group_2'],