# 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 pygp.covar.combinators import SumCF
from pygp.covar.noise import NoiseCFISO
from pygp.covar.se import SqexpCFARD
from pygp.gp.gpcEP import GPCEP
from gptwosample.data.data_base import common_id, individual_id
import scipy as SP
        return SP.exp(-0.5 * (x - mu) ** 2 / v) * SP.sqrt(2 * SP.pi / v)

    def _resample_interval_index(self, interval_index, hyperparams):
        """
        Resample all input values corresponding to ones in interval_indices, 
        w.r.t. already sampled values.
        
        **Parameters:**
        
        interval_index : int
            Index of input value, which shall be resampled            
        """
        interval_indicator = SP.zeros_like(self._indicators)
        interval_indicator[interval_index] = 1
        interval_indicator = SP.array(interval_indicator,dtype='bool')
        
        # make sure to predict on all data given (including all replicates)
        ind_interval_indicator = self._indicators & ~interval_indicator
        comm_interval_indicator = ~self._indicators & ~interval_indicator

        # predict output at interval_indicator
        target_prediction = \
            self._twosample_object.predict_mean_variance(\
                 self._input[interval_indicator],\
                 interval_indices={individual_id: ~SP.tile(ind_interval_indicator,self._n_replicates_ind),\
                                   common_id: ~SP.tile(comm_interval_indicator, self._n_replicates_comm)},
                 )
        
        ind1 = [target_prediction[individual_id]['mean'][0], target_prediction[individual_id]['var'][0]]
        ind2 = [target_prediction[individual_id]['mean'][1], target_prediction[individual_id]['var'][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/interval_smooth.py
from pygp.covar.combinators import SumCF
from pygp.covar.noise import NoiseCFISO
from pygp.covar.se import SqexpCFARD
from pygp.gp.gpcEP import GPCEP
from gptwosample.data.data_base import common_id, individual_id
import scipy as SP

        return SP.exp(-0.5 * (x - mu) ** 2 / v) * SP.sqrt(2 * SP.pi / v)

    def _resample_interval_index(self, interval_index, hyperparams):
        """
        Resample all input values corresponding to ones in interval_indices, 
        w.r.t. already sampled values.
        
        **Parameters:**
        
        interval_index : int
            Index of input value, which shall be resampled            
        """
        interval_indicator = SP.zeros_like(self._indicators)
        interval_indicator[interval_index] = 1
        interval_indicator = SP.array(interval_indicator,dtype='bool')
        
        # make sure to predict on all data given (including all replicates)
        ind_interval_indicator = self._indicators & ~interval_indicator
        comm_interval_indicator = ~self._indicators & ~interval_indicator

        # predict output at interval_indicator
        target_prediction = \
            self._twosample_object.predict_mean_variance(\
                 self._input[interval_indicator],\
                 interval_indices={individual_id: ~SP.tile(ind_interval_indicator,self._n_replicates_ind),\
                                   common_id: ~SP.tile(comm_interval_indicator, self._n_replicates_comm)},
                 )
        
        ind1 = [target_prediction[individual_id]['mean'][0], target_prediction[individual_id]['var'][0]]
        ind2 = [target_prediction[individual_id]['mean'][1], target_prediction[individual_id]['var'][1]]

        comm = [target_prediction[common_id]['mean'], target_prediction[common_id]['var']]