Calibration Dataset - HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models

Calibration dataset for the study case presented in the paper "HPOSS: A hierarchical portfolio optimization stacking strategy<br> to reduce the generalization error of ensembles of models". It encompasses a .h5 file with a dataset called "Calibrations_LHS", which consists of a 80x5 numpy array of float numbers corresponding to (d1(m),d2(m),d3(m),d4(m),zeta_max(Pa)), where d_i, i = 1,...,4 are dimensions (in meters) of the I-beam and zeta_max is the maximum bending stress (in Pascals) developed in a simply such supported beam with 1 m of length after a point load of 1000N is applied at its center.