Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022 (Q6720588)

Data is in HDF5 format (with LZF compression). For specifics and details, please seehttps://github.com/GilesStrong/mode_diffprog_22_challenge The training file contains two datasets: `x0`: a set of voxelwise X0 predictions (float32) `targs`: a set of voxelwise classes (int): 0 = soil 1 = wall The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below: with h5py.File(train.h5, r) as f: inputs = h5[x0][()] targets = h5[targs][()] The test file only contains the X0 inputs: with h5py.File(test.h5, r)as h5: inputs = h5[x0][()] The private testing sample also contains targets.The private and public splits can be recovered using: from sklearn.model_selection import train_test_split pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)