"""

assert train_test_ratio >= 1, "Duplex split: train-test ratio needs to be no less than one!"

inputs, outputs = all_data[:]

train_set_size = round(inputs.size(0) * train_test_ratio / (train_test_ratio + 1))

test_set_size = int(inputs.size(0) - train_set_size)

train_idxs = -np.ones(train_set_size, dtype=int)

test_idxs = -np.ones(test_set_size, dtype=int)

# process the inputs(3D) and outputs(3D) from the TensorDataset into 2-D matrices

# inputs: took only the Q and SeaLev sequences, calculate the max() and min() for both sequences,

# use them as inputs indicators to avoid massive calculation efforts

inputs = np.reshape(inputs.numpy()[:, :, [0, -1]], (inputs.size(0), inputs.size(1) * 2)) # (batch, 192, 15)

inputs = np.array([[max(curr[:192]), min(curr[:192]), max(curr[192:]), min(curr[192:])] for curr in inputs])

outputs = np.reshape(outputs.numpy(), (outputs.size(0), 1)) # (batch, 1, 1)

# arrange inputs and outputs into one matrix by join columns

flattened_all_data = np.concatenate((inputs, outputs), axis=1) # format=np.ndarray()

# allocate the first two data sets to training set

dist_m = dist.cdist(flattened_all_data, flattened_all_data, 'sqeuclidean')

remaining_idxs = np.arange(dist_m.shape[0])

r1, r2 = np.argwhere(dist_m == dist_m.max())[0]

dist_m[r1, :] = 0

dist_m[r2, :] = 0 # remove distances along deleting item rows

train_idxs[[0, 1]] = [r1, r2]

remaining_idxs[[r1, r2]] = -1

# allocate the first two data sets to testing set

dist_m2 = dist_m[:, [r1, r2]].copy()

dist_m[:, r1] = 0

dist_m[:, r2] = 0

r1, r2 = np.argwhere(dist_m == dist_m.max())[0]

test_idxs[[0, 1]] = [r1, r2]

remaining_idxs[[r1, r2]] = -1

dist_m[:, train_idxs[:2]] = dist_m2

dist_m[dist_m == 0] = dist_m.max()

# while loop, fill test set

for i in range(test_set_size-2):

train_idxs, test_idxs, remaining_idxs = allocating_items(dist_m, train_idxs, test_idxs, remaining_idxs, i)

print(i)

# put the rest of the data into train set

if len(remaining_idxs) > 0:

train_idxs[test_set_size:] = remaining_idxs[remaining_idxs != -1]

assert np.min(train_idxs) > -1, "Duplex: train set size not matching expectation!"

assert np.min(test_idxs) > -1, "Duplex: test set size not matching expectation!"

# train = size(batch, 192, 15), test = size(batch, 1, 1)

train_data = TensorDataset(all_data[train_idxs][0], all_data[train_idxs][1])

test_data = TensorDataset(all_data[test_idxs][0], all_data[test_idxs][1])