def feature_add(param, datasets):
data1, data2, data3 = datasets
plabel = data1[:, -2]
tlabel = data1[:, -1]
# dataset index
data1 = data1[:, :-2]
data2 = data2[:, :-2]
data3 = data3[:, :-2]
nb_class = int(max(tlabel)) + 1
nb_people = int(max(plabel)) + 1
unit_step = cc.get_unit_step(data1)
cc.get_index_sampling(param, data1, step_index=unit_step)
def create_configuration_latency(param, file, nb_comb):
df, step_index = cc.dataset_init(param, file)
sampled = cc.get_index_sampling(param, df, step_index)
resized = cc.resize_samples(param, sampled)
combined = cc.combined_samples(resized, comb_degree=nb_comb)
vectorized = cc.vectorized_samples(param, combined, nb_comb)
return [file, vectorized]