import numpy as np

error_1 = dict()
error_2 = dict()
#end2end with reg
# error_1[0] = {'Jumpsuit': 0.00888, 'Trousers': 0.01193, 'Tshirt': 0.01065, 'Top': 0.00725}
# error_1[1] = {'Jumpsuit': 0.00491, 'Trousers': 0.00704, 'Tshirt': 0.00531, 'Top': 0.00393}
# error_1[2] = {'Jumpsuit': 0.00379, 'Trousers': 0.00547, 'Tshirt': 0.00404, 'Top': 0.00309}
# error_1[3] = {'Jumpsuit': 0.00349, 'Trousers': 0.00502, 'Tshirt': 0.00382, 'Top': 0.00281}
# error_2[0] = {'Dress': 0.01009, 'Skirt': 0.01379}
# error_2[1] = {'Dress': 0.0055, 'Skirt': 0.0077}
# error_2[2] = {'Dress': 0.00418, 'Skirt': 0.00582}
# error_2[3] = {'Dress': 0.00359, 'Skirt': 0.00496}

#end2end without reg
error_1[0] = {'Jumpsuit': 0.00927, 'Trousers': 0.01229, 'Tshirt': 0.01113, 'Top': 0.0076}
error_1[1] = {'Jumpsuit': 0.00503, 'Trousers': 0.00721, 'Tshirt': 0.0054, 'Top': 0.004}
error_1[2] = {'Jumpsuit': 0.00402, 'Trousers': 0.0059, 'Tshirt': 0.00421, 'Top': 0.00319}
error_1[3] = {'Jumpsuit': 0.00347, 'Trousers': 0.00528, 'Tshirt': 0.00365, 'Top': 0.00271}
error_2[0] = {'Dress': 0.01005, 'Skirt': 0.01395}
error_2[1] = {'Dress': 0.0056, 'Skirt': 0.00787}
error_2[2] = {'Dress': 0.00441, 'Skirt': 0.00634}
error_2[3] = {'Dress': 0.00379, 'Skirt': 0.0054}

for i in range(4):
    errors_mean = dict()
    error = {**error_1[i], **error_2[i]}
    for category in error.keys():
        errors_mean[category] = round(np.mean(error[category]), 5)
    print(f'Level {i}: {errors_mean}')

    mean = round(np.mean(list(errors_mean.values())), 5)

    print(f'Final level {i}: {mean}')