configuration.py

import argparse
from definitions import Topology
import os

# a variavel abaixo evita mostrar uso de memoria de 10% excedente e tal
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

use_gpu = False
use_cnn = False

seed_count = 5
epoch_count = 1
topology = Topology.USANET

_batch_count = 1
_training_files_length = 10080

_algorithms = {
    'low_load': ['K1SP_Random', 'K1SP_FirstFit',
                 'K3SP_BestFit', '', '', '', '', '', '', ''],
    'medium_load': ['K1SP_Random', 'K1SP_DP', 'K1SP_FirstFit', 'K3SP_BestFit',
                    'K3SP_PP', 'AP', 'K3SP_LastFit', 'K5SP_FirstFit', 'K5SP_CS', 'MAdapSPV'],
    'high_load': ['K5SP_Random', 'K3SP_DP', 'K2SP_BestFit', 'K1SP_FirstFit',
                  'K4SP_PP', 'K3SP_AP', 'K2SP_LastFit', 'K7SP_FirstFit', 'K5SP_CS', 'MAdapSPV'],
}

_algorithm_choice = 'high_load'

_loads = {
    'low': ['75', '100', '125', '150', '175', '200', '225', '250', '275', '300', '325', '350', '375', '400'],
    'high': ['150', '200', '250', '300', '350', '400', '450',
             '500', '550', '600', '650', '700', '750', '800']
}

_load_choice = 'high'


def batch_size():
    sample_amount = LEN_FILENAMES_TRAINING * SEED_COUNT
    return sample_amount // BATCH_COUNT


def algorithms():
    return _algorithms[_algorithm_choice]


def loads():
    return _loads[_load_choice]