from pandas import datetime

MFCC_FEATURE_START = 1
MFCC_FEATURE_END = 21

TRAINING_DATA_DIRECTORY_NAME = 'DemoTrainingDataset'
TESTING_DATA_DIRECTORY_NAME = 'TEST'


CAR = 'Car'
BUS = 'Bus'
TRUCK = 'Truck'
MOTORCYCLE = 'Motorcycle'
TRAM = 'Tram'

LIGHT_WEIGHT = 'Light-Weight'
MEDIUM_WEIGHT = 'Medium-Weight'
HEAVY_WEIGHT = 'Heavy-Weight'
TWO_WHEELED = 'Two-Wheeled'
RAIL_BOUND = 'Rail-Bound'

FEATURES_CSV_NAME = 'features.csv'
TEST_CSV_NAME = 'test.csv'

HIDDEN_LAYER_1_DIMENSIONS = 32
HIDDEN_LAYER_2_DIMENSIONS = 32
HIDDEN_LAYER_3_DIMENSIONS = 32
OUTPUT_LAYER_DIMENSIONS = 5

ACTIVATION_RELU = 'relu'
ACTIVATION_SOFTMAX = 'softmax'

OPTIMIZER_ADAM = 'adam'
LOSS_FUNCTION_SPARSE = 'sparse_categorical_crossentropy'
ACCURACY_METRICS = 'accuracy'

LOG_DIR_PATH = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")

TRAINED_MODEL = 'trained_model.h5'