def run_eval_iter(config, iter_id):
# Prepare network model to be tested
print("Loading network models for testing...")
pre_net, net = load_models(config, iter_id, testing=True)
print("Model loading completed!")
# Load test dataset
print("Preparing dataset manager...")
dataManager = data.DataManager(config)
print("Dataset manager ready!")
print("Preparing test dataset...")
test_set = dataManager.get_test()
print("Test dataset ready!")
print("Testing...")
test_acc = eval_pass(net, test_set, config, pre_net)
print("Accuracy of the network on the test images: {:.2f}%".format(
100 * test_acc))
print("Saving test result...")
utils.update_csv(str(iter_id), test_acc, config.CSV_PATH)
print("Saved!")
def __init__(self, calibration_data_manager, ratio_threshold=2.0):
"""
:param calibration_data_manager: data.DataManager
:param ratio_threshold: float
"""
total_ssc = calibration_data_manager.get_variable(self._ssc_key)
percent_fines = calibration_data_manager.get_variable(
self._percent_fines_key)
sand_concentration = self._calc_sand_concentration(
total_ssc, percent_fines)
fines_to_sands_ratio = self._calc_fines_to_sands_ratio(percent_fines)
new_data = pd.concat([sand_concentration, fines_to_sands_ratio],
axis=1)
new_data_origin = calibration_data_manager.get_origin()
new_data_manager = data.DataManager(new_data, new_data_origin)
calibration_data_manager.add_data(new_data_manager, keep_curr_obs=True)
self._model = self._create_model(calibration_data_manager,
ratio_threshold)
def __init__(self, bot: commands.Bot):
self.bot = bot
reload(data)
self.instance = data.DataManager(
getattr(bot.config, "ASSETS_BASE_URL", None))
def __init__(self, bot: commands.Bot):
self.bot = bot
reload(data)
self.instance = data.DataManager()
def main():
## start pygame setup stuff
width = 1366
height = 768
pygame.init()
os.environ['SDL_VIDEO_WINDOW_POS'] = "0,0" # set window start pos to screen corner
screen = pygame.display.set_mode((width, height), pygame.NOFRAME)
done = False
clock = pygame.time.Clock()
framerate = 60
dir = os.path.dirname(__file__)
def set_done(value):
done = value
## end pygame setup stuff
## start game setup stuff
## examples
sun = univ.Star(pygame.image.load(os.path.join(dir,'res','star2.png')), 0, " testnameA ")
planets = [univ.Planet(pygame.image.load(os.path.join(dir,'res','planet.png')), i, " testname%d "%i)for i in range(5)]
system = univ.System([sun], planets, 0)
for i in range(5):
system.planets[i].set_system(system)
sun.set_system(system)
galaxy = univ.Galaxy(system)
tmap_list = []
for i in range(50):
row = []
for j in range(50):
row.append(0)
tmap_list.append(row)
tile_map = tiles.TileMap(tmap_list, 64, 64)
world = cloud.World(tile_map)
the_cloud = cloud.Cloud([world])
build_manager = buildings.BuildingManager()
sphere1 = buildings.Sphere()
build_manager.add('spheres', sphere1)
data_manager = data.DataManager()
state_manager = util.GameStateManager(3, system, sun, world, the_cloud)
ui_manager = ui.UIManager(state_manager, data_manager, screen)
drawer = util.Drawer(state_manager, data_manager, ui_manager, galaxy, screen)
event_handler = util.EventHandler(state_manager, data_manager, ui_manager, build_manager, drawer, galaxy, screen)
random_event_manager = util.RandomEventManager(state_manager, data_manager)
## end game setup stuff
while not state_manager.get_done():
screen.fill((0,0,0))
clock.tick(framerate)
#print(clock.get_fps())
event_handler.update()
drawer.draw()
data_manager.update_data(build_manager, clock.get_time())
random_event_manager.update()
pygame.display.flip()
def run_train_iter(config, iter_id):
if config.CONFIG_FAMILY == P.CONFIG_FAMILY_HEBB:
torch.set_grad_enabled(False)
# Seed rng
torch.manual_seed(iter_id)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Load datasets
print("Preparing dataset manager...")
dataManager = data.DataManager(config)
print("Dataset manager ready!")
print("Preparing training dataset...")
train_set = dataManager.get_train()
print("Training dataset ready!")
print("Preparing validation dataset...")
val_set = dataManager.get_val()
print("Validation dataset ready!")
# Prepare network model to be trained
print("Preparing network...")
pre_net, net = load_models(config, iter_id, testing=False)
criterion = None
optimizer = None
scheduler = None
if config.CONFIG_FAMILY == P.CONFIG_FAMILY_GDES:
# Instantiate optimizer if we are going to train with gradient descent
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=config.LEARNING_RATE,
momentum=config.MOMENTUM,
weight_decay=config.L2_PENALTY,
nesterov=True)
scheduler = sched.MultiStepLR(optimizer,
gamma=config.LR_DECAY,
milestones=config.MILESTONES)
print("Network ready!")
# Train the network
print("Starting training...")
train_acc_data = []
val_acc_data = []
best_acc = 0.0
best_epoch = 0
start_time = time.time()
for epoch in range(1, config.NUM_EPOCHS + 1):
# Update LR scheduler
if scheduler is not None: scheduler.step()
# Print overall progress information at each epoch
utils.print_train_progress(epoch, config.NUM_EPOCHS,
time.time() - start_time, best_acc,
best_epoch)
# Training phase
print("Training...")
train_acc = train_pass(net, train_set, config, pre_net, criterion,
optimizer)
print("Training accuracy: {:.2f}%".format(100 * train_acc))
# Validation phase
print("Validating...")
val_acc = eval_pass(net, val_set, config, pre_net)
print("Validation accuracy: {:.2f}%".format(100 * val_acc))
# Update training statistics and saving plots
train_acc_data += [train_acc]
val_acc_data += [val_acc]
utils.save_figure(train_acc_data, val_acc_data,
config.ACC_PLT_PATH[iter_id])
# If validation accuracy has improved update best model
if val_acc > best_acc:
print("Top accuracy improved! Saving new best model...")
best_acc = val_acc
best_epoch = epoch
utils.save_dict(net.state_dict(), config.MDL_PATH[iter_id])
if hasattr(net, 'conv1') and net.input_shape == P.INPUT_SHAPE:
utils.plot_grid(net.conv1.weight, config.KNL_PLT_PATH[iter_id])
if hasattr(net, 'fc') and net.input_shape == P.INPUT_SHAPE:
utils.plot_grid(net.fc.weight.view(-1, *P.INPUT_SHAPE),
config.KNL_PLT_PATH[iter_id])
print("Model saved!")
""" ** Author: Xiao Yue ** Date: 2020-08-23 """ import data import illestration import statistic import plan current_month = '2020-09' # Load Data # ---------------------------------------------------------------------------------------------------------------------- data_manager = data.DataManager() daily_spend_sum = data_manager.get_daily_spend_sum() monthly_spend_sum = data_manager.get_monthly_spend_sum() spend_by_category = data_manager.get_spend_by_category_by_month(current_month) income = data_manager.get_monthly_income_by_month(current_month) # Load Plan # ---------------------------------------------------------------------------------------------------------------------- plan_manager = plan.PlanManager() income_arrangement_plan = plan_manager.get_income_arrangement_plan() investment_plan = plan_manager.get_investment_plan() total_asset_arrangement_plan = plan_manager.get_total_asset_arrangement_plan() # Display Figure # ----------------------------------------------------------------------------------------------------------------------