Compare commits
17 Commits
Author | SHA1 | Date | |
---|---|---|---|
69df0198da | |||
![]() |
9443c03e0a | ||
![]() |
530b221763 | ||
![]() |
868ba1bfd7 | ||
![]() |
5004058725 | ||
![]() |
b37847b304 | ||
23950ba5b5 | |||
![]() |
153f16bcc0 | ||
![]() |
d0cde0beab | ||
3ae78c6ee5 | |||
6866e825ce | |||
![]() |
abd60f9c15 | ||
![]() |
5cb4dee25e | ||
![]() |
53cf8c9937 | ||
![]() |
566a8cd868 | ||
![]() |
e5a7a975e8 | ||
![]() |
c5e0b65445 |
137
ForkliftAgent.py
137
ForkliftAgent.py
@ -1,8 +1,10 @@
|
||||
from copy import deepcopy
|
||||
from typing import Tuple, List
|
||||
|
||||
from AgentBase import AgentBase
|
||||
from PatchAgent import PatchAgent
|
||||
from PatchType import PatchType
|
||||
from data.GameConstants import GameConstants
|
||||
from data.Item import Item
|
||||
from data.Order import Order
|
||||
from data.enum.Direction import Direction
|
||||
@ -16,7 +18,7 @@ from util.PathDefinitions import GridLocation, GridWithWeights
|
||||
|
||||
class ForkliftAgent(AgentBase):
|
||||
|
||||
def __init__(self, model, game_constants, client_delivery: PatchAgent, drop_off: PatchAgent,
|
||||
def __init__(self, model, game_constants: GameConstants, client_delivery: PatchAgent, drop_off: PatchAgent,
|
||||
graph: GridWithWeights):
|
||||
super().__init__(model)
|
||||
self.action_queue: List[Action] = []
|
||||
@ -28,11 +30,20 @@ class ForkliftAgent(AgentBase):
|
||||
self.drop_off: PatchAgent = drop_off
|
||||
self.graph = graph
|
||||
self.game_constants = game_constants
|
||||
self.current_order: Order = None
|
||||
self.current_item = None
|
||||
self.item_station_completed = False
|
||||
self.provided_items: List[Item] = []
|
||||
self.current_order_delivered_items: List[Item] = []
|
||||
self.ready_for_execution = False
|
||||
self.last_delviered_item = None
|
||||
|
||||
self.current_item: Item = None
|
||||
self.current_order = None
|
||||
self.base: GridLocation = None
|
||||
self.goal: GridLocation = None
|
||||
|
||||
def set_base(self, drop_off: PatchAgent):
|
||||
self.drop_off = drop_off
|
||||
self.base = self.drop_off.location
|
||||
self.goal = self.base
|
||||
|
||||
def queue_movement_actions(self, movement_actions: List[Action]):
|
||||
self.action_queue.extend(movement_actions)
|
||||
@ -43,120 +54,94 @@ class ForkliftAgent(AgentBase):
|
||||
action_type = action.action_type
|
||||
|
||||
if action_type == ActionType.ROTATE_UP:
|
||||
print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
# print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
self.current_rotation = Direction.top
|
||||
|
||||
elif action_type == ActionType.ROTATE_RIGHT:
|
||||
print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
# print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
self.current_rotation = Direction.right
|
||||
|
||||
elif action_type == ActionType.ROTATE_DOWN:
|
||||
print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
# print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
self.current_rotation = Direction.down
|
||||
|
||||
elif action_type == ActionType.ROTATE_LEFT:
|
||||
print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
# print("rotate {} --> {}".format(self.current_rotation, action_type))
|
||||
self.current_rotation = Direction.left
|
||||
|
||||
elif action_type == ActionType.MOVE:
|
||||
if self.current_rotation == Direction.top:
|
||||
print("move {} --> {}".format(self.current_position, action_type))
|
||||
# print("move {} --> {}".format(self.current_position, action_type))
|
||||
self.current_position = (self.current_position[0], self.current_position[1] + 1)
|
||||
|
||||
elif self.current_rotation == Direction.down:
|
||||
print("move {} --> {}".format(self.current_position, action_type))
|
||||
# print("move {} --> {}".format(self.current_position, action_type))
|
||||
self.current_position = (self.current_position[0], self.current_position[1] - 1)
|
||||
|
||||
elif self.current_rotation == Direction.right:
|
||||
print("move {} --> {}".format(self.current_position, action_type))
|
||||
# print("move {} --> {}".format(self.current_position, action_type))
|
||||
self.current_position = (self.current_position[0] + 1, self.current_position[1])
|
||||
|
||||
elif self.current_rotation == Direction.left:
|
||||
print("move {} --> {}".format(self.current_position, action_type))
|
||||
# print("move {} --> {}".format(self.current_position, action_type))
|
||||
self.current_position = (self.current_position[0] - 1, self.current_position[1])
|
||||
|
||||
def plan_actions(self):
|
||||
if len(self.current_order.items) > 0:
|
||||
i = self.current_order.items.pop(0)
|
||||
if self.current_item is None:
|
||||
self.provided_items.clear()
|
||||
self.current_item = i
|
||||
print("PLAN MOVEMENT")
|
||||
|
||||
# get item
|
||||
def step(self) -> None:
|
||||
if len(self.action_queue) > 0:
|
||||
self.move()
|
||||
elif self.ready_for_execution:
|
||||
if self.current_position != self.goal:
|
||||
pathFinder = PathFinderOnStates(
|
||||
self.game_constants,
|
||||
self.drop_off.location,
|
||||
PathFinderState(self.current_position,
|
||||
self.current_rotation,
|
||||
0,
|
||||
Action(ActionType.NONE),
|
||||
[])
|
||||
)
|
||||
actions = pathFinder.get_action_list()
|
||||
self.queue_movement_actions(actions)
|
||||
|
||||
elif not self.item_station_completed:
|
||||
# go through station
|
||||
|
||||
packing_station: GridLocation = None
|
||||
stations = dict(self.graph.packingStations)
|
||||
if i.real_type == ItemType.SHELF:
|
||||
packing_station = stations[PatchType.packingA]
|
||||
elif i.real_type == ItemType.REFRIGERATOR:
|
||||
packing_station = stations[PatchType.packingB]
|
||||
elif i.real_type == ItemType.DOOR:
|
||||
packing_station = stations[PatchType.packingC]
|
||||
|
||||
pathFinder = PathFinderOnStates(
|
||||
self.game_constants,
|
||||
packing_station,
|
||||
self.goal,
|
||||
PathFinderState(self.current_position,
|
||||
self.current_rotation,
|
||||
0,
|
||||
Action(
|
||||
desired_item=i,
|
||||
desired_item=None,
|
||||
action_type=ActionType.PICK_ITEM
|
||||
),
|
||||
[])
|
||||
)
|
||||
actions = pathFinder.get_action_list()
|
||||
self.queue_movement_actions(actions)
|
||||
self.item_station_completed = True
|
||||
|
||||
else:
|
||||
# go to client delivery area
|
||||
pathFinder = PathFinderOnStates(
|
||||
self.game_constants,
|
||||
self.client_delivery.location,
|
||||
PathFinderState(self.current_position,
|
||||
self.current_rotation,
|
||||
0,
|
||||
Action(ActionType.NONE),
|
||||
[])
|
||||
)
|
||||
actions = pathFinder.get_action_list()
|
||||
self.queue_movement_actions(actions)
|
||||
self.queue_movement_actions(
|
||||
[Action(ActionType.DROP_ITEM)]
|
||||
)
|
||||
if self.current_order is not None and self.goal == self.base:
|
||||
self.current_item = self.current_order.items.pop(0)
|
||||
packing_station: GridLocation = None
|
||||
stations = dict(self.graph.packingStations)
|
||||
|
||||
if self.current_item.real_type == ItemType.SHELF:
|
||||
packing_station = stations[PatchType.packingShelf]
|
||||
elif self.current_item.real_type == ItemType.REFRIGERATOR:
|
||||
packing_station = stations[PatchType.packingRefrigerator]
|
||||
elif self.current_item.real_type == ItemType.DOOR:
|
||||
packing_station = stations[PatchType.packingDoor]
|
||||
|
||||
self.goal = packing_station
|
||||
|
||||
elif self.goal in [i[1] for i in self.graph.packingStations]:
|
||||
self.goal = self.client_delivery.location
|
||||
|
||||
elif self.goal == self.client_delivery.location:
|
||||
if self.current_order is not None and len(self.current_order.items) == 0:
|
||||
|
||||
self.current_order_delivered_items.append(self.current_item)
|
||||
self.current_order.items = deepcopy(self.current_order_delivered_items)
|
||||
self.fulfilled_orders.append(self.current_order)
|
||||
|
||||
self.current_item = None
|
||||
self.provided_items.append(self.current_item)
|
||||
self.item_station_completed = False
|
||||
|
||||
def step(self) -> None:
|
||||
if len(self.action_queue) > 0:
|
||||
self.move()
|
||||
elif self.ready_for_execution and len(self.orderList) > 0:
|
||||
if (self.current_order is not None and len(self.current_order.items)) == 0:
|
||||
self.fulfilled_orders.append(self.current_order)
|
||||
self.current_order = None
|
||||
|
||||
if self.current_order is None:
|
||||
self.current_order = self.orderList.pop(0)
|
||||
self.goal = self.base
|
||||
else:
|
||||
self.current_order_delivered_items.append(self.current_item)
|
||||
self.goal = self.base
|
||||
self.current_item = None
|
||||
|
||||
self.plan_actions()
|
||||
elif self.goal == self.base and self.current_order is None:
|
||||
self.current_order_delivered_items.clear()
|
||||
self.current_order = self.orderList.pop(0)
|
||||
|
||||
def creation_log(self):
|
||||
print("Created Forklift Agent [id: {}]".format(self.unique_id))
|
||||
|
120
GameModel.py
120
GameModel.py
@ -9,6 +9,7 @@ from mesa.time import RandomActivation
|
||||
from AgentBase import AgentBase
|
||||
from ForkliftAgent import ForkliftAgent
|
||||
from InitialStateFactory import InitialStateFactory
|
||||
from ItemDisplayAgent import ItemDisplayAgent
|
||||
from PatchAgent import PatchAgent
|
||||
from PatchType import PatchType
|
||||
from PictureVisualizationAgent import PictureVisualizationAgent
|
||||
@ -18,11 +19,12 @@ from data.Order import Order
|
||||
from data.enum.ItemType import ItemType
|
||||
from decision.Action import Action
|
||||
from decision.ActionType import ActionType
|
||||
from genetic_order.GeneticOrder import GeneticOrder
|
||||
from imageClasification.Classificator import image_classification
|
||||
from pathfinding.PathfinderOnStates import PathFinderOnStates, PathFinderState
|
||||
from tree.DecisionTree import DecisionTree
|
||||
from util.PathByEnum import PathByEnum
|
||||
from util.PathDefinitions import GridLocation, GridWithWeights
|
||||
from imageClasification.Classificator import image_clasification
|
||||
|
||||
|
||||
|
||||
class Phase(Enum):
|
||||
@ -35,7 +37,9 @@ class Phase(Enum):
|
||||
|
||||
class GameModel(Model):
|
||||
|
||||
def __init__(self, width, height, graph: GridWithWeights, items: int, orders: int, classificator):
|
||||
def __init__(self, width, height, graph: GridWithWeights, items: int, orders: int, classificator,
|
||||
item_display_pos: List[GridLocation]):
|
||||
|
||||
# self.num_agents = 5
|
||||
self.first = True
|
||||
self.item_recognised = False
|
||||
@ -43,10 +47,12 @@ class GameModel(Model):
|
||||
self.grid = MultiGrid(height, width, True)
|
||||
self.schedule = RandomActivation(self)
|
||||
self.current_item_recognition = None
|
||||
self.current_item = None
|
||||
|
||||
self.client_delivery: PatchAgent = None
|
||||
self.drop_off: PatchAgent = None
|
||||
self.graph = graph
|
||||
self.cut_orders : List[Order] = []
|
||||
|
||||
self.game_constants = GameConstants(
|
||||
width,
|
||||
@ -67,15 +73,16 @@ class GameModel(Model):
|
||||
|
||||
self.schedule.add(self.forklift_agent)
|
||||
self.agents.append(self.forklift_agent)
|
||||
self.item_display_agents: List[ItemDisplayAgent] = []
|
||||
|
||||
# INITIALIZATION #
|
||||
print("############## INITIALIZATION ##############")
|
||||
self.phase = Phase.INIT
|
||||
self.initialize_grid(graph)
|
||||
self.initialize_grid(graph, item_display_pos)
|
||||
self.orderList: List[Order] = InitialStateFactory.generate_order_list(orders)
|
||||
self.fulfilled_orders: List[Order] = []
|
||||
self.forklift_agent.orderList = self.orderList
|
||||
self.forklift_agent.fulfilled_orders = self.fulfilled_orders
|
||||
self.forklift_agent.set_base(self.drop_off)
|
||||
self.classificator = classificator
|
||||
|
||||
print("############## RECOGNISE ITEMS ##############")
|
||||
@ -84,6 +91,8 @@ class GameModel(Model):
|
||||
self.items_for_recognization = copy.deepcopy(self.provided_items)
|
||||
self.recognised_items: List[Item] = []
|
||||
|
||||
self.current_order_delivered_items = self.forklift_agent.current_order_delivered_items
|
||||
|
||||
print("Relocate forklift agent to loading area for item recognition")
|
||||
|
||||
pathFinder = PathFinderOnStates(
|
||||
@ -97,8 +106,9 @@ class GameModel(Model):
|
||||
print("PATHFINDING")
|
||||
print(actions)
|
||||
self.forklift_agent.queue_movement_actions(actions)
|
||||
self.current_order = self.forklift_agent.current_order
|
||||
|
||||
def initialize_grid(self, graph: GridWithWeights):
|
||||
def initialize_grid(self, graph: GridWithWeights, item_display_pos):
|
||||
print("INITIALIZING GRID")
|
||||
# Add the agent to a random grid cell
|
||||
x = 5
|
||||
@ -119,6 +129,7 @@ class GameModel(Model):
|
||||
self.place_walls_agents(graph.walls)
|
||||
self.place_puddles(graph.puddles)
|
||||
self.place_packing_stations(graph.packingStations)
|
||||
self.place_order_items_display(item_display_pos)
|
||||
|
||||
def place_dividers(self):
|
||||
for i in range(0, 10):
|
||||
@ -159,10 +170,24 @@ class GameModel(Model):
|
||||
self.agents.append(agent)
|
||||
self.grid.place_agent(agent, p[1])
|
||||
|
||||
def place_order_items_display(self, item_positions: List[GridLocation]):
|
||||
for p in item_positions:
|
||||
agent = ItemDisplayAgent(self, p)
|
||||
self.item_display_agents.append(agent)
|
||||
self.grid.place_agent(agent, p)
|
||||
|
||||
def update_item_display(self):
|
||||
self.current_item = self.forklift_agent.current_item
|
||||
for i in range(4):
|
||||
self.item_display_agents[i].image = None
|
||||
if len(self.forklift_agent.current_order_delivered_items) > i:
|
||||
self.item_display_agents[i].image = self.forklift_agent.current_order_delivered_items[i].image
|
||||
|
||||
def step(self):
|
||||
self.schedule.step()
|
||||
self.grid.remove_agent(self.forklift_agent)
|
||||
self.grid.place_agent(self.forklift_agent, self.forklift_agent.current_position)
|
||||
self.update_item_display()
|
||||
|
||||
if self.phase == Phase.ITEM_RECOGNITION:
|
||||
if not self.item_recognised and self.forklift_agent.current_position == self.drop_off.location:
|
||||
@ -180,17 +205,90 @@ class GameModel(Model):
|
||||
self.recognised_items.append(recognised)
|
||||
|
||||
if self.phase == Phase.CLIENT_SORTING:
|
||||
# TODO GENERICS SORTING
|
||||
sorted(self.orderList, key=lambda x: len(x.items))
|
||||
orders: [Order] = self.orderList
|
||||
tree: DecisionTree = DecisionTree()
|
||||
|
||||
# CLIENT RECOGNITION
|
||||
orders_with_prio = tree.get_data_good(orders)
|
||||
|
||||
# print("before:" )
|
||||
# for i in range(len(orders_with_prio)):
|
||||
# print("ORDER {}, PRIO: {}".format(orders_with_prio[i].id, orders_with_prio[i].priority))
|
||||
|
||||
# GENERICS SORTING
|
||||
genericOrder: GeneticOrder = GeneticOrder(orders_with_prio)
|
||||
new_orders = genericOrder.get_orders_sorted(orders)
|
||||
|
||||
# print("after:" )
|
||||
# for i in range(len(new_orders)):
|
||||
# print("ORDER {}, PRIO: {}".format(new_orders[i].id, new_orders[i].priority))
|
||||
|
||||
self.orderList = new_orders
|
||||
self.count_recognised_items()
|
||||
|
||||
self.sort_orders()
|
||||
|
||||
self.forklift_agent.orderList = self.orderList
|
||||
|
||||
print("FINISHED CLIENT ORDER SORTING")
|
||||
self.phase = Phase.EXECUTION
|
||||
|
||||
if self.phase == Phase.EXECUTION:
|
||||
print("Execution")
|
||||
self.current_order = self.forklift_agent.current_order
|
||||
pass
|
||||
# print("Execution")
|
||||
|
||||
def sort_orders(self):
|
||||
orders_to_fill: [Order] = []
|
||||
cut_orders: [Order] = []
|
||||
|
||||
for i in range(len(self.orderList)):
|
||||
o: Order = self.orderList[i]
|
||||
refrige = self.count_item_type(o, ItemType.REFRIGERATOR)
|
||||
shelf = self.count_item_type(o, ItemType.SHELF)
|
||||
door = self.count_item_type(o, ItemType.DOOR)
|
||||
|
||||
if self.count_shelf - shelf >= 0 and self.count_refrige - refrige >= 0 and self.count_door - door >= 0:
|
||||
self.count_shelf -= shelf
|
||||
self.count_door -= door
|
||||
self.count_refrige -= refrige
|
||||
orders_to_fill.append(o)
|
||||
else:
|
||||
cut_orders.append(o)
|
||||
|
||||
self.cut_orders = cut_orders
|
||||
self.orderList = orders_to_fill
|
||||
self.forklift_agent.orderList = orders_to_fill
|
||||
|
||||
def count_item_type(self, o: Order, itemType: ItemType) -> int:
|
||||
res = 0
|
||||
for i in range(len(o.items)):
|
||||
it: Item = o.items[i]
|
||||
if it.guessed_type == itemType:
|
||||
res += 1
|
||||
|
||||
return res
|
||||
|
||||
def count_recognised_items(self):
|
||||
count_refrige: int = 0
|
||||
count_door: int = 0
|
||||
count_shelf: int = 0
|
||||
|
||||
for i in range(len(self.recognised_items)):
|
||||
item: Item = self.recognised_items[i]
|
||||
if item.guessed_type == ItemType.DOOR:
|
||||
count_door += 1
|
||||
elif item.guessed_type == ItemType.SHELF:
|
||||
count_shelf += 1
|
||||
else:
|
||||
count_refrige += 1
|
||||
|
||||
self.count_door = count_door
|
||||
self.count_shelf = count_shelf
|
||||
self.count_refrige = count_refrige
|
||||
|
||||
def recognise_item(self, item: Item):
|
||||
# TODO IMAGE PROCESSING
|
||||
val = image_clasification(self.picture_visualization.img, self.classificator)
|
||||
val = image_classification(self.picture_visualization.img, self.classificator)
|
||||
print("VAL: {}".format(val))
|
||||
|
||||
if val == ItemType.DOOR:
|
||||
|
@ -3,7 +3,9 @@ import random
|
||||
from data.Item import Item
|
||||
from data.Order import Order
|
||||
from data.enum.ItemType import ItemType
|
||||
from data.enum.Priority import Priority
|
||||
from util.ClientParamsFactory import ClientParamsFactory
|
||||
from util.PathByEnum import PathByEnum
|
||||
|
||||
|
||||
class InitialStateFactory:
|
||||
@ -24,6 +26,38 @@ class InitialStateFactory:
|
||||
|
||||
return order_list
|
||||
|
||||
@staticmethod
|
||||
def generate_order_list_XD(output_order_list_size: int):
|
||||
order_list: [Order] = []
|
||||
for i in range(output_order_list_size):
|
||||
order_list.append(InitialStateFactory.__generate_order_XD())
|
||||
|
||||
return order_list
|
||||
|
||||
@staticmethod
|
||||
def __generate_order_XD() -> Order:
|
||||
order_size = random.randint(1, 4)
|
||||
|
||||
items: [Item] = []
|
||||
for i in range(order_size):
|
||||
items.append(InitialStateFactory.__generate_item())
|
||||
|
||||
time_base = random.randint(8, 20)
|
||||
final_time = time_base * order_size
|
||||
|
||||
client_params = ClientParamsFactory.get_client_params()
|
||||
|
||||
x = random.randint(0, 3)
|
||||
type = Priority.LOW
|
||||
if x == 0:
|
||||
type = Priority.MEDIUM
|
||||
elif x == 1:
|
||||
type = Priority.HIGH
|
||||
|
||||
x = random.randint(20, 300)
|
||||
|
||||
return Order(final_time, items, type, x, client_params)
|
||||
|
||||
@staticmethod
|
||||
def __generate_order() -> Order:
|
||||
order_size = random.randint(1, 4)
|
||||
@ -37,7 +71,7 @@ class InitialStateFactory:
|
||||
|
||||
client_params = ClientParamsFactory.get_client_params()
|
||||
|
||||
return Order(final_time, items, None, client_params)
|
||||
return Order(final_time, items, Priority.LOW, 0, client_params)
|
||||
|
||||
@staticmethod
|
||||
def generate_input_sequence(self, input_sequence_size):
|
||||
@ -50,4 +84,6 @@ class InitialStateFactory:
|
||||
@staticmethod
|
||||
def __generate_item() -> Item:
|
||||
randomly_picked_type = random.choice(list(ItemType))
|
||||
return Item(randomly_picked_type)
|
||||
item = Item(randomly_picked_type, PathByEnum.get_random_path(randomly_picked_type))
|
||||
item.guessed_type = item.real_type
|
||||
return item
|
||||
|
10
ItemDisplayAgent.py
Normal file
10
ItemDisplayAgent.py
Normal file
@ -0,0 +1,10 @@
|
||||
from PatchAgent import PatchAgent
|
||||
from PatchType import PatchType
|
||||
from util.PathDefinitions import GridLocation
|
||||
|
||||
|
||||
class ItemDisplayAgent(PatchAgent):
|
||||
|
||||
def __init__(self, model, location: GridLocation):
|
||||
self.image = None
|
||||
super().__init__(model, location, patch_type=PatchType.itemDisplay)
|
@ -7,7 +7,8 @@ class PatchType(enum.Enum):
|
||||
item = 3
|
||||
wall = 4
|
||||
diffTerrain = 5
|
||||
packingA = 6
|
||||
packingB = 7
|
||||
packingC = 8
|
||||
packingShelf = 6
|
||||
packingRefrigerator = 7
|
||||
packingDoor = 8
|
||||
divider = 9
|
||||
itemDisplay = 10
|
||||
|
@ -1,5 +1,7 @@
|
||||
from typing import Dict
|
||||
|
||||
from data.Item import Item
|
||||
from data.Order import Order
|
||||
from data.enum.ItemType import ItemType
|
||||
from util.PathDefinitions import GridLocation
|
||||
|
||||
@ -9,16 +11,10 @@ class GameConstants:
|
||||
self,
|
||||
grid_width: int,
|
||||
grid_height: int,
|
||||
# delivery_pos: GridLocation,
|
||||
# order_pos: GridLocation,
|
||||
# special_positions: Dict[ItemType, GridLocation],
|
||||
walls: [GridLocation],
|
||||
diffTerrain: [GridLocation]
|
||||
):
|
||||
self.grid_width = grid_width
|
||||
self.grid_height = grid_height
|
||||
# self.delivery_pos = delivery_pos
|
||||
# self.order_pos = order_pos
|
||||
# self.special_positions = special_positions
|
||||
self.walls = walls
|
||||
self.diffTerrain = diffTerrain
|
||||
|
@ -6,9 +6,10 @@ from data.enum.ItemType import ItemType
|
||||
class Item:
|
||||
id_counter = count(start=0)
|
||||
|
||||
def __init__(self, item_type: ItemType):
|
||||
def __init__(self, item_type: ItemType, image):
|
||||
self.id = next(self.id_counter)
|
||||
self.real_type = item_type
|
||||
self.image = image
|
||||
self.guessed_type = None
|
||||
|
||||
def __repr__(self) -> str:
|
||||
|
@ -9,12 +9,18 @@ from data.enum.Priority import Priority
|
||||
class Order:
|
||||
id_counter = count(start=0)
|
||||
|
||||
def __init__(self, time: int, items: [Item], priority: Priority, client_params: ClientParams):
|
||||
def __init__(self, time: int, items: [Item], priority: Priority, sum: int, client_params: ClientParams):
|
||||
self.id = next(self.id_counter)
|
||||
self.time = time
|
||||
self.items: List[Item] = items
|
||||
self.client_params = client_params
|
||||
self.priority = priority
|
||||
self.sum = sum
|
||||
|
||||
# def sum_items(self, items: [Item]):
|
||||
# result = 0
|
||||
# for i in range(len(items)):
|
||||
# result += items[i]
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return "items: {} priority: {}".format(self.items, self.priority)
|
||||
|
7
data/enum/GeneticMutationType.py
Normal file
7
data/enum/GeneticMutationType.py
Normal file
@ -0,0 +1,7 @@
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class GeneticMutationType(Enum):
|
||||
MUTATION = 1
|
||||
CROSS = 2
|
||||
REVERSE = 3
|
@ -1,6 +1,7 @@
|
||||
from data.enum.Direction import Direction
|
||||
from data.Item import Item
|
||||
from data.Order import Order
|
||||
from data.enum.Priority import Priority
|
||||
from decision.ActionType import ActionType
|
||||
from util.PathDefinitions import GridLocation
|
||||
|
||||
@ -10,7 +11,7 @@ class State:
|
||||
action_taken: ActionType,
|
||||
forklift_position: GridLocation,
|
||||
forklift_rotation: Direction,
|
||||
pending_orders: [Order],
|
||||
pending_orders: [Priority, [Order]],
|
||||
filled_orders: [Order],
|
||||
input_items: [Item]
|
||||
):
|
||||
|
9
decision/test/ForkliftActions.py
Normal file
9
decision/test/ForkliftActions.py
Normal file
@ -0,0 +1,9 @@
|
||||
from data.GameConstants import GameConstants
|
||||
|
||||
|
||||
class ForkliftActions:
|
||||
|
||||
def __init__(self, game: GameConstants,
|
||||
) -> None:
|
||||
self.game = game
|
||||
|
218
genetic_order/GeneticOrder.py
Normal file
218
genetic_order/GeneticOrder.py
Normal file
@ -0,0 +1,218 @@
|
||||
import itertools
|
||||
import random
|
||||
|
||||
from data.Order import Order
|
||||
from data.enum.GeneticMutationType import GeneticMutationType
|
||||
from data.enum.Priority import Priority
|
||||
|
||||
|
||||
class GeneticOrder:
|
||||
mutation_chance = 10
|
||||
reverse_chance = 60
|
||||
cross_chance = 5
|
||||
|
||||
best_fit_special = 50
|
||||
best_fit_super_special = 20
|
||||
|
||||
population_size = 200
|
||||
number_of_populations = 1000
|
||||
|
||||
punish_low = 500
|
||||
punish_med = 300
|
||||
|
||||
punish_sum = 50
|
||||
|
||||
def __init__(self, orders: [Order]) -> None:
|
||||
self.orders = orders
|
||||
|
||||
def get_mutation_type(self) -> GeneticMutationType:
|
||||
x = random.randint(0, self.mutation_chance + self.cross_chance + self.reverse_chance)
|
||||
|
||||
if x < self.mutation_chance:
|
||||
return GeneticMutationType.MUTATION
|
||||
|
||||
if x > self.mutation_chance + self.cross_chance:
|
||||
return GeneticMutationType.REVERSE
|
||||
|
||||
return GeneticMutationType.CROSS
|
||||
|
||||
def mutation(self, population: [int]) -> [int]:
|
||||
x = random.randint(0, len(population) - 1)
|
||||
y = random.randint(0, len(population) - 1)
|
||||
while x == y:
|
||||
y = random.randint(0, len(population) - 1)
|
||||
|
||||
result = population
|
||||
|
||||
pom = population[x]
|
||||
result[x] = population[y]
|
||||
result[y] = pom
|
||||
|
||||
if (result[x] == result[y]):
|
||||
print("PIZDA I CHUJ")
|
||||
|
||||
return result
|
||||
|
||||
def cross(self, population: [int]) -> [int]:
|
||||
x = random.randint(1, len(population) - 1)
|
||||
|
||||
result = []
|
||||
|
||||
for i in range(len(population)):
|
||||
result.append(population[(i + x) % len(population)])
|
||||
|
||||
return result
|
||||
|
||||
def reverse(self, population: [int]) -> [int]:
|
||||
x = random.randint(0, len(population))
|
||||
y = random.randint(0, len(population) - 1)
|
||||
while y - x > 2 or x >= y:
|
||||
x = random.randint(0, len(population))
|
||||
y = random.randint(0, len(population) - 1)
|
||||
|
||||
result = []
|
||||
# print("X: ", x, " y: ", y)
|
||||
|
||||
for i in range(len(population)):
|
||||
if x <= i <= y:
|
||||
new_i = i - x
|
||||
# print("len:", len(population), " new_i: ", new_i)
|
||||
result.append(population[y - new_i])
|
||||
else:
|
||||
result.append(population[i])
|
||||
|
||||
return result
|
||||
|
||||
def generate_first_population(self, k: int) -> [[int]]:
|
||||
result = []
|
||||
|
||||
s = range(len(self.orders))
|
||||
p = itertools.permutations(s)
|
||||
while len(result) < k:
|
||||
n = p.__next__()
|
||||
if n not in result:
|
||||
result.append(n)
|
||||
|
||||
return [list(x) for x in result]
|
||||
|
||||
# result = itertools.permutations(range(len(self.orders)))
|
||||
#
|
||||
# return [list(x) for x in result]
|
||||
|
||||
def correct_sum(self, last_prio: Priority, last_sum: float, o: Order) -> bool:
|
||||
if o.priority == last_prio:
|
||||
return last_sum > o.sum / o.time
|
||||
return True
|
||||
|
||||
def sum_wrong(self, member: [int]) -> int:
|
||||
last_high = 0
|
||||
last_med = 0
|
||||
last_prio = Priority.HIGH
|
||||
last_sum = 0
|
||||
counter = 0
|
||||
|
||||
for i in range(len(member)):
|
||||
o: Order = self.orders[member[i]]
|
||||
if o.priority == Priority.HIGH:
|
||||
last_high = i
|
||||
elif o.priority == Priority.MEDIUM:
|
||||
last_med = i
|
||||
|
||||
if not self.correct_sum(last_prio, last_sum, o):
|
||||
counter += int(last_sum - (o.sum / o.time))
|
||||
last_prio = o.priority
|
||||
last_sum = o.sum / o.time
|
||||
|
||||
for i in range(last_high):
|
||||
o: Order = self.orders[member[i]]
|
||||
if o.priority == Priority.MEDIUM:
|
||||
counter += self.punish_med
|
||||
elif o.priority == Priority.LOW:
|
||||
counter += self.punish_low
|
||||
|
||||
for i in range(last_med):
|
||||
o: Order = self.orders[member[i]]
|
||||
if o.priority == Priority.LOW:
|
||||
counter += self.punish_low
|
||||
|
||||
return counter
|
||||
|
||||
def evaluate(self, member: [int]) -> int:
|
||||
# result = 0
|
||||
# for i in range(len(self.orders) - 1):
|
||||
# x: Order = self.orders[member[i]]
|
||||
# y: Order = self.orders[member[i + 1]]
|
||||
#
|
||||
# if ((x.priority == Priority.MEDIUM or x.priority == Priority.LOW) and y.priority == Priority.HIGH) or (x.priority == Priority.LOW and y.priority == Priority.MEDIUM):
|
||||
# result += 30
|
||||
#
|
||||
# if x.sum / x.time < y.sum / y.time:
|
||||
# result += int(y.sum / y.time)
|
||||
|
||||
# return result
|
||||
|
||||
return self.sum_wrong(member)
|
||||
|
||||
def mutate_population(self, order_population: [[int]]) -> [[int]]:
|
||||
result = []
|
||||
|
||||
for i in range(len(order_population)):
|
||||
member: [int] = order_population[i]
|
||||
operation: GeneticMutationType = self.get_mutation_type()
|
||||
|
||||
if operation == GeneticMutationType.MUTATION:
|
||||
member = self.mutation(member)
|
||||
elif operation == GeneticMutationType.REVERSE:
|
||||
member = self.reverse(member)
|
||||
else:
|
||||
member = self.cross(member)
|
||||
|
||||
result.append(member)
|
||||
|
||||
return result
|
||||
|
||||
def get_next_population(self, population: [[int]]) -> [[int]]:
|
||||
result = []
|
||||
|
||||
for i in range(len(population) - self.best_fit_special - self.best_fit_super_special):
|
||||
result.append(population[i])
|
||||
|
||||
for i in range(self.best_fit_special):
|
||||
x = random.randint(0, self.best_fit_special)
|
||||
result.append(population[x])
|
||||
|
||||
for i in range(self.best_fit_super_special):
|
||||
x = random.randint(0, self.best_fit_super_special)
|
||||
result.append(population[x])
|
||||
|
||||
return result
|
||||
|
||||
def get_orders_sorted(self, orders: [Order]) -> [Order]:
|
||||
self.orders = orders
|
||||
|
||||
population: [[int]] = self.generate_first_population(self.population_size)
|
||||
# print(population)
|
||||
|
||||
population.sort(key=self.evaluate)
|
||||
best_fit: [int] = population[0]
|
||||
|
||||
for i in range(self.number_of_populations):
|
||||
# print("population: ", i)
|
||||
population = self.mutate_population(population)
|
||||
population.sort(key=self.evaluate)
|
||||
|
||||
if self.evaluate(best_fit) > self.evaluate(population[0]):
|
||||
best_fit = population[0]
|
||||
|
||||
# population = self.get_next_population(population).sort(key=self.evaluate)
|
||||
|
||||
if self.evaluate(best_fit) < self.evaluate(population[0]):
|
||||
population[0] = best_fit
|
||||
|
||||
best: [int] = population[0]
|
||||
result: [Order] = []
|
||||
|
||||
for i in range(len(best)):
|
||||
result.append(self.orders[best[i]])
|
||||
|
||||
return result
|
@ -1,23 +1,22 @@
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
|
||||
|
||||
def image_clasification(image_path, model):
|
||||
# loaded_model = keras.models.load_model("my_model")
|
||||
|
||||
def image_classification(path, model):
|
||||
class_names = ['door', 'refrigerator', 'shelf']
|
||||
|
||||
img = tf.keras.utils.load_img(
|
||||
image_path, target_size=(180, 180)
|
||||
path, target_size=(180, 180)
|
||||
)
|
||||
img_array = tf.keras.utils.img_to_array(img)
|
||||
img_array = tf.expand_dims(img_array, 0) # Create a batch
|
||||
|
||||
predictions = model.predict(img_array)
|
||||
score = tf.nn.softmax(predictions[0])
|
||||
# print(
|
||||
# "This image most likely belongs to {} with a {:.2f} percent confidence."
|
||||
# .format(class_names[np.argmax(score)], 100 * np.max(score))
|
||||
# )
|
||||
# print(class_names[np.argmax(score)])
|
||||
return class_names[np.argmax(score)]
|
||||
|
||||
|
||||
|
@ -9,7 +9,7 @@ from tensorflow.keras import layers
|
||||
from tensorflow.keras.models import Sequential
|
||||
|
||||
|
||||
class ClassificatorInside():
|
||||
class TrainClassificator():
|
||||
|
||||
def __init__(self, data_dir: str) -> None:
|
||||
super().__init__()
|
BIN
img/door_d.jpg
Normal file
BIN
img/door_d.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 30 KiB |
BIN
img/fridge_f.jpg
Normal file
BIN
img/fridge_f.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 23 KiB |
BIN
img/shelf_s.jpg
Normal file
BIN
img/shelf_s.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 35 KiB |
45
main.py
45
main.py
@ -6,6 +6,7 @@ from tensorflow import keras
|
||||
|
||||
from ForkliftAgent import ForkliftAgent
|
||||
from GameModel import GameModel
|
||||
from ItemDisplayAgent import ItemDisplayAgent
|
||||
from PatchAgent import PatchAgent
|
||||
from PatchType import PatchType
|
||||
from PictureVisualizationAgent import PictureVisualizationAgent
|
||||
@ -13,6 +14,7 @@ from data.enum.Direction import Direction
|
||||
from util.PathDefinitions import GridWithWeights
|
||||
from visualization.DisplayAttributeElement import DisplayAttributeElement
|
||||
from visualization.DisplayItemListAttribute import DisplayItemListAttributeElement
|
||||
from visualization.DisplayOrder import DisplayOrder
|
||||
from visualization.DisplayOrderList import DisplayOrderList
|
||||
|
||||
colors = [
|
||||
@ -33,7 +35,7 @@ def agent_portrayal(agent):
|
||||
elif agent.current_rotation == Direction.left:
|
||||
shape = "img/image_left.png"
|
||||
|
||||
portrayal = {"Shape": shape, "scale": 1.0, "Layer": 0}
|
||||
portrayal = {"Shape": shape, "scale": 1.0, "Layer": 2}
|
||||
|
||||
if isinstance(agent, PatchAgent):
|
||||
color = colors[0]
|
||||
@ -45,6 +47,13 @@ def agent_portrayal(agent):
|
||||
portrayal = {"Shape": "img/okB00mer.png", "scale": 1.0, "Layer": 0}
|
||||
elif agent.patch_type == PatchType.diffTerrain:
|
||||
portrayal = {"Shape": "img/puddle.png", "scale": 1.0, "Layer": 0}
|
||||
elif agent.patch_type == PatchType.packingShelf:
|
||||
portrayal = {"Shape": "img/shelf_s.jpg", "scale": 1.0, "Layer": 1}
|
||||
elif agent.patch_type == PatchType.packingRefrigerator:
|
||||
portrayal = {"Shape": "img/fridge_f.jpg", "scale": 1.0, "Layer": 1}
|
||||
elif agent.patch_type == PatchType.packingDoor:
|
||||
portrayal = {"Shape": "img/door_d.jpg", "scale": 1.0, "Layer": 1}
|
||||
|
||||
elif agent.patch_type == PatchType.divider:
|
||||
portrayal = \
|
||||
{"Shape": "rect",
|
||||
@ -65,6 +74,18 @@ def agent_portrayal(agent):
|
||||
if isinstance(agent, PictureVisualizationAgent):
|
||||
portrayal = {"Shape": f"{agent.img}", "scale": 3.0, "Layer": 0}
|
||||
|
||||
if isinstance(agent, ItemDisplayAgent):
|
||||
if agent is not None and agent.image is not None:
|
||||
portrayal = {"Shape": f"{agent.image}", "scale": 1.0, "Layer": 0}
|
||||
else:
|
||||
portrayal = \
|
||||
{"Shape": "rect",
|
||||
"Filled": "true",
|
||||
"Layer": 0,
|
||||
"Color": "black",
|
||||
"w": 1,
|
||||
"h": 1}
|
||||
|
||||
return portrayal
|
||||
|
||||
|
||||
@ -75,26 +96,32 @@ if __name__ == '__main__':
|
||||
scale = base / gridWidth
|
||||
|
||||
diagram = GridWithWeights(gridWidth, gridHeight)
|
||||
diagram.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
|
||||
diagram.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
|
||||
diagram.packingStations = [(PatchType.packingA, (4, 8)), (PatchType.packingB, (4, 6)), (PatchType.packingC, (4, 2))]
|
||||
diagram.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (2, 6), (4, 7), (3, 4), (4, 4), (6, 4)]
|
||||
diagram.puddles = [(2, 2), (2, 5), (5, 4), (4, 8), (4, 6), (4, 2)]
|
||||
diagram.packingStations = [(PatchType.packingShelf, (4, 8)), (PatchType.packingRefrigerator, (4, 6)),
|
||||
(PatchType.packingDoor, (4, 2))]
|
||||
|
||||
grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
|
||||
|
||||
display_items = [(6, 11), (7, 11), (8, 11), (9, 11)]
|
||||
|
||||
readyText = DisplayAttributeElement("phase")
|
||||
# current_item = DisplayPictureElement("current_item_recognition")
|
||||
provided_itesm = DisplayItemListAttributeElement("provided_items")
|
||||
recognised_items = DisplayItemListAttributeElement("recognised_items")
|
||||
current_order = DisplayOrder("current_order")
|
||||
current_item = DisplayAttributeElement("current_item")
|
||||
ordersText = DisplayOrderList("orderList")
|
||||
fulfilled_orders = DisplayOrderList("fulfilled_orders")
|
||||
cut_orders = DisplayOrderList("cut_orders") # MTR!
|
||||
|
||||
loaded_model = keras.models.load_model("./imageClasification/my_model")
|
||||
model = keras.models.load_model("imageClasification/my_model")
|
||||
|
||||
server = ModularServer(GameModel,
|
||||
[grid, readyText, provided_itesm, recognised_items, ordersText,
|
||||
fulfilled_orders],
|
||||
[grid, readyText, current_item, current_order, fulfilled_orders, ordersText, provided_itesm,
|
||||
recognised_items, cut_orders],
|
||||
"Automatyczny Wózek Widłowy",
|
||||
dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=loaded_model))
|
||||
dict(width=gridHeight, height=gridWidth, graph=diagram, items=60, orders=20,
|
||||
classificator=model, item_display_pos=display_items))
|
||||
|
||||
server.port = 8888
|
||||
server.launch()
|
||||
|
@ -57,7 +57,7 @@ class PathFinderOnStates:
|
||||
|
||||
if action == action.action_type.MOVE:
|
||||
if curr_state.agent_position in self.game_constants.diffTerrain:
|
||||
cost = curr_state.cost + 20
|
||||
cost = curr_state.cost + 45
|
||||
# tutaj koszt kaluzy
|
||||
else:
|
||||
cost = curr_state.cost + 1
|
||||
|
@ -1,11 +1,17 @@
|
||||
import csv
|
||||
|
||||
import numpy as np
|
||||
import pandas
|
||||
import sklearn
|
||||
from sklearn import metrics, preprocessing
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
|
||||
from InitialStateFactory import InitialStateFactory
|
||||
from data.ClientParams import ClientParams
|
||||
from data.Order import Order
|
||||
from data.enum.CompanySize import CompanySize
|
||||
from data.enum.Priority import Priority
|
||||
from util.ClientParamsFactory import ClientParamsFactory
|
||||
|
||||
|
||||
@ -58,6 +64,63 @@ class DecisionTree:
|
||||
print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y, prediction))
|
||||
|
||||
|
||||
def get_data_good(self, orders: [Order]) -> [Order]:
|
||||
|
||||
n_array_input = []
|
||||
for i in range(len(orders)):
|
||||
o:Order = orders[i]
|
||||
cp: ClientParams = o.client_params
|
||||
pom = []
|
||||
|
||||
pom.append(cp.payment_delay)
|
||||
pom.append(cp.payed)
|
||||
pom.append(cp.net_worth)
|
||||
pom.append(cp.infuence_rate)
|
||||
pom.append(cp.is_skarbowka)
|
||||
pom.append(cp.membership)
|
||||
pom.append(cp.is_hat)
|
||||
|
||||
size: CompanySize = cp.company_size
|
||||
if(size == CompanySize.NO):
|
||||
pom.append(0)
|
||||
if (size == CompanySize.SMALL):
|
||||
pom.append(1)
|
||||
if (size == CompanySize.NORMAL):
|
||||
pom.append(2)
|
||||
if (size == CompanySize.BIG):
|
||||
pom.append(3)
|
||||
if (size == CompanySize.HUGE):
|
||||
pom.append(4)
|
||||
if (size == CompanySize.GIGANTISHE):
|
||||
pom.append(5)
|
||||
|
||||
|
||||
|
||||
n_array_input.append(pom)
|
||||
|
||||
n_array = np.array(n_array_input)
|
||||
# print(n_array)
|
||||
|
||||
# print(n_array[0])
|
||||
tree = self.get_decision_tree()
|
||||
priority = tree.predict(n_array)
|
||||
|
||||
|
||||
for i in range(len(orders)):
|
||||
print(orders[i].priority)
|
||||
orders[i].priority = priority[i]
|
||||
|
||||
if priority[i] == "LOW":
|
||||
orders[i].priority = Priority.LOW
|
||||
if priority[i] == "MEDIUM":
|
||||
orders[i].priority = Priority.MEDIUM
|
||||
if priority[i] == "HIGH":
|
||||
orders[i].priority = Priority.HIGH
|
||||
|
||||
print(orders[i].priority)
|
||||
|
||||
|
||||
return orders
|
||||
|
||||
|
||||
def get_decision_tree(self) -> DecisionTreeClassifier:
|
||||
@ -72,15 +135,25 @@ class DecisionTree:
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, train_size=0.8)
|
||||
|
||||
# print(len(X_train[0]))
|
||||
# print(X_train[0])
|
||||
|
||||
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
|
||||
|
||||
clf = drugTree.fit(X_train, y_train)
|
||||
predicted = drugTree.predict(X_test)
|
||||
|
||||
# print(type(X_test))
|
||||
|
||||
y_test = y_test.to_list()
|
||||
|
||||
self.print_logs(X_test, y_test, predicted)
|
||||
# self.print_logs(X_test, y_test, predicted)
|
||||
|
||||
print(sklearn.tree.export_text(clf, feature_names=X_headers))
|
||||
# print(sklearn.tree.export_text(clf, feature_names=X_headers))
|
||||
|
||||
return drugTree
|
||||
|
||||
|
||||
|
||||
# kurwa = DecisionTree()
|
||||
# kurwa.get_data_good(InitialStateFactory.generate_order_list(50))
|
46
visualization/DisplayOrder.py
Normal file
46
visualization/DisplayOrder.py
Normal file
@ -0,0 +1,46 @@
|
||||
from collections import Counter
|
||||
from typing import List
|
||||
|
||||
from mesa.visualization.modules import TextElement
|
||||
|
||||
from data.Order import Order
|
||||
|
||||
|
||||
class DisplayOrder(TextElement):
|
||||
def __init__(self, attr_name):
|
||||
'''
|
||||
Create a new text attribute element.
|
||||
|
||||
Args:
|
||||
attr_name: The name of the attribute to extract from the model.
|
||||
|
||||
Example return: "happy: 10"
|
||||
'''
|
||||
self.attr_name = attr_name
|
||||
|
||||
def render(self, model):
|
||||
val = getattr(model, self.attr_name)
|
||||
res = self.attr_name
|
||||
if val is not None:
|
||||
o = val
|
||||
itemList = map(lambda x: x.real_type, o.items)
|
||||
itemCounter = Counter(itemList)
|
||||
|
||||
item_str = "<ul>"
|
||||
for e in itemCounter:
|
||||
key = e
|
||||
val = itemCounter[key]
|
||||
|
||||
# key_str = ""
|
||||
# if key == ItemType.DOOR:
|
||||
# key_str = "Door"
|
||||
# elif key == ItemType.SHELF:
|
||||
# key_str = "Shelf"
|
||||
|
||||
item_str += f"<li>{str(key)}:{str(val)}</li>"
|
||||
|
||||
item_str += "</ul>"
|
||||
|
||||
res += f"<li> items: {item_str} priority: {o.priority} <br> Client: {vars(o.client_params)} </li>"
|
||||
|
||||
return res
|
@ -4,7 +4,6 @@ from typing import List
|
||||
from mesa.visualization.modules import TextElement
|
||||
|
||||
from data.Order import Order
|
||||
from data.enum.ItemType import ItemType
|
||||
|
||||
|
||||
class DisplayOrderList(TextElement):
|
||||
@ -21,7 +20,8 @@ class DisplayOrderList(TextElement):
|
||||
|
||||
def render(self, model):
|
||||
val = getattr(model, self.attr_name)
|
||||
|
||||
res = ""
|
||||
if val is not None:
|
||||
orderList: List[Order] = val
|
||||
|
||||
res = self.attr_name + ": <ol>"
|
||||
|
Loading…
Reference in New Issue
Block a user