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engine_imp
138
ForkliftAgent.py
@ -1,10 +1,8 @@
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from copy import deepcopy
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from typing import Tuple, List
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from AgentBase import AgentBase
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from PatchAgent import PatchAgent
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from PatchType import PatchType
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from data.GameConstants import GameConstants
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from data.Item import Item
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from data.Order import Order
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from data.enum.Direction import Direction
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@ -18,7 +16,7 @@ from util.PathDefinitions import GridLocation, GridWithWeights
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class ForkliftAgent(AgentBase):
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def __init__(self, model, game_constants: GameConstants, client_delivery: PatchAgent, drop_off: PatchAgent,
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def __init__(self, model, game_constants, client_delivery: PatchAgent, drop_off: PatchAgent,
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graph: GridWithWeights):
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super().__init__(model)
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self.action_queue: List[Action] = []
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@ -30,20 +28,10 @@ class ForkliftAgent(AgentBase):
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self.drop_off: PatchAgent = drop_off
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self.graph = graph
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self.game_constants = game_constants
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self.current_order: Order = None
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self.current_item = None
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self.item_station_completed = False
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self.current_order_delivered_items: List[Item] = []
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self.ready_for_execution = False
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self.last_delviered_item = None
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self.current_item: Item = None
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self.current_order = None
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self.base: GridLocation = None
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self.goal: GridLocation = None
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def set_base(self, drop_off: PatchAgent):
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self.drop_off = drop_off
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self.base = self.drop_off.location
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self.goal = self.base
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self.provided_items: List[Item] = []
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def queue_movement_actions(self, movement_actions: List[Action]):
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self.action_queue.extend(movement_actions)
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@ -54,94 +42,120 @@ class ForkliftAgent(AgentBase):
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action_type = action.action_type
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if action_type == ActionType.ROTATE_UP:
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# print("rotate {} --> {}".format(self.current_rotation, action_type))
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print("rotate {} --> {}".format(self.current_rotation, action_type))
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self.current_rotation = Direction.top
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elif action_type == ActionType.ROTATE_RIGHT:
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# print("rotate {} --> {}".format(self.current_rotation, action_type))
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print("rotate {} --> {}".format(self.current_rotation, action_type))
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self.current_rotation = Direction.right
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elif action_type == ActionType.ROTATE_DOWN:
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# print("rotate {} --> {}".format(self.current_rotation, action_type))
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print("rotate {} --> {}".format(self.current_rotation, action_type))
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self.current_rotation = Direction.down
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elif action_type == ActionType.ROTATE_LEFT:
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# print("rotate {} --> {}".format(self.current_rotation, action_type))
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print("rotate {} --> {}".format(self.current_rotation, action_type))
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self.current_rotation = Direction.left
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elif action_type == ActionType.MOVE:
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if self.current_rotation == Direction.top:
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# print("move {} --> {}".format(self.current_position, action_type))
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print("move {} --> {}".format(self.current_position, action_type))
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self.current_position = (self.current_position[0], self.current_position[1] + 1)
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elif self.current_rotation == Direction.down:
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# print("move {} --> {}".format(self.current_position, action_type))
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print("move {} --> {}".format(self.current_position, action_type))
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self.current_position = (self.current_position[0], self.current_position[1] - 1)
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elif self.current_rotation == Direction.right:
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# print("move {} --> {}".format(self.current_position, action_type))
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print("move {} --> {}".format(self.current_position, action_type))
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self.current_position = (self.current_position[0] + 1, self.current_position[1])
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elif self.current_rotation == Direction.left:
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# print("move {} --> {}".format(self.current_position, action_type))
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print("move {} --> {}".format(self.current_position, action_type))
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self.current_position = (self.current_position[0] - 1, self.current_position[1])
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def step(self) -> None:
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if len(self.action_queue) > 0:
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self.move()
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elif self.ready_for_execution:
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if self.current_position != self.goal:
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def plan_actions(self):
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if len(self.current_order.items) > 0:
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i = self.current_order.items.pop(0)
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if self.current_item is None:
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self.provided_items.clear()
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self.current_item = i
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print("PLAN MOVEMENT")
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# get item
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pathFinder = PathFinderOnStates(
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self.game_constants,
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self.goal,
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self.drop_off.location,
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PathFinderState(self.current_position,
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self.current_rotation,
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0,
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Action(ActionType.NONE),
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[])
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)
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actions = pathFinder.get_action_list()
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self.queue_movement_actions(actions)
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elif not self.item_station_completed:
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# go through station
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packing_station: GridLocation = None
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stations = dict(self.graph.packingStations)
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if i.real_type == ItemType.SHELF:
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packing_station = stations[PatchType.packingA]
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elif i.real_type == ItemType.EGG:
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packing_station = stations[PatchType.packingB]
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elif i.real_type == ItemType.DOOR:
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packing_station = stations[PatchType.packingC]
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pathFinder = PathFinderOnStates(
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self.game_constants,
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packing_station,
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PathFinderState(self.current_position,
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self.current_rotation,
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0,
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Action(
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desired_item=None,
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desired_item=i,
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action_type=ActionType.PICK_ITEM
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),
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[])
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)
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actions = pathFinder.get_action_list()
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self.queue_movement_actions(actions)
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self.item_station_completed = True
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else:
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if self.current_order is not None and self.goal == self.base:
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self.current_item = self.current_order.items.pop(0)
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packing_station: GridLocation = None
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stations = dict(self.graph.packingStations)
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if self.current_item.real_type == ItemType.SHELF:
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packing_station = stations[PatchType.packingShelf]
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elif self.current_item.real_type == ItemType.REFRIGERATOR:
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packing_station = stations[PatchType.packingRefrigerator]
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elif self.current_item.real_type == ItemType.DOOR:
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packing_station = stations[PatchType.packingDoor]
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self.goal = packing_station
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elif self.goal in [i[1] for i in self.graph.packingStations]:
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self.goal = self.client_delivery.location
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elif self.goal == self.client_delivery.location:
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if self.current_order is not None and len(self.current_order.items) == 0:
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self.current_order_delivered_items.append(self.current_item)
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self.current_order.items = deepcopy(self.current_order_delivered_items)
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self.fulfilled_orders.append(self.current_order)
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# go to client delivery area
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pathFinder = PathFinderOnStates(
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self.game_constants,
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self.client_delivery.location,
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PathFinderState(self.current_position,
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self.current_rotation,
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0,
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Action(ActionType.NONE),
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[])
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)
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actions = pathFinder.get_action_list()
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self.queue_movement_actions(actions)
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self.queue_movement_actions(
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[Action(ActionType.DROP_ITEM)]
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)
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self.current_item = None
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self.provided_items.append(self.current_item)
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self.item_station_completed = False
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def step(self) -> None:
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if len(self.action_queue) > 0:
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self.move()
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elif len(self.orderList) > 0:
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if (self.current_order is not None and len(self.current_order.items)) == 0:
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self.fulfilled_orders.append(self.current_order)
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self.current_order = None
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self.goal = self.base
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else:
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self.current_order_delivered_items.append(self.current_item)
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self.goal = self.base
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self.current_item = None
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elif self.goal == self.base and self.current_order is None:
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self.current_order_delivered_items.clear()
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if self.current_order is None:
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self.current_order = self.orderList.pop(0)
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self.plan_actions()
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def creation_log(self):
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print("Created Forklift Agent [id: {}]".format(self.unique_id))
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149
GameModel.py
@ -1,6 +1,5 @@
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import copy
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from enum import Enum
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from typing import List, Tuple
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from typing import List
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from mesa import Model
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from mesa.space import MultiGrid
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@ -9,21 +8,15 @@ from mesa.time import RandomActivation
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from AgentBase import AgentBase
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from ForkliftAgent import ForkliftAgent
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from InitialStateFactory import InitialStateFactory
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from ItemDisplayAgent import ItemDisplayAgent
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from PatchAgent import PatchAgent
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from PatchType import PatchType
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from PictureVisualizationAgent import PictureVisualizationAgent
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from data.GameConstants import GameConstants
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from data.Item import Item
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from data.Order import Order
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from data.enum.ItemType import ItemType
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from decision.Action import Action
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from decision.ActionType import ActionType
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from genetic_order.GeneticOrder import GeneticOrder
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from imageClasification.Classificator import image_classification
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from pathfinding.PathfinderOnStates import PathFinderOnStates, PathFinderState
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from tree.DecisionTree import DecisionTree
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from util.PathByEnum import PathByEnum
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from util.PathDefinitions import GridLocation, GridWithWeights
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@ -37,9 +30,7 @@ class Phase(Enum):
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class GameModel(Model):
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def __init__(self, width, height, graph: GridWithWeights, items: int, orders: int, classificator,
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item_display_pos: List[GridLocation]):
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def __init__(self, width, height, graph: GridWithWeights):
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# self.num_agents = 5
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self.first = True
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self.item_recognised = False
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@ -47,12 +38,10 @@ class GameModel(Model):
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self.grid = MultiGrid(height, width, True)
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self.schedule = RandomActivation(self)
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self.current_item_recognition = None
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self.current_item = None
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self.client_delivery: PatchAgent = None
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self.drop_off: PatchAgent = None
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self.graph = graph
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self.cut_orders : List[Order] = []
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self.game_constants = GameConstants(
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width,
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@ -73,26 +62,21 @@ class GameModel(Model):
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self.schedule.add(self.forklift_agent)
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self.agents.append(self.forklift_agent)
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self.item_display_agents: List[ItemDisplayAgent] = []
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# INITIALIZATION #
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print("############## INITIALIZATION ##############")
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self.phase = Phase.INIT
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self.initialize_grid(graph, item_display_pos)
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self.orderList: List[Order] = InitialStateFactory.generate_order_list(orders)
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self.initialize_grid(graph)
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self.orderList: List[Order] = InitialStateFactory.generate_order_list(3)
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self.fulfilled_orders: List[Order] = []
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self.forklift_agent.orderList = self.orderList
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self.forklift_agent.fulfilled_orders = self.fulfilled_orders
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self.forklift_agent.set_base(self.drop_off)
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self.classificator = classificator
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print("############## RECOGNISE ITEMS ##############")
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self.phase = Phase.ITEM_RECOGNITION
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self.provided_items = InitialStateFactory.generate_item_list(items)
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self.items_for_recognization = copy.deepcopy(self.provided_items)
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self.provided_items = InitialStateFactory.generate_item_list(3)
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self.recognised_items: List[Item] = []
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self.current_order_delivered_items = self.forklift_agent.current_order_delivered_items
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print("Relocate forklift agent to loading area for item recognition")
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pathFinder = PathFinderOnStates(
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@ -106,9 +90,8 @@ class GameModel(Model):
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print("PATHFINDING")
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print(actions)
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self.forklift_agent.queue_movement_actions(actions)
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self.current_order = self.forklift_agent.current_order
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def initialize_grid(self, graph: GridWithWeights, item_display_pos):
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def initialize_grid(self, graph: GridWithWeights):
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print("INITIALIZING GRID")
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# Add the agent to a random grid cell
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x = 5
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@ -129,7 +112,6 @@ class GameModel(Model):
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self.place_walls_agents(graph.walls)
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self.place_puddles(graph.puddles)
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self.place_packing_stations(graph.packingStations)
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self.place_order_items_display(item_display_pos)
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def place_dividers(self):
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for i in range(0, 10):
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@ -164,138 +146,41 @@ class GameModel(Model):
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self.agents.append(agent)
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self.grid.place_agent(agent, p)
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def place_packing_stations(self, packing_stations: List[Tuple[PatchType, GridLocation]]):
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def place_packing_stations(self, packing_stations: List[tuple[PatchType, GridLocation]]):
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for p in packing_stations:
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agent = PatchAgent(self, p[1], p[0])
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self.agents.append(agent)
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self.grid.place_agent(agent, p[1])
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def place_order_items_display(self, item_positions: List[GridLocation]):
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for p in item_positions:
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agent = ItemDisplayAgent(self, p)
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self.item_display_agents.append(agent)
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self.grid.place_agent(agent, p)
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def update_item_display(self):
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self.current_item = self.forklift_agent.current_item
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for i in range(4):
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self.item_display_agents[i].image = None
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if len(self.forklift_agent.current_order_delivered_items) > i:
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self.item_display_agents[i].image = self.forklift_agent.current_order_delivered_items[i].image
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def step(self):
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self.schedule.step()
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self.grid.remove_agent(self.forklift_agent)
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self.grid.place_agent(self.forklift_agent, self.forklift_agent.current_position)
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self.update_item_display()
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if self.phase == Phase.ITEM_RECOGNITION:
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if not self.item_recognised and self.forklift_agent.current_position == self.drop_off.location:
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if len(self.items_for_recognization) == 0:
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if len(self.provided_items) == 0:
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print("FINISHED ITEM RECOGNITION")
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self.item_recognised = True
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self.phase = Phase.CLIENT_SORTING
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self.forklift_agent.ready_for_execution = True
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else:
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print("BEGIN ITEM RECOGNITION, left: {}".format(len(self.items_for_recognization)))
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item_to_recognise = self.items_for_recognization.pop()
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self.picture_visualization.img = PathByEnum.get_random_path(item_to_recognise.real_type)
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print("BEGIN ITEM RECOGNITION, left: {}".format(len(self.provided_items)))
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item_to_recognise = self.provided_items.pop()
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self.picture_visualization.img = "item_images/door/drzwi1.jpg"
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recognised = self.recognise_item(item_to_recognise)
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self.recognised_items.append(recognised)
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if self.phase == Phase.CLIENT_SORTING:
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orders: [Order] = self.orderList
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tree: DecisionTree = DecisionTree()
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# CLIENT RECOGNITION
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orders_with_prio = tree.get_data_good(orders)
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# print("before:" )
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# for i in range(len(orders_with_prio)):
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# print("ORDER {}, PRIO: {}".format(orders_with_prio[i].id, orders_with_prio[i].priority))
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# GENERICS SORTING
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genericOrder: GeneticOrder = GeneticOrder(orders_with_prio)
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new_orders = genericOrder.get_orders_sorted(orders)
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# print("after:" )
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# for i in range(len(new_orders)):
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# print("ORDER {}, PRIO: {}".format(new_orders[i].id, new_orders[i].priority))
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self.orderList = new_orders
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self.count_recognised_items()
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self.sort_orders()
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self.forklift_agent.orderList = self.orderList
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# TODO GENERICS SORTING
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sorted(self.orderList, key=lambda x: len(x.items))
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print("FINISHED CLIENT ORDER SORTING")
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self.phase = Phase.EXECUTION
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if self.phase == Phase.EXECUTION:
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self.current_order = self.forklift_agent.current_order
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pass
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# print("Execution")
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def sort_orders(self):
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orders_to_fill: [Order] = []
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cut_orders: [Order] = []
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for i in range(len(self.orderList)):
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o: Order = self.orderList[i]
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refrige = self.count_item_type(o, ItemType.REFRIGERATOR)
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shelf = self.count_item_type(o, ItemType.SHELF)
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door = self.count_item_type(o, ItemType.DOOR)
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if self.count_shelf - shelf >= 0 and self.count_refrige - refrige >= 0 and self.count_door - door >= 0:
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self.count_shelf -= shelf
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self.count_door -= door
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self.count_refrige -= refrige
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orders_to_fill.append(o)
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else:
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cut_orders.append(o)
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self.cut_orders = cut_orders
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self.orderList = orders_to_fill
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self.forklift_agent.orderList = orders_to_fill
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def count_item_type(self, o: Order, itemType: ItemType) -> int:
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res = 0
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for i in range(len(o.items)):
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it: Item = o.items[i]
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if it.guessed_type == itemType:
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res += 1
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return res
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def count_recognised_items(self):
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count_refrige: int = 0
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count_door: int = 0
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count_shelf: int = 0
|
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|
||||
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
|
||||
print("Execution")
|
||||
|
||||
def recognise_item(self, item: Item):
|
||||
val = image_classification(self.picture_visualization.img, self.classificator)
|
||||
print("VAL: {}".format(val))
|
||||
|
||||
if val == ItemType.DOOR:
|
||||
item.guessed_type = ItemType.DOOR
|
||||
elif val == ItemType.REFRIGERATOR:
|
||||
item.guessed_type = ItemType.REFRIGERATOR
|
||||
elif val == ItemType.SHELF:
|
||||
item.guessed_type = ItemType.SHELF
|
||||
|
||||
# TODO IMAGE PROCESSING
|
||||
item.guessed_type = item.real_type
|
||||
return item
|
||||
|
@ -3,9 +3,7 @@ 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:
|
||||
@ -26,38 +24,6 @@ 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)
|
||||
@ -71,7 +37,7 @@ class InitialStateFactory:
|
||||
|
||||
client_params = ClientParamsFactory.get_client_params()
|
||||
|
||||
return Order(final_time, items, Priority.LOW, 0, client_params)
|
||||
return Order(final_time, items, None, client_params)
|
||||
|
||||
@staticmethod
|
||||
def generate_input_sequence(self, input_sequence_size):
|
||||
@ -84,6 +50,4 @@ class InitialStateFactory:
|
||||
@staticmethod
|
||||
def __generate_item() -> Item:
|
||||
randomly_picked_type = random.choice(list(ItemType))
|
||||
item = Item(randomly_picked_type, PathByEnum.get_random_path(randomly_picked_type))
|
||||
item.guessed_type = item.real_type
|
||||
return item
|
||||
return Item(randomly_picked_type)
|
||||
|
@ -1,10 +0,0 @@
|
||||
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)
|
@ -7,8 +7,7 @@ class PatchType(enum.Enum):
|
||||
item = 3
|
||||
wall = 4
|
||||
diffTerrain = 5
|
||||
packingShelf = 6
|
||||
packingRefrigerator = 7
|
||||
packingDoor = 8
|
||||
packingA = 6
|
||||
packingB = 7
|
||||
packingC = 8
|
||||
divider = 9
|
||||
itemDisplay = 10
|
||||
|
@ -1,7 +1,5 @@
|
||||
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
|
||||
|
||||
@ -11,10 +9,16 @@ 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
|
||||
|
@ -6,10 +6,9 @@ from data.enum.ItemType import ItemType
|
||||
class Item:
|
||||
id_counter = count(start=0)
|
||||
|
||||
def __init__(self, item_type: ItemType, image):
|
||||
def __init__(self, item_type: ItemType):
|
||||
self.id = next(self.id_counter)
|
||||
self.real_type = item_type
|
||||
self.image = image
|
||||
self.guessed_type = None
|
||||
|
||||
def __repr__(self) -> str:
|
||||
|
@ -9,18 +9,12 @@ from data.enum.Priority import Priority
|
||||
class Order:
|
||||
id_counter = count(start=0)
|
||||
|
||||
def __init__(self, time: int, items: [Item], priority: Priority, sum: int, client_params: ClientParams):
|
||||
def __init__(self, time: int, items: [Item], priority: Priority, 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)
|
||||
|
@ -1,7 +0,0 @@
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class GeneticMutationType(Enum):
|
||||
MUTATION = 1
|
||||
CROSS = 2
|
||||
REVERSE = 3
|
@ -2,6 +2,6 @@ from enum import Enum
|
||||
|
||||
|
||||
class ItemType(Enum):
|
||||
DOOR = "door"
|
||||
SHELF = "shelf"
|
||||
REFRIGERATOR = "refrigerator"
|
||||
DOOR = 1
|
||||
SHELF = 2
|
||||
EGG = 3
|
@ -1,7 +1,6 @@
|
||||
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
|
||||
|
||||
@ -11,7 +10,7 @@ class State:
|
||||
action_taken: ActionType,
|
||||
forklift_position: GridLocation,
|
||||
forklift_rotation: Direction,
|
||||
pending_orders: [Priority, [Order]],
|
||||
pending_orders: [Order],
|
||||
filled_orders: [Order],
|
||||
input_items: [Item]
|
||||
):
|
||||
|
@ -1,9 +0,0 @@
|
||||
from data.GameConstants import GameConstants
|
||||
|
||||
|
||||
class ForkliftActions:
|
||||
|
||||
def __init__(self, game: GameConstants,
|
||||
) -> None:
|
||||
self.game = game
|
||||
|
@ -1,218 +0,0 @@
|
||||
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
|
@ -1,22 +0,0 @@
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
|
||||
|
||||
# loaded_model = keras.models.load_model("my_model")
|
||||
|
||||
def image_classification(path, model):
|
||||
class_names = ['door', 'refrigerator', 'shelf']
|
||||
|
||||
img = tf.keras.utils.load_img(
|
||||
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(class_names[np.argmax(score)])
|
||||
return class_names[np.argmax(score)]
|
||||
|
||||
|
Before Width: | Height: | Size: 5.9 KiB |
Before Width: | Height: | Size: 8.6 KiB |
Before Width: | Height: | Size: 7.4 KiB |
Before Width: | Height: | Size: 5.9 KiB |
Before Width: | Height: | Size: 3.0 KiB |
Before Width: | Height: | Size: 9.8 KiB |
Before Width: | Height: | Size: 2.7 KiB |
Before Width: | Height: | Size: 4.5 KiB |
Before Width: | Height: | Size: 9.3 KiB |
Before Width: | Height: | Size: 1.9 KiB |
Before Width: | Height: | Size: 3.2 KiB |
Before Width: | Height: | Size: 3.0 KiB |
Before Width: | Height: | Size: 3.0 KiB |
Before Width: | Height: | Size: 3.7 KiB |
Before Width: | Height: | Size: 2.8 KiB |
Before Width: | Height: | Size: 6.5 KiB |
Before Width: | Height: | Size: 3.6 KiB |
Before Width: | Height: | Size: 2.9 KiB |
Before Width: | Height: | Size: 4.5 KiB |
Before Width: | Height: | Size: 7.5 KiB |
Before Width: | Height: | Size: 3.4 KiB |
Before Width: | Height: | Size: 7.1 KiB |
Before Width: | Height: | Size: 4.1 KiB |
Before Width: | Height: | Size: 4.9 KiB |
Before Width: | Height: | Size: 9.2 KiB |
Before Width: | Height: | Size: 2.7 KiB |
Before Width: | Height: | Size: 8.6 KiB |
Before Width: | Height: | Size: 3.6 KiB |
Before Width: | Height: | Size: 6.4 KiB |
Before Width: | Height: | Size: 8.5 KiB |
Before Width: | Height: | Size: 8.4 KiB |
Before Width: | Height: | Size: 3.7 KiB |
Before Width: | Height: | Size: 6.0 KiB |
Before Width: | Height: | Size: 7.0 KiB |
Before Width: | Height: | Size: 1.2 KiB |
Before Width: | Height: | Size: 6.9 KiB |
Before Width: | Height: | Size: 5.4 KiB |
Before Width: | Height: | Size: 5.4 KiB |
Before Width: | Height: | Size: 5.6 KiB |
Before Width: | Height: | Size: 4.3 KiB |
Before Width: | Height: | Size: 5.7 KiB |
Before Width: | Height: | Size: 2.7 KiB |
Before Width: | Height: | Size: 10 KiB |
Before Width: | Height: | Size: 2.6 KiB |
Before Width: | Height: | Size: 7.9 KiB |
Before Width: | Height: | Size: 12 KiB |
Before Width: | Height: | Size: 4.5 KiB |
Before Width: | Height: | Size: 1.5 KiB |
Before Width: | Height: | Size: 5.7 KiB |
Before Width: | Height: | Size: 6.8 KiB |
Before Width: | Height: | Size: 3.3 KiB |
Before Width: | Height: | Size: 1.5 KiB |
Before Width: | Height: | Size: 7.8 KiB |
Before Width: | Height: | Size: 10 KiB |
Before Width: | Height: | Size: 8.5 KiB |
Before Width: | Height: | Size: 5.8 KiB |
Before Width: | Height: | Size: 8.4 KiB |
Before Width: | Height: | Size: 6.4 KiB |
Before Width: | Height: | Size: 6.8 KiB |
Before Width: | Height: | Size: 5.9 KiB |
Before Width: | Height: | Size: 6.0 KiB |
Before Width: | Height: | Size: 5.5 KiB |
Before Width: | Height: | Size: 7.1 KiB |
Before Width: | Height: | Size: 5.8 KiB |
Before Width: | Height: | Size: 6.7 KiB |
Before Width: | Height: | Size: 5.9 KiB |
Before Width: | Height: | Size: 10 KiB |
Before Width: | Height: | Size: 6.8 KiB |
Before Width: | Height: | Size: 4.2 KiB |
Before Width: | Height: | Size: 5.7 KiB |
Before Width: | Height: | Size: 4.3 KiB |
Before Width: | Height: | Size: 8.3 KiB |
Before Width: | Height: | Size: 5.6 KiB |
Before Width: | Height: | Size: 5.8 KiB |
Before Width: | Height: | Size: 3.7 KiB |
Before Width: | Height: | Size: 2.4 KiB |
Before Width: | Height: | Size: 3.0 KiB |
Before Width: | Height: | Size: 3.8 KiB |
Before Width: | Height: | Size: 5.4 KiB |
Before Width: | Height: | Size: 3.0 KiB |
Before Width: | Height: | Size: 8.5 KiB |
Before Width: | Height: | Size: 4.7 KiB |
Before Width: | Height: | Size: 9.8 KiB |
Before Width: | Height: | Size: 6.9 KiB |
Before Width: | Height: | Size: 9.1 KiB |
Before Width: | Height: | Size: 7.6 KiB |