Merge pull request 'order_visualization' (#5) from order_visualization into master

Reviewed-on: #5

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))

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:

InitialStateFactory.py

@@ -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

ItemDisplayAgent.py

@@ -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)

PatchType.py

@@ -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

data/GameConstants.py

@@ -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

data/Item.py

@@ -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:

data/Order.py

@@ -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)

data/enum/GeneticMutationType.py

@@ -0,0 +1,7 @@
+from enum import Enum
+
+
+class GeneticMutationType(Enum):
+    MUTATION = 1
+    CROSS = 2
+    REVERSE = 3

decision/State.py

@@ -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]
                  ):

decision/test/ForkliftActions.py

@@ -0,0 +1,9 @@
+from data.GameConstants import GameConstants
+
+
+class ForkliftActions:
+
+    def __init__(self, game: GameConstants,
+                 ) -> None:
+        self.game = game
+

genetic_order/GeneticOrder.py

@@ -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

imageClasification/Classificator.py

@@ -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)]
 
 

imageClasification/usedAssets/TrainClassificator.py

@@ -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__()

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()

pathfinding/PathfinderOnStates.py

@@ -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

tree/DecisionTree.py

@@ -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))

visualization/DisplayOrder.py

@@ -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

visualization/DisplayOrderList.py

@@ -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>"