GameModel.py

@@ -18,8 +18,10 @@ 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
 
@@ -179,8 +181,18 @@ 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()
+            readyTree = tree.get_decision_tree()
+
+
+            # TODO CLIENT RECOGNITION
+
+            # GENERICS SORTING
+            genericOrder: GeneticOrder = GeneticOrder(orders)
+            self.orderList = genericOrder.get_orders_sorted(orders)
+
+
             print("FINISHED CLIENT ORDER SORTING")
             self.phase = Phase.EXECUTION
 

genetic_order/GeneticOrder.py

@@ -15,10 +15,12 @@ class GeneticOrder:
     best_fit_super_special = 20
 
     population_size = 200
-    number_of_populations = 1000
+    number_of_populations = 10000
 
-    punish_low = 5
-    punish_med = 3
+    punish_low = 500
+    punish_med = 300
+
+    punish_sum = 50
 
     def __init__(self, orders: [Order]) -> None:
         self.orders = orders
@@ -35,10 +37,10 @@ class GeneticOrder:
         return GeneticMutationType.CROSS
 
     def mutation(self, population: [int]) -> [int]:
-        x = random.randint(0, len(population)-1)
-        y = random.randint(0, len(population)-1)
+        x = random.randint(0, len(population) - 1)
+        y = random.randint(0, len(population) - 1)
         while x == y:
-            y = random.randint(0, len(population)-1)
+            y = random.randint(0, len(population) - 1)
 
         result = population
 
@@ -46,13 +48,13 @@ class GeneticOrder:
         result[x] = population[y]
         result[y] = pom
 
-        if(result[x] == result[y]):
+        if (result[x] == result[y]):
             print("PIZDA I CHUJ")
 
         return result
 
     def cross(self, population: [int]) -> [int]:
-        x = random.randint(1, len(population)-1)
+        x = random.randint(1, len(population) - 1)
 
         result = []
 
@@ -63,7 +65,7 @@ class GeneticOrder:
 
     def reverse(self, population: [int]) -> [int]:
         x = random.randint(0, len(population))
-        y = random.randint(0, len(population)-1)
+        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)
@@ -97,18 +99,30 @@ class GeneticOrder:
         #
         # 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 :
+            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:
@@ -123,7 +137,6 @@ class GeneticOrder:
 
         return counter
 
-
     def evaluate(self, member: [int]) -> int:
         # result = 0
         # for i in range(len(self.orders) - 1):
@@ -196,11 +209,10 @@ class GeneticOrder:
             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
+        return result

main.py

@@ -106,8 +106,8 @@ if __name__ == '__main__':
         return order.id
 
 
-    punish_low = 5
-    punish_med = 3
+    punish_low = 500
+    punish_med = 300
     def sum_wrong(member: [Order]) -> int:
         last_high = 0
         last_med = 0
@@ -152,17 +152,17 @@ if __name__ == '__main__':
 
     print("SIEMA before: ")
     sum_wrong(orders)
-    # for i in orders:
-    #     # print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
-    #     print("id:", i.id, "priority:", i.priority)
+    for i in orders:
+        print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
+        # print("id:", i.id, "priority:", i.priority)
 
     newOrders = test.get_orders_sorted(orders)
 
     print("NAURA after:")
     sum_wrong(newOrders)
-    # for i in newOrders:
-    #     # print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
-    #     print("id:", i.id, "priority:", i.priority)
+    for i in newOrders:
+        print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
+        # print("id:", i.id, "priority:", i.priority)