tsp v2

genetic_algorithm/TSP.py

@@ -1,10 +1,13 @@
 import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
+from path_search_algorthms import a_star
+from decision_tree import decisionTree
 
 # klasa tworząca miasta czy też śmietniki
 class City:
-    def __init__(self, x, y):
+    def __init__(self, x, y, dist):
         self.x = x
         self.y = y
+        # self.dist = distance
     
     def distance(self, city):
         xDis = abs(self.x - city.x)

main.py

@@ -17,7 +17,7 @@ from path_search_algorthms import a_star, a_star_utils
 from decision_tree import decisionTree
 from NeuralNetwork import prediction
 from game_objects.trash import Trash
-
+from genetic_algorithm import TSP
 from game_objects import aiPlayer
 import itertools
 
@@ -155,7 +155,14 @@ class Game():
         # print(self.positive_actions[0])
 
         # self.t.startAiController(self.positive_actions[0])
+    def init_TSP(self):
+        city_list = self.positive_decision
+        dist = a_star.path_dist
         
+        for i in range(0,25):
+            city_list.append(TSP.City(x=int(random.random() * 200), y=int(random.random() * 200)))
+
+        tsp_list = TSP.geneticAlgorithm(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
 
     def load_data(self):
         game_folder = path.dirname(__file__)