From 91693d2b19d0c5cdfb0159dd6f190564e80200bf Mon Sep 17 00:00:00 2001
From: Gabriela Piekarska <gabpie2@st.amu.edu.pl>
Date: Thu, 9 Jun 2022 10:12:52 +0200
Subject: [PATCH] Update 'src/board.py'
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łączenie z algorytmem genetycznym
---
 src/board.py | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/src/board.py b/src/board.py
index 4261d97..87e1966 100644
--- a/src/board.py
+++ b/src/board.py
@@ -7,6 +7,7 @@ import pygame
 import snn
 import joblib
 import os
+import gen_algorithms
 
 screen = []
 objectArray = []
@@ -176,6 +177,7 @@ def kb_listen(objectArray, gridLength, path):
     agent = objectArray[0]
     #agent.move(gridLength, path)
 
+
 if __name__ == '__main__':
     pygame.init()  # inicjalizacja modułów, na razie niepotrzebna
     gridSize = 15
@@ -222,6 +224,39 @@ if __name__ == '__main__':
 
     pathPos = 0
     nextCheckpoint = 1
+
+#od tad proboje
+    # parametry
+    num_of_houses = 8  # ilość domków
+    routes_num = 40  # ilość ścieżek, które będziemy generować
+    # rate = 0.3 # do mutacji, by liczby były ładniejsze
+    houses_coordinates = [[7, 4], [3, 10], [8, 10], [4, 5], [1, 2], [10, 4], [13, 14], [6, 9]]  # generowanie losowych współrzędnych między 1, a 9
+    names = np.array(['Dom A', 'Dom B', 'Dom C', 'Dom D', 'Dom E', 'Dom F', 'Dom G', 'Dom H'])  # nazwy domów
+    houses_info = {x: y for x, y in
+                   zip(names, houses_coordinates)}  # zawiera nazwę domu i jego współrzędne X, Y - słownik
+
+    population_set = gen_algorithms.generate_routes(names, routes_num)
+    list_of_sums = gen_algorithms.sums_for_all_routes(population_set, houses_info)
+    progenitor_list = gen_algorithms.selection(population_set, list_of_sums)
+    new_population_set = gen_algorithms.population_mating(progenitor_list)
+    final_mutated_population = gen_algorithms.mutate_population(new_population_set)
+    final_route = [-1, np.inf, np.array([])]  # format listy
+    for i in range(400):
+        list_of_sums = gen_algorithms.sums_for_all_routes(final_mutated_population, houses_info)
+        # zapisujemy najlepsze rozwiązanie
+        if list_of_sums.min() < final_route[1]:
+            final_route[0] = i
+            final_route[1] = list_of_sums.min()
+            final_route[2] = np.array(final_mutated_population)[list_of_sums.min() == list_of_sums]
+
+        progenitor_list = gen_algorithms.selection(population_set, list_of_sums)
+        new_population_set = gen_algorithms.population_mating(progenitor_list)
+
+        final_mutated_population = gen_algorithms.mutate_population(new_population_set)
+    print("tutaj")
+    print(final_route)
+    print(houses_info) # tu nazwa domu i lokalizacja
+
     while True:
         agent_x, agent_y = astarPath[pathPos]
         checkpoint_x, checkpoint_y = checkpoints[nextCheckpoint]