Merge branch 'genetic_algorithm' of https://git.wmi.amu.edu.pl/s462072/Trashmaster into genetic_algorithm
This commit is contained in:
commit
f55886a199
@ -4,12 +4,21 @@
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<<<<<<< HEAD
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=======
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| | | | |--- feature_2 <= 2.50
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| | | | | | |--- class: 1
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| | | | | |--- feature_4 > 2.50
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>>>>>>> a16ec52642067a2be0b41a5c3bcab24193122343
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@ -26,24 +35,42 @@
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| | | | | | | |--- feature_1 > 1.50
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| | | | | | | | |--- class: 0
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| | | | | | |--- feature_0 > 2.50
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<<<<<<< HEAD
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| | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_2 > 2.50
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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=======
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| | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_2 > 2.50
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>>>>>>> a16ec52642067a2be0b41a5c3bcab24193122343
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| | | | | | | | | |--- class: 0
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- feature_1 <= 3.50
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| | | | | | | |--- feature_0 <= 3.50
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_0 > 3.50
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<<<<<<< HEAD
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- feature_4 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_4 > 2.00
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=======
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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| | | | | | | | | |--- feature_2 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_2 > 2.00
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>>>>>>> a16ec52642067a2be0b41a5c3bcab24193122343
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| | | | | | | | | | |--- class: 0
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| | | | | | |--- feature_1 > 3.50
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| | | | | | | |--- class: 0
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@ -59,6 +86,7 @@
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| | | | | | |--- class: 1
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| | | | |--- feature_3 > 3.50
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| | | | | |--- feature_1 <= 2.50
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<<<<<<< HEAD
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | | |--- class: 0
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| | | | | | |--- feature_0 > 2.50
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@ -66,6 +94,15 @@
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_3 > 4.50
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| | | | | | | | |--- class: 0
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=======
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| | | | | | |--- feature_3 <= 4.50
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| | | | | | | |--- feature_0 <= 2.50
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_0 > 2.50
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| | | | | | | | |--- class: 1
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| | | | | | |--- feature_3 > 4.50
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| | | | | | | |--- class: 0
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>>>>>>> a16ec52642067a2be0b41a5c3bcab24193122343
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- class: 0
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| | | |--- feature_4 > 4.50
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@ -76,6 +113,7 @@
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| |--- feature_1 <= 1.50
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| | |--- feature_4 <= 1.50
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| | | |--- feature_2 <= 4.50
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<<<<<<< HEAD
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| | | | |--- feature_0 <= 1.50
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| | | | | |--- class: 0
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| | | | |--- feature_0 > 1.50
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@ -83,6 +121,15 @@
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| | | | | | |--- class: 1
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| | | | | |--- feature_3 > 4.50
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| | | | | | |--- class: 0
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=======
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| | | | |--- feature_3 <= 4.50
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| | | | | |--- feature_0 <= 1.50
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| | | | | | |--- class: 0
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| | | | | |--- feature_0 > 1.50
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| | | | | | |--- class: 1
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| | | | |--- feature_3 > 4.50
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| | | | | |--- class: 0
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>>>>>>> a16ec52642067a2be0b41a5c3bcab24193122343
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| | | |--- feature_2 > 4.50
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| | | | |--- class: 0
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| | |--- feature_4 > 1.50
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@ -6,21 +6,23 @@ import math
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# klasa tworząca miasta czy też śmietniki
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class City:
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def __init__(self, x, y):
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def __init__(self, x, y, array):
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self.x = x
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self.y = y
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#self.array = array
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self.array = array
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# self.dist = distance
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#dystans to d = sqrt(x^2 + y^2)
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def distance(self, city):
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#getting distance by astar gives wrong final distance (intial = final)
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#return get_cost(math.floor(self.x / TILESIZE), math.floor(self.y / TILESIZE), math.floor(city.x / TILESIZE), math.floor(city.y / TILESIZE), self.array)
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xDis = abs(self.x - city.x)
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yDis = abs(self.y - city.y)
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distance = np.sqrt((xDis ** 2) + (yDis ** 2))
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return distance
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return get_cost(math.floor(self.x / TILESIZE), math.floor(self.y / TILESIZE), math.floor(city.x / TILESIZE), math.floor(city.y / TILESIZE), self.array)
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# xDis = abs(self.x - city.x)
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# yDis = abs(self.y - city.y)
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# distance = np.sqrt((xDis ** 2) + (yDis ** 2))
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# return distance
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def __repr__(self):
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return "(" + str(self.x) + "," + str(self.y) + ")"
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@ -30,10 +32,11 @@ class City:
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# inverse of route distance
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# we want to minimize distance so the larger the fitness the better
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class Fitness:
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def __init__(self, route):
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def __init__(self, route, distanceArray):
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self.route = route
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self.distance = 0
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self.fitness = 0.0
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self.distanceArray = distanceArray
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def routeDistance(self):
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if self.distance == 0:
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@ -45,7 +48,8 @@ class Fitness:
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toCity = self.route[i + 1]
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else:
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toCity = self.route[0]
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pathDistance += fromCity.distance(toCity)
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# pathDistance += fromCity.distance(toCity)
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pathDistance += self.distanceArray[str(fromCity.x)+" "+str(fromCity.y)+" "+str(toCity.x)+" "+str(toCity.y)]
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self.distance = pathDistance
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return self.distance
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@ -71,10 +75,10 @@ def initialPopulation(popSize, cityList):
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# ranking fitness of given route, output is ordered list with route id and its fitness score
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def rankRoutes(population):
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def rankRoutes(population, distanceArray):
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fitnessResults = {}
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for i in range(0, len(population)):
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fitnessResults[i] = Fitness(population[i]).routeFitness()
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fitnessResults[i] = Fitness(population[i], distanceArray).routeFitness()
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return sorted(fitnessResults.items(), key=operator.itemgetter(1), reverse=True)
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@ -177,8 +181,8 @@ def mutatePopulation(population, mutationRate):
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# creating new generation
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def nextGeneration(currentGen, eliteSize, mutationRate):
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popRanked = rankRoutes(currentGen) # rank routes in current gen
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def nextGeneration(currentGen, eliteSize, mutationRate, distanceArray):
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popRanked = rankRoutes(currentGen, distanceArray) # rank routes in current gen
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selectionResults = selection(popRanked, eliteSize) # determining potential parents
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matingpool = matingPool(currentGen, selectionResults) # creating mating pool
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children = breedPopulation(matingpool, eliteSize) # creating new gen
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@ -186,15 +190,15 @@ def nextGeneration(currentGen, eliteSize, mutationRate):
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return nextGeneration
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def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations):
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def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations, distanceArray):
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pop = initialPopulation(popSize, population)
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print("Initial distance: " + str(1 / rankRoutes(pop)[0][1]))
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print("Initial distance: " + str(1 / rankRoutes(pop, distanceArray)[0][1]))
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for i in range(0, generations):
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pop = nextGeneration(pop, eliteSize, mutationRate)
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pop = nextGeneration(pop, eliteSize, mutationRate, distanceArray)
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print("Final distance: " + str(1 / rankRoutes(pop)[0][1]))
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bestRouteIndex = rankRoutes(pop)[0][0]
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print("Final distance: " + str(1 / rankRoutes(pop, distanceArray)[0][1]))
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bestRouteIndex = rankRoutes(pop, distanceArray)[0][0]
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bestRoute = pop[bestRouteIndex]
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return bestRoute
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@ -212,18 +216,29 @@ cityList = []
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# plotting the progress
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def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations):
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def distanceFromCityToCity(cityFrom, city, array):
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return get_cost(math.floor(cityFrom.x / TILESIZE), math.floor(cityFrom.y / TILESIZE), math.floor(city.x / TILESIZE), math.floor(city.y / TILESIZE), array)
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def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations, array):
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a_star_distances = {}
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for city in population:
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for target in population:
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if city == target:
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continue
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else:
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a_star_distances[str(city.x)+" "+str(city.y)+" "+str(target.x)+" "+str(target.y)] = distanceFromCityToCity(city, target, array)
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pop = initialPopulation(popSize, population)
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progress = []
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progress.append(1 / rankRoutes(pop)[0][1])
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print("Initial distance: " + str(1 / rankRoutes(pop)[0][1]))
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progress.append(1 / rankRoutes(pop, a_star_distances)[0][1])
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print("Initial distance: " + str(1 / rankRoutes(pop, a_star_distances)[0][1]))
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for i in range(0, generations):
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pop = nextGeneration(pop, eliteSize, mutationRate)
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progress.append(1 / rankRoutes(pop)[0][1])
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pop = nextGeneration(pop, eliteSize, mutationRate, a_star_distances)
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progress.append(1 / rankRoutes(pop, a_star_distances)[0][1])
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print("Final distance: " + str(1 / rankRoutes(pop)[0][1]))
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bestRouteIndex = rankRoutes(pop)[0][0]
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print("Final distance: " + str(1 / rankRoutes(pop, a_star_distances)[0][1]))
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bestRouteIndex = rankRoutes(pop, a_star_distances)[0][0]
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bestRoute = pop[bestRouteIndex]
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plt.plot(progress)
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10
main.py
10
main.py
@ -148,13 +148,15 @@ class Game():
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result = prediction.getPrediction(dir + '/' + file, 'trained_nn_20.pth')
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img = pg.image.load(dir + '/' + file).convert_alpha()
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img = pg.transform.scale(img, (128, 128))
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trash = Trash(img, 0, 0, 128, 128)
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offset_x, offset_y = self.camera.offset()
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trash = Trash(img, math.floor(-offset_x * TILESIZE), math.floor(-offset_y * TILESIZE), 128, 128)
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self.trashDisplay.empty()
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self.trashDisplay.add(trash)
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self.text_display = result
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self.draw()
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# print(result + ' ' + file)
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pg.time.wait(100)
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self.text_display = ''
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self.trashDisplay.empty()
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self.draw()
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# print(self.positive_actions[0])
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@ -167,11 +169,11 @@ class Game():
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for i in self.positive_decision:
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trash_x, trash_y = i.get_coords()
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# city_list.append(TSP.City(x=int(trash_x), y=int(trash_y), array=self.mapArray))
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city_list.append(TSP.City(x=int(trash_x), y=int(trash_y)))
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city_list.append(TSP.City(x=trash_x, y=trash_y, array=self.mapArray))
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# dist = a_star.get_cost
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self.tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=300)
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self.tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=300, array=self.mapArray)
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print(self.tsp_list)
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def load_data(self):
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@ -4,8 +4,11 @@ from path_search_algorthms import a_star_utils as utils
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def get_cost(start_x: int, start_y: int, target_x: int, target_y: int, array):
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actions = search_path(start_x, start_y, utils.Rotation.NONE, target_x, target_y, array)
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print('length')
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if actions is None:
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print('0')
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return 1
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print(len(actions))
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return len(actions)
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Reference in New Issue
Block a user