211 lines
7.2 KiB
Python
211 lines
7.2 KiB
Python
import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
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from path_search_algorthms import a_star
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from decision_tree import decisionTree
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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, dist):
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self.x = x
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self.y = y
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# self.dist = distance
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def distance(self, city):
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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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# fitness function,
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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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self.route = route
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self.distance = 0
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self.fitness= 0.0
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def routeDistance(self):
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if self.distance ==0:
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pathDistance = 0
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for i in range(0, len(self.route)):
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fromCity = self.route[i]
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toCity = None
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if i + 1 < len(self.route): # for returning to point 0?
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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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self.distance = pathDistance
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return self.distance
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def routeFitness(self):
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if self.fitness == 0:
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self.fitness = 1 / float(self.routeDistance())
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return self.fitness
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# creating one individual - single route from city to city (trash to trash)
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def createRoute(cityList):
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route = random.sample(cityList, len(cityList))
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return route
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# creating initial population of given size
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def initialPopulation(popSize, cityList):
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population = []
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for i in range(0, popSize):
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population.append(createRoute(cityList))
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return population
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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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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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return sorted(fitnessResults.items(), key = operator.itemgetter(1), reverse = True)
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# selecting "mating pool"
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# we are using here "Firness proportionate selection", its fitness-weighted probability of being selected
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# moreover we are using elitism to ensure that the best of the best will preserve
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def selection(popRanked, eliteSize):
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selectionResults = []
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# roulette wheel
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df = pd.DataFrame(np.array(popRanked), columns=["Index","Fitness"])
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df['cum_sum'] = df.Fitness.cumsum()
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df['cum_perc'] = 100*df.cum_sum/df.Fitness.sum()
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for i in range(0, eliteSize): # elitism
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selectionResults.append(popRanked[i][0])
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for i in range(0, len(popRanked) - eliteSize): # comparing randomly drawn number to weights for selection for mating pool
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pick = 100*random.random()
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for i in range(0, len(popRanked)):
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if pick <= df.iat[i,3]:
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selectionResults.append(popRanked[i][0])
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break
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return selectionResults # returns list of route IDs
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# creating mating pool from list of routes IDs from "selection"
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def matingPool(population, selectionResults):
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matingpool = []
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for i in range(0, len(selectionResults)):
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index = selectionResults[i]
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matingpool.append(population[index])
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return matingpool
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# creating new generation
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# ordered crossover bc we need to include all locations exactly one time
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# randomly selecting a subset of the first parent string and then filling the remainder of route
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# with genes from the second parent in the order in which they appear, without duplicating any genes from the first parent
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def breed(parent1, parent2):
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child = []
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childP1 = []
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childP2 = []
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geneA = int(random.random() * len(parent1))
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geneB = int(random.random() * len(parent1))
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startGene = min(geneA, geneB)
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endGene = max(geneA, geneB)
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for i in range(startGene, endGene): # ordered crossover
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childP1.append(parent1[i])
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childP2 = [item for item in parent2 if item not in childP1]
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child = childP1 + childP2
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return child
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# creating whole offspring population
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def breedPopulation(matingpool, eliteSize):
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children = []
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length = len(matingpool) - eliteSize
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pool = random.sample(matingpool, len(matingpool))
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# using elitism to retain best genes (routes)
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for i in range(0,eliteSize):
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children.append(matingpool[i])
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# filling rest generation
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for i in range(0, length):
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child = breed(pool[i], pool[len(matingpool)-i-1])
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children.append(child)
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return children
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# using swap mutation
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# with specified low prob we swap two cities in route
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def mutate(individual, mutationRate):
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for swapped in range(len(individual)):
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if(random.random() < mutationRate):
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swapWith = int(random.random() * len(individual))
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city1 = individual[swapped]
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city2 = individual[swapWith]
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individual[swapped] = city2
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individual[swapWith] = city1
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return individual
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# extending mutate function to run through new pop
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def mutatePopulation(population, mutationRate):
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mutatedPop = []
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for ind in range(0, len(population)):
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mutatedInd = mutate(population[ind], mutationRate)
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mutatedPop.append(mutatedInd)
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return mutatedPop
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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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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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nextGeneration = mutatePopulation(children, mutationRate) # applying mutation to new gen
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return nextGeneration
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def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations):
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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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for i in range(0, generations):
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pop = nextGeneration(pop, eliteSize, mutationRate)
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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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bestRoute = pop[bestRouteIndex]
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return bestRoute
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# tutaj ma być lista kordów potencjalnych śmietników z drzewa decyzyjnego
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cityList = []
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for i in range(0,25):
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cityList.append(City(x=int(random.random() * 200), y=int(random.random() * 200)))
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geneticAlgorithm(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
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# plotting the progress
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def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations):
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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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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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plt.plot(progress)
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plt.ylabel('Distance')
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plt.xlabel('Generation')
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plt.show()
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# geneticAlgorithmPlot(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000) |