随机游走 & ortools demo

1 files changed, 217 insertions, 0 deletions

diff --git a/ga_demo/tsp_demo.py b/ga_demo/tsp_demo.py
new file mode 100644
index 0000000..5a5a406
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+++ b/ga_demo/tsp_demo.py
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+import numpy as np
+import random
+import operator
+import pandas as pd
+import matplotlib.pyplot as plt
+from operator import itemgetter
+
+
+class City:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+    def distance(self, city):
+        x_dis = abs(self.x - city.x)
+        y_dis = abs(self.y - city.y)
+        distance = np.sqrt((x_dis ** 2) + (y_dis ** 2))
+        return distance
+
+    def __repr__(self):
+        return "(" + str(self.x) + "," + str(self.y) + ")"
+
+
+# 越小越好
+class Fitness:
+    def __init__(self, route):
+        self.route = route
+        self.distance = 0
+        self.fitness = 0.0
+
+    def route_distance(self):
+        if self.distance == 0:
+            path_distance = 0
+            for i in range(0, len(self.route)):
+                from_city = self.route[i]
+                to_city = None
+                if i + 1 < len(self.route):
+                    to_city = self.route[i + 1]
+                else:
+                    to_city = self.route[0]
+                path_distance += from_city.distance(to_city)
+            self.distance = path_distance
+        return self.distance
+
+    def route_fitness(self):
+        if self.fitness == 0:
+            self.fitness = 1 / float(self.route_distance())
+        return self.fitness
+
+
+def createRoute(cityList):
+    route = random.sample(cityList, len(cityList))
+    return route
+
+
+def initialPopulation(popSize, cityList):
+    population = []
+
+    for i in range(0, popSize):
+        population.append(createRoute(cityList))
+    return population
+
+
+def rankRoutes(population):
+    fitnessResults = {}
+    for i in range(0,len(population)):
+        fitnessResults[i] = Fitness(population[i]).route_fitness()
+    return sorted(fitnessResults.items(), key=operator.itemgetter(1), reverse=True)
+
+
+def selection(popRanked, eliteSize):
+    selectionResults = []
+    df = pd.DataFrame(np.array(popRanked), columns=["Index", "Fitness"])
+    df['cum_sum'] = df.Fitness.cumsum()
+    df['cum_perc'] = 100 * df.cum_sum / df.Fitness.sum()
+
+    for i in range(0, eliteSize):
+        selectionResults.append(popRanked[i][0])
+    for i in range(0, len(popRanked) - eliteSize):
+        pick = 100 * random.random()
+        for i in range(0, len(popRanked)):
+            if pick <= df.iat[i, 3]:
+                selectionResults.append(popRanked[i][0])
+                break
+    return selectionResults
+
+
+def matingPool(population, selectionResults):
+    matingpool = []
+    for i in range(0, len(selectionResults)):
+        index = selectionResults[i]
+        matingpool.append(population[index])
+    return matingpool
+
+
+def breed(parent1, parent2):
+    child = []
+    childP1 = []
+    childP2 = []
+
+    geneA = int(random.random() * len(parent1))
+    geneB = int(random.random() * len(parent1))
+
+    startGene = min(geneA, geneB)
+    endGene = max(geneA, geneB)
+
+    # save_sub_city_list = parent1[startGene: endGene]
+    # target_index = 0
+    # for i in range(len(parent2)):
+    #     while startGene < len(child) < endGene-1:
+    #         child.append(save_sub_city_list[target_index])
+    #         target_index += 1
+    #         continue
+    #     if parent2[i] not in save_sub_city_list:
+    #         child.append(parent2[i])
+    for i in range(startGene, endGene):
+        childP1.append(parent1[i])
+
+    childP2 = [item for item in parent2 if item not in childP1]
+
+    child = childP1 + childP2
+    return child
+
+
+def breedPopulation(matingpool, eliteSize):
+    children = []
+    length = len(matingpool) - eliteSize
+    pool = random.sample(matingpool, len(matingpool))
+
+    for i in range(0, eliteSize):
+        children.append(matingpool[i])
+
+    for i in range(0, length):
+        child = breed(pool[i], pool[len(matingpool) - i - 1])
+        children.append(child)
+    return children
+
+
+def mutate(individual, mutationRate):
+    for swapped in range(len(individual)):
+        if (random.random() < mutationRate):
+            swapWith = int(random.random() * len(individual))
+
+            city1 = individual[swapped]
+            city2 = individual[swapWith]
+
+            individual[swapped] = city2
+            individual[swapWith] = city1
+    return individual
+
+
+def mutatePopulation(population, mutationRate):
+    mutatedPop = []
+
+    for ind in range(0, len(population)):
+        mutatedInd = mutate(population[ind], mutationRate)
+        mutatedPop.append(mutatedInd)
+    return mutatedPop
+
+
+
+def nextGeneration(currentGen, eliteSize, mutationRate):
+    popRanked = rankRoutes(currentGen)
+    selectionResults = selection(popRanked, eliteSize)
+    matingpool = matingPool(currentGen, selectionResults)
+    children = breedPopulation(matingpool, eliteSize)
+    nextGeneration = mutatePopulation(children, mutationRate)
+    return nextGeneration
+
+
+def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations):
+    pop = initialPopulation(popSize, population)
+    print("Initial distance: " + str(1 / rankRoutes(pop)[0][1]))
+
+    for i in range(0, generations):
+        pop = nextGeneration(pop, eliteSize, mutationRate)
+        print(f"generation: {i}, distance: {1 / rankRoutes(pop)[0][1]}")
+
+    print("Final distance: " + str(1 / rankRoutes(pop)[0][1]))
+    bestRouteIndex = rankRoutes(pop)[0][0]
+    bestRoute = pop[bestRouteIndex]
+    return bestRoute
+
+
+def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations):
+    pop = initialPopulation(popSize, population)
+    progress = []
+    progress.append(1 / rankRoutes(pop)[0][1])
+
+    for i in range(0, generations):
+        pop = nextGeneration(pop, eliteSize, mutationRate)
+        progress.append(1 / rankRoutes(pop)[0][1])
+
+    plt.plot(progress)
+    plt.ylabel('Distance')
+    plt.xlabel('Generation')
+    plt.show()
+
+
+if __name__ == '__main__':
+
+    cityList = []
+
+    for i in range(0, 25):
+        cityList.append(City(x=int(random.random() * 200), y=int(random.random() * 200)))
+
+    # plt.scatter(list(map(lambda city: city.x, cityList)), list(map(lambda city: city.y, cityList)))
+    # plt.show()
+    best_route = geneticAlgorithm(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=400)
+    # geneticAlgorithmPlot(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=500)
+    print()
+    # for i in range(0, len(best_route), 2):
+    x = list(map(lambda city: city.x, best_route))
+    y = list(map(lambda city: city.y, best_route))
+    plt.scatter(x, y)
+    plt.plot(x, y, 'ro-')
+    plt.show()