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tsp v4.1a, getting distance from astar not from pythagoras

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trzmielewskiR 2022-06-10 04:21:42 +02:00
parent 75746f077e
commit 65f469465a
6 changed files with 95 additions and 73 deletions
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60
decision_tree/tree_as_txt.txt
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@ -4,12 +4,12 @@
| | | |--- class: 0
| | |--- feature_0 > 1.50
| | | |--- feature_3 <= 3.50
| | | | |--- feature_4 <= 2.50
| | | | |--- feature_2 <= 2.50
| | | | | |--- class: 1
| | | | |--- feature_4 > 2.50
| | | | | |--- feature_2 <= 2.50
| | | | |--- feature_2 > 2.50
| | | | | |--- feature_4 <= 2.50
| | | | | | |--- class: 1
| | | | | |--- feature_2 > 2.50
| | | | | |--- feature_4 > 2.50
| | | | | | |--- class: 0
| | | |--- feature_3 > 3.50
| | | | |--- feature_3 <= 4.50
@ -26,18 +26,18 @@
| | | | | | | |--- feature_1 > 1.50
| | | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 2.50
| | | | | | | |--- feature_2 <= 2.50
| | | | | | | |--- feature_4 <= 2.50
| | | | | | | | |--- class: 1
| | | | | | | |--- feature_2 > 2.50
| | | | | | | | |--- feature_4 <= 2.50
| | | | | | | |--- feature_4 > 2.50
| | | | | | | | |--- feature_2 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_4 > 2.50
| | | | | | | | |--- feature_2 > 2.50
| | | | | | | | | |--- class: 0
| | | | | |--- feature_1 > 2.50
| | | | | | |--- feature_1 <= 3.50
| | | | | | | |--- feature_0 <= 3.50
| | | | | | | | |--- class: 0
| | | | | | | |--- feature_0 > 3.50
| | | | | | |--- feature_0 <= 3.50
| | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 3.50
| | | | | | | |--- feature_1 <= 3.50
| | | | | | | | |--- feature_2 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_2 > 2.50
@ -45,8 +45,8 @@
| | | | | | | | | | |--- class: 1
| | | | | | | | | |--- feature_4 > 2.00
| | | | | | | | | | |--- class: 0
| | | | | | |--- feature_1 > 3.50
| | | | | | | |--- class: 0
| | | | | | | |--- feature_1 > 3.50
| | | | | | | | |--- class: 0
| | | | |--- feature_3 > 4.50
| | | | | |--- class: 0
| |--- feature_4 > 3.50
@ -59,13 +59,13 @@
| | | | | | |--- class: 1
| | | | |--- feature_3 > 3.50
| | | | | |--- feature_1 <= 2.50
| | | | | | |--- feature_3 <= 4.50
| | | | | | | |--- feature_0 <= 2.50
| | | | | | | | |--- class: 0
| | | | | | | |--- feature_0 > 2.50
| | | | | | | | |--- class: 1
| | | | | | |--- feature_3 > 4.50
| | | | | | |--- feature_0 <= 2.50
| | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 2.50
| | | | | | | |--- feature_3 <= 4.50
| | | | | | | | |--- class: 1
| | | | | | | |--- feature_3 > 4.50
| | | | | | | | |--- class: 0
| | | | | |--- feature_1 > 2.50
| | | | | | |--- class: 0
| | | |--- feature_4 > 4.50
@ -73,19 +73,19 @@
| | |--- feature_2 > 1.50
| | | |--- class: 0
|--- feature_2 > 3.50
| |--- feature_1 <= 1.50
| | |--- feature_4 <= 1.50
| |--- feature_4 <= 1.50
| | |--- feature_1 <= 1.50
| | | |--- feature_2 <= 4.50
| | | | |--- feature_3 <= 4.50
| | | | | |--- feature_0 <= 1.50
| | | | | | |--- class: 0
| | | | | |--- feature_0 > 1.50
| | | | | | |--- class: 1
| | | | |--- feature_3 > 4.50
| | | | |--- feature_0 <= 1.50
| | | | | |--- class: 0
| | | | |--- feature_0 > 1.50
| | | | | |--- feature_3 <= 4.50
| | | | | | |--- class: 1
| | | | | |--- feature_3 > 4.50
| | | | | | |--- class: 0
| | | |--- feature_2 > 4.50
| | | | |--- class: 0
| | |--- feature_4 > 1.50
| | |--- feature_1 > 1.50
| | | |--- class: 0
| |--- feature_1 > 1.50
| |--- feature_4 > 1.50
| | |--- class: 0

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decision_tree/tree_model
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27
genetic_algorithm/TSP.py
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@ -1,15 +1,22 @@
import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
from path_search_algorthms import a_star
from path_search_algorthms.a_star import get_cost
from decision_tree import decisionTree
from settings import *
import math
# klasa tworząca miasta czy też śmietniki
class City:
def __init__(self, x, y):
self.x = x
self.y = y
#self.array = array
# self.dist = distance
#dystans to d = sqrt(x^2 + y^2)
def distance(self, city):
#getting distance by astar gives wrong final distance (intial = final)
#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)
xDis = abs(self.x - city.x)
yDis = abs(self.y - city.y)
distance = np.sqrt((xDis ** 2) + (yDis ** 2))
@ -18,6 +25,7 @@ class City:
def __repr__(self):
return "(" + str(self.x) + "," + str(self.y) + ")"
# fitness function,
# inverse of route distance
# we want to minimize distance so the larger the fitness the better
@ -46,11 +54,13 @@ class Fitness:
self.fitness = 1 / float(self.routeDistance())
return self.fitness
# creating one individual - single route from city to city (trash to trash)
def createRoute(cityList):
route = random.sample(cityList, len(cityList))
return route
# creating initial population of given size
def initialPopulation(popSize, cityList):
population = []
@ -59,6 +69,7 @@ def initialPopulation(popSize, cityList):
population.append(createRoute(cityList))
return population
# ranking fitness of given route, output is ordered list with route id and its fitness score
def rankRoutes(population):
fitnessResults = {}
@ -66,6 +77,7 @@ def rankRoutes(population):
fitnessResults[i] = Fitness(population[i]).routeFitness()
return sorted(fitnessResults.items(), key=operator.itemgetter(1), reverse=True)
# selecting "mating pool"
# we are using here "Firness proportionate selection", its fitness-weighted probability of being selected
# moreover we are using elitism to ensure that the best of the best will preserve
@ -79,7 +91,8 @@ def selection(popRanked, eliteSize):
for i in range(0, eliteSize): # elitism
selectionResults.append(popRanked[i][0])
for i in range(0, len(popRanked) - eliteSize): # comparing randomly drawn number to weights for selection for mating pool
for i in range(0,
len(popRanked) - eliteSize): # comparing randomly drawn number to weights for selection for mating pool
pick = 100 * random.random()
for i in range(0, len(popRanked)):
if pick <= df.iat[i, 3]:
@ -87,6 +100,7 @@ def selection(popRanked, eliteSize):
break
return selectionResults # returns list of route IDs
# creating mating pool from list of routes IDs from "selection"
def matingPool(population, selectionResults):
matingpool = []
@ -95,6 +109,7 @@ def matingPool(population, selectionResults):
matingpool.append(population[index])
return matingpool
# creating new generation
# ordered crossover bc we need to include all locations exactly one time
# randomly selecting a subset of the first parent string and then filling the remainder of route
@ -118,6 +133,7 @@ def breed(parent1, parent2):
child = childP1 + childP2
return child
# creating whole offspring population
def breedPopulation(matingpool, eliteSize):
children = []
@ -134,6 +150,7 @@ def breedPopulation(matingpool, eliteSize):
children.append(child)
return children
# using swap mutation
# with specified low prob we swap two cities in route
def mutate(individual, mutationRate):
@ -148,6 +165,7 @@ def mutate(individual, mutationRate):
individual[swapWith] = city1
return individual
# extending mutate function to run through new pop
def mutatePopulation(population, mutationRate):
mutatedPop = []
@ -157,6 +175,7 @@ def mutatePopulation(population, mutationRate):
mutatedPop.append(mutatedInd)
return mutatedPop
# creating new generation
def nextGeneration(currentGen, eliteSize, mutationRate):
popRanked = rankRoutes(currentGen) # rank routes in current gen
@ -179,18 +198,18 @@ def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations):
bestRoute = pop[bestRouteIndex]
return bestRoute
# tutaj ma być lista kordów potencjalnych śmietników z drzewa decyzyjnego
cityList = []
# for i in range(0,25):
# cityList.append(City(x=int(random.random() * 200), y=int(random.random() * 200)))
# geneticAlgorithm(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
# plotting the progress
def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations):

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last_map.nparr
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1
main.py
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@ -166,6 +166,7 @@ class Game():
for i in self.positive_decision:
trash_x, trash_y = i.get_coords()
# city_list.append(TSP.City(x=int(trash_x), y=int(trash_y), array=self.mapArray))
city_list.append(TSP.City(x=int(trash_x), y=int(trash_y)))

2
path_search_algorthms/a_star.py
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@ -4,6 +4,8 @@ from path_search_algorthms import a_star_utils as utils
def get_cost(start_x: int, start_y: int, target_x: int, target_y: int, array):
actions = search_path(start_x, start_y, utils.Rotation.NONE, target_x, target_y, array)
if actions is None:
return 1
return len(actions)