TSP v5
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| | | | | | | |--- class: 0
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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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| | | | | | | | | | |--- class: 0
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| |--- feature_4 > 3.50
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| | |--- feature_2 <= 1.50
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| | | |--- feature_4 <= 4.50
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| | | | | | |--- class: 0
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| | | | | | | |--- class: 0
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| | | | | | | |--- feature_3 <= 4.50
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_3 > 4.50
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| | |--- feature_2 > 1.50
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| | | |--- class: 0
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|--- feature_2 > 3.50
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| |--- feature_4 <= 1.50
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| | |--- feature_1 <= 1.50
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| | | | | |--- class: 0
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| | | | | |--- feature_3 <= 4.50
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| | | | | | |--- class: 1
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| | | | | |--- feature_3 > 4.50
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| | | | | | |--- class: 0
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| | | |--- feature_2 > 4.50
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| | |--- feature_1 > 1.50
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| | | |--- class: 0
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| |--- feature_4 > 1.50
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| | |--- class: 0
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|--- feature_2 <= 3.50
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| |--- feature_4 <= 3.50
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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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| | | |--- feature_3 <= 3.50
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| | | | | |--- class: 1
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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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| | | | | | |--- class: 0
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| | | | |--- feature_3 <= 4.50
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| | | | | |--- feature_1 <= 2.50
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | | |--- feature_1 <= 1.50
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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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| | | | | | | | | |--- feature_2 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_2 > 2.00
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| | | | | | | | | | |--- class: 0
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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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| | | | | | | |--- 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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| | | | | | | | | |--- class: 0
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- feature_1 <= 3.50
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_0 > 3.50
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| | | | | | | | | |--- class: 1
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| | |--- feature_2 <= 1.50
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| | |--- feature_2 > 1.50
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|--- feature_2 > 3.50
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| |--- feature_1 <= 1.50
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| | |--- feature_4 <= 1.50
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Binary file not shown.
@ -1,235 +1,250 @@
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import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
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from path_search_algorthms.a_star import get_cost
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from decision_tree import decisionTree
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from settings import *
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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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self.x = x
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self.y = y
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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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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,
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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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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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progress.append(1 / rankRoutes(pop)[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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bestRoute = pop[bestRouteIndex]
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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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return bestRoute
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# geneticAlgorithmPlot(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
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import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
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from path_search_algorthms.a_star import get_cost
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from decision_tree import decisionTree
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from settings import *
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import math
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# klasa tworząca miasta czy też śmietniki
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class City:
|
||||
def __init__(self, x, y, array):
|
||||
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))
|
||||
# return distance
|
||||
|
||||
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
|
||||
class Fitness:
|
||||
def __init__(self, route, distanceArray):
|
||||
self.route = route
|
||||
self.distance = 0
|
||||
self.fitness = 0.0
|
||||
self.distanceArray = distanceArray
|
||||
|
||||
def routeDistance(self):
|
||||
if self.distance == 0:
|
||||
pathDistance = 0
|
||||
for i in range(0, len(self.route)):
|
||||
fromCity = self.route[i]
|
||||
toCity = None
|
||||
if i + 1 < len(self.route): # for returning to point 0?
|
||||
toCity = self.route[i + 1]
|
||||
else:
|
||||
toCity = self.route[0]
|
||||
# pathDistance += fromCity.distance(toCity)
|
||||
pathDistance += self.distanceArray[str(fromCity.x)+" "+str(fromCity.y)+" "+str(toCity.x)+" "+str(toCity.y)]
|
||||
self.distance = pathDistance
|
||||
return self.distance
|
||||
|
||||
def routeFitness(self):
|
||||
if self.fitness == 0:
|
||||
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 = []
|
||||
|
||||
for i in range(0, popSize):
|
||||
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, distanceArray):
|
||||
fitnessResults = {}
|
||||
for i in range(0, len(population)):
|
||||
fitnessResults[i] = Fitness(population[i], distanceArray).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
|
||||
|
||||
def selection(popRanked, eliteSize):
|
||||
selectionResults = []
|
||||
# roulette wheel
|
||||
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): # 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
|
||||
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 # returns list of route IDs
|
||||
|
||||
|
||||
# creating mating pool from list of routes IDs from "selection"
|
||||
def matingPool(population, selectionResults):
|
||||
matingpool = []
|
||||
for i in range(0, len(selectionResults)):
|
||||
index = selectionResults[i]
|
||||
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
|
||||
# with genes from the second parent in the order in which they appear, without duplicating any genes from the first parent
|
||||
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)
|
||||
|
||||
for i in range(startGene, endGene): # ordered crossover
|
||||
childP1.append(parent1[i])
|
||||
|
||||
childP2 = [item for item in parent2 if item not in childP1]
|
||||
|
||||
child = childP1 + childP2
|
||||
return child
|
||||
|
||||
|
||||
# creating whole offspring population
|
||||
def breedPopulation(matingpool, eliteSize):
|
||||
children = []
|
||||
length = len(matingpool) - eliteSize
|
||||
pool = random.sample(matingpool, len(matingpool))
|
||||
|
||||
# using elitism to retain best genes (routes)
|
||||
for i in range(0, eliteSize):
|
||||
children.append(matingpool[i])
|
||||
|
||||
# filling rest generation
|
||||
for i in range(0, length):
|
||||
child = breed(pool[i], pool[len(matingpool) - i - 1])
|
||||
children.append(child)
|
||||
return children
|
||||
|
||||
|
||||
# using swap mutation
|
||||
# with specified low prob we swap two cities in route
|
||||
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
|
||||
|
||||
|
||||
# extending mutate function to run through new pop
|
||||
def mutatePopulation(population, mutationRate):
|
||||
mutatedPop = []
|
||||
|
||||
for ind in range(0, len(population)):
|
||||
mutatedInd = mutate(population[ind], mutationRate)
|
||||
mutatedPop.append(mutatedInd)
|
||||
return mutatedPop
|
||||
|
||||
|
||||
# creating new generation
|
||||
def nextGeneration(currentGen, eliteSize, mutationRate, distanceArray):
|
||||
popRanked = rankRoutes(currentGen, distanceArray) # rank routes in current gen
|
||||
selectionResults = selection(popRanked, eliteSize) # determining potential parents
|
||||
matingpool = matingPool(currentGen, selectionResults) # creating mating pool
|
||||
children = breedPopulation(matingpool, eliteSize) # creating new gen
|
||||
nextGeneration = mutatePopulation(children, mutationRate) # applying mutation to new gen
|
||||
return nextGeneration
|
||||
|
||||
|
||||
def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations, distanceArray):
|
||||
pop = initialPopulation(popSize, population)
|
||||
print("Initial distance: " + str(1 / rankRoutes(pop, distanceArray)[0][1]))
|
||||
|
||||
for i in range(0, generations):
|
||||
pop = nextGeneration(pop, eliteSize, mutationRate, distanceArray)
|
||||
|
||||
print("Final distance: " + str(1 / rankRoutes(pop, distanceArray)[0][1]))
|
||||
bestRouteIndex = rankRoutes(pop, distanceArray)[0][0]
|
||||
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 distanceFromCityToCity(cityFrom, city, array):
|
||||
return get_cost(math.floor(cityFrom.x / TILESIZE), math.floor(cityFrom.y / TILESIZE), math.floor(city.x / TILESIZE), math.floor(city.y / TILESIZE), array)
|
||||
|
||||
def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations, array):
|
||||
a_star_distances = {}
|
||||
for city in population:
|
||||
for target in population:
|
||||
if city == target:
|
||||
continue
|
||||
else:
|
||||
a_star_distances[str(city.x)+" "+str(city.y)+" "+str(target.x)+" "+str(target.y)] = distanceFromCityToCity(city, target, array)
|
||||
|
||||
pop = initialPopulation(popSize, population)
|
||||
progress = []
|
||||
progress.append(1 / rankRoutes(pop, a_star_distances)[0][1])
|
||||
print("Initial distance: " + str(1 / rankRoutes(pop, a_star_distances)[0][1]))
|
||||
|
||||
for i in range(0, generations):
|
||||
pop = nextGeneration(pop, eliteSize, mutationRate, a_star_distances)
|
||||
progress.append(1 / rankRoutes(pop, a_star_distances)[0][1])
|
||||
|
||||
print("Final distance: " + str(1 / rankRoutes(pop, a_star_distances)[0][1]))
|
||||
bestRouteIndex = rankRoutes(pop, a_star_distances)[0][0]
|
||||
bestRoute = pop[bestRouteIndex]
|
||||
|
||||
plt.plot(progress)
|
||||
plt.ylabel('Distance')
|
||||
plt.xlabel('Generation')
|
||||
plt.show()
|
||||
return bestRoute
|
||||
|
||||
# geneticAlgorithmPlot(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
|
||||
|
||||
BIN
last_map.nparr
BIN
last_map.nparr
Binary file not shown.
512
main.py
512
main.py
@ -1,256 +1,258 @@
|
||||
import os
|
||||
import sys
|
||||
from random import randint
|
||||
import math
|
||||
|
||||
import pygame as pg
|
||||
import numpy
|
||||
|
||||
from game_objects.player import Player
|
||||
from game_objects.aiPlayer import aiPlayer
|
||||
from game_objects.trash import Trash
|
||||
|
||||
from map import map
|
||||
from map import map_utils
|
||||
from settings import *
|
||||
from path_search_algorthms import bfs
|
||||
from path_search_algorthms import a_star_controller, a_star
|
||||
from decision_tree import decisionTree
|
||||
from NeuralNetwork import prediction
|
||||
from game_objects.trash import Trash
|
||||
from genetic_algorithm import TSP
|
||||
from game_objects import aiPlayer
|
||||
import itertools
|
||||
|
||||
|
||||
def getTree():
|
||||
tree = decisionTree.tree()
|
||||
decisionTree.tree_as_txt(tree)
|
||||
# decisionTree.tree_to_png(tree)
|
||||
decisionTree.tree_to_structure(tree)
|
||||
drzewo = decisionTree.tree_from_structure('./decision_tree/tree_model')
|
||||
# print("Dla losowych danych predykcja czy wziąć kosz to: ")
|
||||
# dec = decisionTree.decision(drzewo, *(4,1,1,1))
|
||||
# print('---')
|
||||
# print(f"decision is{dec}")
|
||||
# print('---')
|
||||
|
||||
return drzewo
|
||||
|
||||
|
||||
class Game():
|
||||
|
||||
def __init__(self):
|
||||
pg.init()
|
||||
pg.font.init()
|
||||
self.clock = pg.time.Clock()
|
||||
self.dt = self.clock.tick(FPS) / 333.0
|
||||
self.screen = pg.display.set_mode((WIDTH, HEIGHT))
|
||||
pg.display.set_caption("Trashmaster")
|
||||
self.load_data()
|
||||
self.init_game()
|
||||
# because dont work without data.txt
|
||||
# self.init_bfs()
|
||||
# self.init_a_star()
|
||||
self.t = aiPlayer.aiPlayer(self.player, game=self)
|
||||
|
||||
def init_game(self):
|
||||
# initialize all variables and do all the setup for a new game
|
||||
|
||||
self.text_display = ''
|
||||
|
||||
# sprite groups and map array for calculations
|
||||
(self.roadTiles, self.wallTiles, self.trashbinTiles), self.mapArray = map.get_tiles()
|
||||
|
||||
# save current map
|
||||
file = open('last_map.nparr', 'wb')
|
||||
numpy.save(file, self.mapArray, allow_pickle=True)
|
||||
file.close
|
||||
|
||||
self.trashDisplay = pg.sprite.Group()
|
||||
self.agentSprites = pg.sprite.Group()
|
||||
# player obj
|
||||
self.player = Player(self, 32, 32)
|
||||
# camera obj
|
||||
self.camera = map_utils.Camera(MAP_WIDTH_PX, MAP_HEIGHT_PX)
|
||||
|
||||
# other
|
||||
self.debug_mode = False
|
||||
|
||||
def init_bfs(self):
|
||||
start_node = (0, 0)
|
||||
target_node = (18, 18)
|
||||
find_path = bfs.BreadthSearchAlgorithm(start_node, target_node, self.mapArray)
|
||||
path = find_path.bfs()
|
||||
# print(path)
|
||||
realPath = []
|
||||
nextNode = target_node
|
||||
for i in range(len(path) - 1, 0, -1):
|
||||
node = path[i]
|
||||
if node[0] == nextNode:
|
||||
realPath.insert(0, node[0])
|
||||
nextNode = node[1]
|
||||
print(realPath)
|
||||
|
||||
def init_decision_tree(self):
|
||||
# logika pracy z drzewem
|
||||
self.positive_decision = []
|
||||
self.negative_decision = []
|
||||
|
||||
for i in self.trashbinTiles:
|
||||
atrrs_container = i.get_attributes()
|
||||
x, y = i.get_coords()
|
||||
dec = decisionTree.decision(getTree(), *atrrs_container)
|
||||
# if dec[0] == 1:
|
||||
self.positive_decision.append(i) # zmiana po to by losowało wszystkie smietniki a nie poprawne tylko, zeby ladniej bylo widac algorytm genetyczny
|
||||
# else:
|
||||
# self.negative_decision.append(i)
|
||||
|
||||
print('positive actions')
|
||||
print(len(self.positive_decision))
|
||||
# print('positive actions')
|
||||
# for i in self.positive_actions:
|
||||
# print('----')
|
||||
# print(i)
|
||||
# print('----')
|
||||
self.draw()
|
||||
def decsion_tree_move(self):
|
||||
|
||||
for i in range(0,len(self.positive_decision)):
|
||||
# print(i.get_coords())
|
||||
print('action')
|
||||
|
||||
|
||||
# trash_x, trash_y = i.get_coords()
|
||||
|
||||
# for ii in self.tsp_list:
|
||||
temp_tsp = str(self.tsp_list[i])
|
||||
temp_tsp = temp_tsp.strip("()")
|
||||
temp_tsp = temp_tsp.split(",")
|
||||
trash_x = int(temp_tsp[0])
|
||||
trash_y = int(temp_tsp[1])
|
||||
|
||||
|
||||
print(trash_x, trash_y)
|
||||
|
||||
action = a_star_controller.get_actions_for_target_coords(trash_x, trash_y, self)
|
||||
|
||||
print(action)
|
||||
self.t.startAiController(action)
|
||||
|
||||
print('')
|
||||
print('--rozpoczecie sortowania smietnika--')
|
||||
dir = "./resources/trash_dataset/test/all"
|
||||
files = os.listdir(dir)
|
||||
for j in range(0, 10):
|
||||
random = randint(0, 48)
|
||||
file = files[random]
|
||||
result = prediction.getPrediction(dir + '/' + file, 'trained_nn_20.pth')
|
||||
img = pg.image.load(dir + '/' + file).convert_alpha()
|
||||
img = pg.transform.scale(img, (128, 128))
|
||||
trash = Trash(img, 0, 0, 128, 128)
|
||||
self.trashDisplay.add(trash)
|
||||
self.text_display = result
|
||||
self.draw()
|
||||
# print(result + ' ' + file)
|
||||
pg.time.wait(100)
|
||||
self.text_display = ''
|
||||
self.draw()
|
||||
|
||||
# print(self.positive_actions[0])
|
||||
|
||||
# self.t.startAiController(self.positive_actions[0])
|
||||
def init_TSP(self):
|
||||
|
||||
city_list =[]
|
||||
|
||||
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)))
|
||||
|
||||
|
||||
# dist = a_star.get_cost
|
||||
self.tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=300)
|
||||
print(self.tsp_list)
|
||||
|
||||
def load_data(self):
|
||||
game_folder = os.path.dirname(__file__)
|
||||
img_folder = os.path.join(game_folder, 'resources/textures')
|
||||
|
||||
self.player_img = pg.image.load(os.path.join(img_folder, PLAYER_IMG)).convert_alpha()
|
||||
self.player_img = pg.transform.scale(self.player_img, (PLAYER_WIDTH, PLAYER_HEIGHT))
|
||||
|
||||
def run(self):
|
||||
# game loop - set self.playing = False to end the game
|
||||
self.playing = True
|
||||
self.init_decision_tree()
|
||||
self.init_TSP()
|
||||
self.decsion_tree_move()
|
||||
|
||||
while self.playing:
|
||||
self.dt = self.clock.tick(FPS) / 1000.0
|
||||
self.events()
|
||||
self.update()
|
||||
self.draw()
|
||||
|
||||
def quit(self):
|
||||
pg.quit()
|
||||
sys.exit()
|
||||
|
||||
def update(self):
|
||||
# update portion of the game loop
|
||||
self.agentSprites.update()
|
||||
self.camera.update(self.player)
|
||||
# pygame.display.update()
|
||||
|
||||
def draw(self):
|
||||
# display fps as window title
|
||||
pg.display.set_caption("{:.2f}".format(self.clock.get_fps()))
|
||||
|
||||
# rerender map
|
||||
map.render_tiles(self.roadTiles, self.screen, self.camera)
|
||||
map.render_tiles(self.wallTiles, self.screen, self.camera, self.debug_mode)
|
||||
map.render_tiles(self.trashbinTiles, self.screen, self.camera)
|
||||
map.render_tiles(self.trashDisplay, self.screen, self.camera)
|
||||
|
||||
# draw text
|
||||
text_surface = pg.font.SysFont('Comic Sans MS', 30).render(self.text_display, False, (255, 255, 255))
|
||||
self.screen.blit(text_surface, (0, 128))
|
||||
|
||||
# rerender additional sprites
|
||||
for sprite in self.agentSprites:
|
||||
self.screen.blit(sprite.image, self.camera.apply(sprite))
|
||||
if self.debug_mode:
|
||||
pg.draw.rect(self.screen, CYAN, self.camera.apply_rect(sprite.hit_rect), 1)
|
||||
|
||||
# self.player.hud_group.draw(self.screen)
|
||||
# finally update screen
|
||||
pg.display.flip()
|
||||
|
||||
def events(self):
|
||||
for event in pg.event.get():
|
||||
if event.type == pg.QUIT:
|
||||
self.quit()
|
||||
if event.type == pg.KEYDOWN:
|
||||
if event.key == pg.K_ESCAPE:
|
||||
self.quit()
|
||||
if event.key == pg.K_h:
|
||||
self.debug_mode = not self.debug_mode
|
||||
if event.type == pg.MOUSEBUTTONUP:
|
||||
pos = pg.mouse.get_pos()
|
||||
offset_x, offset_y = self.camera.offset()
|
||||
clicked_coords = [math.floor(pos[0] / TILESIZE) - offset_x, math.floor(pos[1] / TILESIZE) - offset_y]
|
||||
actions = a_star_controller.get_actions_by_coords(clicked_coords[0], clicked_coords[1], self)
|
||||
|
||||
if (actions != None):
|
||||
self.t.startAiController(actions)
|
||||
|
||||
|
||||
# create the game object
|
||||
|
||||
if __name__ == "__main__":
|
||||
g = Game()
|
||||
|
||||
g.run()
|
||||
import os
|
||||
import sys
|
||||
from random import randint
|
||||
import math
|
||||
|
||||
import pygame as pg
|
||||
import numpy
|
||||
|
||||
from game_objects.player import Player
|
||||
from game_objects.aiPlayer import aiPlayer
|
||||
from game_objects.trash import Trash
|
||||
|
||||
from map import map
|
||||
from map import map_utils
|
||||
from settings import *
|
||||
from path_search_algorthms import bfs
|
||||
from path_search_algorthms import a_star_controller, a_star
|
||||
from decision_tree import decisionTree
|
||||
from NeuralNetwork import prediction
|
||||
from game_objects.trash import Trash
|
||||
from genetic_algorithm import TSP
|
||||
from game_objects import aiPlayer
|
||||
import itertools
|
||||
|
||||
|
||||
def getTree():
|
||||
tree = decisionTree.tree()
|
||||
decisionTree.tree_as_txt(tree)
|
||||
# decisionTree.tree_to_png(tree)
|
||||
decisionTree.tree_to_structure(tree)
|
||||
drzewo = decisionTree.tree_from_structure('./decision_tree/tree_model')
|
||||
# print("Dla losowych danych predykcja czy wziąć kosz to: ")
|
||||
# dec = decisionTree.decision(drzewo, *(4,1,1,1))
|
||||
# print('---')
|
||||
# print(f"decision is{dec}")
|
||||
# print('---')
|
||||
|
||||
return drzewo
|
||||
|
||||
|
||||
class Game():
|
||||
|
||||
def __init__(self):
|
||||
pg.init()
|
||||
pg.font.init()
|
||||
self.clock = pg.time.Clock()
|
||||
self.dt = self.clock.tick(FPS) / 333.0
|
||||
self.screen = pg.display.set_mode((WIDTH, HEIGHT))
|
||||
pg.display.set_caption("Trashmaster")
|
||||
self.load_data()
|
||||
self.init_game()
|
||||
# because dont work without data.txt
|
||||
# self.init_bfs()
|
||||
# self.init_a_star()
|
||||
self.t = aiPlayer.aiPlayer(self.player, game=self)
|
||||
|
||||
def init_game(self):
|
||||
# initialize all variables and do all the setup for a new game
|
||||
|
||||
self.text_display = ''
|
||||
|
||||
# sprite groups and map array for calculations
|
||||
(self.roadTiles, self.wallTiles, self.trashbinTiles), self.mapArray = map.get_tiles()
|
||||
|
||||
# save current map
|
||||
file = open('last_map.nparr', 'wb')
|
||||
numpy.save(file, self.mapArray, allow_pickle=True)
|
||||
file.close
|
||||
|
||||
self.trashDisplay = pg.sprite.Group()
|
||||
self.agentSprites = pg.sprite.Group()
|
||||
# player obj
|
||||
self.player = Player(self, 32, 32)
|
||||
# camera obj
|
||||
self.camera = map_utils.Camera(MAP_WIDTH_PX, MAP_HEIGHT_PX)
|
||||
|
||||
# other
|
||||
self.debug_mode = False
|
||||
|
||||
def init_bfs(self):
|
||||
start_node = (0, 0)
|
||||
target_node = (18, 18)
|
||||
find_path = bfs.BreadthSearchAlgorithm(start_node, target_node, self.mapArray)
|
||||
path = find_path.bfs()
|
||||
# print(path)
|
||||
realPath = []
|
||||
nextNode = target_node
|
||||
for i in range(len(path) - 1, 0, -1):
|
||||
node = path[i]
|
||||
if node[0] == nextNode:
|
||||
realPath.insert(0, node[0])
|
||||
nextNode = node[1]
|
||||
print(realPath)
|
||||
|
||||
def init_decision_tree(self):
|
||||
# logika pracy z drzewem
|
||||
self.positive_decision = []
|
||||
self.negative_decision = []
|
||||
|
||||
for i in self.trashbinTiles:
|
||||
atrrs_container = i.get_attributes()
|
||||
x, y = i.get_coords()
|
||||
dec = decisionTree.decision(getTree(), *atrrs_container)
|
||||
# if dec[0] == 1:
|
||||
self.positive_decision.append(i) # zmiana po to by losowało wszystkie smietniki a nie poprawne tylko, zeby ladniej bylo widac algorytm genetyczny
|
||||
# else:
|
||||
# self.negative_decision.append(i)
|
||||
|
||||
print('positive actions')
|
||||
print(len(self.positive_decision))
|
||||
# print('positive actions')
|
||||
# for i in self.positive_actions:
|
||||
# print('----')
|
||||
# print(i)
|
||||
# print('----')
|
||||
self.draw()
|
||||
def decsion_tree_move(self):
|
||||
|
||||
for i in range(0,len(self.positive_decision)):
|
||||
# print(i.get_coords())
|
||||
print('action')
|
||||
|
||||
|
||||
# trash_x, trash_y = i.get_coords()
|
||||
|
||||
# for ii in self.tsp_list:
|
||||
temp_tsp = str(self.tsp_list[i])
|
||||
temp_tsp = temp_tsp.strip("()")
|
||||
temp_tsp = temp_tsp.split(",")
|
||||
trash_x = int(temp_tsp[0])
|
||||
trash_y = int(temp_tsp[1])
|
||||
|
||||
|
||||
print(trash_x, trash_y)
|
||||
|
||||
action = a_star_controller.get_actions_for_target_coords(trash_x, trash_y, self)
|
||||
|
||||
print(action)
|
||||
self.t.startAiController(action)
|
||||
|
||||
print('')
|
||||
print('--rozpoczecie sortowania smietnika--')
|
||||
dir = "./resources/trash_dataset/test/all"
|
||||
files = os.listdir(dir)
|
||||
for j in range(0, 10):
|
||||
random = randint(0, 48)
|
||||
file = files[random]
|
||||
result = prediction.getPrediction(dir + '/' + file, 'trained_nn_20.pth')
|
||||
img = pg.image.load(dir + '/' + file).convert_alpha()
|
||||
img = pg.transform.scale(img, (128, 128))
|
||||
offset_x, offset_y = self.camera.offset()
|
||||
trash = Trash(img, math.floor(-offset_x * TILESIZE), math.floor(-offset_y * TILESIZE), 128, 128)
|
||||
self.trashDisplay.empty()
|
||||
self.trashDisplay.add(trash)
|
||||
self.text_display = result
|
||||
self.draw()
|
||||
pg.time.wait(100)
|
||||
self.text_display = ''
|
||||
self.trashDisplay.empty()
|
||||
self.draw()
|
||||
|
||||
# print(self.positive_actions[0])
|
||||
|
||||
# self.t.startAiController(self.positive_actions[0])
|
||||
def init_TSP(self):
|
||||
|
||||
city_list =[]
|
||||
|
||||
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=trash_x, y=trash_y, array=self.mapArray))
|
||||
|
||||
|
||||
# dist = a_star.get_cost
|
||||
self.tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=300, array=self.mapArray)
|
||||
print(self.tsp_list)
|
||||
|
||||
def load_data(self):
|
||||
game_folder = os.path.dirname(__file__)
|
||||
img_folder = os.path.join(game_folder, 'resources/textures')
|
||||
|
||||
self.player_img = pg.image.load(os.path.join(img_folder, PLAYER_IMG)).convert_alpha()
|
||||
self.player_img = pg.transform.scale(self.player_img, (PLAYER_WIDTH, PLAYER_HEIGHT))
|
||||
|
||||
def run(self):
|
||||
# game loop - set self.playing = False to end the game
|
||||
self.playing = True
|
||||
self.init_decision_tree()
|
||||
self.init_TSP()
|
||||
self.decsion_tree_move()
|
||||
|
||||
while self.playing:
|
||||
self.dt = self.clock.tick(FPS) / 1000.0
|
||||
self.events()
|
||||
self.update()
|
||||
self.draw()
|
||||
|
||||
def quit(self):
|
||||
pg.quit()
|
||||
sys.exit()
|
||||
|
||||
def update(self):
|
||||
# update portion of the game loop
|
||||
self.agentSprites.update()
|
||||
self.camera.update(self.player)
|
||||
# pygame.display.update()
|
||||
|
||||
def draw(self):
|
||||
# display fps as window title
|
||||
pg.display.set_caption("{:.2f}".format(self.clock.get_fps()))
|
||||
|
||||
# rerender map
|
||||
map.render_tiles(self.roadTiles, self.screen, self.camera)
|
||||
map.render_tiles(self.wallTiles, self.screen, self.camera, self.debug_mode)
|
||||
map.render_tiles(self.trashbinTiles, self.screen, self.camera)
|
||||
map.render_tiles(self.trashDisplay, self.screen, self.camera)
|
||||
|
||||
# draw text
|
||||
text_surface = pg.font.SysFont('Comic Sans MS', 30).render(self.text_display, False, (255, 255, 255))
|
||||
self.screen.blit(text_surface, (0, 128))
|
||||
|
||||
# rerender additional sprites
|
||||
for sprite in self.agentSprites:
|
||||
self.screen.blit(sprite.image, self.camera.apply(sprite))
|
||||
if self.debug_mode:
|
||||
pg.draw.rect(self.screen, CYAN, self.camera.apply_rect(sprite.hit_rect), 1)
|
||||
|
||||
# self.player.hud_group.draw(self.screen)
|
||||
# finally update screen
|
||||
pg.display.flip()
|
||||
|
||||
def events(self):
|
||||
for event in pg.event.get():
|
||||
if event.type == pg.QUIT:
|
||||
self.quit()
|
||||
if event.type == pg.KEYDOWN:
|
||||
if event.key == pg.K_ESCAPE:
|
||||
self.quit()
|
||||
if event.key == pg.K_h:
|
||||
self.debug_mode = not self.debug_mode
|
||||
if event.type == pg.MOUSEBUTTONUP:
|
||||
pos = pg.mouse.get_pos()
|
||||
offset_x, offset_y = self.camera.offset()
|
||||
clicked_coords = [math.floor(pos[0] / TILESIZE) - offset_x, math.floor(pos[1] / TILESIZE) - offset_y]
|
||||
actions = a_star_controller.get_actions_by_coords(clicked_coords[0], clicked_coords[1], self)
|
||||
|
||||
if (actions != None):
|
||||
self.t.startAiController(actions)
|
||||
|
||||
|
||||
# create the game object
|
||||
|
||||
if __name__ == "__main__":
|
||||
g = Game()
|
||||
|
||||
g.run()
|
||||
g.show_go_screen()
|
||||
@ -1,76 +1,79 @@
|
||||
from data_structures.heap import Heap
|
||||
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)
|
||||
|
||||
|
||||
def search_path(start_x: int, start_y: int, agent_rotation: utils.Rotation, target_x: int, target_y: int, array):
|
||||
start_node = utils.Node(start_x, start_y, agent_rotation)
|
||||
target_node = utils.Node(target_x, target_y, utils.Rotation.NONE)
|
||||
|
||||
# heap version
|
||||
|
||||
# nodes for check
|
||||
search_list = Heap()
|
||||
search_list.append(start_node, 0)
|
||||
|
||||
# checked nodes
|
||||
searched_list: list[(int, int)] = []
|
||||
|
||||
while (search_list.length() > 0):
|
||||
node: utils.Node = search_list.take_first()
|
||||
|
||||
searched_list.append((node.x, node.y))
|
||||
|
||||
# check for target node
|
||||
if ((node.x, node.y) == (target_x, target_y)):
|
||||
return trace_path(node)
|
||||
|
||||
# neightbours processing
|
||||
neighbours = utils.get_neighbours(node, searched_list, array)
|
||||
for neighbour in neighbours:
|
||||
|
||||
# calculate new g cost for neightbour (start -> node -> neightbour)
|
||||
new_neighbour_cost = node.g_cost + utils.get_neighbour_cost(node, neighbour)
|
||||
|
||||
if (new_neighbour_cost < neighbour.g_cost or not search_list.contains(neighbour)):
|
||||
|
||||
# replace cost and set parent node
|
||||
neighbour.g_cost = new_neighbour_cost
|
||||
neighbour.h_cost = utils.get_h_cost(neighbour, target_node)
|
||||
neighbour.parent = node
|
||||
|
||||
# add to search
|
||||
if (not search_list.contains(neighbour)):
|
||||
search_list.append(neighbour, neighbour.f_cost())
|
||||
|
||||
|
||||
def trace_path(end_node: utils.Node):
|
||||
path = []
|
||||
node = end_node
|
||||
|
||||
# set final rotation of end_node because we don't do it before
|
||||
node.rotation = utils.get_needed_rotation(node.parent, node)
|
||||
|
||||
while (node.parent != False):
|
||||
if (node.parent == utils.Rotation.NONE):
|
||||
path += "forward"
|
||||
else:
|
||||
path += utils.get_move(node.parent, node)
|
||||
node = node.parent
|
||||
|
||||
# delete move on initial tile
|
||||
path.pop()
|
||||
|
||||
# we found path from end, so we need to reverse it (get_move reverse move words)
|
||||
path.reverse()
|
||||
|
||||
# last forward to destination
|
||||
path.append("forward")
|
||||
|
||||
return path
|
||||
from data_structures.heap import Heap
|
||||
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)
|
||||
print('length')
|
||||
if actions is None:
|
||||
print('0')
|
||||
return 1
|
||||
print(len(actions))
|
||||
return len(actions)
|
||||
|
||||
|
||||
def search_path(start_x: int, start_y: int, agent_rotation: utils.Rotation, target_x: int, target_y: int, array):
|
||||
start_node = utils.Node(start_x, start_y, agent_rotation)
|
||||
target_node = utils.Node(target_x, target_y, utils.Rotation.NONE)
|
||||
|
||||
# heap version
|
||||
|
||||
# nodes for check
|
||||
search_list = Heap()
|
||||
search_list.append(start_node, 0)
|
||||
|
||||
# checked nodes
|
||||
searched_list: list[(int, int)] = []
|
||||
|
||||
while (search_list.length() > 0):
|
||||
node: utils.Node = search_list.take_first()
|
||||
|
||||
searched_list.append((node.x, node.y))
|
||||
|
||||
# check for target node
|
||||
if ((node.x, node.y) == (target_x, target_y)):
|
||||
return trace_path(node)
|
||||
|
||||
# neightbours processing
|
||||
neighbours = utils.get_neighbours(node, searched_list, array)
|
||||
for neighbour in neighbours:
|
||||
|
||||
# calculate new g cost for neightbour (start -> node -> neightbour)
|
||||
new_neighbour_cost = node.g_cost + utils.get_neighbour_cost(node, neighbour)
|
||||
|
||||
if (new_neighbour_cost < neighbour.g_cost or not search_list.contains(neighbour)):
|
||||
|
||||
# replace cost and set parent node
|
||||
neighbour.g_cost = new_neighbour_cost
|
||||
neighbour.h_cost = utils.get_h_cost(neighbour, target_node)
|
||||
neighbour.parent = node
|
||||
|
||||
# add to search
|
||||
if (not search_list.contains(neighbour)):
|
||||
search_list.append(neighbour, neighbour.f_cost())
|
||||
|
||||
|
||||
def trace_path(end_node: utils.Node):
|
||||
path = []
|
||||
node = end_node
|
||||
|
||||
# set final rotation of end_node because we don't do it before
|
||||
node.rotation = utils.get_needed_rotation(node.parent, node)
|
||||
|
||||
while (node.parent != False):
|
||||
if (node.parent == utils.Rotation.NONE):
|
||||
path += "forward"
|
||||
else:
|
||||
path += utils.get_move(node.parent, node)
|
||||
node = node.parent
|
||||
|
||||
# delete move on initial tile
|
||||
path.pop()
|
||||
|
||||
# we found path from end, so we need to reverse it (get_move reverse move words)
|
||||
path.reverse()
|
||||
|
||||
# last forward to destination
|
||||
path.append("forward")
|
||||
|
||||
return path
|
||||
|
||||
Loading…
Reference in New Issue
Block a user