我想制作一个简单的程序来提高我对这类编程的了解.
我发现了一个非常有用的库,pyeasyGA,并且我尝试使用graphics.py创建一个简单的程序,从随机生成的“pass”序列创建一个收敛到一个点的序列.
这就是它的工作原理：
def create_individual(data):
a = [(randint(0,5),randint(0,5)) for n in range(len(data))]
print(a)
return a
此函数创建一系列传递,因为graphics.py库允许您通过为对象移动它想要移动它来移动对象.那是我的“个人”.
为了计算健身,我使用了这个：
def fitness(individual,data):
totX=0
totY=0
for elem in individual:
totX+=elem[0]
totY+=elem[1]
tot = (totX,totY)
return distEuclidea(arrivo,tot)
def distEuclidea(p1,p2):
x1 = p1[0]
y1 = p1[1]
x2 = p2[0]
y2 = p2[1]
return ((x2-x1)**2+(y2-y1)**2)**(1/2)
此功能计算距所需到达点的距离.
在这些过程之后,程序会产生很多代,并且会使个体具有最低的适应度,但它不起作用.
有谁可以帮助我吗？
编辑：
该计划似乎有效.唯一的问题是几代人.
最佳答案
我发现你的健身功能最难理解.而不是平均角落或找到中心,它将角落相加然后找到距离.什么是几何解释？
此外,您的代码是指ga.logGenerations,它不是当前pyeasyga 0.3.1版本的一部分.
以下是我对你的要求的近似.如果它没有标记,那么请用示例和/或图表来扩充您的解释：
from time import sleep
from random import randint
from itertools import cycle
from graphics import *
from pyeasyga import pyeasyga
NUMBER_OF_RECTANGLES = 4 # make one more than what you want to see
NUMBER_OF_POINTS = 2
arrivo = (90,90)
colori = ["red","green","blue","cyan","magenta","yellow"]
X,Y = 0,1
def distEuclidea(p1,p2):
x1,y1 = p1
x2,y2 = p2
return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
def create_individual(colors):
color = next(colors)
while color in rectangles and rectangles[color] is None: # skip over deleted rectangle
color = next(colors)
if color in rectangles:
rectangle = rectangles[color]
p1,p2 = rectangle.getP1(),rectangle.getP2()
points = [[p1.getX(),p1.getY()],[p2.getX(),p2.getY()]]
else:
points = [[randint(0,20),20)] for _ in range(NUMBER_OF_POINTS)]
rectangle = Rectangle(*[Point(x,y) for x,y in points])
rectangle.setOutline(color)
rectangle.draw(win)
rectangles[color] = rectangle
return [color,points]
def fitness(individual,colors):
_,points = individual
rectangle = Rectangle(*[Point(x,y in points])
center = rectangle.getCenter()
return distEuclidea(arrivo,(center.getX(),center.getY()))
def mutate(individual):
_,points = individual
mutate_index = randint(0,NUMBER_OF_POINTS - 1)
points[mutate_index][X] += randint(-1,1)
points[mutate_index][Y] += randint(-1,1)
def is_point_inside_rectangle(point,rectangle):
p1,rectangle.getP2()
return min(p1.getX(),p2.getX()) < point.getX() < max(p1.getX(),p2.getX()) and \
min(p1.getY(),p2.getY()) < point.getY() < max(p1.getY(),p2.getY())
win = GraphWin("Genetic Graphics",500,500)
win.setCoords(0,100,100)
rectangles = {}
color_generator = cycle(colori[0:NUMBER_OF_RECTANGLES])
arrivoC = Circle(Point(*arrivo),1)
arrivoC.setFill("orange")
arrivoC.draw(win)
number_of_rectangles = NUMBER_OF_RECTANGLES
while True:
ga = pyeasyga.GeneticAlgorithm(color_generator,\
elitism=False,\
maximise_fitness=False,\
crossover_probability=0.0,\
population_size=number_of_rectangles)
ga.create_individual = create_individual
ga.fitness_function = fitness
ga.mutate_function = mutate
ga.run()
for member in ga.last_generation():
my_fitness,(my_color,my_points) = member
if rectangles[my_color] is None:
continue # skip over deleted rectangle
rectangle = Rectangle(*[Point(x,y in my_points])
rectangle.setOutline(my_color)
rectangle.draw(win)
rectangles[my_color] = rectangle
if is_point_inside_rectangle(arrivoC.getCenter(),rectangle):
rectangles[my_color] = None # delete finished rectangle
number_of_rectangles -= 1
if number_of_rectangles < 2:
break
sleep(0.1)
for value in rectangles.values():
if value is not None:
value.undraw() # delete unfinished rectangle
win.getMouse()
win.close()
以上是粗略的代码(例如,它并不总是保持通用域点和矩形独立于graphics.py点和矩形.)但它应该给你一些实验：
它在窗口的左下角创建矩形,遗传算法在右上角向目标突变,当它们到达目标时丢弃矩形.
我的代码的一部分复杂性是pyeasyga没有提供一个功能钩子来可视化每一代发生的事情.更好的方法可能是将pyeasyga子类化为添加这样的钩子以简化代码的逻辑.