遗传编程pyeasyGA和Zelle图形在Python上

前端之家收集整理的这篇文章主要介绍了遗传编程pyeasyGA和Zelle图形在Python上前端之家小编觉得挺不错的,现在分享给大家,也给大家做个参考。

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

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)

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.

它没有发展.每个传递序列似乎都是随机生成的.

有谁可以帮助我吗?

Here’s the full code

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:

enter image description here

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.

原文链接:https://www.f2er.com/python/438694.html

猜你在找的Python相关文章