我正在编写基本的PSO(粒子群优化),并且已经得到粒子实例没有属性__getitem__
的错误。我认为一切都很好,但particle
类似乎有一些错误。看一下particle
课程。
from numpy import array
from random import random
from math import sin, sqrt, cos, pi
import matplotlib.pyplot as plt
import pylab
## Settings
c1 = 2
c2 = 2
size = 100
bad_size = 10
dim = 10
max_iterations = 20
Error_limit = 0.00001
def functR(k):
val = 10*dim
for i in range(dim):
val = val + (k[i])**2 - 10*cos(2*pi*k[i])
return val
#print functR([0]*20)
class particle():
def __init__(self, pos, fitness,vel, pbestpos, pbestfit):
self.pos = pos
self.fitness = fitness
self.vel = vel
self.pbestpos = pbestpos
self.pbestfitness = pbestfit
class swarm():
def __init__(self, size, bad_size, dim):
#self.gbest = gbest
self.size = size
self.bad_size = bad_size
self.dim = dim
def create(self):
particles = []
for i in range(size + bad_size):
p = particle()
p.pos = array([random() for i in range(dim)])
p.vel = 0.0
p.fitness = 0.0
p.pbestpos = p.pos
p.pbestfit = p.fitness
#p = particle(pos, fitness,vel, pbestpos, pbestfit)
particles.append(p)
return particles
def optimizer():
s = swarm(size, bad_size, dim)
new_swarm = s.create()
gbest = new_swarm[0]
gbestfit = functR(gbest)
i = 0
## The iterative loop
while i < max_iterations:
for p in s:
fitness = functR(p.pos)
if fitness > p.fitness:
p.fitness = fitness
p.pbestpos = p.pos
if fitness > gbestfit:
gbest = p
## Plotting
pylab.xlim([0,10])
pylab.ylim([0,1.5])
plt.plot(i,gbest.fitness, "bo")
## Velocity and Position update
vel = p.vel + c1 * random() * (p.pbestpos - p.pos) \
+ c2 * random() * (gbest.pos - p.pos)
p.pos = p.pos + vel
plt.show()
i += 1
print "gbest fitness :", gbestfit
print "Best particle :", gbest.pos
print optimizer()
答案 0 :(得分:1)
您将单个particle()
实例视为列表:
val = val + (k[i])**2 - 10*cos(2*pi*k[i])
k
是particle()
的一个实例,[i]
语法转换为该实例上的__getitem__
调用。
你在这里传递了这个例子:
gbest = new_swarm[0]
gbestfit = functR(gbest)
而在其他地方你传递了.pos
参数:
for p in s:
fitness = functR(p.pos)
所以也许你打算对gbestfit
行做同样的事情:
gbest = new_swarm[0]
gbestfit = functR(gbest.pos)