我是python中的初学者。我想在python中生成遗传算法源代码。老实说，我从internet下载了这段代码。我在pycharm中编译了这段代码。 显示错误 配置功能配置设置中的主要功能和RAW_INPUT 任何人都可以请检查主功能中的错误并检查原始输入。我附上了代码。谢谢预先
from operator import itemgetter, attrgetter
import random
import sys
import os
import math
import re
# GLOBAL VARIABLES
genetic_code = {
'0000':'0',
'0001':'1',
'0010':'2',
'0011':'3',
'0100':'4',
'0101':'5',
'0110':'6',
'0111':'7',
'1000':'8',
'1001':'9',
'1010':'+',
'1011':'-',
'1100':'*',
'1101':'/'
}
solution_found = False
popN = 100 # n number of chromos per population
genesPerCh = 75
max_iterations = 1000
target = 1111.0
crossover_rate = 0.7
mutation_rate = 0.05
"""Generates random population of chromos"""
def generatePop ():
chromos, chromo = [], []
for eachChromo in range(popN):
chromo = []
for bit in range(genesPerCh * 4):
chromo.append(random.randint(0,1))
chromos.append(chromo)
return chromos
"""Takes a binary list (chromo) and returns a protein (mathematical expression in string)"""
def translate (chromo):
protein, chromo_string = '',''
need_int = True
a, b = 0, 4 # ie from point a to point b (start to stop point in string)
for bit in chromo:
chromo_string += str(bit)
for gene in range(genesPerCh):
if chromo_string[a:b] == '1111' or chromo_string[a:b] == '1110':
continue
elif chromo_string[a:b] != '1010' and chromo_string[a:b] != '1011' and chromo_string[a:b] != '1100' and chromo_string[a:b] != '1101':
if need_int == True:
protein += genetic_code[chromo_string[a:b]]
need_int = False
a += 4
b += 4
continue
else:
a += 4
b += 4
continue
else:
if need_int == False:
protein += genetic_code[chromo_string[a:b]]
need_int = True
a += 4
b += 4
continue
else:
a += 4
b += 4
continue
if len(protein) %2 == 0:
protein = protein[:-1]
return protein
"""Evaluates the mathematical expressions in number + operator blocks of two"""
def evaluate(protein):
a = 3
b = 5
output = -1
lenprotein = len(protein) # i imagine this is quicker than calling len everytime?
if lenprotein == 0:
output = 0
if lenprotein == 1:
output = int(protein)
if lenprotein >= 3:
try :
output = eval(protein[0:3])
except ZeroDivisionError:
output = 0
if lenprotein > 4:
while b != lenprotein+2:
try :
output = eval(str(output)+protein[a:b])
except ZeroDivisionError:
output = 0
a+=2
b+=2
return output
"""Calulates fitness as a fraction of the total fitness"""
def calcFitness (errors):
fitnessScores = []
totalError = sum(errors)
i = 0
# fitness scores are a fraction of the total error
for error in errors:
fitnessScores.append (float(errors[i])/float(totalError))
i += 1
return fitnessScores
def displayFit (error):
bestFitDisplay = 100
dashesN = int(error * bestFitDisplay)
dashes = ''
for j in range(bestFitDisplay-dashesN):
dashes+=' '
for i in range(dashesN):
dashes+='+'
return dashes
"""Takes a population of chromosomes and returns a list of tuples where each chromo is paired to its fitness scores and ranked accroding to its fitness"""
def rankPop (chromos):
proteins, outputs, errors = [], [], []
i = 1
# translate each chromo into mathematical expression (protein), evaluate the output of the expression,
# calculate the inverse error of the output
print ('%s: %s\t=%s \t%s %s' %('n'.rjust(5), 'PROTEIN'.rjust(30), 'OUTPUT'.rjust(10), 'INVERSE ERROR'.rjust(17), 'GRAPHICAL INVERSE ERROR'.rjust(105)))
for chromo in chromos:
protein = translate(chromo)
proteins.append(protein)
output = evaluate(protein)
outputs.append(output)
try:
error = 1/math.fabs(target-output)
except ZeroDivisionError:
global solution_found
solution_found = True
error = 0
print ('\nSOLUTION FOUND' )
print ('%s: %s \t=%s %s' %(str(i).rjust(5), protein.rjust(30), str(output).rjust(10), displayFit(1.3).rjust(130)))
break
else:
#error = 1/math.fabs(target-output)
errors.append(error)
print ('%s: %s \t=%s \t%s %s' %(str(i).rjust(5), protein.rjust(30), str(output).rjust(10), str(error).rjust(17), displayFit(error).rjust(105)))
i+=1
fitnessScores = calcFitness (errors) # calc fitness scores from the erros calculated
pairedPop = zip ( chromos, proteins, outputs, fitnessScores) # pair each chromo with its protein, ouput and fitness score
rankedPop = sorted ( pairedPop,key = itemgetter(-1), reverse = True ) # sort the paired pop by ascending fitness score
return rankedPop
""" taking a ranked population selects two of the fittest members using roulette method"""
def selectFittest (fitnessScores, rankedChromos):
while 1 == 1: # ensure that the chromosomes selected for breeding are have different indexes in the population
index1 = roulette (fitnessScores)
index2 = roulette (fitnessScores)
if index1 == index2:
continue
else:
break
ch1 = rankedChromos[index1] # select and return chromosomes for breeding
ch2 = rankedChromos[index2]
return ch1, ch2
"""Fitness scores are fractions, their sum = 1. Fitter chromosomes have a larger fraction. """
def roulette (fitnessScores):
index = 0
cumalativeFitness = 0.0
r = random.random()
for i in range(len(fitnessScores)): # for each chromosome's fitness score
cumalativeFitness += fitnessScores[i] # add each chromosome's fitness score to cumalative fitness
if cumalativeFitness > r: # in the event of cumalative fitness becoming greater than r, return index of that chromo
return i
def crossover (ch1, ch2):
# at a random chiasma
r = random.randint(0,genesPerCh*4)
return ch1[:r]+ch2[r:], ch2[:r]+ch1[r:]
def mutate (ch):
mutatedCh = []
for i in ch:
if random.random() < mutation_rate:
if i == 1:
mutatedCh.append(0)
else:
mutatedCh.append(1)
else:
mutatedCh.append(i)
#assert mutatedCh != ch
return mutatedCh
"""Using breed and mutate it generates two new chromos from the selected pair"""
def breed (ch1, ch2):
newCh1, newCh2 = [], []
if random.random() < crossover_rate: # rate dependent crossover of selected chromosomes
newCh1, newCh2 = crossover(ch1, ch2)
else:
newCh1, newCh2 = ch1, ch2
newnewCh1 = mutate (newCh1) # mutate crossovered chromos
newnewCh2 = mutate (newCh2)
return newnewCh1, newnewCh2
""" Taking a ranked population return a new population by breeding the ranked one"""
def iteratePop (rankedPop):
fitnessScores = [ item[-1] for item in rankedPop ] # extract fitness scores from ranked population
rankedChromos = [ item[0] for item in rankedPop ] # extract chromosomes from ranked population
newpop = []
newpop.extend(rankedChromos[:popN/15]) # known as elitism, conserve the best solutions to new population
while len(newpop) != popN:
ch1, ch2 = [], []
ch1, ch2 = selectFittest (fitnessScores, rankedChromos) # select two of the fittest chromos
ch1, ch2 = breed (ch1, ch2) # breed them to create two new chromosomes
newpop.append(ch1) # and append to new population
newpop.append(ch2)
return newpop
def configureSettings ():
configure = raw_input ('T - Enter Target Number \tD - Default settings: ')
match1 = re.search( 't',configure, re.IGNORECASE )
if match1:
global target
target = input('Target int: ' )
def main():
configureSettings ()
chromos = generatePop() #generate new population of random chromosomes
iterations = 0
while iterations != max_iterations and solution_found != True:
# take the pop of random chromos and rank them based on their fitness score/proximity to target output
rankedPop = rankPop(chromos)
print ('\nCurrent iterations:', iterations)
if solution_found != True:
# if solution is not found iterate a new population from previous ranked population
chromos = []
chromos = iteratePop(rankedPop)
iterations += 1
else:
break
if __name__ == "__main__":
main()
答案 0 :(得分：0)
可能你使用的是Python3，而不是Python2。
函数raw_input适用于Python2。
在Python3中，raw_input（）被重命名为input（）
来自http://docs.python.org/dev/py3k/whatsnew/3.0.html
因此，将 raw_input 替换为输入并尝试一下。