python - 为什么该对象没有已在另一个模块中定义的属性？

Question

我正在测试编写并发布在网站上的代码

http://foreverlearning.altervista.org/genetic-programming-symbolic-regression-pt-3/

该部分代码位于网页的底部。运行测试代码 mainpova.py 时，出现语法错误。

语法错误是，

python mainprova4.py
Best solution is ((3*(1*2))+x) with error 20.0...
Producing gen number 2...
Traceback (most recent call last):
 File "mainprova4.py", line 68, in <module>
main()
 File "mainprova4.py", line 62, in main
gen.next(crossoverPerc, mutationPerc, randomPerc, copyPerc,shouldPruneForMaxHeight, minHeight, maxHeight, minValue, maxValue, variables, operators)
AttributeError: 'Generation' object has no attribute 'next'

mainprova4.py 的代码是

import generation as gn
import tree as tr
import generator as gtr
import math

xs = [-1, 1, 0, 3, -2, 0, -1, 3, 2, -2] # Values of x
ys = [1, 1, 0, 2, -2, 5, 3, -1, 5, -4] # Values of y
zs = [3, 3, 1, 12, 3, 6, 5, 9, 10, 1] # Values of z, from z = x^2 + y + 1

def main():
    minHeight = 1
    maxHeight = 5
    minValue = 1
    maxValue = 3
    variables = ["x", "y"]
    operators = ["+", "-", "*"]

    numOfMembers = 150
    maxNumOfGenerations = 500
    currentGen = 1
    crossoverPerc = 0.5
    mutationPerc = 0.3
    randomPerc = 0.1
    copyPerc = 0.1
    shouldPruneForMaxHeight = True



    # Step 1: create first generation
    gen = gn.Generation()
    for i in range(0, numOfMembers):
    gen.addMember(gtr.getTree(minHeight, maxHeight, minValue, maxValue, variables, operators))


    for genNum in range(1, maxNumOfGenerations + 1):
       """ Step 2: evaluate all members """
       for memberNum in range(0, gen.size()):
       member = gen.getMember(memberNum)
       totalError = 0
       for i in range(0, len(xs)):
           res = member.eval({"x": xs[i], "y": ys[i]})
           error = math.fabs(zs[i] - res)
           totalError += error
           gen.setError(memberNum, totalError)

       """ Step 3: sort solutions according to errors """
       gen.sort(descending = False)

       """ Step 4: if best solution has error zero, then stop """
       print("Best solution is " + str(gen.getMember(0)) + " with error " + str(gen.getError(0)) + "...")
       if gen.getError(0) == 0:
       break

       """ If limit reached, then stop process """
       if currentGen == maxNumOfGenerations:
          print("LIMIT REACHED")
      break

       """ Step 5: produce next generation """
       currentGen += 1
       print("Producing gen number " + str(currentGen) + "...")
       gen.next(crossoverPerc, mutationPerc, randomPerc, copyPerc, shouldPruneForMaxHeight, minHeight, maxHeight, minValue, maxValue, variables, operators)

    print("END ~~~~~~~~~~~~~~~~~~~~~~~~")
    print("Best solution found is " + str(gen.getMember(0)) + " with error " + str(gen.getError(0)))

if __name__ == "__main__":
    main()

我不认为这是缩进问题。我在这里缺少什么。具有下一个定义的代码是

import random as rnd
import generator as gtr
import treeOperations as trop

class Generation(object):
    def __init__(self):
      self.membersWithErrors = []

    def addMember(self, member):
      """ Add a tree to the generation """
      self.membersWithErrors.append([member, 0])

    def setMember(self, member, index):
      """ Updates the member at the specified position """
      self.membersWithErrors[index] = member

    def setError(self, index, error):
      """ Sets the error of the member at the specified position """
      self.membersWithErrors[index][1] = error

    def getMember(self, index):
      """ Returns the member at the specified position """
      return self.membersWithErrors[index][0]

    def getError(self, index):
      """ Returns the error of the member at the specified position """
      return self.membersWithErrors[index][1]

    def size(self):
      """ Returns the number of members curently in the generation """
      return len(self.membersWithErrors)

    def clear(self):
      """ Clears the generation, i.e. removes all the members """
      self.membersWithErrors.clear()

    def sort(self, descending):
      """ Sorts the members of the generation according the their score """
      self.membersWithErrors.sort(key = lambda l: l[1], reverse = descending)

def getMembersForReproduction(self, numMembers, pickProb):
    """ Returns a certain number of distinct members from the generation.
    The first member is selected with probability pickProb. If it's not chosen, the 
    second member is selected with probability pickProb, and so on. """
    selectedMembers = []
    while len(selectedMembers) < numMembers: 
      indexSelected = 0  
      while rnd.randint(0, 100) > int(pickProb * 100) and indexSelected != len(self.membersWithErrors) - 1:
    indexSelected += 1
    memberWithErrorSelected = self.membersWithErrors[indexSelected]
    if memberWithErrorSelected[0] not in selectedMembers:
       selectedMembers.append(memberWithErrorSelected[0])
    return selectedMembers

def next(self, crossoverPerc, mutationPerc, randomPerc, copyPerc, shouldPruneForMaxHeight, minHeight, maxHeight, minValue, maxValue, variables, operators):
    """ It proceeds to the next generation with the help of genetic operations """
    oldMembersWithError = self.membersWithErrors
    newMembersWithError = []
    maxMembers = len(oldMembersWithError)

    numCrossover = int(maxMembers * crossoverPerc)
    numMutation = int(maxMembers * mutationPerc)
    numRandom = int(maxMembers * randomPerc)
    numCopy = maxMembers - numCrossover - numMutation - numRandom

    # Crossover
    for i in range(0, numCrossover):
    members = self.getMembersForReproduction(2, 0.3)
    m1 = members[0]
    m2 = members[1]
    newMember = trop.crossover(m1, m2)
    newMembersWithError.append([newMember, 0])

    # Crossover
    for i in range(0, numCrossover):
        members = self.getMembersForReproduction(2, 0.3)
        m1 = members[0]
        m2 = members[1]
        newMember = trop.crossover(m1, m2)
        if shouldPruneForMaxHeight and newMember.height() > maxHeight:
       newMember = trop.pruneTreeForMaxHeight(newMember, maxHeight, minValue, maxValue, variables)
    newMembersWithError.append([newMember, 0])

    # Mutation
    for i in range(0, numMutation):
        m1 = self.getMembersForReproduction(1, 0.3)[0]
        newMembersWithError.append([trop.mutation(m1, minValue, maxValue, variables, operators), 0])

    # Random
    for i in range(0, numRandom):
    newMembersWithError.append([gtr.getTree(minHeight, maxHeight, minValue, maxValue, variables, operators), 0])

    # Copy
    members = self.getMembersForReproduction(numCopy, 0.3)
    for m in members:
        ewMembersWithError.append([m.clone(), 0])

    self.membersWithErrors = newMembersWithError

标签： python

Answer 1

您可以在此处看到突出显示的错误：

import random as rnd
import generator as gtr
import treeOperations as trop

class Generation(object):
    def __init__(self):
      self.membersWithErrors = []

    def addMember(self, member):
      """ Add a tree to the generation """
      self.membersWithErrors.append([member, 0])

    def setMember(self, member, index):
      """ Updates the member at the specified position """
      self.membersWithErrors[index] = member

    def setError(self, index, error):
      """ Sets the error of the member at the specified position """
      self.membersWithErrors[index][1] = error

    def getMember(self, index):
      """ Returns the member at the specified position """
      return self.membersWithErrors[index][0]

    def getError(self, index):
      """ Returns the error of the member at the specified position """
      return self.membersWithErrors[index][1]

    def size(self):
      """ Returns the number of members curently in the generation """
      return len(self.membersWithErrors)

    def clear(self):
      """ Clears the generation, i.e. removes all the members """
      self.membersWithErrors.clear()

    def sort(self, descending):
      """ Sorts the members of the generation according the their score """
      self.membersWithErrors.sort(key = lambda l: l[1], reverse = descending)

############ INDENTATION PROBLEM ################
def getMembersForReproduction(self, numMembers, pickProb):
    """ Returns a certain number of distinct members from the generation.
    The first member is selected with probability pickProb. If it's not chosen, the 
    second member is selected with probability pickProb, and so on. """
    selectedMembers = []
    while len(selectedMembers) < numMembers: 
      indexSelected = 0  
      while rnd.randint(0, 100) > int(pickProb * 100) and indexSelected != len(self.membersWithErrors) - 1:
    indexSelected += 1
    memberWithErrorSelected = self.membersWithErrors[indexSelected]
    if memberWithErrorSelected[0] not in selectedMembers:
       selectedMembers.append(memberWithErrorSelected[0])
    return selectedMembers

############ HERE IS THE INDENTATION PROBLEM ########## 
def next(self, crossoverPerc, mutationPerc, randomPerc, copyPerc, shouldPruneForMaxHeight, minHeight, maxHeight, minValue, maxValue, variables, operators):
    """ It proceeds to the next generation with the help of genetic operations """
    oldMembersWithError = self.membersWithErrors
    newMembersWithError = []
    maxMembers = len(oldMembersWithError)

    numCrossover = int(maxMembers * crossoverPerc)
    numMutation = int(maxMembers * mutationPerc)
    numRandom = int(maxMembers * randomPerc)
    numCopy = maxMembers - numCrossover - numMutation - numRandom

    # Crossover
    for i in range(0, numCrossover):
    members = self.getMembersForReproduction(2, 0.3)
    m1 = members[0]
    m2 = members[1]
    newMember = trop.crossover(m1, m2)
    newMembersWithError.append([newMember, 0])

    # Crossover
    for i in range(0, numCrossover):
        members = self.getMembersForReproduction(2, 0.3)
        m1 = members[0]
        m2 = members[1]
        newMember = trop.crossover(m1, m2)
        if shouldPruneForMaxHeight and newMember.height() > maxHeight:
       newMember = trop.pruneTreeForMaxHeight(newMember, maxHeight, minValue, maxValue, variables)
    newMembersWithError.append([newMember, 0])

    # Mutation
    for i in range(0, numMutation):
        m1 = self.getMembersForReproduction(1, 0.3)[0]
        newMembersWithError.append([trop.mutation(m1, minValue, maxValue, variables, operators), 0])

    # Random
    for i in range(0, numRandom):
    newMembersWithError.append([gtr.getTree(minHeight, maxHeight, minValue, maxValue, variables, operators), 0])

    # Copy
    members = self.getMembersForReproduction(numCopy, 0.3)
    for m in members:
        ewMembersWithError.append([m.clone(), 0])

    self.membersWithErrors = newMembersWithError

应该是这样的：

import random as rnd
import generator as gtr
import treeOperations as trop

class Generation(object):
    def __init__(self):
      self.membersWithErrors = []

    def addMember(self, member):
      """ Add a tree to the generation """
      self.membersWithErrors.append([member, 0])

    def setMember(self, member, index):
      """ Updates the member at the specified position """
      self.membersWithErrors[index] = member

    def setError(self, index, error):
      """ Sets the error of the member at the specified position """
      self.membersWithErrors[index][1] = error

    def getMember(self, index):
      """ Returns the member at the specified position """
      return self.membersWithErrors[index][0]

    def getError(self, index):
      """ Returns the error of the member at the specified position """
      return self.membersWithErrors[index][1]

    def size(self):
      """ Returns the number of members curently in the generation """
      return len(self.membersWithErrors)

    def clear(self):
      """ Clears the generation, i.e. removes all the members """
      self.membersWithErrors.clear()

    def sort(self, descending):
      """ Sorts the members of the generation according the their score """
      self.membersWithErrors.sort(key = lambda l: l[1], reverse = descending)

    ########## YOU HAVE TO FIX THIS TOO ############
    def getMembersForReproduction(self, numMembers, pickProb):
        """ Returns a certain number of distinct members from the generation.
        The first member is selected with probability pickProb. If it's not chosen, the 
        second member is selected with probability pickProb, and so on. """
        selectedMembers = []
        while len(selectedMembers) < numMembers: 
          indexSelected = 0  
          while rnd.randint(0, 100) > int(pickProb * 100) and indexSelected != len(self.membersWithErrors) - 1:
        indexSelected += 1
        memberWithErrorSelected = self.membersWithErrors[indexSelected]
        if memberWithErrorSelected[0] not in selectedMembers:
           selectedMembers.append(memberWithErrorSelected[0])
        return selectedMembers

    ############ This is the proper identation #############
    def next(self, crossoverPerc, mutationPerc, randomPerc, copyPerc, shouldPruneForMaxHeight, minHeight, maxHeight, minValue, maxValue, variables, operators):
        """ It proceeds to the next generation with the help of genetic operations """
        oldMembersWithError = self.membersWithErrors
        newMembersWithError = []
        maxMembers = len(oldMembersWithError)

        numCrossover = int(maxMembers * crossoverPerc)
        numMutation = int(maxMembers * mutationPerc)
        numRandom = int(maxMembers * randomPerc)
        numCopy = maxMembers - numCrossover - numMutation - numRandom

        # Crossover
        for i in range(0, numCrossover):
        members = self.getMembersForReproduction(2, 0.3)
        m1 = members[0]
        m2 = members[1]
        newMember = trop.crossover(m1, m2)
        newMembersWithError.append([newMember, 0])

        # Crossover
        for i in range(0, numCrossover):
        members = self.getMembersForReproduction(2, 0.3)
        m1 = members[0]
        m2 = members[1]
        newMember = trop.crossover(m1, m2)
        if shouldPruneForMaxHeight and newMember.height() > maxHeight:
           newMember = trop.pruneTreeForMaxHeight(newMember, maxHeight, minValue, maxValue, variables)
        newMembersWithError.append([newMember, 0])

        # Mutation
        for i in range(0, numMutation):
            m1 = self.getMembersForReproduction(1, 0.3)[0]
            newMembersWithError.append([trop.mutation(m1, minValue, maxValue, variables, operators), 0])

        # Random
        for i in range(0, numRandom):
        newMembersWithError.append([gtr.getTree(minHeight, maxHeight, minValue, maxValue, variables, operators), 0])

        # Copy
        members = self.getMembersForReproduction(numCopy, 0.3)
        for m in members:
            ewMembersWithError.append([m.clone(), 0])

        self.membersWithErrors = newMembersWithError