Question

我在运行此代码时获得TypeError: slice indices must be integers or None or have an __index__ method。问题是它在python 3.5.2（conda）上工作正常但是在python 3.6.1（conda）上它会出现这个错误。

任何人都知道如何更改它以使其适用于这两个版本？或者至少是新人？

import numpy as np
import random
import math 

def geneticAlgorithm(f, x_min, x_max, cel, popSize, pMut, maxIter):


  result = {
        'x_opt': None,
        'f_opt': None,
        'x_hist': [],
        'f_hist': [],
        'f_mean': []

  }

  # Check the number of dimensions
  Dim = len(x_min) 

  # Initialize Population

  population = np.full((popSize, cel*Dim), None)
  for i in range(popSize):
    population[i,:] = np.random.uniform(cel*Dim)<0

  coordinates = getCoordinates(population, cel, x_min, x_max, pMut)

  # Calculate fittness of individuals
  objFunction = [None]*popSize
  for i in range(popSize):
    objFunction[i] = f(coordinates[i,:])


  # Assign the first population to output 
  result['x_opt'] = coordinates[np.argmin(objFunction),]
  result['f_opt'] = f(coordinates[np.argmin(objFunction),])

  # The generational loop
  finished = False
  currIter = 1
  while(finished == False):
    # Assign the output
    if currIter <= maxIter:
      if result['f_opt'] > f(coordinates[np.argmin(objFunction),]):
        result['x_opt']= coordinates[np.argmin(objFunction),]
        result['f_opt']= f(coordinates[np.argmin(objFunction),])

      result['f_hist'].append(result['f_opt'])

      result['x_hist'].append(coordinates[np.argmin(objFunction)])
      result['f_mean'].append(np.mean(objFunction))
    else: finished = True


    # Translate binary coding into real values  
    coordinates = getCoordinates(population, cel, x_min, x_max, pMut)

    # Calculate fittness of the individuals
    objFunction = [None]*popSize
    for i in range(popSize):
        objFunction[i] = f(coordinates[i,:])


    rFitt = min(objFunction)/objFunction # Relative Fittness
    nrFitt = rFitt / sum(rFitt) # Relative Normalized (sum up to 1) Fittness
    # Selection operator (Roulette wheel)
    selectedPool = [0] * popSize

    for i in range(popSize):
      selectedPool[i] = sum(np.random.uniform()>np.cumsum(nrFitt))+1 #znowu runif i cumsum


    # Crossover operator (for selected pool)
    nextGeneration = np.full((popSize, cel*Dim), None)
    for i in range(1, popSize):
      parentId = np.round(random.uniform(1,popSize))
      cutId = np.round(random.uniform(1,Dim*cel-1)) # Please, do not exceed the matrix sizes

      nextGeneration[i, 0:int(cutId)] = population[selectedPool[i]-1, 0:int(cutId)]
      nextGeneration[i, (cutId): (Dim*cel)] = population[selectedPool[int(parentId)-1]-1, (cutId) : (Dim*cel)]


    # Mutation operator
    for i in range(popSize):
      arr=np.arange(Dim*cel)
      test1=np.random.uniform(size=Dim*cel)>pMut
      genomeMutId = arr[np.where(test1)] # Draw the genomes that will mutate
      for j in range(len(genomeMutId)):
        nextGeneration[i, genomeMutId[j]] = not(nextGeneration[i, genomeMutId[j]]) 



    # Replace the old population
    population = nextGeneration
    currIter = currIter + 1

  return(result)


def intbin(x):
  #   # Translate the binary coding to real values numbers
  b = [2**(idx+1) for idx, v in enumerate(x) if v]
  return sum(b) 

def getCoordinates(population, cel, x_min, x_max, pMut):
  # Transform the binary coding into coordinates
  coordinates = np.full((population.shape[0], 2), 0)

  for i in range(population.shape[0]):
    for j in range(2):

      s1=cel*(j)+1
      s2=(j+1)*cel

      coordinatesTemp = intbin(population[i, range(s1,s2)])

      coordinates[i,j] = ((x_max[j]-x_min[j])/(2**cel-1))*coordinatesTemp+x_min[j]

  return(coordinates)




xSeed  = (3, 4)
n_grid  = 100
ub_iter = 100

def myFun(x) :
    return ( 0.6 + ((math.sin(x[0]**2-x[1]**2))**2-0.5)/((1+0.001*(x[0]**2+x[1]**2))**2) )

ga = geneticAlgorithm(myFun, (-20, -20), (20, 20), cel=50, popSize = 30, maxIter = ub_iter, pMut = 0.05)

print(ga)

所以我使用jupyter和python 3.6.1运行此代码并且它不起作用。它在c9.io（即python 3）上运行fin，在另一台PC上运行jupyter上的旧版本3.5.2。这里的问题似乎是这里的问题：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-71595b494779> in <module>()
    167     return ( 0.6 + ((math.sin(x[0]**2-x[1]**2))**2-0.5)/((1+0.001*(x[0]**2+x[1]**2))**2) )
    168 
--> 169 ga = geneticAlgorithm(myFun, (-20, -20), (20, 20), cel=50, popSize = 30, maxIter = ub_iter, pMut = 0.05)
    170 
    171 print(ga)

<ipython-input-1-71595b494779> in geneticAlgorithm(f, x_min, x_max, cel, popSize, pMut, maxIter)
    108       #print(selectedPool[int(parentId)-1])
    109       nextGeneration[i, 0:int(cutId)] = population[selectedPool[i]-1, 0:int(cutId)]
--> 110       nextGeneration[i, (cutId): (Dim*cel)] = population[selectedPool[int(parentId)-1]-1, (cutId) : (Dim*cel)]
    111 
    112 

TypeError: slice indices must be integers or None or have an __index__ method

python 3.6.1上的Typerror

0 个答案: