将下面MATLAB代码转换成python代码 %% 基于logistic映射的种群初始化子函数 function Positions=logisticInitialization(popsize,dim,ub,lb) %input：popsize 种群数量 % dim 变量维度 % ub 变量上限 % lb 变量下限 %return：Positions 生成的初始种群位置 %初始化位置0数组 Positions=zeros(popsize,dim); %对每个个体，混沌映射产生位置 for i = 1:popsize value = Logistic(dim); %混沌映射序列 Positions(i,:)=value.*(ub-lb)+lb; %位置越界限制 Positions(i,:)=min(Positions(i,:),ub); %上界调整 Positions(i,:)=max(Positions(i,:),lb); %下界调整 end end %混沌映射子函数 function sequence=Logistic(n) %input：n 混沌序列长度 %return：value 生成的混沌序列 %初始化数组 sequence=zeros(1,n); sequence(1)=rand; %序列起点 %x0不为(0,0.25,0.5,0.75,1) while max(sequence(1)==[0 0.25 0.5 0.75 1])==1 sequence(1)=rand; end mu =3.8 ;%参数mu范围(0,4) for i=1:n-1 sequence(i+1)=mu*sequence(i)*(1-sequence(i)); end end

Here's the Python code equivalent to the given MATLAB code:

import numpy as np

def logisticInitialization(popsize, dim, ub, lb):
    # popsize: population size
    # dim: dimension of the variables
    # ub: upper bound of the variables
    # lb: lower bound of the variables
    # Returns: positions - the generated initial population
    
    # Initializing the positions array with zeros
    positions = np.zeros((popsize, dim))
    
    # Generating position for each individual using chaotic Logistic mapping
    for i in range(popsize):
        value = Logistic(dim)
        positions[i,:] = value * (ub - lb) + lb
        
        # Position boundary constraint
        positions[i,:] = np.minimum(positions[i,:], ub) # upper bound adjustment
        positions[i,:] = np.maximum(positions[i,:], lb) # lower bound adjustment
    
    return positions

def Logistic(n):
    # n: length of the chaotic sequence
    # Returns: sequence - the generated chaotic sequence
    
    # Initializing the sequence array with zeros
    sequence = np.zeros(n)
    
    # Setting the initial point of the sequence
    sequence[0] = np.random.rand()
    
    # Checking if the initial point is not in (0, 0.25, 0.5, 0.75, 1)
    while sequence[0] in [0, 0.25, 0.5, 0.75, 1]:
        sequence[0] = np.random.rand()
    
    # Parameter mu range is (0, 4)
    mu = 3.8
    
    # Generating the chaotic sequence using Logistic mapping
    for i in range(n-1):
        sequence[i+1] = mu * sequence[i] * (1 - sequence[i])
    
    return sequence