python return "%s: %s" % (
时间: 2023-11-06 15:43:12 浏览: 31
It seems like you were trying to write a code snippet, but the rest of the code is missing. Can you provide me with more context or information so I can better assist you?
相关问题
def time_now_string(): """ 返回现在时间 """ return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
这段代码是一个函数,其功能是返回当前的本地时间,并将其格式化为字符串形式。具体来说,该函数使用了Python标准库中的time模块,调用了其中的strftime()函数。该函数可以将时间元组(由localtime()函数返回)格式化为指定的字符串。"%Y-%m-%d %H:%M:%S"是一个格式字符串,它指定了输出时间的具体格式。其中,%Y表示4位数的年份,%m表示2位数的月份,%d表示2位数的日期,%H表示2位数的小时数(24小时制),%M表示2位数的分钟数,%S表示2位数的秒数。该函数可以用于记录日志、生成文件名等需要时间戳的场合。
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:
```python
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
```