帮我注释代码Algorithm 1: Adaptive Large Neighborhood search 1 Initialization: 2 s← InitialSolution (), s*←s; 3 ρ← InitialWeights(); 4 Ω← InitialScores(); 5 ν← InitialAttempts(); 6 While stop criteria not met do 7 Select neighborhood n ∈ N using ρ;
时间: 2023-03-25 14:01:11 浏览: 61
我可以为您注释代码。以下是代码的注释:
Algorithm 1: 自适应大邻域搜索
1 初始化:
2 s← InitialSolution (), s*←s; // 初始化当前解和最优解
3 ρ← InitialWeights(); // 初始化权重
4 Ω← InitialScores(); // 初始化分数
5 ν← InitialAttempts(); // 初始化尝试次数
6 While stop criteria not met do // 当停止条件未满足时执行以下操作
7 Select neighborhood n ∈ N using ρ; // 使用权重ρ选择邻域n
相关问题
二、实验内容 1、编写:first fit algorithm 2、编写:best fit algorithm 3、编写:
二、实验内容:
1、编写First Fit算法:
First Fit算法是一种内存分配算法,用于将可用的内存块分配给需要的进程。该算法从第一个可用的内存块开始查找,并分配给第一个满足需求的进程。具体步骤如下:
- 遍历可用内存块列表,找到第一个大小大于等于所需内存的块。
- 如果找到了满足条件的块,将进程分配到该块中,更新内存块的状态,并返回分配的起始地址。
- 如果找不到满足条件的块,表示没有足够的内存可供分配,返回错误信息。
2、编写Best Fit算法:
Best Fit算法是一种内存分配算法,它选择满足进程需求最小的可用内存块来进行分配。具体步骤如下:
- 遍历可用内存块列表,找到满足进程需求的最小内存块。
- 如果找到了满足条件的块,将进程分配到该块中,更新内存块的状态,并返回分配的起始地址。
- 如果找不到满足条件的块,表示没有足够的内存可供分配,返回错误信息。
3、编写(略)
CEEMDAN.ceemdan() missing 1 required positional argument: 'S'
This error message means that the method `ceemdan()` of the `CEEMDAN` class is missing a required positional argument 'S'. This argument is necessary for the method to work properly.
To solve this error, you need to provide the missing argument 'S' to the `ceemdan()` method. The argument 'S' is usually the input signal that needs to be decomposed using the CEEMDAN algorithm.
Here's an example code snippet that shows how to use the `ceemdan()` method with an input signal 'x':
```
from ceemdan import CEEMDAN
# create a CEEMDAN object
ceemdan = CEEMDAN()
# input signal
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# decompose the signal using CEEMDAN
imfs = ceemdan.ceemdan(x, S=None)
```
In this example, the `ceemdan()` method is called with the input signal 'x' and the argument 'S' is set to None. The 'S' argument is optional and can be used to provide an initial guess for the mode mixing matrix. If 'S' is not provided, the algorithm will use a random matrix as the initial guess.