Yield Usage Understanding

本文通过对比使用与未使用yield的方法,展示了yield在C#编程中的应用效果,并解释了Task.Yield的作用。

When would I use Task.Yield()?

http://stackoverflow.com/questions/22645024/when-would-i-use-task-yield

 

In order to have a better understanding for above page, we need first know yield return, below is the code example, put it in a console project, and then build, run to have a look the result:

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;

namespace TestYieldReturn
{
class TestYield
{

/// <summary>
/// 使用 yield 的例子.
/// </summary>
/// <returns></returns>
public IEnumerable<int> GetDataListWithYield()
{
for (int i = 0; i < 5; i++)
{
// 这里模拟一个操作
// 假设 这个方法, 对于每一行数据,都要花费一段时间处理, 才能返回.
Thread.Sleep(1000);
yield return i;
}
}

 

/// <summary>
/// 不使用 yield 的例子.
/// </summary>
/// <returns></returns>
public IEnumerable<int> GetDataListWithoutYield()
{
List<int> result = new List<int>();
for (int i = 0; i < 5; i++)
{
// 这里模拟一个操作
// 假设 这个方法, 对于每一行数据,都要花费一段时间处理, 才能返回.
Thread.Sleep(1000);
result.Add(i);
}
return result;
}
}

class Program
{
static void Main(string[] args)
{
TestYield test = new TestYield();
Console.WriteLine("测试不使用 yield 的例子!");
Console.WriteLine("==开始时间:{0}", DateTime.Now);
foreach (int data in test.GetDataListWithoutYield())
{
Console.WriteLine("====处理时间:{0}, 处理结果:{1}", DateTime.Now, data);
}
Console.WriteLine("==结束时间:{0}", DateTime.Now);


Console.WriteLine();
Console.WriteLine("测试使用 yield 的例子!");
Console.WriteLine("==开始时间:{0}", DateTime.Now);
foreach (int data in test.GetDataListWithYield())
{
Console.WriteLine("====处理时间:{0}, 处理结果:{1}", DateTime.Now, data);
}
Console.WriteLine("==结束时间:{0}", DateTime.Now);

Console.ReadLine();
}
}
}

转载于:https://www.cnblogs.com/researcher/p/5919527.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
### Python Generators Usage and Examples #### Understanding Generators A generator is a special type of iterator that allows programmers to declare functions that behave like iterators, i.e., they can be used in a `for` loop. The main difference between a regular function and a generator lies in the use of `yield`. When called, it returns an object (iterator) but does not start execution immediately[^1]. ```python def simple_generator(): yield 1 yield 2 yield 3 gen = simple_generator() print(next(gen)) # Output: 1 print(next(gen)) # Output: 2 ``` #### Infinite Sequences with Generators Generators are particularly useful when dealing with infinite or very large sequences as these do not require all elements to reside simultaneously in memory. An example would involve generating Fibonacci numbers indefinitely. ```python def fibonacci_numbers(nums): x, y = 0, 1 for _ in range(nums): x, y = y, x+y yield x fib_sequence = fibonacci_numbers(10) for num in fib_sequence: print(num) ``` #### Thread Safety Considerations When using generators within multi-threaded applications under Python versions prior to Python 3, encountering issues such as `ValueError: generator already executing` may occur due to concurrent access from multiple threads. To address this issue specifically for Python 2 environments, implementing thread-safe iteration through custom classes has been suggested where methods like `next()` could include synchronization mechanisms via locks[^3]. ```python class threadsafe_iter(object): """Takes an iterator/generator and makes it thread-safe by serializing call to the `next` method of given iterator/generator.""" def __init__(self, it): self.it = it self.lock = threading.Lock() def __iter__(self): return self def next(self): # For compatibility with Python 2 with self.lock: return self.it.next() ```
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