Used to and Would

本文介绍了如何使用used to + 动词原形和would + 动词原形来描述过去的习惯行为以及如何用didn't use to + 动词原形表达过去不常做的事情。注意,在否定句和疑问句中通常不用would。

used to / would + infinitive
Example: I used to / would smoke.
 
did not + use to + infinitive
Example: I did't use to smoke.
 
did ... use to + infinitive?
Example: did you use to smoke?



We use used to + infinitive or would + infinitive to describe repeated actions in the past:
I used to keep the windows closed when I first moved in. (but I stopped doing this)
I would leave the windows open whenever I was at home.

Note: we do not usually use would in the negative form and in Yes/No questions.

  • We use used to + infinitive to describe past states that are usually no longer true:
    We used to live in London when I was a kid. (but we don’t now: not We would live in London when I was a kid.)

  • We do not use used to to refer to specific restricted periods in the past or saying how long it took or how many times:
    I lived in New York City for ten years. (not I used to live in New York City for ten years.)
    I went to London twice when I was young. (not I used to go to London twice when I was young.)
    Note: we do not use would with state verbs.


    
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。
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