Flex event 官方文档重点小结(不全)

本文深入探讨了FlexPriority的概念及其在事件监听中的应用,包括如何通过设置优先级来控制事件处理顺序,并以键盘事件为例,展示了如何通过焦点设置触发事件及不同按键的编码方式。同时介绍了如何通过MouseEvent类及其相关属性来处理鼠标事件。
 

Flex priority
1.You can register any number of event listenres with a single event;
2.If you register some event listeners inline and some with the
addEventListener() method, the order in which the listeners are called for a single event can be unpredictable.  ?
3.Change the order by using the priority parameter of the addEventListerner()
method. The default value is 0. The highest priority event is called first.
4.If you want to change the priority of an event listener, you must remove the
listerner by calling the removeEventListerner() method, and then add the listener again with the new priority.
5.The priority can guarantee the order in which listener functions will be called, 
 but it can't ensure that the listener will finish executing before the next listener is called. So you should ensure your listeners don't rely on each other. (asynchronous)

Keyboard events
1.If you want to dispatch the keyboard event, you must set the focus first.
2.keyCode: a numeric value that corresponds to the value of a key on the
keyboard. It depends on the device and operating system.
   charCode: ASCII value.
3.When handling a key or key combination that the underlying operating
system or browser recognizes, the operating system or browser generally processes the event first.
4.The MouseEvent class and all MouseEvent subclasses have the three
properties(altKey, ctrlKey, shiftKey) that you can use to determine if a specific key was held down when the event occurred.

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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