14.9.9 The is operator

本文详细解析了类型检查运算符is的使用方法及其在运行时确定对象类型兼容性的过程。该运算符通过引用转换、装箱转换和拆箱转换来判断对象是否可以成功转换为目标类型。
The is operator is used to dynamically check if the run-time type of an
object is compatible with a given
type. The result of the operation e is T, where e is an expression and T is
a type, is a boolean value
indicating whether e can successfully be converted to type T by a reference
conversion, a boxing conversion,
or an unboxing conversion. The operation is evaluated as follows:
?If the compile-time type of e is the same as T, or if an implicit
reference conversion (?3.1.4) or boxing
conversion (?3.1.5) exists from the compile-time type of e to T:
If e is of a reference type, the result of the operation is equivalent to
evaluating e != null.
If e is of a value type, the result of the operation is true.
?Otherwise, if an explicit reference conversion (?3.2.3) or unboxing
conversion (?3.2.4) exists from
the compile-time type of e to T, a dynamic type check is performed:
If the value of e is null, the result is false.
Otherwise, let R be the run-time type of the instance referenced by e. If R
and T are the same type, if R is a
reference type and an implicit reference conversion from R to T exists, or
if R is a value type and T is an
interface type that is implemented by R, the result is true.
Otherwise, the result is false.
?Otherwise, no reference or boxing conversion of e to type T is possible,
and the result of the operation is
false.
The is operator only considers reference conversions, boxing conversions,
and unboxing conversions. Other
conversions, such as user defined conversions, are not considered by the is
operator.
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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