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第二篇杂录 侧重最佳实践.

最近 (2021/10/28) 发现官方文档有 Programming FAQ — Python 3.10.0 documentation, 很有用.

Misc

一些语法糖

2023/1/10

(1,) + (2, 3)
# (1, 2, 3)

a = {1: 1}
b = {2: 2}
{**a, **b}
# {1: 1, 2: 2}

nonlocal

2022/7/5

忘了看哪个源码的时候读到的

Python の nonlocal と global の違い - Qiita

functools

lru_cache 以及 singledispatch

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算法分析的那个定理.

Master Theorem

\[T(n) = \begin{cases} \Theta(1), & \text{if } n = 1,\\ aT(n/b) + f(n), & \text{if } n>1. \end{cases}\]

where $a\ge1$, $b>1$ are constants and $f$ is nonnegative. Then

  1. If $f(n) = O(n^{\log_b a-\varepsilon})$ for some constant $\varepsilon >0$, then $T(n) = \Theta(n^{\log_b a})$.
  2. If $f(n) = \Theta(n^{\log_b a})$, then $T(n) = \Theta(n^{\log_b a}\log n)$.
  3. If $f(n) = \Omega(n^{\log_b a+\varepsilon})$ for some constant $\varepsilon >0$, and if $af(n/b)\le cf(n)$ for some constant $c<1$ and all sufficiently large $n$, then $T(n) = \Theta(f(n))$.
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因为 TeXworks 用了太多年, 不太想换 IDE, 还是继续用了.

参考 官方文档.

文件地址: TeXworks 菜单栏的 “帮助” -> “TeXworks 配置与资源” -> “资源” -> “completion” 文件夹 -> “tw-latex.txt” 文件.

语法

<alias>:=<text>

The <alias>:= part can be omitted to turn the code text into its own alias. <text> must fit in a single line. Empty lines and lines starting with a % are ignored.

第一句话的意思是, 单纯写 blahblah 相当于 blahblah:=blahblah.

<text> 中连续的空格是有效的.

  • #RET# 表示 return, 换行.
  • #INS# 表示 insert, 光标会被放置在此处.
  • • bullet 是 placeholder, 使用 <Ctrl>+<Tab> 让光标移动到下一个占位符处.
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While this point may seem obvious to the reader, we have seen this blunder committed many times in published papers in top rank journals.

Consider a classification problem with a large number of predictors, as may arise, for example, in genomic or proteomic applications. A typical strategy for analysis might be as follows:

  1. Screen the predictors: find a subset of “good” predictors that show fairly strong (univariate) correlation with the class labels.
  2. Using just this subset of predictors, build a multivariate classifier.
  3. Use cross-validation to estimate the unknown tuning parameters and to estimate the prediction error of the final model.
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我们考虑一个事件 $A$, 它发生的概率是 $p$. 假设我们观测到事件 $A$ 发生, 我们希望定义一个信息量 $I(p)$ 来衡量 “$A$ 发生了” 这件事给了我们多少信息.

  1. $I(p)$ 是关于 $p$ 的递减函数. 如果事件发生概率高, 而且它发生了, 我们得到的信息应该比较少, 因为我们认为它确实容易发生, 这不稀奇.
  2. 考虑另一个独立的事件 $B$, 它发生的概率是 $q$, 则 $I(pq) = I(p) + I(q)$. 也就是说我们希望独立事件同时发生时提供的信息量应该是他们分别提供的信息量之和.
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Each employee of MegaCorp has a separate office in the MegaCorp office building. Each office is equipped with one overhead light and one toggle switch to turn the light on and off.

Every day, the employees turn on all lights when they come to work. Each evening they turn off all lights when they go home.

One day, the employees arrive to discover that someone has played a rather elaborate hoax on them. Though all looks fine when they come in (all lights are off), every time an employee flicks the switch in her office, this not only toggles the light in her office, but also the lights in the offices of all of her friends. (Friendship is a symmetric relationship.)

The question: does there necessarily exist an arrangement of the switches that will turn all lights simultaneously on (so that work can begin)? Prove your answer.

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