張議隆

@bebo1010

Joined on Dec 3, 2021

  • :::spoiler Table of Contents ::: bebo1010 -- python Sorting One Line Sorting :::spoiler Sort by another Dictionary sorted(nums, key = lambda element: key_dict[element])
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  • 5-fold cross validation The original dataset is randomly divided into five subsets, each containing an equal number of samples. The model is trained and evaluated five times. In each iteration, one of the folds is held out as the validation set, while the remaining four folds are used for training. The model is trained on the four training folds and then tested on the held-out fold. This process is repeated five times, with each fold serving as the validation set once. The performance metrics, such as accuracy or error rate, are recorded for each iteration. The performance metrics obtained from the five iterations are averaged to provide an overall assessment of the model's performance. 將資料集拆分成五等分,每次訓練時以四等分作為 training dataset,剩下一份作為 validation dataset。訓練時紀錄每次的表現並在最後回報五次平均的表現以作為模型的整體評估。 AUC
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  • 姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 15.19 :::info Suppose we have the following requirements for a university database that is used to keep track of students’ transcripts:
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  • 姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 6.16 :::info Specify the following queries on the COMPANY relational database schema shown in Figure 3.5, using the relational operators discussed in this chapter. Also show the result of each query as it would apply to the database state in Figure 3.6.
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  • Student ID: F74082125 Student Name: 張議隆 See full output and data in hackmd website, full output was too long to be shown here. Some Test Results :::warning When the sample data grows, sample time grows significantly. ::: mu_prior_params_$\mu$
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  • :::spoiler TOC ::: 資料庫伺服器處理 開啟access control stackoverflow 解釋 原因出自於origin=null(從本地資料夾直接開啟),而host=localhost 這個限制出自於browser的設定 需要在chrome 的捷徑設定裡面加上 --disable-web-security 把 CORS 關閉
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  • 姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 5.6 a. Retrieve the names and major departments of all straight-A students (students who have a grade of A in all their courses).
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  • 姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 4.7 BOOK table 刪除 BOOK 中的一個 tuple 時BOOK_AUTHOR、BOOK_COPIES、BOOK_LOANS 都需要進行 cascade (同時刪除這本書的資料)
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  • 姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 3.11 Insert <‘Robert’, ‘F’, ‘Scott’, ‘943775543’, ‘1972-06-21’, ‘2365 Newcastle Rd, Bellaire, TX’, M, 58000, ‘888665555’, 1 > into EMPLOYEE.有違反 Semantic Integrity Constraints,Robert 的上司是 James(888665555) ,但 Robert 的薪水(58000)比上司(55000)還高。
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  • 論文連結 姓名:張議隆 學號:F74082125 這一小段是我寫到最後才回來寫的,在此跟教授和助教說聲抱歉。這篇報告前段內容是論文相關,我整理得還算勉強可以接受;後半段大多為實驗的紀錄以及我當下遇到的挫折與疑問,因此非常瑣碎。筆記內提到的日期皆為我當下紀錄的時間點,可以從時間點感受到我做這份作業的發展。 另外有關於所有被 spoiler 遮住的部分在麻煩批改時到這個網頁來展開,我將它們全部遮起來是考量到作業要求中提到不可包含程式碼,但是站在我的筆記來看我還是得附上一些程式碼才更好紀錄,不好意思麻煩助教了! :::spoiler TOC :::
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  • 平均值與標準差 原始數據 $S = {2, 4, 6, 8, x}$ 數據共有 $n = 5$ 筆 平均值 $\mu = \dfrac{\displaystyle \sum^n_{i=1} S_i}{n} = \dfrac{20+x}{5}$ 變異數 $\sigma^2$ = 標準差($\sigma$)的平方 = $\dfrac{\displaystyle \sum^n_{i=1} (S_i - \mu)^2}{n}$ 變異數 $\sigma^2$ 也可寫成 $\displaystyle \sum^n_{i=1} S_i^2 - n\mu^2$ $(2^2+4^2+6^2+8^2+x^2)-5*(\dfrac{20+x}{5})^2=2^2$ $\dfrac{4}{5}x^2+8x+36=0$ $D=b^2-4ac=-51.2<0$
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  • Student ID: F74082125 Student Name: 張議隆 More information in hackmd website :::spoiler TOC ::: How to run my code Most commands are already written in Makefile
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  • ::: spoiler 目錄 ::: Birth-Death Process(BDP) 在 CTMC 的基礎上定義出來的一種 process 基礎定義:$P_j(t) = P(X(t) = j), t \geq 0, j \geq 0, X(0) = r, r \geq 0$,其中 $X(0) = r$ 代表 initial state 在 $r$。 $P_r(0) = P(X(0) = r) = 1$ (initial state probability) $P_{i,k}(s,t) = P(X(t) = k | P(s) = i), \ 0 \leq s \leq t < \infty$ (同前面 DTMC 和 CTMC 定義) $P_{i,k}(t) = P_{i,k}(0, t) = P(X(t) = k | P(0) = i)$ (time homogeneous)
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  • styleGAN github link pre-trained model 在 pre-trained model 內有三個物件 _G: 訓練到一半的 image generator _D: 訓練到一半的 image discriminator Gs: 訓練完成的 image generator 前兩個物件可用來繼續訓練,Gs 則是可以直接拿來使用的 generator # Load pre-trained network.
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  • :::spoiler 目錄 ::: Chapter 19. Learning from examples Entropy 討論一個隨機變數不確定性的方式。 $Entropy = -\sum_k P(v_k)*log_2P(v_k)$ 對於 boolean variable 來說, $entropy$ 為底下公式 $Entropy = B(P) = -(P * log_2P + (1-P) * log_2(1-P))$如果 training set 中有 p 個 positive , n 個 negative,整個 set 的 $entropy$ 為 $B(\frac{p}{p+n})$
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  • contributed by < bebo1010 > 實驗環境 $ gcc --version gcc (Ubuntu 9.4.0-1ubuntu1~20.04) 9.4.0 Copyright (C) 2019 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. $ lscpu
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  • :::warning 專題影片參考 ::: Checklist 專題簡介 [ ] 成果預期 [ ] 實作流程簡介
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  • 2022-04-04 contributed by < bebo1010 > Github repository link 測驗一 實際行為應該和 lib/string.c 中的 memchr 相同,要能做到找出第一個相符的字元位置。 我大致上讀了一下程式碼後,看到了 mask 的做法,就意識到應該是一次比對多個 byte 是否與 mask (目標字元) 相同。 實際上再去了解後,發現做法是一次比對 8 個字元是否和目標字元相同。 以下內容是我的實作,使用 DETECT_CHAR macro 幫我找出是否這 8 個字元中含有目標字元
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  • contributed by < bebo1010 > 測驗一 作答思路 EXP1 原本程式碼為return (a >> 1) + (b >> 1) + (EXP1); 我注意到a >> 1和b >> 1會導致最後一位元的資料損失 因此EXP1的目標是找回最後一位元的資料 因此我列出底下的truth table
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  • contributed by < bebo1010 > AAA (a) /* no operation */ (b) n->pprev = first (c) n->next = first (d) n->pprev = n 首先我注意到一件事情,(b)和(d)是不可選的選項 pprev 是指標的指標,而 first 和 n 的型態是指標
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