大学期间完成的课内实习 这个仓库放一些大学期间完成作业实习，没什么太大价值，但是感觉删除了又有些可惜，遂放在一起免得占太多位置 BaggageCalculator是软件测试实习一，重量计数器 BMP为算法实习一 computer-networks 计算机网络实习 computer-graphics 计算机图形学实习 groupMessage是移动计算课写的一个简陋的聊天软件 java-practice是java课上三次课内实习 passometer是移动计算课写的计步器，不怎么灵 QTSync为数据结构大实习哈希树 SA-practice1是软件体系结构实习一 SA-practice2是软件体系结构实习二 SA-practice3是软件体系结构实习三 地理信息系统大实习 移动计算大实习 数据库大实习 java课程设计实习

简单数据库 伯克利大学的数据库作业：实现一个简单的数据库管理系统 您可以在获得更多详细信息和我的博客（中文版）： : （请参阅README中的英文版本） API变更 在Pro1中，我更改了几种方法的参数 在Pro3中，我更改了JoinOptimizer.java中的方法computeCostAndCardOfSubplan（）以获得左深树（我可能在该课程提供的代码中发现了一些错误）。 此外，我在LogicalPlan.java中更改方法physicalPlan（）。 具体来说，我添加的代码将检查查询是否包含联接，如果不包含联接，则不会调用JoinOptimizer。 我的博客的英文版 我在

静态路由 PC0，PC1 所在网段为20.20.20.0/24；PC2所在网段为30.30.30.0/24；PC3所在网段为40.40.40.0/24。 请给各个路由器配上适当IP地址和静态路由使得PC0、PC1、PC2、PC3能够互联互通

模式识别第一章作业题，中科院刘成林，Question 1 (Pattern Classification, Chapter 2, Problem 12) Let ωmax(x) be the state of nature for which P(ωmax|x) ≥ P(ωi|x) for all i, i = 1,...,c. (a) Show that P(ωmax|x) ≥ 1/c (b) Show that for the minimum-error-rate decision rule the average probability of error is given by P(error) = 1−RP(ωmax|x)p(x)dx (c) Use these two results to show that P(error) ≤ (c−1)/c (d) Describe a situation for which P(error) = (c−1)/c Question 2 (Pattern Classification, Chapter 2, Problem 13) In many pattern classification problems one has the option either to assign the pattern to one of c classes, or to reject it as being unrecognizable. If the cost for rejects is not too high, rejection may be a desirable action. Let λ(αi|ωi) =     0 i = j i,j = 1,...,c λr i = c + 1 λs otherwise where λr is the loss incurred for choosing the (c + 1)th action, rejection, and λs is the loss incurred for making a substitution error. Show that the minimum risk is obtained if we decide ωi if P(ωi|x) ≥ P(ωi|x) for all j and if P(ωi|x) ≥ 1− λr λs , and reject otherwise. What happens if λr = 0? What happens if λr > λs? Question 3 Now we have N samples, and each sample xi, i = 1,...,N has d-dimensions. Please provide us the proofs and the pseudo-codes of PCA algorithm Question 4 (Pattern Classification, Chapter 2, Problem 10) Consider the following decision rule for a two-category one-dimensional problem: Decide ω1 if x > θ; otherwise decide ω2. (a)Showtheprobabilityoferrorforthisruleisgivenby P(error) = P(ω1)Rθ−∞p(x|ω1)dx+P(ω2)R∞ θ p(x|ω2)dx (b) By differentiating, show that a necessary condition to minimize P(error) is that θ satisfy p(θ|ω1)P(ω1) = p(θ|ω2)P(ω2) (c) Does this equation define θ uniquely? (d) Give an example where a value of θ satisfying the equation actually maximizes the probability of error. Question 5 (Pattern Classification, Chapter 2, Problem 24) Consider the multivariate normal density for which σij = 0 and σii = σ2 i , i.e., Σ = diag(σ2 1,σ2 2,...,σ2 d). (a) Show that

# Additional Help for SQL++ and Mondial The [AsterixDB documentation on SQL++](https://asterixdb.apache.org/docs/0.9.4/sqlpp/manual.html) is a very valuable resource. This guide is only intended to give more tangible examples and explanation as to how SQL++ works and point out probably useful features of SQL++ for the homework. Completing this HW is not at all dependent on the material covered here. You are allowed to use the entirety of SQL++ to complete the assignment. Understanding Your Data (`mondial.adm`)

CSE 414 Homework 7: Parallel Data Processing and Spark Objectives: To write distributed queries. To learn about Spark and running distributed data processing in the cloud using AWS. What to turn in: Your Spark code in the sparkapp.py file. Spark Programming Assignment (75 points) In this homework, you will be writing Spark and Spark SQL code, to be executed both locally on your machine and also using Amazon Web Services. We will be using a similar flight dataset used in previous homework. This time, however, we will be using the entire data dump from the US Bureau of Transportation Statistics, which consists of information about all domestic US flights from 1987 to 2011 or so. The data is in Parquet format. Your local runs/tests will use a subset of the data (in the flights_small directory) and your cloud jobs will use the full data (stored on Amazon S3)

任务：在空白处补全代码，将照片列表中喜欢的照片拖放到心形当中，并且可以再次拖回照片列表，打造出一个完美的照片墙

基于击键动力学的识别，可以用来辅助学习生物特征学习

加州大学伯克利分校CS188人工智能导论课。 著名的课程对于深度学习AI非常有帮助和重要。 CS188 2019夏季版已于2019/06年完成。 PJ1_搜索 PJ2_multiagent PJ3_钢筋 PJ4_捉鬼敢死队 PJ5_machinelearning 在此项目中，第4季度要求我自己实施RNN，使用ReLu进行激活，包括模型中的偏差。

thoughtwork秋招笔试作业题目，以便于找工作的同学们提前做好准备。