## +21 ## Code Analysis++ ## ANASTASIA KAZAKOVA ## 20 21 October 24-29 • Anastasia Kazakova, @anastasiak2512 C++ Dev: Embedded, Networking C++ Tools PMM and .NET Tools Marketing Lead, JetBrains - https://www.meetup.com/St-Petersburg-CPP-User-Group/ C++ Russia: https://cppconf.ru/en/ ## Why Code Analysis? ## Software Quality While preparing for my workshop at #CppOnSea, I want to ask you: reply with *sk = tun->sk; unsigned int mask = 0; if (!tun) return POLLERR; } ## Why code analysis • Improve software quality • Lower developer frustration Avoid UB ## Language ### • Lifetime

variable's value to be comptime-known, and all loads and stores of the variable to happen during semantic analysis of the program, rather than at runtime. All variables declared in a comptime expression are implicitly instruction_addresses: [N]usize, }; Here, N is the maximum function call depth as determined by call graph analysis. Recursion is ignored and counts for 2. A pointer to StackTrace is passed as a secret parameter expressions when the condition is known at compile-time, and the compiler guarantees that it will skip analysis of the branch not taken. This means that the actual function generated for max in this situation

# Lecture Notes on Gaussian Discriminant Analysis, Naive Bayes and EM Algorithm Feng Li fli@sdu.edu.cn Shandong University, China ## 1 Bayes' Theorem and Inference Bayes’ theorem is stated mathematically \theta = \{ P(X = x \mid Y = y), P(Y = y) \}_{x,y} $ . ## 2 Gaussian Discriminant Analysis In Gaussian Discriminate Analysis (GDA) model, we have the following assumptions: • A1: $ Y \sim \text{Bernoulli}(\psi) ilde{y})p(\tilde{y})}{p(\tilde{x})} $$ where $ \tilde{y}=0,1 $ . ## 3 Gaussian Discriminant Analysis and Logistic Regression By far, we introduce two classification algorithms, Logistic Regression

# Competitor Analysis: KubeSphere vs. Rancher and OpenShift September 2021 ## Table of Contents Competitor Analysis: KubeSphere vs. Rancher and OpenShift ..... 1 I. Overview ..... 3 1. Metrics Comparison of SonarQube for static code analysis supported and analysis results available on the UIManual configurations required to set up SonarQube for static code analysisManual configurations configurations required to set up SonarQube for static code analysisApplicationApplication deploymentApp Store available to support Helm Chart and application repository configu

## From Eager Futures/Promises to Lazy Continuations Evolving an Actor Library Based on Lessons Learned from Large-Scale Deployments ## prologue • past life at UC Berkeley, Twitter, Mesosphere/D2iQ std::move(body), std::move(k)}; ## lazy continuations auto k = http::Post(url, body, /* k */); resulting type is the “computational graph” ## lazy continuations auto k = http::Post(url, body the “computational graph” - the graph is lazy, i.e., nothing has started when we get it (tradeoff for dynamic allocation) and must be explicitly started ## lazy continuations auto k = http::Post(url, body

# pandas: powerful Python data analysis toolkit Release 0.15.1 Wes McKinney & PyData Development Team November 08, 2014 Powered by TCPDF (www.tcpdf.org) ## CONTENTS 1 What's New 3 1.1 v0.15 for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these

# pandas: powerful Python data analysis toolkit Release 0.7.1 Wes McKinney November 08, 2012 Powered by TCPDF (www.tcpdf.org) ## CONTENTS 1 What's New 3 1.1 v.0.7.1 (February 29, 2012) 3 1 for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these

# pandas: powerful Python data analysis toolkit Release 0.7.3 Wes McKinney November 08, 2012 Powered by TCPDF (www.tcpdf.org) ## CONTENTS 1 What's New 3 1.1 v.0.7.3 (April 12, 2012) 3 1.2 v for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these

# pandas: powerful Python data analysis toolkit Release 0.25.0 Wes McKinney& PyData Development Team Jul 18, 2019 Date: Jul 18, 2019 Version: 0.25.0 Download documentation: PDF Version | Zipped open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about what's in Out[2]: Int64Index([1, 2, 3], dtype='int64') ``` New behavior: pandas: powerful Python data analysis toolkit, Release 0.25.0 In [34]: pd.period_range('19910905', periods=2).union(pd.Int64Index([1