## +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

# 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 rowspan="5">ObservabilityMonitoringBuilt-in metrics for multi-tenant and multi-dimensional monitoring; built-in custom monitoring dashboardsSimple metrics displayed only; Grafana for displaying complex metricsLoggingBuilt-in multi-tenant and multi-dimensional log retrieval system that supports on-disk log collection and provides flexibility to integrate

# pandas: powerful Python data analysis toolkit Release 0.19.0 Wes McKinney & PyData Development Team Oct 02,2016 ## What’s New 3 1.1 v0.19.0 (October 2, 2016) 3 1.1.1 New features 5 merge_asof features 288 1.19.4 Wide DataFrame Printing 288 1.19.5 Updated PyTables Support 290 1.19.6 N Dimensional Panels (Experimental) 293 1.20 v0.9.1 (November 14, 2012) 294 1.20.1 New features 294 1.20.2 Internal Guides 375 7.2 pandas Cookbook 375 7.3 Lessons for New pandas Users 376 7.4 Practical data analysis with Python 376 7.5 Modern Pandas 376 7.6 Excel charts with pandas, vincent and xlsxwriter 377

pandas: powerful Python data analysis toolkit Release 0.20.3 Wes McKinney & PyData Development Team Jul 07, 2017 CONTENTS 1 What’s New 3 1.1 v0.20.3 (July 7, 2017) . . . . . . . . . . . . . . . . PyTables Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 1.24.6 N Dimensional Panels (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 1.25 v0.9.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 6.4 Practical data analysis with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 6.5 Exercises

# 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 into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science

pandas: powerful Python data analysis toolkit Release 0.21.1 Wes McKinney & PyData Development Team Dec 12, 2017 CONTENTS 1 What’s New 3 1.1 v0.21.1 (December 12, 2017) . . . . . . . . . . . . . PyTables Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 1.26.6 N Dimensional Panels (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 1.27 v0.9.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 6.4 Practical data analysis with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 6.5 Exercises