#IstioCon Local Istio Development John Howard / @howardjohn / Google #IstioCon Fully Cloud docker push kubectl apply docker pull #IstioCon Fully Cloud docker push kubectl apply docker pull requests #IstioCon Thank you! For more information: ● https://github.com/howardjohn/local-istio-development

Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn the margin is defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating samples are so-called support vector, i.e., the vectors “supporting” the margin boundaries. We can redefine ω by w = � s∈S αsy(s)x(s) where S denotes the set of the indices of the support vectors 4 Kernel

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review parallely along ω (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx Scaling ! and " such that min& ' & !() & + " = 1 Feng Li (SDU) SVM December 28, 2021 14 / 82 Support Vector Machine (Primal Form) Maximizing 1/∥ω∥ is equivalent to minimizing ∥ω∥2 = ωTω min ω,b ωTω

2011) . . . . . . . . . . . . . . . . . . . . . . . . 12 2 Installation 15 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.5 Development Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2011) . . . . . . . . . . . . . . . . . . . . . . . . 12 2 Installation 15 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.5 Development Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2011) . . . . . . . . . . . . . . . . . . . . . . . . 16 2 Installation 19 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.5 Development Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

pandas: powerful Python data analysis toolkit Release 0.12.0 Wes McKinney & PyData Development Team January 31, 2014 CONTENTS 1 What’s New 3 1.1 v0.12.0 (July 24, 2013) . . . . . . . . . . . . . 2011) . . . . . . . . . . . . . . . . . . . . . . . . 63 2 Installation 65 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4

pandas: powerful Python data analysis toolkit Release 0.25.3 Wes McKinney& PyData Development Team Nov 02, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.3 Date: Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. 2.1 Python version support Officially Python 3.5.3 and above, 3.6, 3.7, and 3.8. 2.2 Installing

pandas: powerful Python data analysis toolkit Release 0.13.1 Wes McKinney & PyData Development Team February 03, 2014 CONTENTS 1 What’s New 3 1.1 v0.13.1 (February 3, 2014) . . . . . . . . . . . 2011) . . . . . . . . . . . . . . . . . . . . . . . . 95 2 Installation 97 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 2.2 Binary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 i 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 4

pandas: powerful Python data analysis toolkit Release 1.3.4 Wes McKinney and the Pandas Development Team Oct 17, 2021 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . api.indexers.check_array_indexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2689 4 Development 2691 4.1 Contributing to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2697 4.2 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2697 4.2.1 Creating