Rancher v2.5 CIS 1.5 Benchmark - Self-Assessment Guide - Rancher v2.5 1 4 5 6 6 14 29 33 34 34 37 37 38 38 42 49 49 50 Contents CIS v1.5 Kubernetes Benchmark - Rancher v2.5 with Kubernetes Guide - Rancher v2.5 2 52 53 5.3 Network Policies and CNI 5.6 General Policies CIS 1.5 Benchmark - Self-Assessment Guide - Rancher v2.5 3 CIS v1.5 Kubernetes Benchmark - Rancher v2.5 with Kubernetes to download a PDF version of this document Overview This document is a companion to the Rancher v2.5 security hardening guide. The hardening guide provides prescriptive guidance for hardening a production

unpack them when decoding the data. Now let’s run the code for the range [-10, 10], incrementing by 2.5 each time and find the quantized values for b = 3. First, let’s create our x. # Construct the array quantize. # We slightly exceed 10.0 to include 10.0 in our range. x = np.arange(-10.0, 10.0 + 1e-6, 2.5) print(x) We do this using NumPy’s arange method, which allows us to generate a range of floating endpoint defined, along with a step value. This returns the following result. [-10. -7.5 -5. -2.5 0. 2.5 5. 7.5 10. ] Now let’s quantize x. # Quantize the entire array in one go. x_q = quantize(x

෍ ??∈?2 ?? 1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5 ? ? = min ?1 ෍ ??∈?1 (?? − ?1)2 + min ?2 ෍ ??∈?2 (?? − ?2)2 ? = 1.5，?1 = 1 , ?2 = 2,3, … , 10 , ?1 = 5.56, ?2 = 7.5 s 1.5 2.5 3.5 4.5 5.5 6.5 7 5 0.07 -0.11 ?6 ? = x<2.5 -0.15 0.04 ? ?, ?3 ? =0.47 ? ?, ?4 ? =0.30 ? ?, ?5 ? =0.23 ?6 ? = ?5 ? + ?6 ? =?1 ? +…+?6 ? = ? ?, ?6 ? =0.17 x<6.5 x<4.5 8.95 x<3.5 x<2.5 5.63 6.83 6.56 5.82 （7）不断地重复（1）~（6）步骤直到达到规定的迭代次数或者收敛为止。 40 4.LightGBM 样本序号 样本的特征取值 样本的一阶导 样本的二阶导 ? 1 2 3 4 5 6 7 8 ?? 0.1 2.1 2.5 3.0 3.0 4.0 4.5 5.0 ?? 0.01 0.03 0.06 0.05 0.04 0.7 0.6 0.07 ℎ? 0.2 0.04 0.05 0.02 0.08 0.02 0.03

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rows x 12 columns] This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data section on pivoting DataFrame objects. Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table(values="value", index="location"

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373450 8.0500 NaN S This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data Python data analysis toolkit, Release 1.1.0 Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table(values="value", index="location"

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rows x 12 columns] This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data section on pivoting DataFrame objects. Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table( ....: values="value", index="location"

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373450 8.0500 NaN S This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data section on pivoting DataFrame objects. Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table( ....: values="value", index="location"

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rows x 12 columns] This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data section on pivoting DataFrame objects. Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table( ....: values="value", index="location"

Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 2.5 Indexing and selecting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rows x 12 columns] This tutorial uses air quality data about ??2 and Particulate matter less than 2.5 micrometers, made available by openaq and using the py-openaq package. The air_quality_long.csv data section on pivoting DataFrame objects. Pivot table I want the mean concentrations for ??2 and ??2.5 in each of the stations in table form In [14]: air_quality.pivot_table( ....: values="value", index="location"

}} 142 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.0 2.5 Intro to data structures We’ll start with a quick, non-comprehensive overview of the fundamental data Series(np.random.randn(5)) Out[6]: 0 -1.202857 1 -1.577769 2 0.645254 (continues on next page) 2.5. Intro to data structures 143 pandas: powerful Python data analysis toolkit, Release 1.0.0 (continued section. Like a NumPy array, a pandas Series has a dtype. In [18]: s.dtype Out[18]: dtype('float64') 2.5. Intro to data structures 145 pandas: powerful Python data analysis toolkit, Release 1.0.0 This is