目录网盘文件永久链接
1.云数据中心融合资源池概述
2.云数据中心OpenStack架构与原理_OpenStack综述
3.云数据中心OpenStack架构与原理_Nova、Cinder、Swift
4.云数据中心OpenStack架构与原理_Glance、Keystone
5.云数据中心OpenStack架构与原理_FusionSphere介绍
6.云数据中心OpenStack组件部署_FusionSphere OpenStack介绍
7.云数据中心OpenStack组件部署_安装FusionSphere OpenStack
8.云数据中心OpenStack组件部署_部署FusionSphere OpenStack
9.云数据中心OpenStack组件部署_安装和配置FusionSphere OpenStack
10.云数据中心FusionCompute原理与部署_FusionCompute安装与介绍
11.云数据中心FusionCompute原理与部署_配置FusionCompute
12.FusionStorage分布式存储原理与架构_FusionStorage架构和功能....
2022-05-21 19:03:06
355B
Synthetic_Chinese_String_Dataset 中文识别数据集 1 for https://gitee.com/chenyang918/Lets_OCR
2022-05-21 18:41:46
145.81MB
1
#Fec Data Loader这是我编写的一些代码，用于从FEC下载数据，对其进行归一化和解码，并将其编入弹性搜索中以便在kibana中进行查看。 您也可以在熊猫中使用输出的csv文件，而不必对数据进行任何连接。 请注意，这是非常快捷和肮脏的。
在此处查看第一个分析： :
##用法：1.下载代码2.运行build2.py，这将在data子目录中生成文件。 生成的文件将以下划线（_）开头。 文件是_indiv16.csv，_exp16.csv，_pas216.csv。 （16是数据集年份）3.运行index_to_es.py（可选）
##要求：1. Python 3 2. index_to_es.py的elasticsearch-py（pip安装elasticsearch）
## build2.py从FEC（ ）下载数据，对数据进行非规范化并将数据作为csv写入文件系统。 这
2022-05-21 17:04:36
59.59MB
1
springboot.zip+springboot使用+spring thymeleaf+semantic UI使用+spring data jpa
2022-05-21 16:35:25
3.74MB
1
An Introduction to Analysis of Financial Data with R
2022-05-21 16:07:59
16.41MB
1
四叉树
这是Quadtree的Java实现，Quadtree是一种树数据结构，可用于存储2D位置数据。
用法
创建新的四叉树
从点（0,0）开始以400 x 400尺寸初始化世界
// init.
Dimension dimension = new Dimension ( 400 , 400 );
Position2D position = new Position2D ( 0 , 0 );
QuadTree< Point> KD = new QuadTree< Point> (position, dimension);
// populate with random points.
Random random = new Random ();
List< Point> pointList = new LinkedList<> ();
for ( int i = 0 ;
2022-05-21 14:41:16
14KB
1
Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning.
Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them.
By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.
What You Will Learn
• Understand how to install and manage Anaconda
• Read, sort, and map data using NumPy and pandas
• Find out how to create and slice data arrays using NumPy
• Discover how to subset your DataFrames using pandas
• Handle missing data in a pandas DataFrame
• Explore hierarchical indexing and plotting with pandas
2022-05-21 14:35:08
8.83MB
1