Basic concepts CRT (C Runtime) refers to the C runtime library, which provides C and C++ programs with a set of basic building blocks for initializing and terminating the program. These building blocks ensure proper initialization and cleanup before and after the main() function is executed. The main tasks of CRT include: Initialize static data: […]
Tag: ime
[Comprehensive explanation of Linux commands] 120. Master the badblocks command and check hard disk bad sectors in time
Article directory badblocks Additional information grammar Options parameter Example other Learn `python` from scratch badblocks Find corrupted blocks on disk Supplementary instructions The badblock command is used to find damaged blocks on the disk. The hard disk is a wear-and-tear device, and physical failures such as bad sectors may occur after being used for a […]
Pandas data analysis at a glance – a guide to quickly learn data analysis in a short time (book given at the end of the article)
Foreword After three years of working as a data analyst in a major company, some tools must be mastered, especially the three swordsmen of data analysis in Python: Pandas, Numpy and Matplotlib. Just from personal experience, Pandas is a must to master. It provides easy-to-use data structures and data manipulation tools, making processing structured data […]
Switch configuration and Lab2 experiment in Packet Tracer
A switch is a network device in a computer network that is used to transmit and forward data packets within a local area network. It has functions such as packet forwarding, packet filtering, VLAN division and port management, can achieve high-speed, reliable and secure data transmission, and supports network management and control. Switches play an […]
kafkaStream real-time streaming computing
2 Real-time streaming computing 2.1 Concept Streaming computing is generally compared to batch computing. In the streaming computing model, the input is continuous and can be considered unbounded in time, which means that the full amount of data can never be obtained for calculation. At the same time, the calculation results are continuously output, that […]
Vue conference room time picker (modified)
Vue conference room time picker Original link: https://blog.csdn.net/Alan0728/article/details/123326325 Compare the modified content of the original text 1. The original article used a time interval selector based on 5-minute intervals, but here it has been changed to a half-hour interval. 2. The css style was modified, and the original timeline was changed to box selection. 3. […]
Analysis of the principle of Netty time wheel HashedWheelTimer
HashedWheelTimer initialization public HashedWheelTimer( ThreadFactory threadFactory, long tickDuration, TimeUnit unit, int ticksPerWheel, boolean leakDetection, long maxPendingTimeouts) { checkNotNull(threadFactory, “threadFactory”); checkNotNull(unit, “unit”); checkPositive(tickDuration, “tickDuration”); checkPositive(ticksPerWheel, “ticksPerWheel”); // Normalize ticksPerWheel to a power of 2 and initialize the time wheel wheel = createWheel(ticksPerWheel); mask = wheel.length – 1; // Convert the clock ticking frequency to nanoseconds long […]
python: excel holiday time extraction statistics
# encoding: utf-8 # Copyright 2023 Tu Juwen Co., Ltd. # View license information: # describe: # Author: geovindu,Geovin Du Tu Juwen. # IDE: PyCharm 2023.1 python 311 # Datetime: 2023/9/3 7:04 # User : geovindu #Product: PyCharm # Project : LukfookLeaveCalculation # File : EmpLoyeeHolidaysGet.py # explain : learn importsys import os import Common.Utils […]
Network design for network engineering data communication training (HCIA comprehensive experiment: communication between company intranet, external network, and personal network)
Experimental environment requirements: Huawei ensp, virtualbox are as follows: Experimental topology: Terminal naming method: Routers are devices that start with R, and switches are devices that start with S. For example: The corresponding name of AR1 is: R1 The corresponding name of LSW2 is: S2 I. Exchange part 1.S1, R1 interconnection link set up LACP […]
Feature dimensionality reduction using feature extraction
1. Use principal components for feature dimensionality reduction For a given set of features, reduce the number of features while retaining the amount of information. from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn import datasets digits=datasets.load_digits() features=StandardScaler().fit_transform(digits.data) #Create a PCA that can retain 99% of the information (expressed as variance) pca=PCA(n_components=0.99,whiten=True) features_pca=pca.fit_transform(features) print(“Original […]