mitos riscv for xv6
【论文阅读】Run, Don't Walk- Chasing Higher FLOPS for Faster Neural Networks1
> # 🚩前言 > > - 🐳博客主页:😚[睡晚不猿序程](https://www.cnblogs.com/whp135/)😚 > - ⌚首发时间: > - ⏰最近更新时间: > - 🙆本文由 **睡晚不猿序程** 原创 > - 🤡作者是蒻蒟本蒟,如果文章里有任何错误或者表述不清,请 t ......
docker deployment for openldap
openldap https://github.com/osixia/docker-openldap/tree/master Run OpenLDAP docker image: docker run --name my-openldap-container --detach osixia/open ......
Is Docker-Compose Suited For Production?
Is Docker-Compose Suited For Production? https://vsupalov.com/docker-compose-production/ Hidden Assumptions Production means different things to diffe ......
Go 语言 for-range 的两个坑,你踩过吗?
#### 坑一:迭代时协程引用索引和值 先看看下面的例子,你知道最终输出的结果是什么吗? ```go package main import ( "fmt" "time" ) func main() { var m = []int{1, 3, 5} for i, v := range m { go ......
Paper Reading: Ensemble of Classifiers based on Multiobjective Genetic Sampling for Imbalanced Data
大多数处理不平衡学习的技术都是针对二分类问题提出的,这些方法并不一定适用于不平衡的多分类任务。针对这些问题,本文提出了一种新的自适应方法——基于多目标遗传抽样的分类器集成(E-MOSAIC)。E-MOSAIC 将训练数据集中提取的样本编码为个体进行进化,通过多目标优化过程搜索能够在所有类别中产生具有... ......
Final Cut Pro for Mac(fcpx视频剪辑)永久激活版
软件下载:Final Cut Pro for Mac Final Cut Pro是由苹果公司开发的一款专业视频编辑软件,主要用于影片的后期剪辑、调色、特效、音频处理等方面。以下是Final Cut Pro的特点: 高效的视频编辑功能:Final Cut Pro提供了丰富的视频编辑工具,包括多轨道编辑 ......
苹果Mac最好用的视频下载工具:Downie 4 for Mac v4.6.20直装版
Downie 4 for Mac软件下载 Downie是一款Mac平台上非常实用的视频下载工具。它支持下载各种视频网站上的视频,并且具有快速、稳定、易于使用的特点。 Downie支持下载各种视频网站上的视频,包括YouTube、Vimeo、Netflix、Hulu、Amazon等等。它具有快速、稳定 ......
【论文解析】EJOR 2011 A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems
> 论文名称:A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems ### 动机 假 ......
for 循环
# 字符串的for 循环 str1 = 'abc' for i in str1: print(i) ''' a b c ''' # 字典的for 循环 dict1 = {'name':'fqs','age':18,'address':'beijing'} for key in dict1: prin ......
mysql报错ERROR 1062 (23000): Duplicate entry '0' for key 'PRIMARY'
创建表语句: ```sql CREATE TABLE `mytable` ( `id` int(11) NOT NULL PRIMARY KEY AUTO_INCREMENT, `col` varchar(50) NOT NULL DEFAULT '未知', `col1` int(11) NOT N ......
Vue(十二):列表渲染—— v-for
1.基础使用 <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>基本列表</title> <script type="text/javascript" src="../js/vue.js"></script> </head> <! ......
nvm for windows
[TOC] # nvm for windows 基础设置 ## 淘宝镜像 > nvm npm_mirror https://npmmirror.com/mirrors/npm/ > nvm node_mirror https://npmmirror.com/mirrors/node/ ## 默认安装 ......
KingbaseES数据库导入数据invalid byte sequence for encoding
## 一、适用版本: KingbaseES数据库所有版本。 ## 二、问题现象: 使用备份的数据进行还原,还原过程中发生异常。 日志信息: ``` sys_restore: connecting to database for restore sys_restore: creating TABLE ......
Educational Codeforces Round 151 (Rated for Div. 2) D. Rating System
贪心 由题可得,对于k的选择一定是单调递增的,对于前面选定的k后面选的k必须大于之前选的才会发生新的变化,因此k的选择其实是一个单调栈,由前缀和组成 我们要想最后的结果最大,则k值一定要尽可能的高,例如当选中i为k值时,如果从i后面某个原本的前缀和要大于选k之后所得到的前缀和的话,说明k不是最优的 ......
ML-for-AGV-Dispatching:Center.py逐段解读
class Center(object): def __init__(self, env, x, y, routRule, AGV_num, WS_num, AGV_disRuleV, AGV_disRuleW, Ledispatch = "None", Task = ["None", "None" ......
ML-for-AGV-Dispatching:run.py解读
import simpy from ShopFloor import Center import Learn as Le import numpy as np import pandas as pd import pickle import time Task = "None" #TestD, Te ......
ML-for-AGV-Dispatching:Learn.py逐段解读
import numpy as np import Routing import random as rd import tensorflow as tf import matplotlib.pyplot as plt import pandas as pd from sklearn import ......
ML-for-AGV-Dispatching:Dispatcher.py逐段解读
from ShopFloor import Vehicle, Job from Routing import ShortestPath as sp def VID(Controller, Vehicle, rule, routRule, Parameter): if Controller.job_n ......
ML-for-AGV-Dispatching:ShopFloor.py逐段解读
import simpy from Routing import ShortestPath as sp import tkinter as tk import Dispatcher as dp import numpy as np import Learn as Le import copy imp ......
ML-for-AGV-Dispatching:Routing.py逐段解读
# -*- coding: utf-8 -*- """ Created on Mon Nov 20 10:55:40 2017 @author: CIMlab徐孟維 """ ''' Alpha = 0.06 Beta = 0.03 Gamma = 0.01 Dist1 = 3 Dist2 = 7 ' ......
ML-for-Dispatching-Module解释
home unet View code on Github # U-Net This is an implementation of the U-Net model from the paper, U-Net: Convolutional Networks for Biomedical Image ......
Educational Codeforces Round 151 (Rated for Div. 2) C. Strong Password
题目翻译,给定t组数据,每组数据包含一个字符串s,两个长度为m的字符串l和r,要求判断是否存在一个长度为m的字符串res,满足l[i]<=res[i]<=r[i](i->0~m)且不是s的子序列 贪心 首先对于所有满足l<res<r的字符串,我们只需判断是否存在一个字符串不是子序列即可,那么我们让r ......
Grep for multiple patterns
The syntax is: Use extended regular expressions: grep -E 'pattern1|pattern2' *.py Try on older Unix shells/oses: grep -e pattern1 -e pattern2 *.pl Ano ......
mac M2 多个 docker环境 colim 、docker for mac 、orbstack
#### 三个环境存在是会让 docker 命令混乱 #### colim * 真实的路径 ``` /opt/homebrew/bin/docker -> /opt/homebrew/Cellar/docker/24.0.2/bin/docker ``` * docker.sock ``` ~/.c ......
Oracle RAC 19.14 for linux 7.9 基于VSAN平台搭建
Oracle RAC 19.14 for linux 7.9 基于VSAN平台搭建一、虚拟机准备:1、vSAN内针对Oracle RAC的磁盘规划 2、开启虚机UUID参数在虚机“编辑设置”->“虚拟机选项”->高级->配置参数的“编辑配置”->添加以下信息:名称:disk.EnableUUID 值 ......
linux系统报错:系统自己弹出诸如 kernel:NMI watchdog: BUG: soft lockup - CPU#2 stuck for 26s [mysqld:2875]
1、 https://blog.csdn.net/weixin_41752389/article/details/120777145 内核软死锁(soft lockup)Soft lockup:这个bug没有让系统彻底死机,但是若干个进程(或者kernel thread)被锁死在了某个状态(一般在内 ......
apple sicion M2 mac docekr for mac 使用 x86-64 amd64
#### 使用环境变量 指定 docker for mac 适用 Rosetta * DOCKER_DEFAULT_PLATFORM=linux/amd64 * 指定后 docker for mac 使用 Rosetta2 ``` version: '3' services: mule-applic ......
Multi-Modal Attention Network Learning for Semantic Source Code Retrieval 解读
# Multi-Modal Attention Network Learning for Semantic Source Code Retrieva Multi-Modal Attention Network Learning for Semantic Source Code Retrieval,题 ......