cdx 快速切换路径

用python写的命令行工具, 可以保存比较长的路径,或这url, 或者备忘录 为书签, 然后使用 cdx + 书签, 可以切换到保存目录或者网页.

cdx

  • Introduction

    • change derectory fast by ‘cdx bookmark’.
  • Features

    • bookmark saved for dirpath or url
    • cdx a directory by bookmark fast
    • modify, dispaly, delete bookmarks

version

cdx 1.2.3 , Dec 8 2017

setup

linux: sudo pip install cdx   sudo pip install cdx
Windows:  pip install cdx  
if fail to setup or reinstall, try again after remove '~/.cdx/database' or 'C:\User\xx\.cdx\databse'.

Usage

usage: cdx [option] [arg] 
Options and arguments:
cdx -s bookmark [dirpath|url|note1 note2 ..]  # save the CURRENT dirpath or some notes as bookmark (also --save)
cdx bookmark                                 # cdx to a location or retuan notes by bookmark
cdx -l                                       # dispaly the saved bookmarks(also --list)
cdx -m old_bookmark new_bookmark             # modify a bookmark name (also --modify)
cdx -d bookmark1 bookmark2 ...               # delete a bookmark (also --delete)

Example

# save the current dirpath as a bookmark 
js@py:~/Documents/pycodes/myprojects$ cdx -s cdx
cdx cdx >>> /home/js/Documents/pycodes/myprojects

# add a website
js@py:~/Documents/pycodes/myprojects$ cdx -s gh https://github.com/ZhangLijuncn/cdx
notes gh >>> https://github.com/ZhangLijuncn/cdx

# notes some infomation 
js@py:~/Documents/pycodes/myprojects$ cdx -s version v1.1.2 Dec,6,2017
cdx version >>> v1.1.2 Dec,6,2017

# mark a dirpath
js@py:~/Documents/pycodes/myprojects$ cdx -s doc ~/Documents/
cdx doc >>> /home/js/Documents/

# modify a bookmark
js@py:~/Documents/pycodes/myprojects$ cdx -m version v
cdx v >>> v1.1.2 Dec,6,2017

# display all the bookmarks
js@py:~/Documents/pycodes/myprojects$ cdx -l
----------------------------------------------------------------------
Bookmarks          Locations      
----------------------------------------------------------------------
doc                /home/js/Documents
v                   v1.1.2 Dec,6,2017
cdx                /home/js/Documents/pycodes/myprojects
gh                 https://github.com/ZhangLijuncn/cdx
----------------------------------------------------------------------

# change a diretory by bookmark
js@py:~/Documents/pycodes/myprojects$ cdx doc
js@py:~/Documents$ 

# open a url in the default web browser
js@py:~/Documents$ cdx gh

# delete some bookmarks
js@py:~/Documents$ cdx -d doc v cdx 
cdx: 'doc' was removed.
cdx: 'v' was removed.
cdx: 'cdx' was removed.
----------------------------------------------------------------------
Bookmarks          Locations      
----------------------------------------------------------------------
gh                 https://github.com/ZhangLijuncn/cdx
----------------------------------------------------------------------
内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值