关于日期操作

1:获得指定日期指定月份的第一天

formular form:dateadd(month,datediff(month,'19000101',orderdate),'19000101')

select distinct dateadd(month,datediff(month,'19000101',orderdate),'19000101')
from sales.ordervalues

 

 

2:获得指定日期指定月份的最后一天

formular form: dateadd(month,datediff(month,'19000131',orderdate),'19000131')

select distinct dateadd(month,datediff(month,'19000131',orderdate),'19000131')
from sales.ordervalues

 

 

3:获得指定日期指定月份的第一天

select dateadd(month,datediff(month,0,o.orderdate),0)
as ordmonth
from
TSQLFundamentals2008.sales.orders as o

 

3 获得指定日期的年月这两个部分

select convert(nvarchar(6),orderdate,112)
from sales.ordervalues

 

获得员工的生日(nearest birthday)

WITH Args1 AS
(
  SELECT LastName, FirstName, BirthDate,
    DATEDIFF(year, BirthDate, GETDATE()) AS Diff,
    CAST(CONVERT(CHAR(8), GETDATE(), 112) AS DATETIME) AS Today
  FROM dbo.Employees
),

Args2 AS
(
  SELECT LastName, FirstName, BirthDate, Today,
    DATEADD(year, Diff, BirthDate) AS BDCur,
    DATEADD(year, Diff + 1, BirthDate) AS BDNxt
  FROM Args1
),
Args3 AS
(
  SELECT LastName, FirstName, BirthDate, Today,
    BDCur + CASE WHEN DAY(BirthDate) = 29 AND DAY(BDCur) = 28
      THEN 1 ELSE 0 END AS BDCur,
    BDNxt + CASE WHEN DAY(BirthDate) = 29 AND DAY(BDNxt) = 28
      THEN 1 ELSE 0 END AS BDNxt
  FROM Args2
)
SELECT LastName, FirstName, BirthDate,
  CASE WHEN BDCur >= Today THEN BDCur ELSE BDNxt END AS BirthDay
FROM Args3;

 

 

 

计算机组成原理(第三版)唐朔飞-第六章计算机的运算方法-课后习题(1-16) 阅读详情

相关推荐

AI:Coze、Dify、n8n:三款低代码工具如何选?

Coze、Dify、n8n:三款低代码工具如何选?

Kant2048的博客 2806

DATEDIFF() 函数—— 计算时间差

作用:返回两个日期之间的时间差 语法:DATEDIFF(datepart,startdate,enddate) datepart 格式: datepart 缩写 意义 year yy  | yyyy 年 quarter  qq  |  q 季度 month mm  |  m 月 week ww  |  w 周 d...

qq_34498806的博客 5万+

AUTOSAR RTE配置实战:从ECU分区到Task映射的完整指南

本文提供了一份AUTOSAR RTE配置的完整实战指南。从创建ECU分区、定义OS任务,到实现Runnable Entity与Task的精确映射,详细解析了每一步的操作要点与常见陷阱。文章结合配置工具界面,旨在帮助开发者高效完成RTE配置,确保系统实时性与数据一致性,是掌握AUTOSAR核心开发流程的实用参考。

weixin_29228203的博客 486

JS日期操作大全

js操作时间(函数) var myDate = new Date(); myDate.getYear(); //获取当前年份(2位) myDate.getFullYear(); //获取完整的年份(4位,1970-????) myDate.getMonth(); //获取当前月份(0-11,0代表1月) myDate.getDate(); /

一米阳光 3377

避开ego_planner仿真的第一个坑:深度相机、点云与odom话题配置全指南

本文详细解析了ego_planner仿真中深度相机、点云与odom话题配置的关键问题,帮助开发者避开常见陷阱。从数据流架构到具体配置细节,包括深度相机的内外参匹配、点云数据规范要求以及odom话题的验证方法,提供了全面的实战指南和优化建议。

weixin_30335575的博客 493

历年北京理工大学复试专业综合题

。

A place to record sth. 2万+

jquery关于日期的一些操作

jquery比较两个日期的大小 var starttime = $('#txtStorageTimeStart').val(); var endtime = $('#txtStorageTimeEnd').val(); var start = new Date(starttime.replace("-", "/").replace("-", "/")); var end = new Dat

c782699991的博客 666

二叉树的操作之统计二叉树中节点的个数

一,问题描述 给定一颗二叉树,已知其根结点。 ①计算二叉树所有结点的个数 ②计算二叉树中叶子结点的个数 ③计算二叉树中满节点(度为2)的个数   二,算法分析 找出各个问题的基准条件,然后采用递归的方式实现。 ①计算二叉树所有结点的个数 1)当树为空时,结点个数为0,否则为根节点个数 加上 根的左子树中节点个数 再加上 根的右子树中节点的个数 借助遍历二叉树的思路,每访问一个结...

weixin_34200628的博客 4568

SQL-日期操作

本文主要记录关于SQL-日期操作的相关笔记。涉及函数date_add(),date_sub(),datediff(),date_format(),makedate(),last_date()等

Sky-JT的博客 3570

从零到一:i.MX8M Plus开发板全流程开发指南(源码编译、烧录、镜像运行)

本文详细介绍了i.MX8M Plus开发板的全流程开发指南,包括源码编译、烧录和镜像运行等关键步骤。通过实战案例和配置技巧,帮助开发者快速掌握工业级应用开发,提升在边缘计算和AI推理等领域的开发效率。

weixin_30687051的博客 572

IEEE Journal of Biomedical and Health Informatics(JBHI)投稿经验分享

《IEEE Journal of Biomedical and Health Informatics投稿经验分享》详细记录了在影响因子6.7的中科院一区期刊JBHI的投稿历程(2025.1-5月,历时4个月)。文章重点分析:1)审稿特色:4位审稿人差异显著,领域专家意见专业但要求严苛;2)修改要点:需重视大修(补充实验/原理阐述),并注意将回复内容同步至正文;3)期刊特点:送审率高但审稿严格,常规投稿周期约4-5个月;4)实用建议:建议首次大修全力以赴,善用AI工具辅助写作,避免拖延。

LiRongLu_的博客 5089

sql语句中日期相减的操作

sql语句中日期相减的操作 GETDATE() 和 NOW() 查出来的时间一样 select GETDATE(),NOW() FROM t_表名; curdate() 只显示年月日 select curdate(),NOW() FROM TB_表名; 如果做减法,只能得到相差的天数 不想有时分秒,可以考虑用 curdate() select (NOW()-日期时间字段),(curdate()-日期时间字段) FROM T_表名; 两日期间隔年 select datediff(year

sungencheng的博客 6万+

振镜控制卡硬件接口揭秘:XY2-100/SL2-100协议对比

本文对比分析了工业振镜控制领域两种主流数字通信协议XY2-100和SL2-100的硬件接口差异与应用场景。XY2-100作为开放式协议兼容性强,采用LVDS差分信号传输,适用于激光打标等中高精度场景;SL2-100为半封闭协议,采用RS485差分信号,专为高速响应场景设计。文章详细解析了两种协议的引脚定义、通信机制和性能参数,指出XY2-100适配多品牌振镜且调试简单,SL2-100则更适合Nutfield专用设备的高速高精度需求。最后提供了接口调试的实操建议和选型逻辑,强调应根据具体应用场景的精度、速度和

远程部署调试 运行安装 项目调试 二次开发 项目技术新 持续迭代 部分源码免费分享 646

java日期加减操作

 /****       * 传入具体日期 ,返回具体日期增加一个月。       * @param date 日期(2019-01-21)       * @return 2019-02-21      * @throws ParseException       */       private  String subMonth(String date) throws ParseExcepti...

fall_hat的博客 3015

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

大中型超市收银系统(可直接运行)

光步前台收银系统系统功能:商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传,基本资料下载,开钱箱、小票打印、收款结算,支持多币种付款方式(现金、储值卡、礼券、微信等),支持多种促销。运行环境:.net 4.0登陆信息用户名:1001 密码:123456内置商品信息,商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开

python日期操作_Python基础—日期与时间操作

Python标准库中提供了datatime模块来操作日期和时间环境及版本Windows 10 x64Python 2.7Pycharm 4.5.1datetime.date表示日期的类,常用的属性有year, month, dayyear的范围是[1, 9999],month的范围是[1, 12],day的最大值根据给定的year, month参数来决定(区分闰年)常用的属性# 利用构造函数创建日...

weixin_33969103的博客 994

Camera2Demo

在Camera2 的基础上进行封装,使得Camera2一堆初始化逻辑与Activity分离,降低耦合度,完成预览拍照并保存的操作

对日期的一些特殊操作

1、Java获取某年某周的最后一天 public static Date getLastDayOfWeek(int year, int week) { Calendar c = new GregorianCalendar(); c.set(Calendar.YEAR, year); c.set(Calendar.MONTH, Calendar.J...

南巷清风 237

IDEA的使用指南

IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.

python中的日期操作

Python日期的加减等操作 经常会用的,总结一下。 1. 日期输出格式化 所有日期、时间的api都在datetime模块内。 datetime => string now = datetime.datetime.now() now.strftime('%Y-%m-%d %H:%M:%S') #输出2012-03-05 16:26:23.870105 strftime是datetim...

ISxiancai的博客 378

Java中日期的几种常见操作

Java中日期的几种常见操作 JDK 1.7 一、日期取值 java.util.Date类不便于实现国际化。 推荐使用java.util.Calendar类进行时间和日期方面的处理。 由于 Calendar 的构造器方法被 protected 修饰,所以我们会通过 API 中提供的getInstance() 方法来创建 Calendar 对象。 //有多个重载方法创建 Calendar 对象 Calendar now = Calendar.getInstance(); //默认 //指定时区和地区,也可以只

qq_43044552的博客 1588

日期操作类

日期操作类

2301_77118121的博客 1055

java 日期时间操作_java日期时间操作

packagecom.gds.web.util;importjava.text.ParseException;importjava.text.SimpleDateFormat;importjava.util.Calendar;importjava.util.Date;publicclassDateOpUtil{/***@paramargs*@throwsParseExcep...

weixin_29504987的博客 152

日期操作工具类大全

package com.xiejiamin.utils; import java.text.SimpleDateFormat; import java.util.*; /** 日期操作工具类 / public class DateUtils { /* 日期转换- String -> Date @param dateString 字符串时间 @return Date类型信息 @throws Exception 抛出异常 */ public static Date parseString2Dat

weixin_45958353的博客 288

java日期操作

取得指定月份的第一天与取得指定月份的最后一天 /** * 取得指定月份的第一天 * * @param strdate String * @return String */ public String getMonthBegin(String strdate) { java.util.Date date = parseFormatDate(str...

aika7689的博客 190

mysql日期操作

mysql常用的日期操作函数

zhangzd_1981的博客 436

对日期的一些操作

对日期的一些操作,转自:http://www.cnblogs.com/ufo0303/archive/2006/10/22/536277.html时间对象是一个我们经常要用到的对象,无论是做时间输出、时间判断等操作时都与这个对象离不开。除开JavaScript中的时间对象外,在VbScript中也有许多的时间对象,而且非常好用。下面还是按照我们的流程来进行讲解。它是一个内置对象——而不是其

Guogle的专栏 607

mysql 日期详解_MySQL中如何操作日期的详解

日期是数据库存储很重要的一部分,下面总结下在MySQL中常用的日期操作1.select curDate(); #获取当前日期select curTime(); #获取当前时间select now(); #获取当前日期+时间2.列举1个天数加减的例子,其他的看英文意思就可以理解了select date_add(now(), interval 1 day); #当前日期...

weixin_35215870的博客 646

python日期加减一年_Python日期的加减等操作

原文:https://www.cnblogs.com/vampirejt/p/4159267.html1. 日期输出格式化所有日期、时间的api都在datetime模块内。1. datetime => stringnow = datetime.datetime.now()now.strftime('%Y-%m-%d %H:%M:%S')#输出2012-03-05 16:26:23.87010...

weixin_39838302的博客 2908
上一篇: 你不知道的join的语法
下一篇: 再说分页
pmandy
博客等级 码龄18年 13粉丝 155原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
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
实付元
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