墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1.3k累计阅读
1.3k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
1、Hive语句的书写顺序：（从前往后）
(1)select
(2)from
(3)join on
(4) where
(5)group by
(6)having
(7)distribute by/cluster by
(8) sort by
(9) order by
(10) limit
(11) union(去重不排序)/union all（不去重不排序）
2、Hive语句的执行顺序：
(1)from
(2)on
(3)join
(4)where
(5)group by
(6)having
(7)select
(8)distinct
(9)distribute by /cluster by
(10)sort by
(11) order by
(12) limit
(13) union /union all
均值场博弈_平均场博弈（mean field game）
The New Big Fish Called Mean-Field Game Theory原文链接http://www.science4all.org/article/mean-field-games/www.science4all.org主要内容在经典博弈论中，它对附近其他鱼类的行为做出反应。这是非常复杂的，因为不同的鱼类之间会有大量的相互作用。这意味着经典博弈论对应于一长串高度耦合的方程。...
HIVE的执行顺序
hive语句执行顺序 msyql语句执行顺序 代码写的顺序： select ... from... where.... group by... having......
【脑电绘图教程 2】使用Brainstorm进行脑电处理及源定位
Brainstorm 是一个开源应用程序，支持MEG、EEG、fNIRS、ECoG、深度电极和其他电生理学的数据分析，尤其用于MEG、EEG的源定位。Brainstorm的主要优点是其简单直观的图形界面，不需要进行任何编程，即可上手MEG、EEG分析，此外，它还支持多种数据类型（基本上EEGLab支持的，它都支持），并且具有数据批量处理功能，可以很方便的分析自己的数据。
hive执行顺序及优化--四
1.hive执行顺序 hive语句和mysql都可以通过 explain+代码 查看执行计划，这样就可以查看执行顺序 **SQL语句书写顺序：** select … from … where … group by … having … order by … **SQL语句执行顺序：** from … where … group by … having … select … order by … from … on … join … where … group by … having … select … d
避坑指南：OpenWRT改AP模式必做的5个关键设置（DHCP/防火墙/WAN口处理）
本文详细解析了将OpenWRT设备从路由模式切换为AP模式时，必须完成的五个关键设置步骤。核心在于理解AP模式本质是带无线的智能交换机，需彻底关闭DHCP服务、调整防火墙策略、正确桥接所有物理接口（包括WAN口处理），并集成无线网络，以确保设备能透明接入主路由网络，避免出现设备隔离或无法互访等常见问题。
光致发光（Photoluminescence, PL）入门版
关于光致发光的实际应用做出一些解释。
hive语句的书写顺序及执行顺序
1、hive语句的书写顺序：（从前往后） (1)select (2)from (3)join on (4) where (5)group by (6)having (7)distribute by/cluster by (8) sort by (9) order by (10) limit (11) union(去重不排序)/union all（不去重不排序） 2、hive语句的执行顺序：...
EPLAN中2D安装板的绘制方法示例
EPLAN中2D安装板的绘制方法示例
从join on和where执行顺序认识T-SQL查询处理执行顺序
SQL语句中join连表时on和where后都可以跟条件，那么对查询结果集，执行顺序，效率是如何呢？ 通过查询资料发现： 区别： on是对中间结果进行筛选，where是对最终结果筛选。 执行顺序： 先进行on的过滤, 而后才进行join。 效率： 如果是inner join, 放on和放where产生的结果一样, 但没说哪个效率速度更高? 如果有outer join (left or
一文读懂AI协同办公领域发展和趋势
协同办公，又称OA，是指企业内部或外部各类人员之间利用信息技术来进行协作工作的一种形式。这种协作工作既可以由直接员工进行，也可以来自外部的咨询机构、合作伙伴或联营企业。协同办公的优势在于可以对资源进行有效管理和配置，各部门之间可以建立良好的协作关系，跨部门更好地实现资源共享。
第55篇：数据隐私保护技术盘点——差分隐私、同态加密与它们的坑（踩坑总结）
数据隐私保护是AI工程化中必须严肃对待的课题。差分隐私和同态加密是两把利器，但也都是“重武器”，使用不当反而会伤及自身。差分隐私像“模糊处理”，通过加噪来保证统计安全性，要小心管理隐私预算和效用权衡。同态加密像“保险箱里打算盘”，保证了计算的精确性，但必须承受巨大的性能和工程复杂度代价。我的建议是：从小规模实验开始，先用DP-SGD在MNIST/CIFAR-10等标准数据集上感受噪声与精度的关系；再用TenSEAL等易用的库尝试一个加密逻辑回归，体会密文膨胀和速度延迟。
hive语句的书写顺序和执行顺序
1、hive语句的书写顺序：（从前往后） (1)select (2)from (3)join on (4) where (5)group by (6)having (7)distribute by/cluster by (8) sort by (9) order by (10) limit (11) union(去重不排序)/union all（不去重不排序） 2、hive语句的执行顺序： (1)from (2)on (3)join (4)where (5)group by (6)having (7)sel
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
Hive语句中sql条件的书写顺序和执行顺序
在hive和mysql中都可以通过explain+sql语句，来查看执行顺序。 对于一条标准sql语句，它的书写顺序是这样的： select … from … where … group by … having … order by … limit … （1）mysql语句执行顺序： from... where...group by... having.... select ... ...
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');```[](https://bitdeli.com/free "Bitdeli Badge")
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
地表最强系列之Hive的高级查询语句
SELECT基础 SELECT用于映射符合指定查询条件的行 Hive SELECT是数据库标准SQL的子集 1.使用方法类似于MySQL 2.关键字和MySQL一样，不区分大小写 3.limit子句 4.where子句 5.运算符、like、rlike 6.group by子句 7.having子句 Hive的语句的书写顺序 (1)select (2)from (3)join on (4) where (5)group by (6)having (7)distribute by/cluster by (
hive语句执行顺序
SQL语句书写顺序： select … from… where…. group by… having… order by.. MySQL语句执行顺序： from… where… group by… having…. select … order by… hive语句执行顺序： from… where… select… group by… having … ord
hive和mysql书写顺序
mysql写的顺序 select....from....group by....having.....order by.....limit mysql执行顺序 from....on.....join....where....group by.....having....select......distinct.....order by.....limit hive书写顺序 select....
hive中子句的使用
hive中子句的书写顺序 select...from....join.....on....where....group by ....having....order by... sort by....limit....union | union all.... select 后跟要查询的列每个字段为一列(行专列就可以将字段放到hive后就可以完成)。 from 后根表，可以是一个select子句，...
Hive之优化小细节，加快你日常使用！
1.启动hive时采用 nohup hiveserver2 > 文件名 2>&1 & ，此种启动方式不会占用太多的shell窗口，直接将结果传入指定的文件中； 例如： nohup hiveserver2 > log1.txt 2>&1 $ 2.处理小数据集时，可以采用本地模式，提高效率： set hive.exec.mode.local.auto=true 3.新版本的hive启动的时候，默认申请的jvm堆内存大小为256M，jvm堆内存申请的太
只能访问hive默认库_hive知识点重点难点总结(2020/11/26/早上)
必读：1）SQL的书写顺序SELECT-DISTINCT-FROM-JOIN ON-WHERE-GROUP BY-HAVING-ORDER BY-LIMIT2)真正执行的顺序：随着mysql版本的更新换代，其优化器也在不断升级，优化器会分析不同执行顺序产生的性能消耗不同而动态调整执行顺序。下面是经常出现的查询顺序：FROM-ON-JOIN-WHERE-GROUP BY-HAVING-SELECT-...
Hive知识之基本查询
前言：Hive的查询语句和SQL语句非常类似，如果有SQL基础，学习Hive的查询语句会比较容易。 可参考SQL的基本查询： https://blog.youkuaiyun.com/weixin_42384784/article/details/105331826 1、简单查询 SELECT … FROM … WHERE … 需要注意的是，如果该表是一个分区表，则WHERE条件里必须对分区字段进行筛选 ...
Hive SQL语句的正确执行顺序
关于 sql 语句的执行顺序网上有很多资料，但是大多都没进行验证，并且很多都有点小错误，尤其是对于 select 和 group by 执行的先后顺序，有说 select 先执行，有说 group by 先执行，到底它俩谁先执行呢？ 今天我们通过 explain 来验证下 sql 的执行顺序。 在验证之前，先说结论，Hive 中 sql 语句的执行顺序如下： from .. where .. join .. on .. select .. group by .. select .. having ..
hive 语句执行顺序
hive语句执行顺序 msyql语句执行顺序 代码写的顺序： select … from… where…. group by… having… order by.. 或者 from … select … 1 2 3 4 代码的执行顺序： from… where…group by… having…. select … order by… 1 2 hive 语句执...
SQL和HIVE语句的查询顺序、执行顺序以及一些实用小tips
查询顺序： SELECT ... FROM ... WHERE ... GROUP BY ... HAVING ... ORDER BY ... SQL的执行顺序： FROM ... JOIN ... ON ... WHERE ... GROUP BY ... AVG SUM 等聚合函数 ... HAVING ... 计算所有表达式 ... SELECT ... DISTINCT ...
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
3681
扫一扫
