simple

CF558E A Simple Task WOC怎么又一个simple task? 操作就是区间排序+最终询问 第一反应是Splay（不对呀，我明明不会Splay的......😀） 后来看了看，感觉Splay不可做（我连Splay都不会，怎么就觉得不能做了） 感觉线段树比较靠谱 观察题目发现小写字母只有26个（常识） 往元素上去做文章 将排序用另外一种方式呈现 用桶排序的思想 开一个桶记录区间1~26字母出现的个数 然后区间赋值，达到排序目的 输出只要遍历一下线段树的叶子节点即可 代码： #include<bits/stdc++.h> using namespace std; const int N=100005; int n,m; int a[N]; int cal[30]; struct Sugment_Tree{ int LZT[N<<2]; int t[N<<2][30]; #define il inline #define mid (l+r)/2 Sugment_Tree(){memset(t,0,sizeof(t));memset(LZT,0,sizeof(LZT));} il void push_up(int num){ for(int i=1;i<=26;i++){t[num][i]=t[num<<1][i]+t[num<<1|1][i];} } il void

通过不同的设置api 版本环境变量，支持的命令行不同。 Interacting with etcd： https://coreos.com/etcd/docs/latest/dev-guide/interacting_v3.html 1. 未设置 ETCDCTL_API 时，支持的命令 　　缺省使用 ETCDCTL_API=2 [root@vStack etcd-v3.0.15-linux-amd64]# ./etcdctl help NAME: 　　etcdctl - A simple command line client for etcd. USAGE: 　　etcdctl [global options] command [command options] [arguments...] VERSION: 　　3.0.15 COMMANDS: 　　1. backup 　　　　　　　　 　　 　　 backup an etcd directory 　　2. cluster-health 　　　　　　　　　　check the health of the etcd cluster 　　3. mk 　　　　　　　　　　　　　　　 make a new key with a given value 　　4. mkdir 　　　　　　　　　　　　　　make a new

Major CRM Scenarios with most common Business Processes Interaction Center (Telesales, Customer Service & Support, Complaint Management) Field Applications (Mobile Sales, Mobile Service) Web Channel (Mobile) (Internet Sales Business-To-Business (B2B), Business-To-Consumer (B2C)) Marketing (Trade Promotion Management, Campaign Management) Sales (Sales Order Management, Activity Management) Service (Service Order Management, In-House Repair) Differences between CRM Releases CRM 4.0 based on Web AS 6.20 CRM 5.0 based on Web AS 7.00 (IPC runs on VMC which is a kind of Java VM included in kernel)

[MATLAB] Simple TF-IDF implementation Term-Frequency word weighting scheme is one of most used in normalization of document-term matrices in text mining and information retrieval. See wikipedia for details. function Y = tfidf( X ) % FUNCTION computes TF-IDF weighted word histograms. % % Y = tfidf( X ); % % INPUT : % X - document-term matrix (documents in columns) % % OUTPUT : % Y - TF-IDF weighted document-term matrix % % get term frequencies X = tf(X); % get inverse document frequencies I = idf(X); % apply weights for each document for j=1:size(X, 2) X(:, j) = X(:, j)*I(j); end Y = X;