说明,代码摘取自webrtc中的代码库,随机数生成算法:
random.h
#ifndef __RANDOM_H__
#define __RANDOM_H__
#include<limits>
#include<stdint.h>
#include<math.h>
class Random {
public:
// TODO(tommi): Change this so that the seed can be initialized internally,
// e.g. by offering two ways of constructing or offer a static method that
// returns a seed that's suitable for initialization.
// The problem now is that callers are calling clock_->TimeInMicroseconds()
// which calls TickTime::Now().Ticks(), which can return a very low value on
// Mac and can result in a seed of 0 after conversion to microseconds.
// Besides the quality of the random seed being poor, this also requires
// the client to take on extra dependencies to generate a seed.
// If we go for a static seed generator in Random, we can use something from
// webrtc/base and make sure that it works the same way across platforms.
// See also discussion here: https://codereview.webrtc.org/1623543002/
explicit Random(uint64_t seed);
// Return pseudo-random integer of the specified type.
// We need to limit the size to 32 bits to keep the output close to uniform.
template <typename T>
T Rand() {
static_assert(std::numeric_limits<T>::is_integer &&
std::numeric_limits<T>::radix == 2 &&
std::numeric_limits<T>::digits <= 32,
"Rand is only supported for built-in integer types that are "
"32 bits or smaller.");
return static_cast<T>(NextOutput());
}
// Uniformly distributed pseudo-random number in the interval [0, t].
uint32_t Rand(uint32_t t);
// Uniformly distributed pseudo-random number in the interval [low, high].
uint32_t Rand(uint32_t low, uint32_t high);
// Uniformly distributed pseudo-random number in the interval [low, high].
int32_t Rand(int32_t low, int32_t high);
// Normal Distribution.
double Gaussian(double mean, double standard_deviation);
// Exponential Distribution.
double Exponential(double lambda);
private:
// Outputs a nonzero 64-bit random number.
uint64_t NextOutput() {
state_ ^= state_ >> 12;
state_ ^= state_ << 25;
state_ ^= state_ >> 27;
//RTC_DCHECK(state_ != 0x0ULL);
return state_ * 2685821657736338717ull;
}
uint64_t state_;
RTC_DISALLOW_IMPLICIT_CONSTRUCTORS(Random);
};
// Return pseudo-random number in the interval [0.0, 1.0).
template <>
float Random::Rand<float>();
// Return pseudo-random number in the interval [0.0, 1.0).
template <>
double Random::Rand<double>();
// Return pseudo-random boolean value.
template <>
bool Random::Rand<bool>();
#endif // __RANDOM_H__
random.cc
#include"random.h"
Random::Random(uint64_t seed) {
//RTC_DCHECK(seed != 0x0ull);
state_ = seed;
}
uint32_t Random::Rand(uint32_t t) {
// Casting the output to 32 bits will give an almost uniform number.
// Pr[x=0] = (2^32-1) / (2^64-1)
// Pr[x=k] = 2^32 / (2^64-1) for k!=0
// Uniform would be Pr[x=k] = 2^32 / 2^64 for all 32-bit integers k.
uint32_t x = NextOutput();
// If x / 2^32 is uniform on [0,1), then x / 2^32 * (t+1) is uniform on
// the interval [0,t+1), so the integer part is uniform on [0,t].
uint64_t result = x * (static_cast<uint64_t>(t) + 1);
result >>= 32;
return result;
}
uint32_t Random::Rand(uint32_t low, uint32_t high) {
//RTC_DCHECK(low <= high);
return Rand(high - low) + low;
}
int32_t Random::Rand(int32_t low, int32_t high) {
//RTC_DCHECK(low <= high);
// We rely on subtraction (and addition) to be the same for signed and
// unsigned numbers in two-complement representation. Thus, although
// high - low might be negative as an int, it is the correct difference
// when interpreted as an unsigned.
return Rand(high - low) + low;
}
template <>
float Random::Rand<float>() {
double result = NextOutput() - 1;
result = result / 0xFFFFFFFFFFFFFFFEull;
return static_cast<float>(result);
}
template <>
double Random::Rand<double>() {
double result = NextOutput() - 1;
result = result / 0xFFFFFFFFFFFFFFFEull;
return result;
}
template <>
bool Random::Rand<bool>() {
return Rand(0, 1) == 1;
}
double Random::Gaussian(double mean, double standard_deviation) {
// Creating a Normal distribution variable from two independent uniform
// variables based on the Box-Muller transform, which is defined on the
// interval (0, 1]. Note that we rely on NextOutput to generate integers
// in the range [1, 2^64-1]. Normally this behavior is a bit frustrating,
// but here it is exactly what we need.
const double kPi = 3.14159265358979323846;
double u1 = static_cast<double>(NextOutput()) / 0xFFFFFFFFFFFFFFFFull;
double u2 = static_cast<double>(NextOutput()) / 0xFFFFFFFFFFFFFFFFull;
return mean + standard_deviation * sqrt(-2 * log(u1)) * cos(2 * kPi * u2);
}
double Random::Exponential(double lambda) {
double uniform = Rand<double>();
return -log(uniform) / lambda;
}
真随机数的获取在linux可以对设备/dev/urandom进行读取,在windows下则可以调用RtlGenRandom。
还可以采用mt19937算法生成。
C++代码如下:
#include <random>
#include <iostream>
struct MT19937 {
private:
static std::mt19937_64 rng;
public:
// This is equivalent to srand().
static void seed(uint64_t new_seed = std::mt19937_64::default_seed) {
rng.seed(new_seed);
}
// This is equivalent to rand().
static uint64_t get() {
return rng();
}
static double get_double()
{
double result=rng();
return result/std::numeric_limits<uint64_t>::max();
}
};
std::mt19937_64 MT19937::rng;
int main() {
MT19937::seed(/*put your seed here*/);
for (int i = 0; i < 10; ++ i)
std::cout << MT19937::get() << std::endl;
}
文章浏览阅读1.8k次,点赞4次,收藏6次。python简易爬虫v1.0作者:William Ma (the_CoderWM)进阶python的首秀,大部分童鞋肯定是做个简单的爬虫吧,众所周知,爬虫需要各种各样的第三方库,例如scrapy, bs4, requests, urllib3等等。此处,我们先从最简单的爬虫开始。首先,我们需要安装两个第三方库:requests和bs4。在cmd中输入以下代码:pip install requestspip install bs4等安装成功后,就可以进入pycharm来写爬虫了。爬
文章浏览阅读2.6k次。解决方法:解决方法可以去github重新下载一个pyflakes.vim。执行如下命令git clone --recursive git://github.com/kevinw/pyflakes-vim.git然后进入git克降目录,./pyflakes-vim/ftplugin,通过如下命令将python目录下的所有文件复制到~/.vim/ftplugin目录下即可。cp -R ...._freetorn.vim
文章浏览阅读210次,点赞7次,收藏3次。本文简述了hello.c源程序的预处理、编译、汇编、链接和运行的主要过程,以及hello程序的进程管理、存储管理与I/O管理,通过hello.c这一程序周期的描述,对程序的编译、加载、运行有了初步的了解。_hit csapp
文章浏览阅读1w次,点赞2次,收藏27次。来源:机器人小妹 很多时候企业拥有重复,乏味且困难的工作流程,这些流程往往会减慢生产速度并增加运营成本。为了降低生产成本,企业别无选择,只能自动化某些功能以降低生产成本。 通过数字化..._人工智能平台
文章浏览阅读2.2k次。热加载能够在每次保存修改的代码后自动刷新 electron 应用界面,而不必每次去手动操作重新运行,这极大的提升了开发效率。安装 electron 热加载插件热加载虽然很方便,但是不是每个 electron 项目必须的,所以想要舒服的开发 electron 就只能给 electron 项目单独的安装热加载插件[electron-reloader]:// 在项目的根目录下安装 electron-reloader,国内建议使用 cnpm 代替 npmnpm install electron-relo._electron-reloader
文章浏览阅读942次。在11.0 进行定制化开发,会根据需要去掉recovery模式的一些选项 就是在device.cpp去掉一些选项就可以了。_android recovery 删除 部分菜单
文章浏览阅读2.2k次,点赞2次,收藏6次。继续上次的echart博客,由于省会流向图是从echart画廊中直接取来的。所以直接上代码<!DOCTYPE html><html><head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width,initial-scale=1,minimum-scale=1,maximum-scale=1,user-scalable=no" /&_java+echart地图+物流跟踪
文章浏览阅读1.4k次。一、OSD模块简介1.1 消息封装:在OSD上发送和接收信息。cluster_messenger -与其它OSDs和monitors沟通client_messenger -与客户端沟通1.2 消息调度:Dispatcher类,主要负责消息分类1.3 工作队列:1.3.1 OpWQ: 处理ops(从客户端)和sub ops(从其他的OSD)。运行在op_tp线程池。1...._ceph 发送数据到其他副本的源码
文章浏览阅读7.9k次,点赞3次,收藏22次。一 定义这是最早出现的置换算法。该算法总是淘汰最先进入内存的页面,即选择在内存中驻留时间最久的页面予以淘汰。该算法实现简单,只需把一个进程已调入内存的页面,按先后次序链接成一个队列,并设置一个指针,称为替换指针,使它总是指向最老的页面。但该算法与进程实际运行的规律不相适应,因为在进程中,有些页面经常被访问,比如,含有全局变量、常用函数、例程等的页面,FIFO 算法并不能保证这些页面不被淘汰。这里,我_进程调度fifo算法代码
文章浏览阅读133次。rownum是oracle才有的写法,rownum在oracle中可以用于取第一条数据,或者批量写数据时限定批量写的数量等mysql取第一条数据写法SELECT * FROM t order by id LIMIT 1;oracle取第一条数据写法SELECT * FROM t where rownum =1 order by id;ok,上面是mysql和oracle取第一条数据的写法对比,不过..._mysql 替换@rownum的写法
文章浏览阅读790次,点赞3次,收藏4次。官网下载下载链接:http://www.eclipse.org/downloads/点击Download下载完成后双击运行我选择第2个,看自己需要(我选择企业级应用,如果只是单纯学习java选第一个就行)进入下一步后选择jre和安装路径修改jvm/jre的时候也可以选择本地的(点后面的文件夹进去),但是我们没有11版本的,所以还是用他的吧选择接受安装中安装过程中如果有其他界面弹出就点accept就行..._ecjelm
文章浏览阅读245次。原文链接:https://linux.cn/article-7801-1.htmlifconfigping <IP地址>:发送ICMP echo消息到某个主机traceroute <IP地址>:用于跟踪IP包的路由路由:netstat -r: 打印路由表route add :添加静态路由路径routed:控制动态路由的BSD守护程序。运行RIP路由协议gat..._ifconfig 删除vlan