Boost.Accumulators is both a library for incremental statistical computation as well as an extensible framework for incremental calculation in general. The library deals primarily with the concept of an accumulator, which is a primitive computational entity that accepts data one sample at a time and maintains some internal state. These accumulators may offload some of their computations on other accumulators, on which they depend. Accumulators are grouped within an accumulator set. Boost.Accumulators resolves the inter-dependencies between accumulators in a set and ensures that accumulators are processed in the proper order.

The rolling mean is the mean over the last N samples. It is computed by dividing the rolling sum by the rolling count.

Lazy
or iterative calculation of the mean over the last N samples. The lazy
calculation is associated with the tag::lazy_rolling_mean feature, and
the iterative calculation (which is the default) with the
tag::immediate_rolling_mean feature. Both can be extracted using the
tag::rolling_mean() extractor.
 
把连续取得的N个采样值看成一个队列,队列的长度固定为N,
每次采样到一个新数据放入队尾,并扔掉原来队首的一次数据(先进先出原则),
把队列中的N个数据进行算术平均运算,获得新的滤波结果。

#include <iostream>
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics/stats.hpp>
#include <boost/accumulators/statistics/rolling_mean.hpp>

using namespace boost::accumulators;

int main()
{
        accumulator_set<int, stats<tag::rolling_mean> > acc(tag::rolling_window::window_size = 7);

// push in some data ...
        acc(1);
        acc(2);
        acc(3);
        std::cout << "Mean: " << rolling_mean(acc) << std::endl;

acc(4);
        acc(5);
        acc(6);
        acc(7);
        std::cout << "Mean: " << rolling_mean(acc) << std::endl;

return 0;
}
输出
Mean: 2
Mean: 4

#include <iostream>
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics/stats.hpp>
#include <boost/accumulators/statistics/mean.hpp>
#include <boost/accumulators/statistics/moment.hpp>

using namespace boost::accumulators;

int main()
{
    // Define an accumulator set for calculating the mean and the
    // 2nd moment ...
    accumulator_set<double, stats<tag::mean, tag::moment<2> > > acc;

// push in some data ...
    acc(1.2);
    acc(2.3);
    acc(3.4);
    acc(4.5);

// Display the results ...
    std::cout << "Mean: " << mean(acc) << std::endl;
    std::cout << "Moment: " << moment<2>(acc) << std::endl;

return 0;
}

结果

Mean: 2.85
Moment: 9.635

----------------------

Usage of the framework follows the following pattern:

1. Users build a computational object, called an accumulator_set<>, by selecting the
       computations in which they are interested, or authoring their own computational
       primitives which fit within the framework.

2. Users push data into the accumulator_set<> object one sample at a time.
    
   3. The accumulator_set<> computes the requested quantities in the most efficient method
        possible, resolving dependencies between requested calculations, possibly caching
        intermediate results.

The
Accumulators Framework defines the utilities needed for defining
primitive computational elements, called accumulators. It also provides
the accumulator_set<> type, described above.

// In header: <boost/accumulators/framework/accumulator_set.hpp>

template<typename Sample, typename Features, typename Weight>
struct accumulator_set {
  // types
  typedef Sample sample_type; // The type of the samples that will be accumulated.
  typedef Features features_type; // An MPL sequence of the features that should be accumulated.
  typedef Weight weight_type; // The type of the weight parameter. Must be a scalar. Defaults to void.
  typedef void result_type;

// member classes/structs/unions
  template<typename Feature>
  struct apply {
  };

可见 accumulator_set 是个类模板。模板的第一个参数表示样本的类型,
use the features<> template to specify a list of features to be calculated

template <class T>
class stats
{
public:
   stats()
      : m_min(tools::max_value<T>()),
        m_max(-tools::max_value<T>()),
        m_total(0),
        m_squared_total(0),
        m_count(0)
   {}
...

stats 也是一个类模版,T = Tag::mean 是参数类型。

namespace tag
{
    struct mean
      : depends_on<count, sum>
    {
        /// INTERNAL ONLY
        ///
        typedef accumulators::impl::mean_impl<mpl::_1, sum> impl;
    };

struct immediate_mean
      : depends_on<count>
    {
        /// INTERNAL ONLY
        ///
        typedef accumulators::impl::immediate_mean_impl<mpl::_1, tag::sample> impl;
    };

由此可见 tag 是一种命名空间 mean 是一个结构体

-------------------------关于 tag::moment< para > --------------------

Header

#include <boost/accumulators/statistics/moment.hpp>

Example

accumulator_set<int, stats<tag::moment<2> > > acc1;

acc1(2); // 4
acc1(4); // 16
acc1(5); // + 25
         // = 45 / 3 = 15

BOOST_CHECK_CLOSE(15., accumulators::moment<2>(acc1), 1e-5);

-----------------------------------------------------------------
accumulator_set<int, stats<tag::moment<5> > > acc2;

acc2(2); // 32
acc2(3); // 243
acc2(4); // 1024
acc2(5); // + 3125
         // = 4424 / 4 = 1106

BOOST_CHECK_CLOSE(1106., accumulators::moment<5>(acc2), 1e-5);

可见 tag::moment< para > 是一个结构体 就是一个类模板, 模板的参数para指定了矩的类型,
para = 2 是二次矩,也就是方差。

boost的accumulator rolling_mean的使用的更多相关文章

  1. boost number handling

    Boost.Integer defines specialized for integers. 1. types for integers with number of bits #include & ...

  2. boost强分类器的实现

    boost.cpp文件下: bool CvCascadeBoost::train( const CvFeatureEvaluator* _featureEvaluator, int _numSampl ...

  3. Boost信号/槽signals2

    信号槽是Qt框架中一个重要的部分,主要用来解耦一组互相协作的类,使用起来非常方便.项目中有同事引入了第三方的信号槽机制,其实Boost本身就有信号/槽,而且Boost的模块相对来说更稳定. signa ...

  4. 玩转Windows服务系列——使用Boost.Application快速构建Windows服务

    玩转Windows服务系列——创建Windows服务一文中,介绍了如何快速使用VS构建一个Windows服务.Debug.Release版本的注册和卸载,及其原理和服务运行.停止流程浅析分别介绍了Wi ...

  5. boost::function的用法

    本片文章主要介绍boost::function的用法. boost::function 就是一个函数的包装器(function wrapper),用来定义函数对象. 1.  介绍 Boost.Func ...

  6. Boost条件变量condition_variable_any

    Boost条件变量可以用来实现线程同步,它必须与互斥量配合使用.使用条件变量实现生产者消费者的简单例子如下,需要注意的是cond_put.wait(lock)是在等待条件满足.如果条件不满足,则释放锁 ...

  7. 新手,Visual Studio 2015 配置Boost库,如何编译和选择,遇到无法打开文件“libboost_thread-vc140-mt-gd-1_63.lib“的解决办法

    1,到官网下载最新的boost,www.boost.org 这里我下载的1-63版本. 2,安装,解压后运行bootstrap.bat文件.稍等一小会就OK. 3,编译boost库.注意一定要使用VS ...

  8. boost.python笔记

    boost.python笔记 标签: boost.python,python, C++ 简介 Boost.python是什么? 它是boost库的一部分,随boost一起安装,用来实现C++和Pyth ...

  9. vs2013给项目统一配置boost库

    1.打开项目,然后点击菜单中的 视图->其他窗口->属性管理器 2. 打开属性管理器,点击项目前的箭头,展开项目,找到debug或者release下面的Microsoft.Cpp.Win3 ...

随机推荐

  1. Eclipse kepler 安装 Dynamic Web Project差距WTP

    原文地址:http://blog.csdn.net/westrain2010/article/details/25122999, 欢迎转载 Eclipse 标准版是不能创建 Dynamic Web P ...

  2. day17 正则表达式 re模块和hashlib模块

    今日内容 1. re&正则表达式(*****) 注:不要将自定义文件命名为re import re re.findall(正则表达式,被匹配的字符串) 拿着正则表达式去字符串中找,返回一个列表 ...

  3. jQuery 替换元素

    参考https://www.cnblogs.com/halai/p/6868027.html http://www.w3school.com.cn/jquery/manipulation_replac ...

  4. python写机器人玩僵尸骰子

    python写机器人玩僵尸骰子由Al Sweigart用python发布注意:我正在为我的僵尸骰子模拟器寻找反馈,以及这一套指令.如果你觉得有什么地方可以改进,请发邮件到al@inventwithpy ...

  5. 1009 数字1的数量 数位dp

    1级算法题就这样了,前途渺茫啊... 更新一下博客,我刚刚想套用数位dp的模板,发现用那个模板也是可以做到,而且比第二种方法简单很多 第一种方法:我现在用dp[pos][now]来表示第pos位数字为 ...

  6. JMeter快速入门之Badboy录制

    1. 前言 JMeter录制有两种方式,一种是JMeter自带录制方法,另一种是下面要学习的Badboy录制,个人推荐使用此方法 下面教程不设计Badboy安装,可以百度一下. 2. 录制步骤: 2. ...

  7. JavaScript各种继承方式(五):寄生式继承(parasitic)

    一 原理 与原型式继承完全相同,只是对父类的实例(也当作子类的实例使用)进行了增强. function create(obj){ let mango = Object.create(obj); man ...

  8. c#: 以模态窗口显示于其它进程窗体之前

    产品之工具箱,需要工具以模态窗体,显示于主界面之上.记下代码点,以做备忘. 1.IWin32Window internal class Win32Window : IWin32Window { pub ...

  9. xcode 更新svn/Git后发现模拟器显示No Scheme问题

    这个是由于XXX..xcodeproj包中xcuserdata文件夹中user.xcuserdatad文件夹名字的问题...user.xcuserdatad文件夹的名字,不是当前用户的名字,就会显示n ...

  10. mysql 复制原理与实践

    复制功能是将一个mysql数据库上的数据复到一个或多个mysql从数据库上. 复制的原理:在主服务器上执行的所有DDL和DML语句都会被记录到二进制日志中,这些日志由连接到它的从服务器获取,并复制到从 ...