Transducers are composable algorithmic transformations. They are independent from the context of their input and output sources and specify only the essence of the transformation in terms of an individual element. Because transducers are decoupled from input or output sources, they can be used in many different processes - collections, streams, channels, observables, etc. Transducers compose directly, without awareness of input or creation of intermediate aggregates.

OK, short in description... let's see why we need it

  1. Normal Javascript `map`,`filter` create inter variable and loop though array everytime we call `map` or `filter` function, `transducer` can loop thought the array only ONCE and apply all the transofrmations necessary.
const data = [,,];
const inc = x => x + ;
const double = x => * x;
const lessThanThree = x => x < ;
////////////////////
/**
* Problem: We loop over array 3 times! We want to loop over only once
* in order to improve the profermance.
*/
const res1 = data
.filter(lessThanThree)
.map(double)
.map(inc) console.log(res1) // [3,5]

  2. We don't want to introduce any mutation or impure function such as `forEach` does, transducer are mutation free.

/**
* Problem: it is not pure function and we do mutation. But it is faster
* than we do .filter.map.map style, because it only loop the array once.
*/
let res2 = [];
data.forEach((x) => {
let item;
if (lessThanThree(x)) {
item = inc(double(x))
res2.push(item);
}
})
console.log(res2) // [3,5]

  3. We want to style functional style to keep the code more readable, in the meanwhile improve the proferemance:

/**
* Good: We avoid the mutation and can be write as pure function and it only loop once!
* Problem: But we lose our function composion style! We still want .filter.map.map styling.
* Meanwhile it should be profermance wise.
*/
const res3 = data.reduce((acc, curr) => {
if (lessThanThree(curr)) {
acc.push(inc(double(curr)));
}
return acc;
}, []);
console.log(res3); // [3,5]

OK, until now, we have some idea, what kind of code we want. Basiclly it should be composable and efficient.

The question is how to make composable code?

As we might know about, in OOP; if we want to chain multi function calls, from each function, we need to return `this`:

Class Bot {
...
sayName() {
console.log(this,name)
return this;
} sayHello() {
console.log("Hello")
return this;
} } const b = new Bot('Petter') b.sayName().sayHello()

For Array, the reason we can chain calls together is because each call return Array type. The same as String, number...

The key is we need to keep the input and output as the same type!

Therefore for function, we need to keep input function and output function have the same function signature!

//data.reduce(reducer, seed), reducer is something we can compose!
//Because reducer :: (acc, curr) => acc
//For every reducer functions' signature are the same.
//If the function sinature are the same, then we can compose function together!
const _mapReducer = (xf, array) =>
array.reduce((acc, curr) => {
acc.push(xf(curr))
return acc;
}, []);
const _filterReducer = (xf, array) =>
array.reduce((acc, curr) => {
if (xf(curr)) acc.push(curr);
return acc;
}, []);
// To make fns easy to compose, we extract 'array' data & init value
const mapReducer = (xf) => ((acc, curr) => {
acc.push(xf(curr))
return acc;
});
const filterReducer = pred => ((acc, curr) => {
if (pred(curr)) acc.push(curr);
return acc;
});
// now mapReducer and filterReducer both have the same function signature.
console.log(data.reduce(mapReducer(double), [])); // [2,4,6]
console.log(data.reduce(mapReducer(inc), [])); // [2,3,4]
console.log(data.reduce(filterReducer(lessThanThree), [])); // [1,2]
In order to compose reudcers together we need to make mapReducer and filterReducer as high order functions to take reducer as arguement, take a reducer as input and return a reducer signature as output is the key to do composion!
// In order to compose reudcers together we need to make mapReducer and filterReducer as high order functions to take reducer as arguement
// Take a reducer as input and return a reducer signature as output is the key to do composion!
const map = xf => reducer => ((acc, curr) => {
acc = reducer(acc, xf(curr))
return acc;
});
const filter = pred => reducer => ((acc, curr)=> {
if (pred(curr)) acc = reducer(acc, curr)
return acc;
})
// For mapReducer and filterReducer, we both do acc.push()
// therefore we can extrat this as base reducer
const pushReducer = (acc, value) => {
acc.push(value);
return acc;
};
Now we are able to use functional style and loop the array only once!
const doulbeLessThanThree = compose(
map(inc),
map(double),
filter(lessThanThree)
)
const res5 = data.reduce(doulbeLessThanThree(pushReducer), []);
console.log(res5); // [3,5]

Define our transducer!

/**
* transducer :: ((a -> b -> a), (a -> b -> a), [a], [a]) -> [a]
* @param {*} xf: base reducer
* @param {*} reducer: the composion redcuer signature
* @param {*} seed : init value
* @param {*} collection : data
*/
const transducer = (xf, reducer, seed, collection) => {
return collection.reduce(reducer(xf), seed);
}
const res6 = transducer(pushReducer, doulbeLessThanThree, [], data);
console.log(res6); // [3,5]

[Transducer] Step by Step to build a simple transducer的更多相关文章

  1. Build step 'Execute shell' marked build as failure解决

    今天jenkins构建时运行脚本报错如下: Build step 'Execute shell' marked build as failure 脚本没问题后来看了下原因是磁盘空间不足导致报错,清除下 ...

  2. Jenkins Build step 'Execute shell' marked build as failure

    问题出现: Jenkins一直都构建成功,今天突然报错:Jenkins Build step 'Execute shell' marked build as failure 问题原因: By defa ...

  3. [转]Bootstrap 3.0.0 with ASP.NET Web Forms – Step by Step – Without NuGet Package

    本文转自:http://www.mytecbits.com/microsoft/dot-net/bootstrap-3-0-0-with-asp-net-web-forms In my earlier ...

  4. Tomcat Clustering - A Step By Step Guide --转载

    Tomcat Clustering - A Step By Step Guide Apache Tomcat is a great performer on its own, but if you'r ...

  5. Code Understanding Step by Step - We Need a Task

      Code understanding is a task we are always doing, though we are not even aware that we're doing it ...

  6. 课程四(Convolutional Neural Networks),第一周(Foundations of Convolutional Neural Networks) —— 2.Programming assignments:Convolutional Model: step by step

    Convolutional Neural Networks: Step by Step Welcome to Course 4's first assignment! In this assignme ...

  7. Convolutional Neural Networks: Step by Step

    Andrew Ng deeplearning courese-4:Convolutional Neural Network Convolutional Neural Networks: Step by ...

  8. Sequence Models Week 1 Building a recurrent neural network - step by step

    Building your Recurrent Neural Network - Step by Step Welcome to Course 5's first assignment! In thi ...

  9. Step by step Process of creating APD

    Step by step Process of creating APD: Business Scenario: Here we are going to create an APD on top o ...

随机推荐

  1. Windows 消息循环(2) - WPF中的消息循环

    接上文: Windows 消息循环(1) - 概览 win32/MFC/WinForm/WPF 都依靠消息循环驱动,让程序跑起来. 本文介绍 WPF 中是如何使用消息循环来驱动程序的. 4 消息循环在 ...

  2. AOP的自动代理

    Spring的aop机制提供两类方式实现类代理.一种是单个代理,一种是自动代理. 单个代理通过ProxyFactoryBean来实现(就如上面的配置). 自动代理:自动代理能够让切面定义来决定那个be ...

  3. Xcode 7.0正式版发布了

    Xcode 7.0正式版发布了     下载地址:链接: http://pan.baidu.com/s/1FNkPS 密码: ee42     本文由大学霸整理,转载请注明出处,尊重IT人!

  4. FastReport.Net使用:[17]线(Line)控件使用

    FastReport中,线(Line)控件怎么用?怎么画一条美观的线? 认识Line控件 1.线(Line)控件包含于形状(Shape)控件中,有5个可选项,一个标准线和四个对角线,其实都是同一种线, ...

  5. Hibernate 组合主键映射

    在开发过程中创建数据库表时,有时候会发现单纯的创建一个主键是不可行的,有时候就需要多个字段联合保持唯一,本文讲述如何创建组合主键的映射. 例如:记录一个班的考试成绩.学生跟科目是多对多的关系,只有一个 ...

  6. Parse要垮了

    一清早收到邮件就睡不着了... 花了那么多时间熟悉api,第一个基于parse的app也要做完了... 看来国内的类似产品也不敢用了,还是老老实实用阿里云自己写backend吧...

  7. Djangio笔记

    django图解 新创建一个项目后的目录层级

  8. python开发_glob

    ''' 在python中,glob模块是用来查找匹配的文件的 在查找的条件中,需要用到Unix shell中的匹配规则: * : 匹配所所有 ? : 匹配一个字符 *.* : 匹配如:[hello.t ...

  9. PAT甲级1017. Queueing at Bank

    PAT甲级1017. Queueing at Bank 题意: 假设一家银行有K台开放服务.窗前有一条黄线,将等候区分为两部分.所有的客户都必须在黄线后面排队,直到他/她轮到服务,并有一个可用的窗口. ...

  10. Qt on android 蓝牙开发(控制小车)

    因为要做一个用蓝牙控制小车的app,就用着QT搞了下,网上关于QT蓝牙开发的资料比较少,我在这里记录下过程希望对看到了人有所帮助 首先在项目文件里添加 QT += bluetooth 这样就可以用QT ...