深入理解Clojure高级算法：实战指南

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"Clojure.Data.Structures.and.Algorithms.Cookbook.1785281453" 本书《Clojure数据结构与算法实战》是面向已经掌握Clojure编程语言的中级开发者的一本指南，旨在深入理解并实现高级算法。书中通过25个精心设计的食谱（即实践案例）帮助读者探索算法在解决实际计算问题中的应用。书中涵盖了使用Clojure的各种技巧，如拉链、解析、模式匹配等，并介绍了逻辑编程和异步编程等创新方法。书中的章节分布如下： 1. 重访数组：这一章将重新审视数组这一经典数据结构，探讨其在Clojure中的不同应用场景和优化策略。 2. 另类链表：介绍链表的非传统用法，可能包括环形链表、双向链表等，以及如何利用它们解决复杂问题。 3. 遍历数据森林：讨论树形数据结构的应用，可能包括二叉树、森林遍历等，以及Clojure库如何支持这些操作。 4. 科学决策：涉及机器学习和优化技术，如线性回归、神经网络等，以及如何利用Clojure实现这些算法。 5. 逻辑编程：通过Clojure的core.logic库学习逻辑编程，这是一种用逻辑表达式解决问题的方法。 6. 通过通信共享：介绍Clojure的core.async库，用于异步编程，强调如何通过通道实现并发和协调。 7. 转换为一等公民：探讨Clojure的转换器（transducers），这是一种高效的数据处理机制，可以应用于各种集合操作。这本书适合那些对Clojure有一定基础的开发者，不需要预先了解书中的所有算法，因为书中会提供相关的理论解释。通过阅读，读者将能够利用Clojure的特性，如Instaparse（解析）、core.match（模式匹配）、clojure.zip（拉链）和clojure.matrix（矩阵运算）等库，来构建优雅且高效的算法解决方案。书中的每个章节都将理论知识与实践相结合，帮助读者提升算法设计和实现的能力。

Revisiting Arrays

In this chapter, we will see how you can use array abstractions in Clojure to cover the following

topics:

f Efciently compressing a byte array

f Using Pascal's triangle to draw fractals

f Simulating multithreading using time-sharing

f Simulating a call stack using arrays

Introduction

In this book, we will go on a journey through the broad land of algorithms and data structures,

taking a ride on the comfortable vehicle that is Clojure programming language.

First, we will take a look at arrays, exploring their particular structures to tackle problems as

interesting as compression, fractal drawing, multithreading, and call stacks.

Then we will elaborate on linked lists, transforming them in to doubly linked lists. We will do

this to speed up access to their elements, build parsers, and devise fast random access.

The next step of our trip will concern trees of data. We'll show you how to implement self-

balancing red-black trees, how to design efcient key-value stores — thanks to B-trees (way

to go in order to design undo-capable text editors), and lastly, a methodology to construct

autocomplete text typing systems.

After that, we'll focus on exploring some optimization and machine-learning techniques. We

will see how to set up a recommendation engine, the way to go to optimize a problem where

costs and prots are involved, a methodology to nd the best possible paths in graphs, and

how to summarize texts.

Revisiting Arrays

Then we'll study the topic of logic programming, analyzing some website trafc logs to detect

visitors of interest to us. Doing this, we'll dive into the problem of type inferencing for the Java

language, and simulate a turn of a checkers game.

At that point, we'll talk about asynchronous programming as a means of tackling difcult

problems. We'll build a tiny web spider, design an interactive HTML5 game, and design a

complex online taxi-booking solution.

The last rally point of this trip, but certainly not the least, is that we'll have a look at the higher

order functions and transducers at the heart of Clojure. We'll design a recursive descent

parser using a trampoline, build a reusable mini rewall thanks to transducers, and lastly,

explore the continuation passing style as a tool to design a simple unication engine.

This will be quite a tour, in which we will bring to life various real-world use cases related to the

essential theory of computing as far as data structures and algorithms are involved, which are

all served by the high expressive power of Clojure. By the end of this book, you'll be familiar

with many of the advanced concepts that fuel most of the nontrivial applications of our world

while you enhance your mastery of Clojure!

Efciently compressing a byte array

Compressing a byte array is a matter of recognizing repeating patterns within a byte sequence

and devising a method that can represent the same underlying information to take advantage

of these discovered repetitions.

To get a rough idea of how this works, imagine having a sequence as:

["a" "a" "a" "b" "b" "b" "b" "b" "b" "b" "c" "c"]

It is intuitively more efcient to represent this as:

[3 times "a", 7 times "b", 2 times "c"]

Now, we are going to use a methodology based on a well-known algorithm, that is, the LZ77

compression method. LZ77 is, despite being quite old, the basis of most of all the well-known

and currently used compression methods, especially the Deate algorithm.

Deate is at the heart of the ZIP family of compression algorithms. It

uses a slightly modied version of LZ77 plus a special encoding, that is,

the Huffman encoding.

The point of LZ77 is to walk a sequence and recognize a pattern in the past elements that will

occur in the upcoming elements, replacing those with a couple of values: how many elements

should go backwards in order to locate the recurring pattern, which is called "distance"; and

how long is the recurring pattern, which is labeled as "length".

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深入理解Clojure高级算法：实战指南

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