C#编程：文本压缩算法实现与解析

文本的压缩

代码

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"这篇文章主要介绍了如何使用C#编程语言实现文本的压缩算法，提供了一种实践性的方法，通过具体的代码示例和算法分析，帮助C#程序员理解和应用数据结构与算法。作者Mike McMillan强调了C#在.NET Framework库中的数据结构和算法实现，并覆盖了数组、链表、哈希表、树、图、排序和搜索算法等基础知识，同时也涉及概率算法和动态规划等进阶主题。这本书适合C#专业人士和学生阅读，旨在提高读者的编程效率和问题解决能力。" 在C#中实现文本压缩，首先要理解压缩的基本原理，这通常涉及到编码理论和信息论的知识，如霍夫曼编码、行程编码或LZ77/78等压缩方法。C#作为一种强大的面向对象的编程语言，提供了丰富的数据结构和算法支持来实现这些压缩技术。例如，霍夫曼编码是一种基于字符频率的无损数据压缩方法，它构建一个霍夫曼树，将频繁出现的字符赋予较短的编码。在C#中，可以通过构造自平衡二叉树（AVL树或红黑树）来实现霍夫曼树，并利用位操作来高效地编码和解码文本。另一方面，行程编码则是寻找连续重复字符并将其替换为字符和长度的组合。C#中的ArrayList或LinkedList可以用于存储字符序列，然后遍历序列查找重复部分，从而进行压缩。哈希表和字典数据结构在压缩过程中用于快速查找和统计字符出现的频率，C#的Dictionary<TKey, TValue>类提供了这样的功能。而二叉搜索树和图数据结构则可能在更复杂的压缩算法如 LZ77/78 中发挥作用，它们可以帮助找到文本中的匹配模式。排序和搜索算法，如快速排序、归并排序和二分查找，是优化压缩过程的关键。在C#中，Array.Sort()方法和List<T>.Sort()方法可以用于排序，而BinarySearch()方法则用于高效的搜索操作。更高级的算法，如概率算法，可能涉及到随机化和概率统计，例如在压缩时根据字符出现的概率进行编码。动态规划则可用于解决一些复杂问题，例如在压缩过程中找到最优的编码方案。通过学习和实践C#中的数据结构和算法，开发者可以有效地实现文本压缩，提高程序的效率，并深入理解数据压缩背后的数学原理。Mike McMillan的书提供了这样一个平台，让C#程序员可以直接在自己的项目中应用这些知识，而无需依赖其他语言的经验。

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Collections Described 5

return lname;

}

set {

lname = lastName;

}

public override string ToString() {

return (String.Format("{0}{1}{2}", fname, mname,

lname));

}

public string Initials() {

return (String.Format("{0}{1}{2}",fname.Substring(0,1),

mname.Substring(0,1), lname.Substring(0,1)));

}

public class NameTest {

static void Main() {

Name myName = new Name("Michael", "Mason", "McMillan");

string fullName, inits;

fullName = myName.ToString();

inits = myName.Initials();

Console.WriteLine("My name is {0}.", fullName);

Console.WriteLine("My initials are {0}.", inits);

}

Although many of the elements in the .NET environment are implemented as

classes (such as arrays and strings), several primary elements of the language

are implemented as structures, such as the numeric data types. The Integer

data type, for example, is implemented as the Int32 structure. One of the

methods you can use with Int32 is the Parse method for converting the string

representation of a number into an integer. Here’s an example:

using System;

public class IntStruct {

static void Main() {

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Collections Described 7

Push

David

Raymond

Mike

Bernica

Pop

David

Raymond

Mike

Bernica

FIGURE

1.3. Stack Operations.

Some types of linear lists restrict access to their data elements. Examples

of these types of lists are stacks and queues. A stack is a list where access is

restricted to the beginning (or top) of the list. Items are placed on the list

at the top and can only be removed from the top. For this reason, stacks are

known as Last-in, First-out structures. When we add an item to a stack, we

call the operation a push. When we remove an item from a stack, we call that

operation a pop. These two stack operations are shown in Figure 1.3.

The stack is a very common data structure, especially in computer systems

programming. Stacks are used for arithmetic expression evaluation and for

balancing symbols, among its many applications.

A queue is a list where items are added at the rear of the list and removed

from the front of the list. This type of list is known as a First-in, First-out struc-

ture. Adding an item to a queue is called an EnQueue, and removing an item

from a queue is called a Dequeue. Queue operations are shown in Figure 1.4.

Queues are used in both systems programming, for scheduling operating

system tasks, and for simulation studies. Queues make excellent structures

for simulating waiting lines in every conceivable retail situation. A special

type of queue, called a priority queue, allows the item in a queue with the

highest priority to be removed from the queue ﬁrst. Priority queues can be

used to study the operations of a hospital emergency room, where patients

with heart trouble need to be attended to before a patient with a broken arm,

for example.

The last category of linear collections we’ll examine are called generalized

indexed collections. The ﬁrst of these, called a hash table, stores a set of data

Mike

Raymond

David

Beata

Bernica

Beata

Mike

Raymond

David

Bernica

En Queue

De Queue

FIGURE 1.4. Queue Operations.

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8 INTRODUCTION TO COLLECTIONS, GENERICS, AND TIMING CLASS

“Paul E. Spencer”

“Information Systems”

37500

FIGURE 1.5. A Record To Be Hashed.

values associated with a key. In a hash table, a special function, called a hash

function, takes one data value and transforms the value (called the key) into

an integer index that is used to retrieve the data. The index is then used to

access the data record associated with the key. For example, an employee

record may consist of a person’s name, his or her salary, the number of years

the employee has been with the company, and the department he or she works

in. This structure is shown in Figure 1.5. The key to this data record is the

employee’s name. C# has a class, called HashTable, for storing data in a hash

table. We explore this structure in Chapter 10.

Another generalized indexed collection is the dictionary. A dictionary is

made up of a series of key–value pairs, called associations. This structure

is analogous to a word dictionary, where a word is the key and the word’s

deﬁnition is the value associated with the key. The key is an index into the

value associated with the key. Dictionaries are often called associative arrays

because of this indexing scheme, though the index does not have to be an

integer. We will examine several Dictionary classes that are part of the .NET

Framework in Chapter 11.

Hierarchical Collections

Nonlinear collections are broken down into two major groups: hierarchical

collections and group collections. A hierarchical collection is a group of items

divided into levels. An item at one level can have successor items located at

the next lower level.

One common hierarchical collection is the tree. A tree collection looks like

an upside-down tree, with one data element as the root and the other data

values hanging below the root as leaves. The elements of a tree are called

nodes, and the elements that are below a particular node are called the node’s

children. A sample tree is shown in Figure 1.6.

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