Higher-Order Perl: Transforming Programs with Programs

Mark Jason Dominus

  • 出版商: Morgan Kaufmann
  • 出版日期: 2005-03-28
  • 售價: $2,800
  • 貴賓價: 9.0$2,520
  • 語言: 英文
  • 頁數: 600
  • 裝訂: Paperback
  • ISBN: 1558607013
  • ISBN-13: 9781558607019
  • 相關分類: Perl 程式語言

立即出貨 (庫存 < 3)




"Higher-Order Perl is the most exciting, most clearly-written, most comprehensive, and most forward-looking programming book I've read in at least ten years. It's your map to the future of programming in any language."
—Sean M. Burke, Leading Programmer, Comprehensive Perl Archive Network (CPAN)
Most Perl programmers were originally trained as C and Unix programmers, so the Perl programs that they write bear a strong resemblance to C programs. However, Perl incorporates many features that have their roots in other languages such as Lisp. These advanced features are not well understood and are rarely used by most Perl programmers, but they are very powerful. They can automate tasks in everyday programming that are difficult to solve in any other way. One of the most powerful of these techniques is writing functions that manufacture or modify other functions. For example, instead of writing ten similar functions, a programmer can write a general pattern or framework that can then create the functions as needed according to the pattern. For several years Mark Jason Dominus has worked to apply functional programming techniques to Perl. Now Mark brings these flexible programming methods that he has successfully taught in numerous tutorials and training sessions to a wider audience.



Table of Contents:

1. Recursion and Callbacks
1.1 Decimal to Binary Conversion
1.2 Factorial
1.2.1 Why Private Variables are Important
1.3 The Tower of Hanoi
1.4 Hierarchical Data
1.5 Applications and Variations of Directory Walking
1.6 Functional vs. Object-Oriented Programming
1.7 HTML
1.7.1 More Flexible Selection
1.8 When Recursion Blows Up
1.8.1 Fibonacci Numbers
1.8.2 Partitioning

2. Dispatch Tables
2.1 Configuration File Handling
2.1.1 Table-driven configuration
2.1.2 Advantages of Dispatch Tables
2.1.3 Dispatch Table Strategies
2.1.4 Default Actions
2.2 Calculator
2.2.1 HTML Processing Revisited

3. Caching and Memoization
3.1 Caching Fixes Recursion
3.2 Inline Caching
3.2.1 Static Variables
3.3 Good Ideas
3.4 Memoization
3.5 The Memoize Module
3.5.1 Scope and Duration Scope Duration
3.5.2 Lexical Closure
3.5.3 Memoization Again
3.6 Caveats
3.6.1 Functions whose Return Values do not Depend on their Arguments
3.6.2 Functions with Side Effects
3.6.3 Functions that Return References
3.6.4 A Memoized Clock?
3.4.5 Very Fast Functions
3.7 Key Generation
3.7.1 More Applications of User-Supplied Key Generators
3.7.2 Inlined Cache Manager with Argument Normalizer
3.7.3 Functions with Reference Arguments
3.7.4 Partioning
3.7.5 Custom Key Generation for Impure Functions
7.8 Caching in Object Methods
3.8.1 Memoization of Object Methods
3.9 Persistent Caches
3.10 Alternatives to Memoization
3.11 Evangelism
3.12 The Benefits of Speed
3.12.1 Profiling and Performance Analysis
3.12.2 Automatic Profiling
3.12.3 Hooks

4. Iterators
4.1 Introduction
4.1.1 Filehandles are Iterators
4.1.2 Iterators are Objects
4.1.3 Other Common Examples of Iterators
4.2 Homemade Iterators
4.2.1 A Trivial Iterator: upto() Syntactic Sugar for Manufacturing Iterators
4.2.2 dir_walk()
4.2.3 On Clever Inspirations
4.3 Examples
4.3.1 Permutations
4.3.2 Genomic Sequence Generator
4.3.3 Filehandle Iterators
4.3.4 A Flat-File Database Improved Database
4.3.5 Searching Databases Backwards A Query Package that Transforms Iterators An Iterator that Reads Files Backwards Putting it Together
4.3.6 Random Number Generation
4.4 Filters and Transforms
4.4.1 imap()
4.4.2 igrep()
4.4.3 list_iterator()
4.4.4 append()
4.5 The Semipredicate Problem
4.5.1 Avoiding the Problem
4.5.2 Alternative undefs
4.5.3 Rewriting Utilities
4.5.4 Iterators that Return Mulitple Values
4.5.5 Explicit Exhaustion Function
4.5.6 Four-Operation Iterators
4.5.7 Iterator Methods
4.6 Alternative Interfaces to Iterators
4.6.1 Using foreach to Loop over more than one Array
4.6.2 An Iterator with an each-like Interface
4.6.3 Tied Variable Interfaces Summary of tie Tied Scalars Tied Filehandle
4.7 An Extended Example: Web Spiders
4.7.1 Pursuing only Interesting Links
4.7.2 Referring URLs
4.7.3 robots.txt
4.7.4 Summary

5. From Recursion to Iterators
5.1 The Partition Problem Revisited
5.1.1 Finding All Possible Partions
5.1.2 Optimizations
5.1.3 Variations
5.2 How to Convert a Recursive Function to an Iterator
5.3 A Generic Search Iterator
5.4 Other General Techniques for Eliminating Recursion
5.4.1 Tail Call Elimination Someone Else's Problem Creating Tail Calls Explicit Stacks Eliminating Recursion from fib()

6. Infinite Streams
6.1 Linked Lists
6.2 Lazy Linked Lists
6.2.1 A Trivial Stream: upto()
6.2.2 Utilities for Streams
6.3 Recursive Streams
6.3.1 Memoizing Streams
6.4 The Hamming Problem
6.5 Regex String Generation
6.5.1 Generating Strings in Order
6.5.2 Regex Matching
6.5.3 Cutsorting Log Files
6.6 The Newton-Raphson Method
6.6.1 Approximation Streams
6.6.2 Derivatives
6.6.3 The Tortoise and the Hare
6.6.4 Finance
6.7 Power Series
6.7.1 Derivatives
6.7.2 Other Functions
6.7.3 Symbolic Computation

7. Higher-Order Functions and Currying
7.1 Currying
7.2 Common Higher-Order Functions
7.2.1 Automatic Currying
7.2.2 Prototypes Prototype Problems
7.2.3 More Currying
7.2.4 Yet More Currying
7.3 reduce() and combine()
7.3.1 Boolean Operators
7.4 Databases
7.4.1 Operator Overloading

8. Parsing
8.1 Lexers
8.1.1 Emulating the <> operator
8.1.2 Lexers More Generally
8.1.3 Chained Lexers
8.1.4 Peeking
8.2 Parsing in General
8.2.1 Grammars
8.2.2 Parsing Grammars
8.3 Recursive Descent Parsers
8.3.1 Very Simple Parsers
8.3.2 Parser Operators
8.3.3 Compound Operators
8.4 Arithmetic Expressions
8.4.1 A Calculator
8.4.2 Left Recursion
8.4.3 A Variation on star()
8.4.4 Generic Operator Parsers
8.4.5 Debugging
8.4.6 The Finished Calculator
8.4.7 Error Diagnosis and Recovery Error Recovery Parsers Exceptions
8.4.8 Big Numbers
8.5 Parsing Regexes
8.6 Outlines
8.7 Databases Query Parsing
8.7.1 The Lexer
8.7.2 The Parser
8.8 Backtracking Parsers
8.8.1 Continuations
8.8.2 Parse Streams
8.9 Overloading

9. Declarative Programming
9.1 Constraint Systems
9.2 Local Propagation Networks
9.2.1 Implementing a Local Propagation Network
9.2.2 Problems with Local Propagation
9.3 Linear Equations
9.4 linogram: a drawing system
9.4.1 Equations ref($base) || $base Solving Equations Constraints
9.4.2 Values Constant Values Tuple Values Feature Values Intrinsic Constraints Synthetic Constraints Feature Value Methods
9.4.3 Feature Types Scalar Types Type methods
9.4.4 The Parser Parser Extensions %TYPES Programs Definitions Declarations Expressions
9.4.5 Missing Features
9.5 Conclusion