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2.8 Efficiency

浏览 64 扫码 分享 2022-07-09 06:07:14
  • 2.8.0 introduction
  • 2.8.1 Measuring Efficiency
  • 2.8.2 Memoization
  • 2.8.3 Orders of Growth
  • 2.8.4 Example: Exponentiation
  • 2.8.5 Growth Categories

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  • Chapter 1: Building Abstractions with Functions
    • 1.1 Getting Started
      • 1.1.0 Introduction
      • 1.1.1 Programming in Python
      • 1.1.2 Installing Python 3
      • 1.1.3 Interactive Sessions
      • 1.1.4 First Example
      • 1.1.5 Errors
    • 1.2 Elements of Programming
      • 1.2.0 Introduction
      • 1.2.1 Expressions
      • 1.2.2 Call Expressions
      • 1.2.3 Importing Library Functions
      • 1.2.4 Names and the Environment
      • 1.2.5 Evaluating Nested Expressions
      • 1.2.6 The Non-Pure Print Function
    • 1.3 Defining New Functions
      • 1.3.0 introduction
      • 1.3.1 Environments
      • 1.3.2 Calling User-Defined Functions
      • 1.3.3 Example: Calling a User-Defined Function
      • 1.3.4 Local Names
      • 1.3.5 Choosing Names
      • 1.3.6 Functions as Abstractions
      • 1.3.7 Operators
    • 1.4 Designing Functions
      • 1.4.0 introduction
      • 1.4.1 Documentation
      • 1.4.2 Default Argument Values
    • 1.5 Control
      • 1.5.0 introduction
      • 1.5.1 Statements
      • 1.5.2 Compound Statements
      • 1.5.3 Defining Functions II: Local Assignment
      • 1.5.4 Conditional Statements
      • 1.5.5 Iteration
      • 1.5.6 Testing
    • 1.6 Higher-Order Functions
      • 1.6.0 introduction
      • 1.6.1 Functions as Arguments
      • 1.6.2 Functions as General Methods
      • 1.6.3 Defining Functions III: Nested Definitions
      • 1.6.4 Functions as Returned Values
      • 1.6.5 Example: Newton's Method
      • 1.6.6 Currying
      • 1.6.7 Lambda Expressions
      • 1.6.8 Abstractions and First-Class Functions
      • 1.6.9 Function Decorators
    • 1.7 Recursive Functions
      • 1.7.0 introduction
      • 1.7.1 The Anatomy of Recursive Functions
      • 1.7.2 Mutual Recursion
      • 1.7.3 Printing in Recursive Functions
      • 1.7.4 Tree Recursion
      • 1.7.5 Example: Partitions
  • Chapter 2: Building Abstractions with Data
    • 2.1 Introduction
      • 2.1.0 Introduction
      • 2.1.1 Native Data Types
    • 2.2 Data Abstraction
      • 2.2.0 introduction
      • 2.2.1 Example: Rational Numbers
      • 2.2.2 Pairs
      • 2.2.3 Abstraction Barriers
      • 2.2.4 The Properties of Data
    • 2.3 Sequences
      • 2.3.0 introduction
      • 2.3.1 Lists
      • 2.3.2 Sequence Iteration
      • 2.3.3 Sequence Processing
      • 2.3.4 Sequence Abstraction
      • 2.3.5 Strings
      • 2.3.6 Trees
      • 2.3.7 Linked Lists
    • 2.4 Mutable Data
      • 2.4.0 introduction
      • 2.4.1 The Object Metaphor
      • 2.4.2 Sequence Objects
      • 2.4.3 Dictionaries
      • 2.4.4 Local State
      • 2.4.5 The Benefits of Non-Local Assignment
      • 2.4.6 The Cost of Non-Local Assignment
      • 2.4.7 Implementing Lists and Dictionaries
      • 2.4.8 Dispatch Dictionaries
      • 2.4.9 Propagating Constraints
    • 2.5 Object-Oriented Programming
      • 2.5.0 introduction
      • 2.5.1 Objects and Classes
      • 2.5.2 Defining Classes
      • 2.5.3 Message Passing and Dot Expressions
      • 2.5.4 Class Attributes
      • 2.5.5 Inheritance
      • 2.5.6 Using Inheritance
      • 2.5.7 Multiple Inheritance
      • 2.5.8 The Role of Objects
    • 2.6 Implementing Classes and Objects
      • 2.6.0 introduction
      • 2.6.1 Instances
      • 2.6.2 Classes
      • 2.6.3 Using Implemented Objects
    • 2.7 Object Abstraction
      • 2.7.0 introduction
      • 2.7.1 String Conversion
      • 2.7.2 Special Methods
      • 2.7.3 Multiple Representations
      • 2.7.4 Generic Functions
    • 2.8 Efficiency
      • 2.8.0 introduction
      • 2.8.1 Measuring Efficiency
      • 2.8.2 Memoization
      • 2.8.3 Orders of Growth
      • 2.8.4 Example: Exponentiation
      • 2.8.5 Growth Categories
    • 2.9 Recursive Objects
      • 2.9.0 introduction
      • 2.9.1 Linked List Class
      • 2.9.2 Tree Class
      • 2.9.3 Sets
  • Chapter 3: Interpreting Computer Programs
    • 3.1 Introduction
      • 3.1.0 introction
      • 3.1.1 Programming Languages
    • 3.2 Functional Programming
      • 3.2.0 introduction
      • 3.2.1 Expressions
      • 3.2.2 Definitions
      • 3.2.3 Compound values
      • 3.2.4 Symbolic Data
      • 3.2.5 Turtle graphics
    • 3.3 Exceptions
      • 3.3.0 introduction
      • 3.3.1 Exception Objects
    • 3.4 Interpreters for Languages with Combination
      • 3.4.0 introduction
      • 3.4.1 A Scheme-Syntax Calculator
      • 3.4.2 Expression Trees
      • 3.4.3 Parsing Expressions
      • 3.4.4 Calculator Evaluation
    • 3.5 Interpreters for Languages with Abstraction
      • 3.5.0 introduction
      • 3.5.1 Structure
      • 3.5.2 Environments
      • 3.5.3 Data as Programs
  • Chapter 4: Data Processing
    • 4.1 Introduction
      • 4.1.0 introduction
    • 4.2 Implicit Sequences
      • 4.2.0 introduction
      • 4.2.1 Iterators
      • 4.2.2 Iterables
      • 4.2.3 Built-in Iterators
      • 4.2.4 For Statements
      • 4.2.5 Generators and Yield Statements
      • 4.2.6 Iterable Interface
      • 4.2.7 Creating Iterables with Yield
      • 4.2.8 Iterator Interface
      • 4.2.9 Streams
      • 4.2.10 Python Streams
    • 4.3 Declarative Programming
      • 4.3.0 introduction
      • 4.3.1 Tables
      • 4.3.2 Select Statements
      • 4.3.3 Joins
      • 4.3.4 Interpreting SQL
      • 4.3.5 Recursive Select Statements
      • 4.3.6 Aggregation and Grouping
    • 4.4 Logic Programming
      • 4.4.0 introduction
      • 4.4.1 Facts and Queries
      • 4.4.2 Recursive Facts
    • 4.5 Unification
      • 4.5.0 introduction
      • 4.5.1 Pattern Matching
      • 4.5.2 Representing Facts and Queries
      • 4.5.3 The Unification Algorithm
      • 4.5.4 Proofs
      • 4.5.5 Search
    • 4.6 Distributed Computing
      • 4.6.0 introduction
      • 4.6.1 Messages
      • 4.6.2 Client/Server Architecture
      • 4.6.3 Peer-to-Peer Systems
    • 4.7 Distributed Data Processing
      • 4.7.0 introduction
      • 4.7.1 MapReduce
      • 4.7.2 Local Implementation
      • 4.7.3 Distributed Implementation
    • 4.8 Parallel Computing
      • 4.8.0 introduction
      • 4.8.1 Parallelism in Python
      • 4.8.2 The Problem with Shared State
      • 4.8.3 When No Synchronization is Necessary
      • 4.8.4 Synchronized Data Structures
      • 4.8.5 Locks
      • 4.8.6 Barriers
      • 4.8.7 Message Passing
      • 4.8.8 Synchronization Pitfalls
      • 4.8.9 Conclusion
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