SICP JS
Structure and Interpretation of Computer Programs, JavaScript Adaptation
Preface of JavaScript Adaptation
JavaScript Adaptation Making-of
References
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1 Building Abstractions with Functions
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1.1 The Elements of Programming
1.1.1 Expressions
1.1.2 Naming and the Environment
1.1.3 Evaluating Operator Combinations
1.1.4 Functions
1.1.5 The Substitution Model for Function Application
1.1.6 Conditional Expressions and Predicates
1.1.7 Example: Square Roots by Newtons Method
1.1.8 Functions as Black-Box Abstractions
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1.2 Functions and the Processes They Generate
1.2.1 Linear Recursion and Iteration
1.2.2 Tree Recursion
1.2.3 Orders of Growth
1.2.4 Exponentiation
1.2.5 Greatest Common Divisors
1.2.6 Example: Testing for Primality
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1.3 Formulating Abstractions with Higher-Order Functions
1.3.1 Functions as Arguments
1.3.2 Function Definition Expressions
1.3.3 Functions as General Methods
1.3.4 Functions as Returned Values
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2 Building Abstractions with Data
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2.1 Introduction to Data Abstraction
2.1.1 Example: Arithmetic Operations for Rational Numbers
2.1.2 Abstraction Barriers
2.1.3 What Is Meant by Data?
2.1.4 Extended Exercise: Interval Arithmetic
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2.2 Hierarchical Data and the Closure Property
2.2.1 Representing Sequences
2.2.2 Hierarchical Structures
2.2.3 Sequences as Conventional Interfaces
2.2.4 Example: A Picture Language
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2.3 Symbolic Data
2.3.1 Strings
2.3.2 Example: Symbolic Differentiation
2.3.3 Example: Representing Sets
2.3.4 Example: Huffman Encoding Trees
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2.4 Multiple Representations for Abstract Data
2.4.1 Representations for Complex Numbers
2.4.2 Tagged data
2.4.3 Data-Directed Programming and Additivity
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2.5 Systems with Generic Operations
2.5.1 Generic Arithmetic Operations
2.5.2 Combining Data of Different Types
2.5.3 Example: Symbolic Algebra
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3 Modularity, Objects, and State
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3.1 Assignment and Local State
3.1.1 Local State Variables
3.1.2 The Benefits of Introducing Assignment
3.1.3 The Costs of Introducing Assignment
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3.2 The Environment Model of Evaluation
3.2.1 The Rules for Evaluation
3.2.2 Applying Simple Functions
3.2.3 Frames as the Repository of Local State
3.2.4 Internal Definitions
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3.3 Modeling with Mutable Data
3.3.1 Mutable List Structure
3.3.2 Representing Queues
3.3.3 Representing Tables
3.3.4 A Simulator for Digital Circuits
3.3.5 Propagation of Constraints
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3.5 Streams
3.5.1 Streams Are Delayed Lists
3.5.2 Infinite Streams
3.5.3 Exploiting the Stream Paradigm
3.5.4 Streams and Delayed Evaluation
3.5.5 Modularity of Functional Programs and Modularity of Objects
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4 Metalinguistic Abstraction
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4.1 The Metacircular Evaluator
4.1.1 The Core of the Evaluator
4.1.2 Representing Statements and Expressions
4.1.3 Evaluator Data Structures
4.1.4 Running the Evaluator as a Program
Content
Preface of JavaScript Adaptation
JavaScript Adaptation Making-of
References
>
⌵
1 Building Abstractions with Functions
>
⌵
1.1 The Elements of Programming
1.1.1 Expressions
1.1.2 Naming and the Environment
1.1.3 Evaluating Operator Combinations
1.1.4 Functions
1.1.5 The Substitution Model for Function Application
1.1.6 Conditional Expressions and Predicates
1.1.7 Example: Square Roots by Newtons Method
1.1.8 Functions as Black-Box Abstractions
>
⌵
1.2 Functions and the Processes They Generate
1.2.1 Linear Recursion and Iteration
1.2.2 Tree Recursion
1.2.3 Orders of Growth
1.2.4 Exponentiation
1.2.5 Greatest Common Divisors
1.2.6 Example: Testing for Primality
>
⌵
1.3 Formulating Abstractions with Higher-Order Functions
1.3.1 Functions as Arguments
1.3.2 Function Definition Expressions
1.3.3 Functions as General Methods
1.3.4 Functions as Returned Values
>
⌵
2 Building Abstractions with Data
>
⌵
2.1 Introduction to Data Abstraction
2.1.1 Example: Arithmetic Operations for Rational Numbers
2.1.2 Abstraction Barriers
2.1.3 What Is Meant by Data?
2.1.4 Extended Exercise: Interval Arithmetic
>
⌵
2.2 Hierarchical Data and the Closure Property
2.2.1 Representing Sequences
2.2.2 Hierarchical Structures
2.2.3 Sequences as Conventional Interfaces
2.2.4 Example: A Picture Language
>
⌵
2.3 Symbolic Data
2.3.1 Strings
2.3.2 Example: Symbolic Differentiation
2.3.3 Example: Representing Sets
2.3.4 Example: Huffman Encoding Trees
>
⌵
2.4 Multiple Representations for Abstract Data
2.4.1 Representations for Complex Numbers
2.4.2 Tagged data
2.4.3 Data-Directed Programming and Additivity
>
⌵
2.5 Systems with Generic Operations
2.5.1 Generic Arithmetic Operations
2.5.2 Combining Data of Different Types
2.5.3 Example: Symbolic Algebra
>
⌵
3 Modularity, Objects, and State
>
⌵
3.1 Assignment and Local State
3.1.1 Local State Variables
3.1.2 The Benefits of Introducing Assignment
3.1.3 The Costs of Introducing Assignment
>
⌵
3.2 The Environment Model of Evaluation
3.2.1 The Rules for Evaluation
3.2.2 Applying Simple Functions
3.2.3 Frames as the Repository of Local State
3.2.4 Internal Definitions
>
⌵
3.3 Modeling with Mutable Data
3.3.1 Mutable List Structure
3.3.2 Representing Queues
3.3.3 Representing Tables
3.3.4 A Simulator for Digital Circuits
3.3.5 Propagation of Constraints
>
⌵
3.5 Streams
3.5.1 Streams Are Delayed Lists
3.5.2 Infinite Streams
3.5.3 Exploiting the Stream Paradigm
3.5.4 Streams and Delayed Evaluation
3.5.5 Modularity of Functional Programs and Modularity of Objects
>
⌵
4 Metalinguistic Abstraction
>
⌵
4.1 The Metacircular Evaluator
4.1.1 The Core of the Evaluator
4.1.2 Representing Statements and Expressions
4.1.3 Evaluator Data Structures
4.1.4 Running the Evaluator as a Program