Berkeley's SICP in python(一)

最新推荐文章于 2025-05-22 10:13:26 发布

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本文探讨如何使用Python实现Berkeley的SICP课程中的思想，重点在于利用函数构建抽象，管理和简化逻辑的复杂性。文章强调了解释器设计、模块化、精确假设和团队合作的重要性，并提倡增量测试和DRY（Don't Repeat Yourself）原则。通过docstrings进行清晰的函数注释，以及展示单元测试示例，展示了良好的程序设计特征。

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一 Building abstractions with Functions
1 Introduction
所有的计算都是表示信息，指定逻辑并处理它，设计抽象和管理这一逻辑的复杂性。

A language isn’t something you learn so much as something you join

Assignment statement ：赋值语句
compound expressions：复合表达式

The design and implementation of interpreters：解释器的设计与实现

Incremental testing, modular design, precise assumptions, and teamwork are themes that persist throughout this course.
Hopefully, they will also persist throughout your computer science career.

As you become familiar with the Python language and vocabulary, this documentation will become a valuable reference source。

Don’t repeat yourself is a central tenet of software engineering。

Docstrings are conventionally triple quoted. The first line describes the job of the function in one line. The following lines can describe arguments and clarify the behavior of the function.

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(unit testing:>>>(space)sum_naturals(10));