Haskelling My Python
The article explains implementing Haskell's lazy infinite lists in Python using generators, covering recursive integer generation, Taylor series for \( e^x \), sine and cosine functions, and memoization for performance.
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The article discusses the implementation of Haskell's lazy infinite lists in Python using generators. It begins with a simple generator for positive integers, demonstrating how to create it recursively. The author explains the integration of a Taylor series, showcasing how to define the exponential function \( e^x \) using its properties. The article also covers the evaluation of the Taylor series against Python's standard library function for \( e^x \), showing that the results are closely aligned. Additionally, the author introduces the sine and cosine functions through their integral relationships, providing Python implementations for both. To enhance performance, a memoization technique is suggested to address the inefficiencies of Python generators compared to Haskell's lists. The article concludes with a note on using Python's fractions module for rational number outputs.
- The article illustrates how to implement Haskell's lazy infinite lists in Python.
- It demonstrates the creation of a recursive generator for positive integers.
- The integration of Taylor series is explained, particularly for the exponential function \( e^x \).
- Sine and cosine functions are derived using their integral relationships.
- A memoization technique is recommended to improve the performance of Python generators.
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- Several commenters express admiration for the use of generators, highlighting their memory efficiency and ability to create transformation pipelines.
- There are concerns about the readability and maintainability of the Python code compared to Haskell, with some suggesting that it can become difficult to debug.
- Some users question the practicality of the recursive function implementation, with one commenter expressing skepticism about its effectiveness.
- Discussions include references to alternative approaches and languages, such as F# and numpy, indicating a broader context of functional programming.
- A few comments critique Python's flexibility, suggesting it can lead to convoluted code that is hard to maintain.
14 commentsI implemented the same thing myself in F#. [1]
[0]: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d...
f = x -> raise if
x :: int => IndexError x
otherwise => ValueError x
"> {}: {}".format "Author" "stop using stale memes"
|> printWhen working with other engineers, I’ve learned to be careful: sometimes it’s better to just materialize things into a list for clarity, even if it’s less “elegant” on paper.
There’s a real balance between cleverness and maintainability here.
Actually, it's even simpler than that: the $ operator is nothing but a function that applies its left argument to its right one! The full definition is
f $ x = f x
def ints():
yield 1
yield from map(lambda x: x + 1, ints())
The reason this all works is that generators plus memoization is "just" an implementation of the lazy sequences that Haskell has built in.
Alternatives that aren't dumb:
for x in range(2**256): # not infinite but go ahead and run it to the end and get back to me
from itertools import repeat
for x, _ in enumerate(repeat(None)): # slightly more annoying but does work infinitely
If you want to code it Haskell, have you considered using Haskell?
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