I was inspired to create it while taking the Fast.ai course and seeing Jeremy Howard share [0] how a "complicated" Frobenius norm equation could be implemented in a single line of Python.
Math to Code uses the Skulpt library to interpret Python in JavaScript.
It's open source here: https://github.com/vthommeret/mathtocode
I would appreciate any and all feedback!
[0] https://youtu.be/4u8FxNEDUeg?t=1390
> It's time to start reading papers. And papers look something like this, which if you're anything like me, that's terrifying. And I'm not going to lie, it's still the case when I start looking at a new paper, every single time, I think, I'm not smart enough to understand this. I just can't get past that immediate reaction. So I just look at this stuff and I go, that's not something I understand. > But then I remember, this is the Adam paper and you've all seen Adam implemented in one cell of Microsoft Excel. > 1. Even familiar stuff looks complex in a paper! > 2. Papers are important for deep learning beyond the basics, but hard to read. > 3. Learn to pronounce Greek letters.
Any chance for a license like MIT?
NotImplementedError: the 'axis' parameter is currently not supported on line 7
I used variations of: np.sqrt(m.sum(m.prod())) np.sqrt(m.sum(m2))
It's been a long time since I've taken linear algebra, so I don't remember some of these operations.
So it should look something like this: `np.sqrt((m * * 2).sum())`
Re: The error message, it looks like it's occurring because `sum` doesn't normally take any parameters and interprets the argument as an "axis" parameter — https://numpy.org/doc/1.18/reference/generated/numpy.sum.htm...
Order of operations is tricky and I could do a better job breaking it down. Still plan to add the Show Solution button but need to get some sleep :-).
In the meantime you can see all the possible solutions in the repo! https://github.com/vthommeret/mathtocode/tree/master/questio...
[0] Remove the space between the asterisks / I had to add it since HN interprets them as italics.
https://github.com/ebertmi/skulpt_numpy
It's fairly outdated (5-years old) and doesn't implement some of the functions I wanted to use (like equality), but otherwise works pretty well.
It has Borel spaces: https://leanprover-community.github.io/mathlib_docs/measure_...
Math to Code is open source, so if you can express a solution with NumPy you can easily add new questions / I'm happy to take pull requests — https://github.com/vthommeret/mathtocode/blob/master/questio...
It's possible that it timed out when computing the result — it evaluates the answer in a web worker with a 2.5s timeout and execution is slower on mobile. I used to get timeouts more regularly, but it's fairly optimized now — it checks all of the test inputs and evaluates both the expected solution and user-provided solution in a single Python function.
https://github.com/vthommeret/mathtocode/blob/0d5f780be4f218...
Is there something specific that did work for you? I tried all the examples on Python 3.7.3 and they worked for me. One notable thing that's missing is that the @ operator for matrix multiplication, but I hope to add that in the future.
[0] http://skulpt.org Python 3 Grammar. The master branch is now building and running using the grammar for Python 3.7.3. There are still lots of things to implement under the hood, but we have made a huge leap forward in Python 3 compatibility. We will still support Python 2 as an option going forward for projects that rely on it.
1. https://github.com/Jam3/math-as-code/blob/master/PYTHON-READ...
The HN thread:
Feedback: Please autofocus to input box - it will be a smoother experience!
Is a cool experiment, and it could be good to lose the fear in papers. Maybe you could add some exercises with formulas or different papers that are a must read.
