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https://github.com/mingrammer/funmath

:umbrella: Implementations of mathematical functions, formulas and concepts
https://github.com/mingrammer/funmath

algorithm awesome formula funny implementation math mathematical-functions mathematics numerical practice python

Last synced: about 2 months ago
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:umbrella: Implementations of mathematical functions, formulas and concepts

Host: GitHub
URL: https://github.com/mingrammer/funmath
Owner: mingrammer
License: mit
Created: 2017-06-30T07:49:23.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2019-12-16T15:28:46.000Z (about 5 years ago)
Last Synced: 2024-08-08T23:23:48.913Z (5 months ago)
Topics: algorithm, awesome, formula, funny, implementation, math, mathematical-functions, mathematics, numerical, practice, python
Language: Python
Homepage:
Size: 29.3 KB
Stars: 90
Watchers: 6
Forks: 7
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        



FUN MATH




  

  

  





  Implementations of mathematical functions, formulas and concepts








It provides the mathematical implementations for various topics which are related to mathematical things such as fibonacci, euclidean, prime numbers and so on. Any mathematical topics or concepts are welcome. If you love the math, join to here with your algorithms and codes!

This repository does not force for using the specific programming languages, but if you are using the Python, you should use the Python 3.6 to contribute or to run. (you wiil use the only Python 3.6+ in the future)

A solution can be implemented in three ways:

- **By definition** solution, a mathematical definition. Good example is the fibonacci series. But the solution by definition of the fibonacci has worst performance

- **Optimal** solution which has better performance than by definition one (or best solution).

- **Creative** solution. This may not be optimal one, but creative or funny way.

## Run

* Python

  * `python3 -m doctest / -v`

## Implementations

* [abs](abs)

  * [abs.py](abs/abs.py)

* combinations

* decomposition

* [differentiation](differentiation)

  * [simple_numerical_differentiation.py](differentiation/simple_numerical_differentiation.py)

* [euclidean](euclidean)

  * [distance.py](euclidean/distance.py)

* [factorial](factorial)

  * [factorial.py](factorial/factorial.py)

  * [factorial_recursion.py](factorial/factorial_recursion.py)

* [fibonacci](fibonacci)

  * [fibonacci.py](fibonacci/fibonacci.py)

  * [fibonacci_optimal_memoization.py](fibonacci/fibonacci_optimal_memoization.py)

  * [fibonacci_optimal_iteration.py](fibonacci/fibonacci_optimal_iteration.py)

* [gcd](gcd)

  * [gcd.py](gcd/gcd.py)

  * [gcd_optimal_euclidean.py](gcd/gcd_optimal_euclidean.py)

* integral

* [lcm](lcm)

  * [lcm.py](lcm/lcm.py)

  * [lcm_optimal_euclidean.py](lcm/lcm_optimal_euclidean.py)

* matrix

  * multiplication

  * multiplication_optimal

* permutations

* [prime](prime)

  * [is_prime.py](prime/is_prime.py)

  * [is_prime_improved.py](prime/is_prime_improved.py)

  * [is_prime_optimal.py](prime/is_prime_optimal.py)

  * [next_prime.py](prime/next_prime.py)

  * [sieve_of_eratosthenes.py](prime/sieve_of_eratosthenes.py)

* [pythagorean_triple](pythagorean_triple)

  * [pythagorean_triple.py](pythagorean_triple/pythagorean_triple.py)

* square_root

* [trigonometric_functions](trigonometric_functions)

  * [trig.py](trigonometric_functions/trig.py)

## Contribution

* You can implement any sort of algorithms for mathematical things.

  * If you add new kind of algorithms, please also add that in README as new category.

  * If you want to add other programming language version of existing solutions, you must name the source code file as same to existing one.

  * It is better to provide the explains of your algorithms in comments of source code files.

* You can use any programming languages you are preferring, but you **SHOULD** take care of handling large numbers. And if you want to contribute the algorithms with Python, you **SHOULD** write the scripts with Python 3.6+.

* You **MUST** have to write the test codes for each algorithms. See the [example](fibonacci/fibonacci_optimal_memoization.py) for writing the tests (the example is written in Python)

* It is not recommended to use the builtin math libraries as possible. Because this repository encourages that implement the mathematical things by ourselves.

* It is better to provide the optimal solutions as well for each algorithms.

## Changelog

> **Enhancement**

> - Link the Travis CI to run the tests when commits [#4](../../issues/4)

>

> **Fixed**

> - Don't check the weird invalid type on functions which accept only specific types [#3](../../issues/3)

> - Remove the stricted type checking on some functions [#2](../../issues/2)

## Contributors

- [@mingrammer](https://github.com/mingrammer)

- [@Xaltonon](https://github.com/Xaltonon )

- [@TsimpDim](https://github.com/TsimpDim)

## License

[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fmingrammer%2Ffunmath.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fmingrammer%2Ffunmath?ref=badge_large)