https://github.com/deftasparagusanaconda/gapprox
python toolkit to find the approximate function of a graph
https://github.com/deftasparagusanaconda/gapprox
approximation graph regression
Last synced: 2 months ago
JSON representation
python toolkit to find the approximate function of a graph
- Host: GitHub
- URL: https://github.com/deftasparagusanaconda/gapprox
- Owner: deftasparagusanaconda
- License: gpl-3.0
- Created: 2025-02-28T15:20:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-02-14T06:39:46.000Z (4 months ago)
- Last Synced: 2026-02-14T23:15:21.507Z (4 months ago)
- Topics: approximation, graph, regression
- Language: Python
- Homepage:
- Size: 1.18 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
[📖 manual][manual] | [🔍 examples][examples] | [📜 license][license] | [💡suggest silly ideas!][contact]
# gapprox
a python toolkit to find the approximate function of any [graph][graph]
instead of "find the graph of the function", youre flipping it: "find the function of the graph"
## 💾 installation
get it from PyPI:
```shell
pip install gapprox
```
or install the latest from GitHub:
```shell
git clone https://github.com/deftasparagusanaconda/gapprox
cd gapprox
pip install .
```
for better performance, install with all optional dependencies:
```shell
pip install gapprox[all]
```
## 📗 basic guide
```python
import gapprox as ga
f = ga.fit([1, 2, 4, 6, 3], [1, 2, 5, 5, 2])
print(f)
print(f(2.5))
```
`.fit()` selects the best approximation method and returns an [Expression][advanced guide]
you can print the Function `print(f)` or call it like a function `f(2.5)`
gapprox can do a lot more. check out more examples [here!][examples] or read the [manual][manual]
## 🚀 roadmap
+ DAG/expression trees
+ multi-objective analysis
+ complex numbers
+ parametric function support
+ multiple-input multiple-output
- [pareto front](https://en.wikipedia.org/wiki/Pareto_front) presentation
- web app
- symbolic regression
- n-dimensional plotters
- surface approximation
- [many-to-many][relation types] relation approximation
- point density evaluators
- hypersonic blasters 🚀
~~in the far far future, ga will support multiple-input multiple-output approximation. for m inputs and n outputs, it runs n approximations of m-dimensional [manifolds][manifold] separately
effectively, this turns it into a general-purpose prediction library, analogous to AI
currently, ga only supports single-input single-output [many-to-one][relation types] functions. see [roadmap][roadmap] for details ~~
gapprox now supports any general mathematical expression as long as it is representable on a directed acyclic graph. you may go ham on tensorial input/output
## 📔 you read all that?!?
this project is still budding 🌱 if you'd like to change something, add something, or suggest ideas—[come say hi!][contact]
with love, and a passion for maths ~
\- [daa][contact] 🌸
[examples]: https://github.com/deftasparagusanaconda/gapprox/tree/main/examples/
[manual]: https://github.com/deftasparagusanaconda/gapprox/tree/main/documentation/manual.md
[license]: https://github.com/deftasparagusanaconda/gapprox/tree/main/LICENSE
[contact]: https://discordapp.com/users/608255432859058177
[graph]: https://en.wikipedia.org/wiki/Graph_of_a_function
[function]: https://en.wikipedia.org/wiki/Function_(mathematics)
[functional analysis]: https://en.wikipedia.org/wiki/Functional_analysis
[approximation]: https://en.wikipedia.org/wiki/Approximation_theory
[manifold]: https://en.wikipedia.org/wiki/Manifold
[smoothness]: https://en.wikipedia.org/wiki/Smoothness
[parsing]: https://en.wikipedia.org/wiki/Parsing
[sampling]: https://en.wikipedia.org/wiki/Sampling_(statistics)
[interpolation]: https://en.wikipedia.org/wiki/Interpolation
[optimization]: https://en.wikipedia.org/wiki/Mathematical_optimization
[iterative method]: https://en.wikipedia.org/wiki/Iterative_method
[expression]: https://en.wikipedia.org/wiki/Expression_(mathematics)
[error analysis]: https://en.wikipedia.org/wiki/Error_analysis_(mathematics)
[relation types]: https://en.wikipedia.org/wiki/Relation_(mathematics)#Combinations_of_properties
[object in cs]: https://en.wikipedia.org/wiki/Object_(computer_science)
[advanced guide]: https://github.com/deftasparagusanaconda/gapprox/blob/main/documentation/manual.md#-advanced-guide-