Metadata-Version: 2.1
Name: prinpy
Version: 0.0.2.2
Summary: A package for fitting principal curves in Python
Home-page: https://github.com/artusoma/prinPy
Author: https://github.com/artusoma/
Author-email: artusoma1@gmail.com
License: MIT
Description: # prinPy
        
        Inspired by [this R package](https://github.com/rcannood/princurve), prinPy brings principal curves to Python. 
        
        ### What prinPy does
        Currently, prinPy has implemented two local ("bottom-up") algorithms from [this paper](https://www.sciencedirect.com/science/article/pii/S0377042715005956). As of now, these only work in 2-dimensional space. 
        
        1. CLPC-g (Greedy Constraint Local Principal Curve)
        2. CLPC-s (One-Dimensional Search Constraint Local Principal Curve)
        
        CLPC-g will be faster and is fine for simpler curves. CLPS-s has the potential to be much more accurate at the expense of speed for more difficult curves. After fitting a curve, prinPy has the ability to project to the curve.
        
        ### What is a Principal Curve?
        A principal curve, simply put, is a smooth line that passes through the middle of a dataset. It then is a one-dimensional summary of a data.
        
        ## Quick-Start
        View the quickstart notebook [here](https://github.com/artusoma/prinPy/blob/master/prinPy%20quickstart.ipynb). Docs will be coming soon!
        
        ## Future:
        1. Add global algorithms, and expand to 3-dimensions+
        2. Move some code to C++
        
        ## References
        \[1\] Dewang Chen, Jiateng Yin, Shiying Yang, Lingxi Li, Peter Pudney,
        Constraint local principal curve: Concept, algorithms and applications,
        Journal of Computational and Applied Mathematics,
        Volume 298,
        2016,
        Pages 222-235,
        ISSN 0377-0427,
        https://doi.org/10.1016/j.cam.2015.11.041.
        
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Description-Content-Type: text/markdown
