# multipolyfit

**Repository Path**: ZhangLanTao/multipolyfit

## Basic Information

- **Project Name**: multipolyfit
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-14
- **Last Updated**: 2024-06-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Multivariate Polynomial Fit
===========================

Holds a python function to perform multivariate polynomial regression in Python
using NumPy

[See related question on
stackoverflow](http://stackoverflow.com/questions/10988082/multivariate-polynomial-regression-with-numpy)

This is similar to numpy's polyfit function but works on multiple covariates

Origin
------

This code originated from the following question on StackOverflow

[http://stackoverflow.com/questions/10988082/multivariate-polynomial-regression-with-numpy](http://stackoverflow.com/questions/10988082/multivariate-polynomial-regression-with-numpy)

Author
------

[Matthew Rocklin](http://matthewrocklin.com)

License
-------

New BSD license. See LICENSE.txt

Disclaimer
----------

This is not a commonly used method.  It often results in a solution with many
non-zero coeffieicients like

    10 x**2 + 0.01 x y - 0.02 x + 20 y - 0.03 y**2

Instead of a sparse solution like

    10 x**2 + 20 y

To obtain sparse solutions (like the second) where near-zero elements are
eliminated you should probably look into L1 regularization


Sorry
-----

But I rarely respond to questions about this repository.  It is oddly popular 
but the implementation is pretty dense and so this project generates a large number
of reasonable questions.  Unfortunately I don't have time to respond to all of these.

I would care more about this project if it contained a useful algorithm.  It doesn't.  
Read the disclaimer above.