用numpy/scipy拟合的6度曲线 [英] 6th degree curve fitting with numpy/scipy

问题描述

对于使用6阶多项式插值非线性数据，我有一个非常具体的要求.我见过numpy/scipy例程(scipy.interpolate.InterpolatedUnivariateSpline)，这些例程最多只能进行5级插值.

I have a very specific requirement for interpolating nonlinear data using a 6th degree polynomial. I've seen numpy/scipy routines (scipy.interpolate.InterpolatedUnivariateSpline) that allow interpolation only up to degree 5.

即使没有直接函数可以执行此操作，是否有办法在Python中复制Excel的LINEST线性回归算法? LINEST允许进行6度曲线拟合，但是我不想在任何东西上使用Excel，因为此计算是更大的Python脚本的一部分.

Even if there's no direct function to do this, is there a way to replicate Excel's LINEST linear regression algorithm in Python? LINEST allows 6th degree curve-fitting but I do NOT want to use Excel for anything as this calculation is part of a much larger Python script.

任何帮助将不胜感激！

推荐答案

使用numpys polyfit例程.

Use numpys polyfit routine.

http://docs.scipy. org/doc/numpy-1.3.x/reference/generation/numpy.polyfit.html

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