Python：为数据列表找到最适合的函数(Python: Find a best fit function for a list of data)

Python：为数据列表找到最适合的函数(Python: Find a best fit function for a list of data)

我知道许多概率函数在python中构建， random模块。

我想知道，如果给出浮标列表，是否有可能找到最适合列表的分布方程式？

我不知道numpy是否会这样做，但是这个函数可以与Excel的“趋势”函数进行比较（不相等，但相似）。

我会怎么做？

I am aware of many probabilistic functions builted-in python, with the random module.

I'd like to know if, given a list of floats, would be possible to find the distribution equation that best fits the list?

I don't know if numpy does it, but this function could be compared (not equal, but similar) with the Excel's "Trend" function.

How would I do that?

最满意答案

看看numpy.polyfit

numpy.polyfit(x, y, deg, rcond=None, full=False)¶ Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.

Look at numpy.polyfit

numpy.polyfit(x, y, deg, rcond=None, full=False)¶ Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.