Python或SQL Logistic回归 [英] Python or SQL Logistic Regression

问题描述

鉴于时间序列数据，我想找到最合适的对数曲线.在Python或SQL中有什么好的库可以做到这一点?

Given time-series data, I want to find the best fitting logarithmic curve. What are good libraries for doing this in either Python or SQL?

具体来说，我正在寻找的是一个可以容纳类似于S形函数的数据的库，该库具有上下水平渐近线.

Specifically, what I'm looking for is a library that can fit data resembling a sigmoid function, with upper and lower horizontal asymptotes.

推荐答案

如果您的数据是分类的，则可以使用逻辑回归来拟合属于某个类(分类)的概率.

If your data were categorical, then you could use a logistic regression to fit the probabilities of belonging to a class (classification).

但是，我了解到您正在尝试将数据拟合为S形曲线，这意味着您只想最小化拟合的均方误差.

However, I understand you are trying to fit the data to a sigmoid curve, which means you just want to minimize the mean squared error of the fit.

我会将您重定向到名为scipy.optimize.leastsq的 SciPy 函数:执行最小二乘拟合.

I would redirect you to the SciPy function called scipy.optimize.leastsq: it is used to perform least squares fits.

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