拟合分布的线性组合 [英] Fitting a linear combination of distributions

问题描述

我有5个数组(一个熊猫数据框的列)我想计算线性分布到指数分布的线性组合的最佳拟合.例如:

I have 5 arrays (columns of a pandas data frame) and I want calculate the best fit for a linear combination of the distributions to an exponential distribution. for example:

a*(d1)+b*(d2)+c*(d3)+d*(d4)+e*(d5)=Y

其中Y具有指数分布(据我所知)，而a，b，c，d，e是要拟合的系数.

where Y has an exponential distribution (which i know) and a,b,c,d,e are the coefficients to fit.

我尝试使用curve_fit或lmfit python库，但是没有有效地做到这一点.

I tried using curve_fit or lmfit python libraries but didn't get how to do it effectively.

推荐答案

您要描述的是线性模型.使用软件包 scikit-learn :

What you're describing is a linear model. Use the package scikit-learn:

from sklearn.linear_model import LinearRegression

X = df[['d1', 'd2', 'd3', 'd4', 'd5']]
reg = LinearRegression().fit(X, Y)
reg.get_params()

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