是否有更好的线性回归的Java库？ （例如，迭代重加权最小二乘） [英] Is there a Java library for better linear regression? (E.g., iteratively reweighted least squares)

问题描述

我正在努力寻找一种更好的线性回归方法。我一直在使用 Moore-Penrose pseudoinverse 和 QR分解， JAMA库，但结果并不理想。 ojAlgo 会有用吗？我一直在达到我知道不应该存在的准确度限制。该算法应该能够将输入变量的影响降低到零。也许这采取迭代重加权最小二乘的形式，但我不知道该算法，也无法找到它的库。输出应该是权重矩阵或向量，使得输入矩阵与权重矩阵的矩阵乘法将产生预测矩阵。我的输入矩阵几乎总是有多行而不是列。谢谢你的帮助。

I am struggling to find a way to perform better linear regression. I have been using the Moore-Penrose pseudoinverse and QR decomposition with JAMA library, but the results are not satisfactory. Would ojAlgo be useful? I have been hitting accuracy limits that I know should not be there. The algorithm should be capable of reducing the impact of an input variable to zero. Perhaps this takes the form of iteratively reweighted least squares, but I do not know that algorithm and cannot find a library for it. The output should be a weight matrix or vector such that matrix multiplication of the input matrix by the weight matrix will yield a prediction matrix. My input matrix will almost always have more rows than columns. Thank you for your help.

推荐答案

我不完全理解你的问题，但我用 Apache Commons Math 之前做过线性回归。

I don't fully understand your question, but I've used Apache Commons Math to do linear regressions before.

这篇关于是否有更好的线性回归的Java库？ （例如，迭代重加权最小二乘）的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！