问题描述
我有一个 Python 脚本，需要经常更新均值和协方差矩阵.我目前正在做的是，每次我得到一个新的数据点 $x$(一个向量)，我重新计算均值和协方差如下:
I have a Python script where I need to frequently update the mean and co-variance matrix. What I am currently doing is that each time I get a new data point $x$ (a vector), I recompute the mean and covariance as follows:
data.append(x) # My `data` is just a list of lists of floats (i.e., x is a list of floats)
self.mean = np.mean( data, axis=0) # self.mean is a list representing the center of data
self.cov = np.cov( data, rowvar=0)
问题是这对我来说还不够快.无论如何，通过增量更新 mean 和 cov 而不根据所有 data 重新计算它们是否会更有效?
The problem is that is not fast enough for me. Is there anyway to be more efficient by incrementally updating mean and cov without re-computing them based on all the data ?
计算均值应该很容易，我可以弄清楚.我的主要问题是如何更新协方差矩阵 self.cov.
Computing mean incrementally should be easy and I can figure it out. My main problem is how to update the covariance matrix self.cov.
推荐答案
我刚刚发现我们可以使用 mdp 库轻松做到这一点http://mdp-toolkit.sourceforge.net/api/mdp.utils.CovarianceMatrix-class.html
I just figured out that we can easily do that using the mdp libraryhttp://mdp-toolkit.sourceforge.net/api/mdp.utils.CovarianceMatrix-class.html
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