What are the most memory efficient open source packages for calculating a geographically weighted regression (GWR)?
I am in a situation where I need to do a geographically weighted regression on a set of points where training data consists of about 40,000 observations and each observation has about 20,000 variables.
I have implemented a version of GWR myself using a combination of Python Numpy/SciPy and PostGIS. I solve the regression using a matrix algebra approach, but this fails due to memory issues when I have dense, feature rich systems with many observations.
One way to get around the memory issue is use an iterative approach for finding a line of best fit, such as an incremental gradient descent. I'm thinking it should work something like (http://www.eecs.wsu.edu/~cook/dm/lectures/l5/node14.html). Incremental Gradient Descent is described pretty well here in pages 4-7 (http://cs229.stanford.edu/notes/cs229-notes1.pdf).
Obviously I could implement this myself, but I was hoping maybe someone else had already coded something similar.
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