Twitter decoding program

[Ether]

Originally Posted by Lalaland1125

The reason why I am using SVD is because that is how Eigen's tutorials describe how to perform a least squares operation(http://eigen.tuxfamily.org/api/Tutor...Leastsqua res)

SVD is just one (of many) ways to compute least squares. The choice of the "best" method to use (like choosing the right tool for a job) depends on the problem domain.

For this application, LDLT would be far faster* and plenty accurate.

I don't think missing scores is going to be that bad. As long as most of the scores are posted, there should be enough data to get a reasonably accurate result.

Be aware: There are already two events (Oregon and Traverse City) for which data is missing for the entire event. No guarantee that won't happen for more events as the season rolls on. Not counting these two events, over 12% of the data is missing for the other events.



* For computing least squares for single events, the matrix is small enough that the time difference is probably not even noticeable. But if you ever intend to expand the functionality to compute least squares for a matrix containing all the data from an entire year's worth of events, I believe there would be a very noticeable difference in speed. If you have the time and are so inclined, it would be interesting if you would try SVD with 2011's data and see what the computation time is. For reference, LDLT takes 12 seconds on my 8-year-old PC to do least squares on a matrix populated with all the qual data from all the events in 2011