Nikhil Ketkar

There was some problem with Greedy approach implementation. As seen from the previous results, for very low training sets, the greedy approach is performing worse than others. The problem was that after getting 100% coverage on the training set, the greedy implementation was not properly picking vertices at random. I think I fixed that and here are the update results.

1% Training on 100 partitions between -3000 and 3000

For really small training sizes here is what is observed. Note that results are no better that random for all three approaches.

25 partitions between -3000 and 3000, 0.25% used for training