TODO PLAN FOR BIRDS          python

 1. I tried to find an example where hypers can be learned
 in small amount of time. Current 'easy' functions params
 have one_step and color_diag, with hypers=[1,0]. if a feature
 can be positive and negative, then it should be possible
 to learn a zero hyper. if the features values are always
 positive, then it's trickier, because a zero weight
 will look similar to a negative weight.

 Once we find an example that can be easily learned, we
 can vary inf programs and compare performance. We
 can get an integration test of Analytics filtering inference.

 2. Need to add store_observes and observes_from_file methods
 to poisson and then generalization testing/inference functions
 below to work with Poisson object. Test Poisson param inference
 on our basic example.