constrainsts on HawkesExpKern

[sumau]

Hey I'm trying to recreate the approach in this paper: https://scholar.harvard.edu/files/bgreen/files/jama-iternmed-17.pdf They use a multivariate hawkes model with an exponential kernel where the baselines are identical for all dimensions and the adjacency is identical for all neighbours. I was wondering if there was a way of tweaking the tick library, perhaps the LearnerOptim() class, to set constraints? Examples:

1. Baseline is identical for all nodes
2. Adjacency is zero for specific elements and identical for the other elements

Thanks!

[KristenMoore]

I would also like constrain some adjacency elements to be zero and the rest to be positive. Did you get anywhere with this @samau?

[sumau]

@KristenMoore I wrote a new Learner in Python. It's not very flexible but you're welcome to use it: https://bitbucket.org/soumau/network_contagion/src/master/src/hawkesexpkernelidentical.py

I'm using it to infer contagion in a network of nodes. It assumes that: (1) Baseline is identical for all nodes (2) Adjacency is identical for neighbours and zero otherwise

[KristenMoore]

Thanks a lot for sharing this, @sumau. I'll try to adapt it to my case.