This week was a mix of discussion on design and extending previous work. I also got to know about some new cool features of
According to the plan proposed in Week 4, I have completed my work on
DiscreteMarkovChain via PR #17083. I used the
as_relational methods which helped me to cover many miscellaneous cases and probably, now
DiscreteMarkovChain is quite dynamic and can handle various generic
expectation queries. I have also started the PR #17163 for adding
ContinuousMarkovChain and I am observing that it’s a bit tricky to maintain both the performance and result quality while working on it. Now, moving on to symbolic
Range,well, the work has been started in the PR #17146 and I have figured out one disparity between
Range and python’s
range(details available at this thread). I will try to fix it by making minimal changes to the code. The tensorflow related PR #17103 which I started in the previous week is also almost complete and is waiting for
Tensorflow 2.0 release. I am also studying a bit about the architecture of the above framework to make changes to
lambdify. Regarding random matrices, I believe that discussion has reached its final stages and I am waiting for the comments from Francesco for improvements at the issue #17039.
Let me share with you about my discoveries and learnings in this week. Well, thanks to Francesco for telling me about,
sympy.multipledispatch. It helps in implementing operator overloading like in C/C++. I liked it very much. I also read about continuous Markov chain and discovered about generator matrix, forward and backward equations. Adding one interesting fact, that Poisson process and continuous Markov chain are very closely related via generator matrices it will make the implementation of the former much easier :D.
Leaving you for now, Bye!!