This week required a lot of thinking before jumping to code the stuff. Interested? Okay move on to next paragraph.
Basically, I worked on three PRs, #17163 for continuous time Markov chains, #17174 for random matrices and #17146 for symbolic Ranges. The first and the last PRs are very much intensive. I developed a new algorithm for the query handler of
ContinuousMarkovChain.probability method, because the previous one which I implemented in
DiscreteMarkovChain.probability, was not easy to maintain, quite ad-hoc, rigid and difficult to extend. The philosophy behind the algorithm is recursion i.e., boil everything down to
Relational query, convert them to sets and then calculate the probability. You can find the complete description here. I am waiting for any critical objections from my mentors and after that I will refactor the code as suggested by oscarbenjamin and jksuom. So, now let’s move on to random matrices. As it was to be implemented from scratch, it required a bit of thinking to reach a decent architecture. Currently, the PR is at a basic level, and some more testing is to be done. Now, coming on to symbolic
Range. Let me tell you, it requires a lot of logical thinking to make
Range accept symbolic parameters. A lot of tests fail, and a lot of debugging has to be done to make a method work. In fact, we might deprecate
xrange support from
Range because we are going to drop
Python 2 support from
This week I learnt to combine the concepts from algorithms and software engineering to develop the stuff I mentioned above. This was the best week of my overall GSoC experience till now.
A lot more lies ahead. Bye!!