So in this lecture, I'm going to assign to you the next exercise for this course, which will be to
implement Tex rank in Python.
Your goal in this exercise is to only make use of Nampai, but not any other libraries which already
implement text rank for you.
As a small hint, recall that you can reuse most of the code from the previous script.
This is because there are many common steps, such as splitting the document into sentences.
Computing the DFAT of Matrix and sorting each sentence by score.
The only difference is in how we compute the scores.
So that should be your main focus as in other hands.
Recall that the standard eigenvalue equation is eight times vehicles Lambda Times V where V is a column
vector, as we normally assume in linear algebra.
But for Markov chains, this is not the convention we use instead.
Since we treat the state distribution as a row vector, this changes how we call the eigenvalue function
and nampai.
Specifically, you'll want to transpose the state transition matrix.
As always, the next lecture will present the solution to the exercise.
So make sure you've completed it before checking the answer.