stint | Simple , suckless-style color grabber

So I am going back to basics with Python, after a long stint in JS. And I just something out which goes against what I thought it would do.

I have the following code:

My XML files have TEI milestone elements like

:

However, this only returns the text node(s) following the final milestone in the text, and only in the for-each iteration that selects for the attribute value matching that final milestone. I've surely got the >> statement wrong and would be grateful for any advice either with this approach or for a different, grouping-based approach.

I should probably mention that once I've mastered this concatenation, I'll have to add any addition-type content (i.e. revisions by hands not matching that of the current stint) into the equation, by excluding nonmatching content of this nature and including matching content situated within nonmatching scribal stints, but I don't necessarily foresee this having to be added to the concatenation in the "correct" place. I thus should be able to account for these in two fairly straightforward additional steps, but the initial concatenation or grouping solution has to allow for the exclusion of nodes with nonmatching attribute values, and of any other elements I may wish to exclude (e.g. in the below example).

Here is a mock XML file:

I have a data frame of baseball player information:

How do I use regular expressions in Notepad++ to place all items belonging to a set on one line?

I have a set of practice question answers in the following format:

For a database table looking something like this:

My goal is to dynamically determine what question should be next in quiz by using statistics of previous answers

So, I have:

• Question with difficulty field (1-100)
• Maximum score you can get in question (let it be 256)
• Score user have reached in question (x out of max)

I want to somehow combine these paramaters in formula to choose most suitable next question for user

How can I do it?

My idea was to give user a question with median difficulty as first one and then check if user scored less than 50% of maximum, then get questions with 25 percentile difficulty else get 75 percentile. Then repeat this schema on a smaller stint (25-50 percentile or 50-75 percentile and so on)

I have the following DataFrame:

I'm using C++17 after a long stint with C# and working through some Project Euler problems to get my feet well. Anyway, can anyone explain why createRandomVector(const int n) is not "moving" the vector created? I output the memory addresses and they only stay the same when passed by reference (obviously). Below is the code:

I am using the Azure ML model available at https://gallery.azure.ai/Experiment/Weather-prediction-model-1 to design a prediction mechanism based on temperature and humidity. I haven't done any changes to the existing model and feeding in data from a simulator. The prediction output is stuck at 0.489944100379944. I have taken over 17k samples and still, the prediction is constant at this value.

Any help will be highly appreciated.

N.B. - This is my first stint with ML