SANTO | based Annotation Tool for Ontology-driven Slot Filling | Machine Learning library

You can simplify the process of adding Regioes but you also seem to have a problem with the encoding of the character sets within the data. I read your data with the following code:

You can use

I see two main issues with how your code passes the data from input_data to filtertuple.

The first issue is that your recursion in input_data is messed up, you never do anything with the results of the recursive calls so only the first row of input data gets included in the final return value. Recursion really isn't an ideal approach to this problem, a loop would be a lot simpler and cleaner. But you could make the recursion work, if you do something with the value returned to you, like tup.extend(intput_data(n-1)). If you stick with recursion, you'll also need to make the base case return something appropriate (or add an extra check for None), like an empty list (or tuple).

The second issue is that filtertuple is written to expect many arguments, but you're only passing it one. So tup will always be a 1-tuple containing the actual argument. If you're expecting the one argument to be a list of tuples (or tuple of tuples, I'm not sure exactly what API you're aiming for), you shouldn't use *tup in the argument list, just tup is good without the star. You could call filtertuple(*input_data(...)) which would unpack your tuple of tuples into many arguments, but that would be silly if the function is just going to pack them back up into tup again.

There may be other issues further along in the code, I was only focused on the input_data and filtertuple interactions, since that's what you were asking about.

Here's my take on solving your problem:

Sounds like you are asking for help on how to get started. You are asking for help on learning to learn. Here is how I would approach your problem:

Apparently you are supposed to use a priority queue.

1. Write a tiny program that makes a priority queue and stores strings into it, then prints them out.
2. Define a class and store instances of that class into the priority queue instead of strings.
3. Modify the sort criteria on the priority queue and notice that the printed sequence changes according to the sort criteria.
4. Write a function that creates one class instance with random values.
5. Write a function that creates all 100 class instances.
6. Declare victory.

Assuming that dist is a tibble and choice is a vector of values (whose length is equal to the number of columns in dist), I would try something like this:

You should focus on your for loop. I would suggest you to create a dictionary for each row you want to define in your final DataFrame, and then create a list to append those dictionaries to.

In example:

I think it because the a tag takes a click instead of button. You should remove the a tag and write a click function for the button to open a new page.

Here is the general concept, given the things you have posted so far

Note that answering a question is much easier if you've provided this in your question beforehand!

In App.js