Ella | Self-improving decision organism | Machine Learning library

Try this:

There are the following issues:

• The height of a leaf node is 1, not 0. So the last return in the height function should be:

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.

I am pretty sure a Module exists that could do this for you, you could try and make it yourself by parsing through the text and creating words like: ["date of birth", "born", "birth date", etc] and you do this for multiple fields. This would thus allow you to find information that would be available.

The idea is:

you grab your text or whatever u have,

you grab what you are looking for (example date of birth)

You then assign a date of birth to a list of similar words,

you look through ur file to see if you find a sentence that has that in it.

I am pretty sure there is no module, maybe I am wrong but smth like this should work.

Here you can add users to the array:

You can filter rows if there are tuples by compare if same length of sets and values of tuples - so removed rows with 2 or more duplicated words:

You could simplify using ListViews for both Columns:

Try this code

I'm not 100% sure this will meet your needs, but perhaps it will be helpful for you. It might help to know more details about your data, including how large your dataset is, how your columns are organized by year, etc.

In this example, you can use dplyr from tidyverse. First, you can group_by Work_2011 (I removed the braces from column names), and filter where the number of distinct values for Work_2012 is greater than 1. This would imply multiple destinations.

Second, you can group_by both Work_2011 and Work_2012 to determine the number of countries for each destination. This will be helpful in a second filter.

Again, please let me know if this is the direction you were interested in.