Moo | Mapping Objects to Objects | Machine Learning library

You can do it like this:

The first argument of passed to init is always implicitly the constructed instance. You should handle this in the definition too:

In order to invoke a script file (.ps1) with arguments, use the PowerShell CLI's -File parameter, not the (implied) -Command parameter:

C++ has a really stupid quirk where functions cannot take a array by value as a parameter.

The clear() method cleans out all the key-value pairs, leaving an empty dict. That refers to the dict repr method. If you want to clean the object completely, just del it: del text. The GC then will destroy the object in the memory. But be careful: after deleting variable is destroyed completely, so you'll get NameError: name 'text' is not defined when print('text =', text) occurs.

Another approach here is to subclass dict class, but implement your own __repr__() method:

Use URLSearchParams to parse the strings into key-value pairs and then combine them.

&Animal is short for &dyn Animal. dyn Animal is a trait object type, and it only exists for a given trait if the trait is object-safe. Traits with associated types are not object-safe, because dyn Animal cannot implement Animal without specifying the associated type Food.¹

This is an inherent limitation of runtime polymorphism (trait objects): you don't know the concrete type, so you can't know its associated type.²

If you want to create a vector of things you can call .talk() on, it's easy enough to create a trait just for that (playground):

I'd use a regex-based lexer, certainly. But you could try to write an unambiguous grammar, based on the observation that you can never have two adjacent chars in a charCode:

As pointed out in the comments, you can use group_by from dplyr to accomplish this.

First, you can extract keywords for each comment/sentence. Then unnest so each keyword is in a separate row with a date.

Then, use group_by with both date and comment included (to get frequency for combination of date and keyword together). The use of summarise with n() will give number of mentions.

Here's a complete example: