Learn With Me: ReasonML, Part 2
This article requires you to have a basic understanding of ReasonML, a functional programming language which I partly covered in one of my previous articles.
For quick navigation, here are the other articles in the same series:
Diving into λ-calculus, you realize it’s foundational for understanding functions as first-class citizens. You can even define natural numbers with just functions, leading us into Church numerals.
Here’s the setup:
- “α” stands for a type representing numbers, like “int” in ReasonML.
- You start with an “induction basis”, “x” or “zero”, which is 0.
- Then there’s a “step function”, “f” or “succ”, basically x -> x + 1.
This way, we can inductively count numbers: “zero” gets you 0, “one” (succ(zero)) is 1, “two” (succ(one)) is 2, and so on.
In λ-calculus, all natural numbers “n” must satisfy the following type: “∏ α : * . α -> (α -> α) -> α”, which can be read as:
The type abstraction of α over a function which takes in zero value of type α and successor function of type α -> α and returns a value of type α.
Now, let’s declare the type of all “natural numbers”:
Implementing natural numbers in ReasonML using natGroup is straightforward:
Typed church numerals look like this:
When fully evaluated, these church numerals yield natural numbers.
The trick was getting polymorphism right. Initially, types and terms had to be explicitly linear, which was a bit limiting. Wrapping the definitions in a local scope helped abstract away the complexity.
Now, generating numbers is just a matter of passing the module. Simple, right?
But exposing all functions, including the ones meant for internal use, could be an issue. That’s where interfaces come into play. Interfaces let you control what the user sees and uses, while implementations handle the functionality behind the scenes. Here’s what users should interact with:
Inheriting without repetition is key. If NatPriv doesn’t show a method, it’s because NatGroup didn’t list it.
Extending interfaces allows for additional methods without redundancy.
Implementing ShowNat by including Nat and adding a toString method.
With interfaces, direct access to values is restricted, enhancing encapsulation.
Interfaces ensure users only see what they need to, maintaining privacy.
Now, onto functors, ReasonML’s way of transforming modules. Functors take a module and return a new, tailored module. The functor interface sets the blueprint for transformation.
Implementing the functor involves crafting custom logic without using “include”.
With NatInt, you’re applying the functor to integer types for a sleek implementation.
NatBool applies the same logic to booleans, showing off the functor’s versatility.
And for a bit of a twist, NatTern demonstrates how you can handle ternary logic, introducing a nuanced state with “null”.
Checking out the implementation in action with NatBool and NatTern, it’s clear how functors bring a level of dynamism and reusability to the table.
Suppose there’s a special case for NatChurch that needs a unique method for creating Nats, illustrating the power of extending modules.
Running NatChurch.printUntil(3) showcases how you can adapt and extend functionality in a modular fashion, tailoring it to specific needs.
This exploration through ReasonML’s type system, modules, and functors is more than just a magic trick. It’s about finding practical, elegant solutions to complex problems. Leveraging these features, you can design code that’s not only efficient and scalable but also clear and maintainable. ReasonML encourages a thoughtful approach to software design, where every line of code serves a purpose, and every module fits neatly into the larger puzzle.
The next use case will be a perfect final for the series, so stay tuned!
For quick navigation, here are the other articles in the same series:
