- I love functional programming; I've been enjoying programs functionally for over 3 years; in LISP and Elixir mostly
- Soon to be SDE at AWS
- I'm 20 years old, graduating this semester once I'm done with my final general class I missed on my grad plan (2credits only, ugh, ask me after if you want to hear the bureacracy)
On our journey to get a valentine, looks like we have stumbled across a black box. What does it do? Well... we could start shoving random numbers in it and see what happens.
We're in love with its predictability, the assurance that no matter what we give as input or how many times it's given, the function behaves in a consistent manner.
Imagine asking your partner about what food they want to eat. But instead of a straightforward answer based on your question alone and some state (i.e. hunger levels, craving ratios, etc), the function's response is influenced by other factors; the day of the week, and the state of the day or week prior. We can simulate this: ((go through the ratios and outputs)).
Let's see what causes this in this black box we don't love; don't pay attention to the implementation (there's some stuff we haven't talked about yet), but check out this line (MATH.RANDOM). This is a side effect; an unpredictible effect on the output.
Side effects introduce unpredictability into our programming relationships. Where output is not just determined by its input but also by the state of the outside world, or the system at the time of execution. Suddenly, the predictability we cherished is compromised.
It was obvious how we could cause unpredictability by including randomness. On the left we have two impure functions.
When we execute the first block, we get the expected result of `fib(5)`.
But because `fact` uses the same cache, we'll run fact(5)... `fact(5)` returns fib(5)!
Now, when I go and update the cache to be correct, we get the correct answer. I was able to change the behavior of the function by modifying stuff outside of it. Get the gist - impure vs pure functions, side effects, etc? Cool.
For now let's move back to studying our pure black boxes.
We love our black boxes a lot, and we want to share them with other people in our lives. So let's create another that takes a list of people and creates a custom valentine's letter for them.
letters.append(f"Dear, {person.name}\nYour smile lights up my world. Happy Valentine's Day!")
return letters
```
Good! Now they will all share our love.
But a few months goes by, and soon all their birthdays are coming up! So we make another black box to generate a letter, and in how many days we should give it to them:
But this is getting annoying; what about Christmas, Thanksgiving, Easter, or another life event such as a wedding? Making this list of new cards and going through each person every single time is getting really tedious.
What if we create a bunch of black boxes that take a person and create them a card, specifically. Then we create a black box that takes a list of people and another black box and creates a new card for each person?
After exploring the simple yet profound beauty of using black boxes in black boxes, our journey into the functional programming romance takes an intriguing turn. What's better than one black box the uses a black box? A black box that creates more black boxes.
All life on Earth contains instructions in the form of DNA. During mitosis, special mechanisms in our cells read the instructions and create new instructions to give to another cell.
Very similarly, we can give our black boxes DNA from parents to reproduce and create new child black boxes. But in our world of black boxes, we call these "closures".
We've broken up the input of the black box ~cardGenerator~ into it and its child. This is called ~currying~, and it's something we'll touch on in more detail later.
There's another way that we could implement the same functionality - "partial function application"; a fancy name for a simple idea:
```
def josephCard(type):
joseph = {"name": "Joseph", birthday: new Date()}
cardGenerator = cardGeneratorFor(joseph, type)
return cardGenerator()
print(josephCardGenerator("valentine")
```
Immutability
===
We briefly mentioned side effects and alluded them to the unpredictability of a partner. We love our black boxes because they're reliable.
But, we've actually had an impostor among us (AMOGUS sound??) throughout this presentation. Specifically in ~buildCards~:
```
def buildCards(people, cardMaker):
cards = []
for person in people: # side effect (mutation of person)
card = cardMaker(person)
cards.append(card) # side effect (mutation of cards)
# someone could do something nasty with "cards" before it's returned here
return cards
cards = buildCards(people, valentineMaker)
```
This function actually has a small side effect - though it's contained entirely within the function - when we append to the cards list. If we were to go about living a side effect free life, that means we can't change _anything_.
Here we're not changing anything at all (except the stack), with the same functionality; this code is "immutable". When we call ~build_cards~ on a list of people, we're 100% certain we're not gonna get something odd short of a bug.
At a high level there are so many benefits to immutability:
+ Concurrency (TODO: go into more detail)
+ Easier to verify correctness.
+ Compiler optimizations.
+ Easy to read and understand.
But there are also downsides:
+ Keeping immutability requires more computation (data structures)
+ More difficult to write (huge selling point for Object Oriented Programming; encapsulation)
But like all relationships, we need to compromise with our black boxes. Side effects are _unavoidable_ as programmers; when people press the button, they don't want the computation to just exist out there in the computer void. No! They want to see the pop up box, and the value updated on their account. We need to be able to mutate state where the tradeoff of extra compute time is unavoidable like in I/O or a relatively large database.
Writing Code Functionally
===
Determining if a natural number is even or odd in the best way possible is a problem that's stood unsolved since the dawn of time. We'll use this incredibly difficult task to show how we can refactor some code to use the paradigms we just learned.
========
Part Two - A Deeper Dive
===
The Lambda Calculus
===
Completeness of the Lambda Calculus
===
Mention how alonzo church actually failed on his first attempt, natural numbers