# Deterministic Finite Automata in Swift

I’ve been reading Understanding Computation by Tom Suart, and it reminds me of my first CS class in grad school where I learned about and struggled to implement finite automata. I think one major lesson from that experience was to forget about the pictures of circles and loops on the blackboard, and figure out the bare minimum needed to represent the idea. In the case of finite automata, the bare minimum was that there was a state, and a set of rules that modified the state; each rule was composed of a current state, an input, and a next state. I decided to implement a simple finite automata in Swift using a list of `Int`s as states, and a single `Int` as input.

# Very Very Verbose Cosine Similarity

This material was a teaching aid for a crash course I gave at work about cosine similarity. Cosine similarity is a blunt instrument used to compare two sets of text. If two the two texts have high numbers of common words, then the texts are assumed to be similar. The ultimate goal is to plug two texts into a function and get an easy to understand number out that describes how similar the texts are, and cosine similarity is one way to skin that cat.

Please note, there are plenty of other very fast implementations for cosine similarity, but this one was written for educational purposes.

# C++14 Range Based For Loops

C++ has changed a lot since I was first learned about it in college, so I’m going to do a series of posts outlining some of those changes as they come up for me. First off: range-based for-loops using auto type-inference. Here’s the TL;DR:

• Use `auto i` when you want to work with copies of the array items
• Use `auto &i` when you want to work with the original items and maybe modify them place
• Use `auto const &i` when you want to work with the original items and not modify them