solves the problem of finding chains of words distance one apart
b413839b — Alec Stein 2 months ago
updated readme
1607e682 — Alec Stein 2 months ago
diameter finder is working now
0bc4a7fc — Alec Stein 2 months ago
works -- painfully slowly finds longest word chain


browse  log 



You can also use your local clone with git send-email.

#What does this do?

A friend asked me to solve the following puzzle:

Get from the word "war" to "peace" by making a sequence of moves: either adding, deleting, or changing a letter. Each move must result in a new English word.

The shortest solution (path from "war" to "peace") is:

war --> par --> pare --> pace --> peace.

word_chains finds chains of words each connected by one letter differences. In other words, it solves the general case of the war/peace puzzle.

#How do I run the program?

If you want to compile word_chains yourself, you need to have zig installed:

$ brew install zig

Then to build, open the terminal and type

$ zig build-exe word_chains.zig -O ReleaseFast

Then follow the prompts. Try typing in "war" and then "peace" for example:

$ ./word_chains
Starting Alec's word-chain-finder. Press Ctrl-C to exit.
Usage: type in a start word and an end word to find the shortest path between them.
Calculating word distances: DONE!
Enter start word: war  
Enter end word: peace
Found the shortest path:


#How does it work?

Each time you run word_chains it builds a graph. Each node in the graph is a word, and each word is connected to all the other words that it's distance 1 away from. Then, word_chains does a breadth-first search to go from the start word start to the target word end. It prints the first result it finds (necessarily the shortest path). It will run exhaustively, which means if no path exists, word_chains will figure that out too.

By changing the words_{size}.txt files you can use any words you like. You can add words, or delete all of the them and change the language. words_med.txt contains about 40k American English words, including some proper nouns. By using a smaller dictionary you can make the program run faster.


For fun. This is my first time writing code in a compiled language with manual memory management and I thought it might be more straightforward than learning C.

In the end, getting through the Zig docs was a challenge and I had to rely on friends and the #zig freenode channel for help. I definitely couldn't have built this basic application by myself and I owe a lot to my friends (Matt and Sam) who patiently learned with me.

This took me about 30-40 hours. I had to learn what an allocator was, how to allocate memory efficiently, how to use pointers, and how to read the Zig source.

For comparison, the python version took me less than 30 minutes.

#What I learned

  • how to implement a queue
  • how to make a breadth-first search algorithm
  • basic bucket sorting algorithm
  • structs, types, casting
  • working with strings
  • calculating distances between strings

#Bonus: longest-shortest word chain

Included is an experimental program which will calculate the longest-shortest word chain (that is, given all the shortest word chains between two words, the longest of those). This is also known as the diameter of a graph. This function will print out longer and longer trial diameters until it loops through the whole graph.

It's not the prettiest program, but it works.

#What next?

It would be nice to have the option to pre-build the graph at compile time so that you don't have to build it on every run.

Unfortunately I was not able to take advantage of Zig's comptime to freeze the graph building into the binary because there's no compile-time hashmap available. Writing the graph to file is an option, perhaps sometime down the line.

Once I figured out the basics memory management I was able to get pretty far, and it turned out that speeding up the program came down to good memory management. I still think there's room to speed up the program, maybe by using a more efficient memory allocator.

#Have suggestions?

Let me know. I'd love to make this code faster, more efficient, simpler, and more beautiful.