Thunderhead Explorer

This is part II of my journey back into SOMs . Actually this journey started with the below picture: It is a map of AIS broadcast points around the harbor of Miami, FL. Now we, sentient creatures, when we look at this map we can quickly and clearly see patterns formed by the points. There is a sequence of points that start from the harbor and go northeast.  There is a sequence of points that starts at the harbor and go south southeast. And there are a plenty of north south paths, some are very close to the shore, others are on the "edge" to the east. And there are path in the "middle". Wouldn't it be wonderful if the Machine can see these pattern and formulate the general paths? That is actually what started this journey. I needed an unsupervised way for the Machine to recognize the patterns and emit the paths. I'm sure there are multiple ways to solve this, but I remembered that a while back I used Self Organizing Maps due to their simplicity and cruci...

Self Organizing Map ( SOM ) is a form of Artificial Neural Network (ANN) belonging to a class of Machine Learning. AI Junkie has a GREAT tutorial about it. What I like about SOMs is that they belong to a class of unsupervised learning models and they hold true to the first law of geography. "E verything is related to everything else, but near things are more related than distant things. " - Tobler I encountered them and used them over 20 years ago, and since AI/ML is the hottest topic these days, I'm reacquainting myself with them. There are plenty of SOM libraries, but I learn (or in this case re-learn) by doing.   This project is my learning journey in implementing SOMs and " Sparkyfing " them. The following is a sample output of the obligatory RGB classifier, where a million random RGB triples are organized by a Spark based SOM into a 10x10 square lattice: And the following is a sample solution to a TSP using SOM: Like usual, all the source code ...