Gosom

Gosom is the memory efficient som implementation of Kohonen Self Organising Map (published in 1990) in pure Go. No external dependencies

Gosom comparison with R library

For developing this library, we analysed the R library SOM. The 2 libraries split the wine dataset as follow

R result

Gosom result

As you can see excluding the initial randomness, the 2 libraries split, in the same way, the data so you can assume we make the same error as R ;)

This library looks like the library I always wanted. How can I use it?

Gosom is a pretty straight forward library as you can see in the following example code we will initialise a new som and train it

    s, err := NewSom(5, 5, Rectangular, nil, 0, Bubble, false, 0, SumOfSquareDistance)
    if err != nil {
        panic("Error: %s\n", err.Error())
    }
    data := loadData()
    err = s.Train(data)
    if err != nil {
        panic("Error: %s\n", err.Error())
    }
    fmt.Printf("Codebooks: %+v\n", s.CodeBooks)

Ok this library seems easy to use, but how about performance?

Sit down, pick a cup of delicious coffee and watch the benchmark for the wine dataset:

BenchmarkSomTrain-4             20      59128343 ns/op    142734051 B/op       35406 allocs/op

in less than 6 milliseconds you have a complete train with the parameter you found in the example section, kinda cool, isn't it?

This library seem very cool! Who create it?

This library was created by me (bestbug) and the best Data Scientist I've ever met Franca Marinelli

If I found a bug or something wrong?

You can create a pull request to us (really appreciate), or if you don't have any idea about what is going wrong you can always open an issue here on GitHub!