Go implementation of Self-Organizing Maps (SOM) alias Kohonen maps. Provides a command line tool and a library for training and visualizing SOMs.

• Multi-layered SOMs, alias XYF, alias super-SOMs.
• Visualization Induced SOMs, alias ViSOMs.
• Training from CSV files without any manual preprocessing.
• Supports continuous and discrete data.
• Fully customizable training and SOM parameters.
• Visualization of SOMs by a wide range of flexible plots.
• Use as command line tool or as Go library.

Please note that the built-in visualizations are not intended for publication-quality output. Instead, they serve as quick tools for inspecting training and prediction results. For high-quality visualizations, we recommend exporting the SOM and other results to CSV files. You can then use dedicated visualization libraries in languages such as Python or R to create more refined and customized graphics.

Pre-compiled binaries for Linux, Windows and MacOS are available in the Releases.

Alternatively, install the latest version using Go:

go install github.com/mlange-42/som/cmd/som@latest

Get help for the command line tool:

som --help

Here are some examples how to use the command line tool, using the World Countries dataset.

Train an SOM with the dataset:

som train _examples/countries/untrained.yml _examples/countries/data.csv > trained.yml

Visualize the trained SOM as heatmaps of components, showing labels of data points (i.e. countries):

som plot heatmap trained.yml heatmap.png --data-file _examples/countries/data.csv --label Country

Export the trained SOM to a CSV file:

som export trained.yml > nodes.csv

Determine the best-matching unit (BMU) for a each row in the dataset:

som bmu trained.yml _examples/countries/data.csv --preserve Country,code,continent > bmu.csv

Taken from the CLI help, here is a tree representation of all currently available (sub)-commands:

som          Self-organizing maps command line tool.
├─train      Trains an SOM on the given dataset.
├─quality    Calculates various quality metrics for a trained SOM.
├─label      Classifies SOM nodes using label propagation.
├─export     Exports an SOM to a CSV table of node vectors.
├─predict    Predicts entire layers or table columns using a trained SOM.
├─bmu        Finds the best-matching unit (BMU) for each table row in a dataset.
├─fill       Fills missing data in the data file based on a trained SOM.
└─plot       Plots visualizations for an SOM in various ways. See sub-commands.
  ├─heatmap  Plots heat maps of multiple SOM variables, a.k.a. components plot.
  ├─codes    Plots SOM node codes in different ways. See sub-commands.
  │ ├─line   Plots SOM node codes as line charts.
  │ ├─bar    Plots SOM node codes as bar charts.
  │ ├─pie    Plots SOM node codes as pie charts.
  │ ├─rose   Plots SOM node codes as rose alias Nightingale charts.
  │ └─image  Plots SOM node codes as images.
  ├─u-matrix Plots the u-matrix of an SOM, showing inter-node distances.
  ├─xy       Plots for pairs of SOM variables as scatter plots.
  ├─density  Plots the data density of an SOM as a heatmap.
  └─error    Plots (root) mean-squared node error as a heatmap.

The command line tool uses a YAML configuration file to specify the SOM parameters.

Here is an example of a configuration file for the Iris dataset. The dataset has these columns: species, sepal_length, sepal_width, petal_length, and petal_width.

som:                      # SOM definitions
  size: [8, 6]            # Size of the SOM
  neighborhood: gaussian  # Neighborhood function
  metric: manhattan       # Distance metric in map space
  visom-metric: euclidean # Distance metric for ViSOM update

  layers:                 # Layers of the SOM
    - name: Scalars       # Name of the layer. Has no meaning for continuous layers
      columns:            # Columns of the layer
        - sepal_length    # Column names as in the dataset
        - sepal_width
        - petal_length
        - petal_width
      norm: [gaussian]    # Normalization function(s) for columns
      metric: euclidean   # Distance metric
      weight: 1           # Weight of the layer

    - name: species       # Name of the layer. Use column name for categorical layers
      metric: hamming     # Distance metric
      categorical: true   # Layer is categorical. Omit columns
      weight: 0.5         # Weight of the layer

training:                 # Training parameters. Optional. Can be overwritten by CLI arguments
  epochs: 2500                        # Number of training epochs
  alpha: polynomial 0.25 0.01 2       # Learning rate decay function
  radius: polynomial 6 1 2            # Neighborhood radius decay function
  weight-decay: polynomial 0.5 0.0 3  # Weight decay coefficient function
  lambda: 0.33                        # ViSOM resolution parameter

See the examples folder for more examples.

This project is distributed under the MIT license.