🗺️ Self-Organising Map

They select which distribution the training colours are drawn from. Rainbow covers the full hue circle, Warm spans reds to yellows, Cool spans cyans to violets, and Random picks uniformly random RGB values. Changing the input resets the map, because the learned layout reflects whatever distribution it was trained on.

The slider sets how many training iterations are executed per animation frame: Slow (10), Medium (50), Fast (200) or Blazing (800). Higher speeds let the map converge in fewer seconds, while slower settings let you watch individual BMU updates and the gradual smoothing of the colour field.

α is the learning rate (how strongly neurons move towards an input) and σ is the neighbourhood radius (how far the influence spreads). Both decay exponentially, here as α₀·exp(−t/4000) and σ₀·exp(−t/2500). Large early values give broad, global ordering; shrinking values refine local structure and let the map settle.

The 576 weight vectors are initialised with random RGB values, so the grid begins as colour static. As training proceeds the neighbourhood updates correlate nearby neurons, and the noise resolves into ordered patches of similar colour, demonstrating how self-organisation emerges purely from local competitive learning.

It means inputs that are similar in the original space map to neurons that are close together on the grid. After training, neighbouring cells hold near-identical colours and the whole lattice forms a continuous gradient, so the 2D grid faithfully reflects the structure of the 3D colour space.

Yes. It implements the standard Kohonen algorithm: competitive BMU selection by Euclidean distance, a Gaussian neighbourhood function, and exponentially decaying learning rate and radius. It is simplified to a 24×24 grid and 3D colour inputs for clarity, but the core update rule and decay schedule match the classic formulation.

Both place reference vectors among data, but k-means treats clusters independently, whereas a SOM imposes a fixed grid topology and updates a neuron together with its neighbours. This neighbourhood coupling makes nearby SOM units learn related values, producing an ordered map rather than a set of unrelated centroids.

SOMs are applied to data visualisation, exploratory clustering and dimensionality reduction across fields such as document and image organisation, financial and customer segmentation, fault detection and bioinformatics. The colour example here is the classic teaching demonstration because the high-dimensional ordering is directly visible as a smooth gradient.