algorithmic modeling for Rhino
Introducing a new component that generates a Self-Organising Map from high dimensional input data.
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretised representation of the input space of training samples. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.
This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network. The Kohonen net is a computationally convenient abstraction building on work on biologically neural models from the 1970s and morphogenesis models dating back to Alan Turing in the 1950s
Files (last updated 18/03/16):
The component will appear under Extra>ANN
For anyone interested in how it works, a short introduction to artificial neural networks (in particular SOMs) written by myself is here:
Copyright 2015 John Harding and released under the GNU General Public License
Thanks to Christian Derix for first introducing me to the SOM and applied neural networks in general.