| sESOM4BMUs {DatabionicSwarm} | R Documentation |
Intern function: Simplified Emergent Self-Organizing Map
Description
Intern function for the simplified ESOM (sESOM) algorithm for fixed BestMatchingUnits.
Usage
sESOM4BMUs(BMUs,Data, esom, toroid, CurrentRadius, ComputeInR=FALSE,
Parallel=TRUE)
Arguments
BMUs |
[1:Lines,1:Columns], BestMAtchingUnits generated by ProjectedPoints2Grid() |
Data |
[1:n,1:d] array of data: n cases in rows, d variables in columns |
esom |
[1:Lines,1:Columns,1:weights] array of NeuronWeights, see ListAsEsomNeurons() |
toroid |
TRUE/FALSE - topology of points |
CurrentRadius |
number betweeen 1 to x |
ComputeInR |
=T: Rcode, =F Cpp Code. |
Parallel |
Optional, =TRUE: Parallel C++ implementation, =FALSE C++ implementation |
Details
Algorithm is described in [Thrun, 2018, p. 48, Listing 5.1].
Value
esom |
numeric array [1:Lines,1:Columns,1:d], d is the dimension of the weights, the same as in the ESOM algorithm. modified esomneuros regarding a predefined neighborhood defined by a radius |
Note
Usually not for seperated usage!
Author(s)
Michael Thrun
References
[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, doi:10.1007/978-3-658-20540-9, 2018.