Finds the weak Nash equilibirium of the data bots for one epoch depending on a radius, which requires the setting of constants, grid, and so on in, see Pswarm.
DataBotsPos: Numeric vector [1:NumJumpsn2] containing the current positions and all positions for considered/possible jumps which can be computed (depending on number of jumps parameter NumJumps) for the databots on two dimensions.
DataDists: Numeric vector with vectorized distance matrix of the datapoints in the original (high-dimensional) data space
AllallowedDBPosR0: NumericMatrix, see AllallowedDBPosR0 in setPolarGrid
IndPossibleDBPosRe: Numeric Vector of possible positions of the 1st coordinate.
IndPossibleDBPosIm: Numeric Vector of possible positions of the 2nd coordinate.
Lines: Integer stating the number of Lines the polar grid consists of.
Columns: Integer stating the number of columns the polar grid consists of.
Radius: Numeric (Integer) stating the moving radius of the databots
NumAllDB: Integer total number of databots
NumChoDB: Integer number of databots chosen for moving/jumps.
NumFreeShape1: Integer stating the first dimension of the numeric matrix book keeping the possible position grid
NumJumps: Integer number of jumps
Origin1: Numeric origin coordinate 1
Origin2: Numeric origin coordinate 2
Happiness: Numeric value indicating the global happiness over all databots
MinIterations: asdf
HappinessInclination: asdf
Eps: optional, double: Stop criterion for convergence of each epoche.
debug: optional, bool: If TRUE prints status every 100 iterations
Returns
list of - AllDataBotsPos: ComplexVector, indizes of DataBot Positions after a weak Nash equlibrium is found
stressverlauf: NumericVector, intern result, for debugging only
fokussiertlaufind: NumericVector, intern result, for debugging only
Details
Algorithm is described in [Thrun, 2018, p. 95, Listing 8.1].
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, tools:::Rd_expr_doi("10.1007/978-3-658-20540-9") , 2018.
[Thrun/Ultsch, 2021] Thrun, M. C., and Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial Intelligence, Vol. 290, pp. 103237, tools:::Rd_expr_doi("10.1016/j.artint.2020.103237") , 2021.
[Stier/Thrun, 2024] Stier, Q. and Thrun, M. C.: An efficient multicore CPU implementation of the DatabionicSwarm, 18th conference of the International Federation of Classification Societies (IFCS), San José, Costa Rica, July 14-19, 2024.