The ESOM (emergent self organizing map) algorithm as defined by [Ultsch 1999]. A set of weigths(neurons) on a two-dimensional grid get trained to adapt the given datastructure. The weights will be used to project data on a two-dimensional space, by seeking the BestMatches for every datapoint.
List with
BestMatches of datapoints
Trained weights
Height of grid
Width of grid
TRUE if grid is a toroid
Nr of DataPoints that jumped to a different BestMatch in every epoch
Data that will be used for training and projection
Height of grid
Width of grid
Number of Epochs the ESOM will run
If TRUE, the grid will be toroid
Type of Neighbourhood; Possible values are: "cone", "mexicanhat" and "gauss"
Initial value for LearningRate
Final value for LearningRate
Start value for the Radius in which will be searched for neighbours
End value for the Radius in which will be searched for neighbours
Cooling method for radius; "linear" is the only available option at the moment
Cooling method for LearningRate; "linear" is the only available option at the moment
Generate progress output for shiny if Progress Object is given
If True, the Umatrix will be shifted so that the point with highest density will be at the center
name of the method that will be used to choose initializations Valid Inputs: "uni_min_max": uniform distribution with minimum and maximum from sampleData "norm_mean_std": normal distribuation based on mean and standard deviation of sampleData
Vector of numeric keys matching the datapoints. Will be added to Bestmatches
If TRUE, Umatrix based on resulting ESOM is calculated and returned
On a toroid grid, opposing borders are connected.
Kohonen, T., Self-organized formation of topologically correct feature maps. Biological cybernetics, 1982. 43(1): p. 59-69.
Ultsch, A., Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. Kohonen maps, 1999. 46: p. 33-46.
data('Hepta')
res=esomTrain(Hepta$Data, Key = 1:nrow(Hepta$Data))
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