esomTrain {Umatrix}

Train an ESOM (emergent self organizing map) and project data

Description

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.

Arguments

Data

Data that will be used for training and projection

Lines

Height of grid

Columns

Width of grid

Epochs

Number of Epochs the ESOM will run

Toroid

If TRUE, the grid will be toroid

NeighbourhoodFunction

Type of Neighbourhood; Possible values are: "cone", "mexicanhat" and "gauss"

StartLearningRate

Initial value for LearningRate

EndLearningRate

Final value for LearningRate

StartRadius

Start value for the Radius in which will be searched for neighbours

EndRadius

End value for the Radius in which will be searched for neighbours

NeighbourhoodCooling

Cooling method for radius; "linear" is the only available option at the moment

LearningRateCooling

Cooling method for LearningRate; "linear" is the only available option at the moment

shinyProgress

Generate progress output for shiny if Progress Object is given

ShiftToHighestDensity

If True, the Umatrix will be shifted so that the point with highest density will be at the center

InitMethod

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

Key

Vector of numeric keys matching the datapoints. Will be added to Bestmatches

UmatrixForEsom

If TRUE, Umatrix based on resulting ESOM is calculated and returned

Details

On a toroid grid, opposing borders are connected.

Value

List with

BestMatches

BestMatches of datapoints

Weights

Trained weights

Lines

Height of grid

Columns

Width of grid

Toroid

TRUE if grid is a toroid

JumpingDataPointsHist

Nr of DataPoints that jumped to a different BestMatch in every epoch

References

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.

Examples

data('Hepta')
res=esomTrain(Hepta$Data, Key = 1:nrow(Hepta$Data))