optimized-gng: Constructor of Optimized GrowingNeuralGas object.
In gmum.r: GMUM Machine Learning Group Package

Description Usage Arguments Examples

Construct simplified and optimized GNG object. Can be used to train offline, or online. Data dimensionality shouldn't be too big, if it is consider using dimensionality reduction techniques.

OptimizedGNG(x = NULL, labels = c(), beta = 0.99, alpha = 0.5,
  max.nodes = 1000, eps.n = 6e-04, eps.w = 0.05, max.edge.age = 200,
  train.online = FALSE, max.iter = 200, dim = 0,
  min.improvement = 0.001, lambda = 200, verbosity = 0, seed = -1,
  value.range = c(0, 1))

`x`	Passed data (matrix of data.frame) for offline training
`labels`	Every example can be associated with labels that are added to nodes later. By default empty
`beta`	Decrease the error variables of all node nodes by this fraction (forgetting rate). Default 0.99
`alpha`	Decrease the error variables of the nodes neighboring to the newly inserted node by this fraction. Default 0.5
`max.nodes`	Maximum number of nodes (after reaching this size it will continue running, but new noes won't be added)
`eps.n`	Strength of adaptation of neighbour node. Default `0.0006`
`eps.w`	Strength of adaptation of winning node. Default `0.05`
`max.edge.age`	Maximum edge age. Decrease to increase speed of change of graph topology. Default `200`
`train.online`	If used will run in online fashion. Default `FALSE`
`max.iter`	If training offline will stop if exceedes max.iter iterations. Default `200`
`dim`	Used for training online, specifies dataset example dimensionality
`min.improvement`	Used for offline (default) training. Controls stopping criterion, decrease if training stops too early. Default `1e-3`
`lambda`	New vertex is added every lambda iterations. Default 200
`verbosity`	How verbose should the process be, as integer from [0,6], default: `0`
`seed`	Seed for internal randomization
`value.range`	All example features should be in this range, required for optimized version of the algorithm. Default `(0,1)`

## Not run: 
# Train online optimizedGNG. All values in this dataset are in the range (-4.3, 4.3)
X <- gng.preset.sphere(100)
gng <- OptimizedGNG(train.online = TRUE, value.range=c(min(X), max(X)), dim=3, max.nodes=20)
insertExamples(gng, X)
run(gng)
Sys.sleep(10)
pause(gng)

## End(Not run)