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

Construct GNG object. Can be used to train offline, or online.

GNG(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 = -1,
  min.improvement = 0.001, lambda = 200, verbosity = 0, seed = -1,
  k = NULL)

`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 won't add new nodes)
`eps.n`	How strongly adapt neighbour node. Default `0.0006`
`eps.w`	How strongly adapt winning node. Default `0.05`
`max.edge.age`	Maximum edge age. Decrease to increase speed of change of graph topology. Default `200`
`train.online`	default FALSE. If used will run in online fashion
`max.iter`	Uf training offline will stop if exceedes max.iter iterations. Default `200`
`dim`	Used for training online, specifies training example size
`min.improvement`	Used for offline (default) training. Controls stopping criterion, decrease if training stops too early. Default `1e-3`
`lambda`	Every lambda iteration is added new vertex. Default 200
`verbosity`	How verbose should the process be, as integer from [0,6], default: `0`
`seed`	Seed for internal randomization
`k`	Utility constant, by default turned off. Good value is 1.3. Constant controlling speed of erasing obsolete nodes, see http://sund.de/netze/applets/gng/full/tex/DemoGNG/node20.html

## Not run: 
X <- gng.preset.sphere(100)
y <- round(runif(100))
# Train in an offline manner
gng <- GNG(X, labels=y, max.nodes=20)
# Plot
plot(gng)

# Train in an online manner with utility (erasing obsolete nodes)
gng <- GNG(max.nodes=20, train.online=TRUE, k=1.3, dim=3)
insertExamples(gng, X, labels=y)
run(gng)
Sys.sleep(10)
terminate(gng)
# Plot
plot(gng)

## End(Not run)