som.init(data, xdim, ydim, init="linear")
som(data, xdim, ydim, init="linear", alpha=NULL, alphaType="inverse",
neigh="gaussian", topol="rect", radius=NULL, rlen=NULL, err.radius=1,
inv.alp.c=NULL)
som.train(data, code, xdim, ydim, alpha=NULL, alphaType="inverse",
neigh="gaussian", topol="rect", radius=NULL, rlen=NULL, err.radius=1, inv.alp.c=NULL)
som.update(obj, alpha = NULL, radius = NULL, rlen = NULL, err.radius =
1, inv.alp.c = NULL)
som.project(obj, newdat)
"sample"
uses a radom sample from the data;
"random"
uses random draws from N(0,1);
"linear"
uses the linear grids upon the first two principle
components directin.
"linear"
) and inverse-time type
function ("inverse"
).
"bubble"
"gaussian"
"hexa"
"rect"
"som"
representing the fit, which is a list
containing the following components:data(yeast)
yeast <- yeast[, -c(1, 11)]
yeast.f <- filtering(yeast)
yeast.f.n <- normalize(yeast.f)
foo <- som(yeast.f.n, xdim=5, ydim=6)
foo <- som(yeast.f.n, xdim=5, ydim=6, topol="hexa", neigh="gaussian")
plot(foo)
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