Description Usage Arguments Author(s) Examples
Function to apply a pipeline of deconstruction, learning, reconstruction on a picture
1 2 3 4 5 6 | SOM.cluster(data, margin, transform = c(F), grid.x = c(3),
grid.y = c(3), topo = c("hexagonal"), toroidal = c(F),
rlen = c(100), alpha.1 = c(0.05), alpha.2 = c(0.01),
radius = c("Default"), action = c("reconstruct"),
count.successif.1 = rep(0, length(margin)),
count.successif.2 = rep(100, length(margin)), use = length(margin))
|
data |
a 3D array, create by the function f.read.image |
margin |
the margin to apply the deconstruction |
transform |
should the array be transformed after deconstruction (interesting value are: margin=3-transform=F and margin=2-transform=T) |
grid.x |
x width of the kohonen map |
grid.y |
y width of the kohonen map |
topo |
topo of the kohonen map |
toroidal |
should the map be toroidal |
rlen |
number of iteration |
alpha.1 |
learning rate begining |
alpha.2 |
learning rate end |
radius |
radius of the neighbourhood |
action |
what the process should return for the next step, choices are: reconstruct, evolve, clusterize, original, original_noirci. |
count.successif.1 |
for original_noirci what is the minimum of successive sample to be consider as sample and note noise or interband |
count.successif.2 |
for original_noirci what is the maximum of successive sample to be consider as sample and note noise or interband |
use |
use to skip a process |
Dimitri Fichou
1 2 3 4 | data <- f.read.image('www/rTLC_demopicture.JPG',format='jpeg',native=F) %>% redim.array(256)
model <- SOM.cluster(data,margin=c(3),transform=F,action='evolve')
str(model)
model$data.recon[[1]][,,1] %>% raster
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