Nothing
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Function:
# ssa20() and ssa30() functions provide a labeling of
# anisotropic 2D & 3D clusters with von Neumann neighborhood.
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Arguments:
# x - linear dimension of the percolation lattice;
# p - vector of p[1:6] values, distributed by lattice
# directions: -x, +x, -y, +y, -z, +z;
# set - vector of linear indexes of starting sites subset;
# all - trigger "Do we mark all starting sites or only accessible?";
# shape - vector of shape parameters of beta-distributed random variables,
# weighting the percolation lattice sites.
# Variables:
# e - linear indexes of sites from 2D & 3D von Neumann neighborhood.
# Value:
# acc - labeled accessibility matrix for the percolation lattice.
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
ssa20 <- function(x=33,
p=runif(4, max=0.9),
set=(x^2+1)/2, all=TRUE,
shape=c(1,1)) {
e <- c(-1, 1,-x, x)
acc <- array(rbeta(x^2,shape[1],shape[2]), rep(x,2))
if (all) acc[set] <- 2
else acc[set <- set[acc[set] < mean(p)]] <- 2
acc[c(1,x),] <- acc[,c(1,x)] <- 1
repeat {
acc[set <- unique(c(
set[acc[set+e[1]] < p[1]] +e[1],
set[acc[set+e[2]] < p[2]] +e[2],
set[acc[set+e[3]] < p[3]] +e[3],
set[acc[set+e[4]] < p[4]] +e[4]))] <- 2
if (length(set) < 1) break
}
return(acc)
}
ssa30 <- function(x=33,
p=runif(6, max=0.6),
set=(x^3+1)/2, all=TRUE,
shape=c(1,1)) {
e <- c(-1, 1,-x, x,-x^2, x^2)
acc <- array(rbeta(x^3,shape[1],shape[2]), rep(x,3))
if (all) acc[set] <- 2
else acc[set <- set[acc[set] < mean(p)]] <- 2
acc[c(1,x),,] <- acc[,c(1,x),] <- acc[,,c(1,x)] <- 1
repeat {
acc[set <- unique(c(
set[acc[set+e[1]] < p[1]] +e[1],
set[acc[set+e[2]] < p[2]] +e[2],
set[acc[set+e[3]] < p[3]] +e[3],
set[acc[set+e[4]] < p[4]] +e[4],
set[acc[set+e[5]] < p[5]] +e[5],
set[acc[set+e[6]] < p[6]] +e[6]))] <- 2
if (length(set) < 1) break
}
return(acc)
}
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