Data commit

Task/Percolation-Mean-cluster-density/Julia/percolation-mean-cluster-density.julia

@@ -0,0 +1,51 @@
+using Printf, Distributions
+
+newgrid(p::Float64, r::Int, c::Int=r) = rand(Bernoulli(p), r, c)
+
+function walkmaze!(grid::Matrix{Int}, r::Int, c::Int, indx::Int)
+    NOT_CLUSTERED = 1 # const
+    N, M = size(grid)
+    dirs = [[1, 0], [-1, 0], [0, 1], [0, -1]]
+    # fill cell
+    grid[r, c] = indx
+    # check for each direction
+    for d in dirs
+        rr, cc = (r, c) .+ d
+        if checkbounds(Bool, grid, rr, cc) && grid[rr, cc] == NOT_CLUSTERED
+            walkmaze!(grid, rr, cc, indx)
+        end
+    end
+end
+
+function clustercount!(grid::Matrix{Int})
+    NOT_CLUSTERED = 1 # const
+    walkind = 1
+    for r in 1:size(grid, 1), c in 1:size(grid, 2)
+        if grid[r, c] == NOT_CLUSTERED
+            walkind += 1
+            walkmaze!(grid, r, c, walkind)
+        end
+    end
+    return walkind - 1
+end
+clusterdensity(p::Float64, n::Int) = clustercount!(newgrid(p, n)) / n ^ 2
+
+function printgrid(G::Matrix{Int})
+    LETTERS = vcat(' ', '#', 'A':'Z', 'a':'z')
+    for r in 1:size(G, 1)
+        println(r % 10, ") ", join(LETTERS[G[r, :] .+ 1], ' '))
+    end
+end
+
+G = newgrid(0.5, 15)
+@printf("Found %i clusters in this %i×%i grid\n\n", clustercount!(G), size(G, 1), size(G, 2))
+printgrid(G)
+println()
+
+const nrange = 2 .^ (4:2:12)
+const p = 0.5
+const nrep = 5
+for n in nrange
+    sim = mean(clusterdensity(p, n) for _ in 1:nrep)
+    @printf("nrep = %2i p = %.2f dim = %-13s sim = %.5f\n", nrep, p, "$n × $n", sim)
+end