Data commit

Task/Percolation-Mean-cluster-density/11l/percolation-mean-cluster-density.11l

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+UInt32 seed = 0
+F nonrandom()
+   :seed = 1664525 * :seed + 1013904223
+   R (:seed >> 16) / Float(FF'FF)
+
+V nn = 15
+V tt = 5
+V pp = 0.5
+V NotClustered = 1
+V Cell2Char = ‘ #abcdefghijklmnopqrstuvwxyz’
+V NRange = [4, 64, 256, 1024, 4096]
+
+F newGrid(n, p)
+   R (0 .< n).map(i -> (0 .< @n).map(i -> Int(nonrandom() < @@p)))
+
+F walkMaze(&grid, m, n, idx) -> N
+   grid[n][m] = idx
+   I n < grid.len - 1 & grid[n + 1][m] == NotClustered
+      walkMaze(&grid, m, n + 1, idx)
+   I m < grid[0].len - 1 & grid[n][m + 1] == NotClustered
+      walkMaze(&grid, m + 1, n, idx)
+   I m > 0 & grid[n][m - 1] == NotClustered
+      walkMaze(&grid, m - 1, n, idx)
+   I n > 0 & grid[n - 1][m] == NotClustered
+      walkMaze(&grid, m, n - 1, idx)
+
+F clusterCount(&grid)
+   V walkIndex = 1
+   L(n) 0 .< grid.len
+      L(m) 0 .< grid[0].len
+         I grid[n][m] == NotClustered
+            walkIndex++
+            walkMaze(&grid, m, n, walkIndex)
+   R walkIndex - 1
+
+F clusterDensity(n, p)
+   V grid = newGrid(n, p)
+   R clusterCount(&grid) / Float(n * n)
+
+F print_grid(grid)
+   L(row) grid
+      print(L.index % 10, end' ‘) ’)
+      L(cell) row
+         print(‘ ’Cell2Char[cell], end' ‘’)
+      print()
+
+V grid = newGrid(nn, 0.5)
+print(‘Found ’clusterCount(&grid)‘ clusters in this ’nn‘ by ’nn" grid\n")
+print_grid(grid)
+print()
+
+L(n) NRange
+   V sum = 0.0
+   L 0 .< tt
+      sum += clusterDensity(n, pp)
+   V sim = sum / tt
+   print(‘t = #.  p = #.2  n = #4  sim = #.5’.format(tt, pp, n, sim))