2016 Update

Task/Percolation-Mean-cluster-density/Go/percolation-mean-cluster-density.go

@@ -0,0 +1,98 @@
+package main
+
+import (
+    "fmt"
+    "math/rand"
+    "time"
+)
+
+var (
+    n_range = []int{4, 64, 256, 1024, 4096}
+    M       = 15
+    N       = 15
+)
+
+const (
+    p             = .5
+    t             = 5
+    NOT_CLUSTERED = 1
+    cell2char     = " #abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
+)
+
+func newgrid(n int, p float64) [][]int {
+    g := make([][]int, n)
+    for y := range g {
+        gy := make([]int, n)
+        for x := range gy {
+            if rand.Float64() < p {
+                gy[x] = 1
+            }
+        }
+        g[y] = gy
+    }
+    return g
+}
+
+func pgrid(cell [][]int) {
+    for n := 0; n < N; n++ {
+        fmt.Print(n%10, ") ")
+        for m := 0; m < M; m++ {
+            fmt.Printf(" %c", cell2char[cell[n][m]])
+        }
+        fmt.Println()
+    }
+}
+
+func cluster_density(n int, p float64) float64 {
+    cc := clustercount(newgrid(n, p))
+    return float64(cc) / float64(n) / float64(n)
+}
+
+func clustercount(cell [][]int) int {
+    walk_index := 1
+    for n := 0; n < N; n++ {
+        for m := 0; m < M; m++ {
+            if cell[n][m] == NOT_CLUSTERED {
+                walk_index++
+                walk_maze(m, n, cell, walk_index)
+            }
+        }
+    }
+    return walk_index - 1
+}
+
+func walk_maze(m, n int, cell [][]int, indx int) {
+    cell[n][m] = indx
+    if n < N-1 && cell[n+1][m] == NOT_CLUSTERED {
+        walk_maze(m, n+1, cell, indx)
+    }
+    if m < M-1 && cell[n][m+1] == NOT_CLUSTERED {
+        walk_maze(m+1, n, cell, indx)
+    }
+    if m > 0 && cell[n][m-1] == NOT_CLUSTERED {
+        walk_maze(m-1, n, cell, indx)
+    }
+    if n > 0 && cell[n-1][m] == NOT_CLUSTERED {
+        walk_maze(m, n-1, cell, indx)
+    }
+}
+
+func main() {
+    rand.Seed(time.Now().Unix())
+    cell := newgrid(N, .5)
+    fmt.Printf("Found %d clusters in this %d by %d grid\n\n",
+        clustercount(cell), N, N)
+    pgrid(cell)
+    fmt.Println()
+
+    for _, n := range n_range {
+        M = n
+        N = n
+        sum := 0.
+        for i := 0; i < t; i++ {
+            sum += cluster_density(n, p)
+        }
+        sim := sum / float64(t)
+        fmt.Printf("t=%3d p=%4.2f n=%5d sim=%7.5f\n", t, p, n, sim)
+    }
+}