Data commit

Task/100-prisoners/Go/100-prisoners.go

@@ -0,0 +1,67 @@
+package main
+
+import (
+    "fmt"
+    "math/rand"
+    "time"
+)
+
+// Uses 0-based numbering rather than 1-based numbering throughout.
+func doTrials(trials, np int, strategy string) {
+    pardoned := 0
+trial:
+    for t := 0; t < trials; t++ {
+        var drawers [100]int
+        for i := 0; i < 100; i++ {
+            drawers[i] = i
+        }
+        rand.Shuffle(100, func(i, j int) {
+            drawers[i], drawers[j] = drawers[j], drawers[i]
+        })
+    prisoner:
+        for p := 0; p < np; p++ {
+            if strategy == "optimal" {
+                prev := p
+                for d := 0; d < 50; d++ {
+                    this := drawers[prev]
+                    if this == p {
+                        continue prisoner
+                    }
+                    prev = this
+                }
+            } else {
+                // Assumes a prisoner remembers previous drawers (s)he opened
+                // and chooses at random from the others.
+                var opened [100]bool
+                for d := 0; d < 50; d++ {
+                    var n int
+                    for {
+                        n = rand.Intn(100)
+                        if !opened[n] {
+                            opened[n] = true
+                            break
+                        }
+                    }
+                    if drawers[n] == p {
+                        continue prisoner
+                    }
+                }
+            }
+            continue trial
+        }
+        pardoned++
+    }
+    rf := float64(pardoned) / float64(trials) * 100
+    fmt.Printf("  strategy = %-7s  pardoned = %-6d relative frequency = %5.2f%%\n\n", strategy, pardoned, rf)
+}
+
+func main() {
+    rand.Seed(time.Now().UnixNano())
+    const trials = 100000
+    for _, np := range []int{10, 100} {
+        fmt.Printf("Results from %d trials with %d prisoners:\n\n", trials, np)
+        for _, strategy := range [2]string{"random", "optimal"} {
+            doTrials(trials, np, strategy)
+        }
+    }
+}