continue preparing for concurrency

probppl.go

@@ -135,29 +135,25 @@ func main() {
 
 	var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
 
-	n_dists := 10_000
+	n_dists := 10
 	var dists = make([]IntProbsWeights, n_dists)
 
-	sum_weights := int64(0)
-	for i := 0; i < 10_000; i++ {
-
+	for i := 0; i < n_dists; i++ {
 		people_known_distribution := generatePeopleKnownDistribution(r)
-		// fmt.Println(people_known_distribution)
 		result := getUnnormalizedBayesianUpdateForDistribution(people_known_distribution, r)
 		if i%10 == 0 {
-			fmt.Printf("%d/10000\n", i)
+			fmt.Printf("%d/%d\n", i, n_dists)
 		}
 		if result > 0 {
-			// fmt.Println(people_known_distribution)
-			// fmt.Println(result)
 			dists[i] = IntProbsWeights{IntProbs: people_known_distribution, w: result}
 		}
-		sum_weights += result
-		// fmt.Println(result)
 	}
-	// fmt.Println(dists)
 
 	// Now calculate the posterior
+	sum_weights := int64(0)
+	for _, dist := range dists {
+		sum_weights += dist.w
+	}
 
 	for i := int64(16); i <= 2048; i *= 2 {
 		p := 0.0