Refactor random functions

utils/random/weighted_random_chooser.go

@@ -0,0 +1,65 @@
+package random
+
+import (
+	"errors"
+)
+
+type WeightedChooser struct {
+	entries     []interface{}
+	weights     []int
+	totalWeight int
+}
+
+func NewWeightedRandomChooser() *WeightedChooser {
+	return &WeightedChooser{}
+}
+
+func (w *WeightedChooser) Add(value interface{}, weight int) {
+	w.entries = append(w.entries, value)
+	w.weights = append(w.weights, weight)
+	w.totalWeight += weight
+}
+
+// GetAndRemove choose a random entry based on their weights, and removes it from the list
+func (w *WeightedChooser) GetAndRemove() (interface{}, error) {
+	if w.totalWeight == 0 {
+		return nil, errors.New("cannot choose from zero weight")
+	}
+	i, err := w.weightedChoice()
+	if err != nil {
+		return nil, err
+	}
+	entry := w.entries[i]
+	w.Remove(i)
+	return entry, nil
+}
+
+// Based on https://eli.thegreenplace.net/2010/01/22/weighted-random-generation-in-python/
+func (w *WeightedChooser) weightedChoice() (int, error) {
+	if w.totalWeight == 0 {
+		return 0, errors.New("no choices available")
+	}
+	rnd := Int64(int64(w.totalWeight))
+	for i, weight := range w.weights {
+		rnd -= int64(weight)
+		if rnd < 0 {
+			return i, nil
+		}
+	}
+	return 0, errors.New("internal error - code should not reach this point")
+}
+
+func (w *WeightedChooser) Remove(i int) {
+	w.totalWeight -= w.weights[i]
+
+	w.weights[i] = w.weights[len(w.weights)-1]
+	w.weights = w.weights[:len(w.weights)-1]
+
+	w.entries[i] = w.entries[len(w.entries)-1]
+	w.entries[len(w.entries)-1] = nil
+	w.entries = w.entries[:len(w.entries)-1]
+}
+
+func (w *WeightedChooser) Size() int {
+	return len(w.entries)
+}