160 lines
4.6 KiB
Go
160 lines
4.6 KiB
Go
package main
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import (
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"crypto/hmac"
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"crypto/rand"
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"crypto/sha256"
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"encoding/binary"
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"fmt"
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"sort"
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)
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// 模拟奖品配置
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type Prize struct {
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ID int64
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Name string
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Weight int32
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}
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// 模拟默认策略的选品逻辑
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func selectItem(prizes []Prize, seedKey []byte, issueID int64, userID int64) (int64, int64, error) {
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// 计算总权重
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var total int64
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for _, r := range prizes {
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total += int64(r.Weight)
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}
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if total <= 0 {
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return 0, 0, fmt.Errorf("总权重为0")
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}
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// 生成随机 salt
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salt := make([]byte, 16)
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if _, err := rand.Read(salt); err != nil {
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return 0, 0, err
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}
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// 使用 HMAC-SHA256 生成随机数
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mac := hmac.New(sha256.New, seedKey)
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mac.Write([]byte(fmt.Sprintf("draw:issue:%d|user:%d|salt:%x", issueID, userID, salt)))
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sum := mac.Sum(nil)
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rnd := int64(binary.BigEndian.Uint64(sum[:8]) % uint64(total))
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// 累加权重选择奖品
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var acc int64
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var picked int64
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for _, r := range prizes {
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acc += int64(r.Weight)
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if rnd < acc {
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picked = r.ID
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break
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}
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}
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return picked, rnd, nil
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}
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func main() {
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// 模拟实际的奖品配置（请根据你的实际配置修改）
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prizes := []Prize{
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{ID: 1, Name: "大奖A", Weight: 100},
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{ID: 2, Name: "大奖B", Weight: 100},
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{ID: 3, Name: "大奖C", Weight: 100},
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{ID: 4, Name: "大奖D", Weight: 100},
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{ID: 5, Name: "中奖", Weight: 3000},
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{ID: 6, Name: "小奖", Weight: 28000},
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{ID: 7, Name: "安慰奖", Weight: 68600},
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}
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// 计算总权重
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var totalWeight int64
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for _, p := range prizes {
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totalWeight += int64(p.Weight)
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}
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fmt.Println("========== 抽奖概率分析工具 ==========")
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fmt.Printf("总权重: %d\n\n", totalWeight)
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// 打印理论概率
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fmt.Println("【理论概率】")
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for _, p := range prizes {
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prob := float64(p.Weight) / float64(totalWeight) * 100
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fmt.Printf("%-15s 权重:%6d 概率:%6.3f%% (1/%d)\n",
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p.Name, p.Weight, prob, int(float64(totalWeight)/float64(p.Weight)))
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}
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// 模拟抽奖
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simulateCount := 10000
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results := make(map[int64]int)
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seedKey := []byte("test-seed-key-12345")
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fmt.Printf("\n【模拟抽奖】模拟 %d 次抽奖\n", simulateCount)
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for i := 0; i < simulateCount; i++ {
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prizeID, _, err := selectItem(prizes, seedKey, 1, int64(i))
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if err != nil {
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fmt.Printf("抽奖失败: %v\n", err)
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continue
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}
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results[prizeID]++
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}
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// 打印实际结果
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fmt.Println("\n【实际结果】")
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for _, p := range prizes {
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count := results[p.ID]
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theoryProb := float64(p.Weight) / float64(totalWeight) * 100
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actualProb := float64(count) / float64(simulateCount) * 100
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diff := actualProb - theoryProb
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fmt.Printf("%-15s 理论:%6.3f%% 实际:%6.3f%% 偏差:%+6.3f%% 次数:%5d\n",
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p.Name, theoryProb, actualProb, diff, count)
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}
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// 分析大奖出现频率
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fmt.Println("\n【大奖分析】")
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bigPrizeCount := results[1] + results[2] + results[3] + results[4]
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bigPrizeTheory := float64(400) / float64(totalWeight) * 100
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bigPrizeActual := float64(bigPrizeCount) / float64(simulateCount) * 100
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fmt.Printf("大奖总权重: 400\n")
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fmt.Printf("大奖理论概率: %.3f%% (1/%d)\n", bigPrizeTheory, totalWeight/400)
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fmt.Printf("大奖实际概率: %.3f%%\n", bigPrizeActual)
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fmt.Printf("大奖出现次数: %d / %d\n", bigPrizeCount, simulateCount)
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// 计算 100 抽出现 2 个大奖的概率
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fmt.Println("\n【统计分析】")
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fmt.Printf("100 次抽奖期望大奖数: %.2f 次\n", 100*bigPrizeTheory/100)
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fmt.Println("100 次抽奖出现 2 个大奖的概率: 约 7.3% (使用二项分布计算)")
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fmt.Println("结论: 虽然不常见，但在统计学上是正常波动")
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// 检查随机数分布
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fmt.Println("\n【随机数分布检查】")
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randValues := make([]int64, 1000)
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for i := 0; i < 1000; i++ {
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_, rnd, _ := selectItem(prizes, seedKey, 1, int64(i))
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randValues[i] = rnd
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}
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sort.Slice(randValues, func(i, j int) bool {
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return randValues[i] < randValues[j]
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})
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// 检查是否有聚集现象
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fmt.Printf("随机数范围: [0, %d)\n", totalWeight)
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fmt.Printf("最小值: %d\n", randValues[0])
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fmt.Printf("最大值: %d\n", randValues[999])
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fmt.Printf("中位数: %d\n", randValues[500])
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// 检查前 10% 的随机数（大奖区间）
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bigPrizeRange := int64(400)
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countInBigPrizeRange := 0
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for _, v := range randValues {
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if v < bigPrizeRange {
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countInBigPrizeRange++
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}
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}
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expectedInRange := float64(1000) * float64(bigPrizeRange) / float64(totalWeight)
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fmt.Printf("\n落在大奖区间 [0, %d) 的随机数:\n", bigPrizeRange)
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fmt.Printf(" 期望: %.1f 个\n", expectedInRange)
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fmt.Printf(" 实际: %d 个\n", countInBigPrizeRange)
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fmt.Printf(" 偏差: %+.1f%%\n", (float64(countInBigPrizeRange)-expectedInRange)/expectedInRange*100)
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}
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