Openjij/openjijtest.py

import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations
import math
import openjij as oj
import random

# ============================
# 1. KDE 函數
# ============================
def kde_1d(samples, num_points=200):
    if len(samples) == 0:
        return np.array([]), np.array([])
    
    xs = np.linspace(min(samples), max(samples), num_points)
    n = len(samples)
    if n < 2:
        return xs, np.zeros_like(xs)
    
    std = np.std(samples)
    if std == 0:
        std = 1.0
        
    h = 1.06 * std * (n ** (-1/5))
    ys = []
    inv_sqrt_2pi = 1.0 / math.sqrt(2 * math.pi)

    for x in xs:
        s = 0.0
        for xi in samples:
            z = (x - xi) / h
            s += math.exp(-0.5 * z * z) * inv_sqrt_2pi
        ys.append(s / (n * h))

    return xs, np.array(ys)


# ============================
# 2. route diversity matrix（支援可變長度路徑）
# ============================
def route_diversity_matrix(routes, N):
    """
    routes: list of routes，每條 route 是城市序列，例如 [0, 2, 5, 1, 9]
    我們只用前 N 個位置來統計（超過 N 的部分忽略）
    """
    freq = np.zeros((N, N), dtype=float)
    valid_routes = 0

    for route in routes:
        if len(route) == 0:
            continue
        valid_routes += 1
        for t in range(min(len(route), N)):
            city = route[t]
            if 0 <= city < N:
                freq[city, t] += 1

    if valid_routes > 0:
        freq /= valid_routes

    return freq


# ============================
# 3. MTSP 變數編碼：x[k,i,p] → index
# ============================
def idx_mtsp(k, i, p, N):
    """ x[k,i,p] → 1D index """
    return k * (N * N) + i * N + p


# ============================
# 4. 建立 2-UAV M-TSP QUBO（城市數不固定）
# ============================
def build_mtsp_qubo_variable(D, penalty=20.0, big_penalty=9999.0):
    """
    - 城市: 0..N-1，其中 0 為 depot
    - 兩台 UAV: k = 0,1
    - 每台 UAV 有 N 個 slot: p = 0..N-1
    - 變數: x_{k,i,p}
    QUBO包含：
      距離成本 + 位置約束 + 城市約束 + 起點約束
    """
    D = np.asarray(D)
    N = D.shape[0]
    Q = {}

    def addQ(u, v, w):
        if w == 0:
            return
        if u > v:
            u, v = v, u
        Q[(u, v)] = Q.get((u, v), 0.0) + w

    # ---- 距離成本 ----
    for k in range(2):
        for p in range(N):
            q = (p + 1) % N
            for i in range(N):
                for j in range(N):
                    dij = D[i, j]
                    if dij == 0:
                        continue
                    u = idx_mtsp(k, i, p, N)
                    v = idx_mtsp(k, j, q, N)
                    addQ(u, v, dij)

    # ---- 位置約束：每 slot 只能一個城市 ----
    for k in range(2):
        for p in range(N):
            vars_pos = [idx_mtsp(k, i, p, N) for i in range(N)]

            for u in vars_pos:
                addQ(u, u, -penalty)

            for a in range(N):
                for b in range(a+1, N):
                    addQ(vars_pos[a], vars_pos[b], 2*penalty)

    # ---- 城市約束：1..N-1 每城恰好一次 ----
    for i in range(1, N):
        vars_city = []
        for k in range(2):
            for p in range(N):
                vars_city.append(idx_mtsp(k, i, p, N))

        for u in vars_city:
            addQ(u, u, -penalty)

        L = len(vars_city)
        for a in range(L):
            for b in range(a+1, L):
                addQ(vars_city[a], vars_city[b], 2*penalty)

    # ---- 起點約束：每台 UAV 的 p=0 必須是 city 0 ----
    for k in range(2):
        for i in range(1, N):
            addQ(idx_mtsp(k, i, 0, N), idx_mtsp(k, i, 0, N), big_penalty)
        addQ(idx_mtsp(k, 0, 0, N), idx_mtsp(k, 0, 0, N), -big_penalty)

    return Q, N


# ============================
# 5. 解碼 slot 序列（不修剪）
# ============================
def decode_slots(sample, N):
    """
    回傳：
      u1_slots, u2_slots
    各自長度 N，裡面是城市 index（可能重複，也可能很多 0）
    """
    slots = []
    for k in range(2):
        row = []
        for p in range(N):
            chosen = 0  # 預設當作待在 depot
            for i in range(N):
                if sample.get(idx_mtsp(k, i, p, N), 0) == 1:
                    chosen = i
                    break
            row.append(chosen)
        slots.append(row)
    return slots[0], slots[1]


# ============================
# 6. 根據 slots 做 repair → 產生合法 uav1 / uav2 path
# ============================
def repair_routes_from_slots(u1_slots, u2_slots, N):
    """
    依據 slots 傾向資訊，把城市分配給兩台 UAV，
    並用暴力 TSP 在各自子集合裡找最短路徑，確保：
      - 每個城市 1..N-1 被恰好一台 UAV 拜訪
      - 兩台 UAV 路徑皆為 0 -> ... -> 0（成本計算時處理）
    """

    # 統計每個城市在兩台 UAV slots 中出現次數
    count1 = [0]*N
    count2 = [0]*N
    for c in u1_slots:
        if 0 <= c < N:
            count1[c] += 1
    for c in u2_slots:
        if 0 <= c < N:
            count2[c] += 1

    # 分配城市：看誰出現次數多，平手則讓城市較少的一邊拿
    assign1 = []
    assign2 = []
    for city in range(1, N):  # city 0 = depot 不分配
        if count1[city] > count2[city]:
            assign1.append(city)
        elif count2[city] > count1[city]:
            assign2.append(city)
        else:
            # 平手 → 給目前負載較少的 UAV
            if len(assign1) <= len(assign2):
                assign1.append(city)
            else:
                assign2.append(city)

    return sorted(assign1), sorted(assign2)


# ============================
# 7. 單 UAV 成本 & 子集合最佳排列
# ============================
def uav_cost(path, D):
    if not path:
        return 0.0
    path = [c for c in path if 0 <= c < len(D)]
    if len(path) == 0:
        return 0.0

    cost = D[0, path[0]]
    for i in range(len(path)-1):
        cost += D[path[i], path[i+1]]
    cost += D[path[-1], 0]
    return float(cost)


def best_order_for_cities(cities, D):
    """
    對給定 city set，暴力搜尋最短 0 -> perm -> 0 的路徑
    cities: list of city index (1..N-1)
    """
    if len(cities) <= 1:
        return list(cities), uav_cost(list(cities), D)

    best_perm = None
    best_cost = float('inf')
    for perm in permutations(cities):
        c = uav_cost(list(perm), D)
        if c < best_cost:
            best_cost = c
            best_perm = list(perm)
    return best_perm, best_cost


# ============================
# 8. 利用 QUBO 取樣 + repair 得到合法解
# ============================
def sample_and_repair(sampler, Q, N, D):
    """
    一次取樣 + repair，即可保證得到合法 2-UAV M-TSP 解。
    不需要反覆重試。
    """
    result = sampler.sample_qubo(Q, num_reads=30)
    sample = result.first.sample

    # 1) 先解出 slots
    u1_slots, u2_slots = decode_slots(sample, N)

    # 2) 依 slots 傾向分配城市給兩台 UAV
    assign1, assign2 = repair_routes_from_slots(u1_slots, u2_slots, N)

    # 3) 各自子集合內找最短路徑
    u1_path, c1 = best_order_for_cities(assign1, D)
    u2_path, c2 = best_order_for_cities(assign2, D)

    total = c1 + c2

    return u1_path, u2_path, c1, c2, total



# ============================
# 9. Classic 2-UAV MTSP 暴力搜尋（全域最佳）
# ============================
def classic_mtsp_two_uav_bruteforce(D):
    N = D.shape[0]
    cities = list(range(1, N))
    best_total = float('inf')
    best_u1, best_u2 = None, None
    best_route = None

    def cost_of(path):
        return uav_cost(path, D)

    for perm in permutations(cities):
        for cut in range(len(perm)+1):
            u1 = list(perm[:cut])
            u2 = list(perm[cut:])
            total = cost_of(u1) + cost_of(u2)
            if total < best_total:
                best_total = total
                best_u1 = u1
                best_u2 = u2
                best_route = [0] + list(perm)

    return best_u1, best_u2, best_total, best_route


# ============================
# 10. 共用：計算兩 UAV 總成本
# ============================
def compute_two_uav_cost(uav1_path, uav2_path, D):
    c1 = uav_cost(uav1_path, D)
    c2 = uav_cost(uav2_path, D)
    return c1 + c2, c1, c2


# ============================
# 11. SA & SQA：每 run 使用 sample_and_repair
# ============================
def run_mtsp_sa_sqa(Q, N, D, NUM_RUNS=10):
    sa_costs = []
    sa_routes = []
    sa_u1_list = []
    sa_u2_list = []
    sa_uav_costs = []   # store tuple (c1, c2)

    sqa_costs = []
    sqa_routes = []
    sqa_u1_list = []
    sqa_u2_list = []
    sqa_uav_costs = []  # store tuple (c1, c2)

    sa = oj.SASampler()
    sqa = oj.SQASampler()

    print("\n🔥 Running SA (with repair, always feasible)...")
    for r in range(NUM_RUNS):
        u1, u2, c1, c2, total = sample_and_repair(sa, Q, N, D)

        full_route = [0] + u1 + u2

        print(f"SA Run {r+1:02d}: UAV1={u1}, UAV2={u2}, "
              f"UAV1_cost={c1:.2f}, UAV2_cost={c2:.2f}, Total={total:.2f}")

        sa_costs.append(total)
        sa_routes.append(full_route)
        sa_u1_list.append(u1)
        sa_u2_list.append(u2)
        sa_uav_costs.append((c1, c2))  # <-- store both costs

    print("\n🔥 Running SQA (with repair, always feasible)...")
    for r in range(NUM_RUNS):
        u1, u2, c1, c2, total = sample_and_repair(sqa, Q, N, D)

        full_route = [0] + u1 + u2

        print(f"SQA Run {r+1:02d}: UAV1={u1}, UAV2={u2}, "
              f"UAV1_cost={c1:.2f}, UAV2_cost={c2:.2f}, Total={total:.2f}")

        sqa_costs.append(total)
        sqa_routes.append(full_route)
        sqa_u1_list.append(u1)
        sqa_u2_list.append(u2)
        sqa_uav_costs.append((c1, c2))  # <-- store both costs

    sa_results = (sa_costs, sa_routes, sa_u1_list, sa_u2_list, sa_uav_costs)
    sqa_results = (sqa_costs, sqa_routes, sqa_u1_list, sqa_u2_list, sqa_uav_costs)

    return sa_results, sqa_results

# ============================
# 12. 綜合分析圖表（你指定的版本）
# ============================
def create_comprehensive_charts(sa_results, sqa_results, classic_results, D, N):
    """創建綜合分析圖表"""
    fig = plt.figure(figsize=(18, 12))
    
    # 解包結果
    sa_costs, sa_routes, sa_uav1, sa_uav2, sa_uav_costs = sa_results
    sqa_costs, sqa_routes, sqa_uav1, sqa_uav2, sqa_uav_costs = sqa_results
    classic_uav1, classic_uav2, classic_cost, classic_route = classic_results
    
    # ---------------------------------------------------------
    # 1. MTSP 成本分布比較 (左上) - 系統總成本
    # ---------------------------------------------------------
    ax1 = plt.subplot(2, 3, 1)
    bins = range(int(min(sa_costs + sqa_costs)) - 1,
                 int(max(sa_costs + sqa_costs)) + 2)
    ax1.hist(sa_costs, bins=bins, alpha=0.6, label="SA Total", density=True, color='lightblue')
    ax1.hist(sqa_costs, bins=bins, alpha=0.6, label="SQA Total", density=True, color='lightcoral')
    
    # 添加KDE
    if len(sa_costs) > 1:
        xs_sa, ys_sa = kde_1d(sa_costs)
        ax1.plot(xs_sa, ys_sa, label="SA KDE", color='blue', linewidth=2)
    if len(sqa_costs) > 1:
        xs_sqa, ys_sqa = kde_1d(sqa_costs)
        ax1.plot(xs_sqa, ys_sqa, label="SQA KDE", color='red', linewidth=2)
    
    ax1.axvline(x=classic_cost, color='green', linestyle='--', 
                label=f'Classic: {classic_cost}', linewidth=2)
    ax1.set_title("Total System Cost Distribution")
    ax1.set_xlabel("Total Cost (UAV1 + UAV2)")
    ax1.set_ylabel("Density")
    ax1.legend()
    ax1.grid(True, alpha=0.3)
    
    # ---------------------------------------------------------
    # 2. 單一 UAV 成本分布比較 (右上) - 修正為「個別 UAV」視角
    # ---------------------------------------------------------
    ax2 = plt.subplot(2, 3, 2)
    
    # 展平數據：將 [(c1, c2), (c1, c2)...] 變成 [c1, c2, c1, c2...]
    # 這樣可以看到「單台無人機」通常飛多遠
    sa_single_costs = [c for costs in sa_uav_costs for c in costs]
    sqa_single_costs = [c for costs in sqa_uav_costs for c in costs]

    # 計算 classic 的單機成本
    c1_classic = uav_cost(classic_uav1, D)
    c2_classic = uav_cost(classic_uav2, D)

    # 設定 bins
    all_single = sa_single_costs + sqa_single_costs
    uav_bins = range(int(min(all_single)) - 1, int(max(all_single)) + 2)

    ax2.hist(sa_single_costs, bins=uav_bins, alpha=0.6, label="SA Single UAV", density=True, color='skyblue')
    ax2.hist(sqa_single_costs, bins=uav_bins, alpha=0.6, label="SQA Single UAV", density=True, color='salmon')

    ax2.axvline(x=c1_classic, color='darkgreen', linestyle=':', label='Classic UAV1')
    ax2.axvline(x=c2_classic, color='limegreen', linestyle=':', label='Classic UAV2')
    
    ax2.set_title("Individual UAV Cost Distribution")
    ax2.set_xlabel("Cost per UAV")
    ax2.set_ylabel("Density")
    ax2.legend()
    ax2.grid(True, alpha=0.3)
    
    # ---------------------------------------------------------
    # 3. 箱型圖比較 (中上) - 修正重點
    # ---------------------------------------------------------
    ax3 = plt.subplot(2, 3, 3)

    # 數據 1 & 2: 系統總成本 (Total Cost)
    # 數據 3 & 4: 單機成本 (Individual UAV Cost) - 使用上面展平的數據
    # 這樣 Box Plot 會有明顯的高低落差（總成本高，單機成本低）
    
    box_data = [sa_costs, sqa_costs, sa_single_costs, sqa_single_costs]

    bp = ax3.boxplot(
        box_data,
        labels=["SA\nTotal", "SQA\nTotal", "SA\nSingle", "SQA\nSingle"],
        patch_artist=True,
        showmeans=True
    )

    colors = ['lightblue', 'lightcoral', 'skyblue', 'salmon']
    for patch, color in zip(bp['boxes'], colors):
        patch.set_facecolor(color)

    ax3.set_title("Cost Comparison: Total vs Single UAV")
    ax3.set_ylabel("Cost")
    ax3.grid(True, alpha=0.3)
    
    # ---------------------------------------------------------
    # 4. Route diversity heatmap - SA (左下)
    # ---------------------------------------------------------
    ax4 = plt.subplot(2, 3, 4)
    sa_mat = route_diversity_matrix(sa_routes, N)
    im1 = ax4.imshow(sa_mat, aspect='auto', origin='lower', cmap='viridis')
    ax4.set_title("SA Route Diversity")
    ax4.set_xlabel("Position")
    ax4.set_ylabel("City")
    plt.colorbar(im1, ax=ax4, shrink=0.8)
    
    # ---------------------------------------------------------
    # 5. Route diversity heatmap - SQA (右下)
    # ---------------------------------------------------------
    ax5 = plt.subplot(2, 3, 5)
    sqa_mat = route_diversity_matrix(sqa_routes, N)
    im2 = ax5.imshow(sqa_mat, aspect='auto', origin='lower', cmap='viridis')
    ax5.set_title("SQA Route Diversity")
    ax5.set_xlabel("Position")
    ax5.set_ylabel("City")
    plt.colorbar(im2, ax=ax5, shrink=0.8)
    
    # ---------------------------------------------------------
    # 6. 最佳路徑可視化 (中下)
    # ---------------------------------------------------------
    ax6 = plt.subplot(2, 3, 6)

    # 找出 SA 或 SQA 的最佳解
    best_sa_idx = sa_costs.index(min(sa_costs))
    best_sqa_idx = sqa_costs.index(min(sqa_costs))

    best_sa_u1 = sa_uav1[best_sa_idx]
    best_sa_u2 = sa_uav2[best_sa_idx]

    best_sqa_u1 = sqa_uav1[best_sqa_idx]
    best_sqa_u2 = sqa_uav2[best_sqa_idx]

    # 用成本較好的那組
    if min(sa_costs) <= min(sqa_costs):
        u1 = best_sa_u1
        u2 = best_sa_u2
        route_name = "SA"
        route_cost = min(sa_costs)
    else:
        u1 = best_sqa_u1
        u2 = best_sqa_u2
        route_name = "SQA"
        route_cost = min(sqa_costs)

    # --- 繪製城市 ---
    angles = np.linspace(0, 2*np.pi, N, endpoint=False)
    x_pos = np.cos(angles)
    y_pos = np.sin(angles)

    ax6.scatter(x_pos, y_pos, s=200, c='red', zorder=5)
    for i, (x, y) in enumerate(zip(x_pos, y_pos)):
        ax6.annotate(str(i), (x, y), ha='center', va='center',
                     fontsize=10, fontweight='bold', color='white', zorder=6)

    # --- UAV1: 藍色 ---
    if len(u1) > 0:
        pts = [0] + u1 + [0]    # 加入 depot
        xs = [x_pos[c] for c in pts]
        ys = [y_pos[c] for c in pts]
        ax6.plot(xs, ys, '-o', color='blue', linewidth=2, alpha=0.8,
                 label=f'UAV1 ({len(u1)})')

    # --- UAV2: 橘色 ---
    if len(u2) > 0:
        pts = [0] + u2 + [0]    # 加入 depot
        xs = [x_pos[c] for c in pts]
        ys = [y_pos[c] for c in pts]
        ax6.plot(xs, ys, '-o', color='orange', linewidth=2, alpha=0.8,
                 label=f'UAV2 ({len(u2)})')

    ax6.set_title(f"Best {route_name} MTSP Solution\nTotal Cost: {route_cost:.2f}")
    ax6.set_xlim(-1.3, 1.3)
    ax6.set_ylim(-1.3, 1.3)
    ax6.set_aspect('equal')
    ax6.axis('off')
    ax6.legend(loc='upper right')

    plt.tight_layout()
    plt.savefig("mtsp_two_uav_comprehensive_analysis.png", dpi=300, bbox_inches='tight')
    print(f"\n📊 綜合分析圖表已保存: mtsp_two_uav_comprehensive_analysis.png")
    plt.show()

# ============================
# 13. main：整合執行
# ============================
if __name__ == "__main__":
    # 距離矩陣：10 城市
    D = np.array([
        [0, 2, 8, 5, 7, 6, 3, 9, 4, 2],
        [2, 0, 5, 7, 3, 8, 4, 6, 9, 1],
        [8, 5, 0, 2, 6, 7, 3, 4, 1, 5],
        [5, 7, 2, 0, 4, 3, 8, 6, 2, 7],
        [7, 3, 6, 4, 0, 5, 9, 2, 7, 3],
        [6, 8, 7, 3, 5, 0, 4, 7, 9, 6],
        [3, 4, 3, 8, 9, 4, 0, 5, 6, 2],
        [9, 6, 4, 6, 2, 7, 5, 0, 8, 3],
        [4, 9, 1, 2, 7, 9, 6, 8, 0, 5],
        [2, 1, 5, 7, 3, 6, 2, 3, 5, 0]
    ])
    N = D.shape[0]

    print("🚀 2-UAV M-TSP (Quantum + Repair vs Classic) 開始運行...\n")
    print(f"Distance Matrix ({N}x{N}):")
    print(D)

    # 建立 QUBO
    Q, _ = build_mtsp_qubo_variable(D, penalty=20.0)

    # SA / SQA，每 run 一定是合法解
    NUM_RUNS = 10
    sa_results, sqa_results = run_mtsp_sa_sqa(Q, N, D, NUM_RUNS=NUM_RUNS)

    # Classic 暴力全域最佳
    print("\n🎯 Running Classic 2-UAV MTSP (Bruteforce ALL solutions)...")
    classic_uav1, classic_uav2, classic_total_cost, classic_route = classic_mtsp_two_uav_bruteforce(D)
    print(f"Classic UAV1={classic_uav1}, UAV2={classic_uav2}, Cost={classic_total_cost}")
    print(f"Classic merged route (0 + perm): {classic_route}")

    # Summary
    sa_costs, _, _, _, _ = sa_results
    sqa_costs, _, _, _, _ = sqa_results
    print("\n📊 Result Summary:")
    print(f"SA   best cost = {min(sa_costs):.2f}, mean = {np.mean(sa_costs):.2f}")
    print(f"SQA  best cost = {min(sqa_costs):.2f}, mean = {np.mean(sqa_costs):.2f}")
    print(f"Classic global optimum = {classic_total_cost:.2f}")

    # 畫圖
    classic_results = (classic_uav1, classic_uav2, classic_total_cost, classic_route)
    print("\n🎨 Creating comprehensive analysis charts...")
    create_comprehensive_charts(sa_results, sqa_results, classic_results, D, N)

    print("\n🏁 Done.")