修改代码及README
This commit is contained in:
@@ -1,99 +1,312 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import beta as beta_dist # 导入beta分布用于绘图
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import scipy.stats as stats
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from scipy.special import gammaln # 用于计算 log(Γ(x))
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# ------------------------------------------------------------------
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# 1. 设置 matplotlib 支持中文显示
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# ------------------------------------------------------------------
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try:
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plt.rcParams['font.sans-serif'] = ['SimHei'] # Windows/Linux
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plt.rcParams['axes.unicode_minus'] = False # 正常显示负号
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except Exception:
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try:
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # MacOS
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plt.rcParams['axes.unicode_minus'] = False
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except Exception:
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print("未找到中文字体，绘图可能显示异常。请安装'SimHei'或'Arial Unicode MS'字体。")
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# --- 1. 定义先验和似然函数 ---
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# ------------------------------------------------------------------
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# 2. 设定模型参数 (为了模拟您图中的效果)
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# ------------------------------------------------------------------
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n = 20 # X的试验总次数
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# 更改 alpha 和 beta 以匹配图中 ~0.72 的均值
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alpha = 8.0 # Beta分布的先验参数 alpha
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beta = 3.0 # Beta分布的先验参数 beta
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# 理论均值 E[Y] = alpha / (alpha + beta) = 8 / 11 ≈ 0.727
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theoretical_mean = alpha / (alpha + beta)
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# 定义先验参数
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# π(λ) ~ Gamma(α, β) (注意：scipy.stats.gamma用 a=shape, scale=1/rate)
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# 我们使用 α=2, β=1 (rate=1) 作为先验
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ALPHA_LAM = 2
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BETA_LAM = 1 # 这是 rate (或 1/scale)
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# MCMC (Gibbs) 抽样参数
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N_samples = 1000 # 总抽样量 N (同您图中的 N=1000)
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N_burn_in = 200 # 预估的老化期(预热期) N1
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# π(r) ~ InverseGamma(α, β) (注意：scipy.stats.invgamma用 a=shape, scale=scale)
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# 我们使用 α=2, β=1 (scale=1) 作为先验
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ALPHA_R = 2
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BETA_R = 1 # 这是 scale
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print(f"模型参数: n={n}, alpha={alpha}, beta={beta}")
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print(f"理论均值 E[Y]: {theoretical_mean:.4f}")
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print(f"抽样设置: 总样本 N={N_samples}")
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# 模型先验
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LOG_PRIOR_K1 = np.log(0.5)
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LOG_PRIOR_K2 = np.log(0.5)
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# ------------------------------------------------------------------
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# 3. 初始化
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# ------------------------------------------------------------------
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# 创建数组来存储所有样本
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samples_X = np.zeros(N_samples, dtype=int)
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samples_Y = np.zeros(N_samples, dtype=float)
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# 模型跳跃提议概率 q(k'|k)
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# q(1|1)=0.5, q(2|1)=0.5, q(1|2)=0.5, q(2|2)=0.5
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LOG_Q_1_GIVEN_1 = np.log(0.5)
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LOG_Q_2_GIVEN_1 = np.log(0.5)
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LOG_Q_1_GIVEN_2 = np.log(0.5)
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LOG_Q_2_GIVEN_2 = np.log(0.5)
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# 设定马尔可夫链的初始状态
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# 故意设置一个远离均值(0.727)的初始值，以观察收敛
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y_t = 0.1
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# ------------------------------------------------------------------
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# 4. 运行 Gibbs 抽样
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# ------------------------------------------------------------------
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print("开始Gibbs抽样...")
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np.random.seed(101) # 使用和您图中一样的随机种子
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for i in range(N_samples):
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x_t = np.random.binomial(n, y_t)
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y_t = np.random.beta(x_t + alpha, n - x_t + beta)
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samples_Y[i] = y_t
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samples_X[i] = x_t
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print("抽样完成。")
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# ------------------------------------------------------------------
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# 5. 定义并计算逐步平均值 (Ergodic Mean)
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# ------------------------------------------------------------------
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def calculate_ergodic_mean(y_samples):
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"""
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计算逐步平均值 (累积平均值)
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y_k_bar = (1/k) * sum(y_i for i=1 to k)
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def get_log_prior(k, params):
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"""计算参数的对数先验概率"""
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if k == 1:
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lam = params[0]
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if lam <= 0:
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return -np.inf
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# π(λ)
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return stats.gamma.logpdf(lam, a=ALPHA_LAM, scale=1.0/BETA_LAM)
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使用 np.cumsum() 可以高效实现
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"""
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n = len(y_samples)
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# 1. 计算累积和 [y1, y1+y2, y1+y2+y3, ...]
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s = np.cumsum(y_samples)
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# 2. 创建 k 数组 [1, 2, 3, ...]
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k_array = np.arange(1, n + 1)
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# 3. 计算 avg[k] = s[k] / k
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return s / k_array
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elif k == 2:
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lam, r = params
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if lam <= 0 or r <= 0:
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return -np.inf
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# π(λ, r) = π(λ) * π(r) (假设先验独立)
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log_p_lam = stats.gamma.logpdf(lam, a=ALPHA_LAM, scale=1.0/BETA_LAM)
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log_p_r = stats.invgamma.logpdf(r, a=ALPHA_R, scale=BETA_R)
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return log_p_lam + log_p_r
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# 计算所有 Y 样本的逐步平均值 (包括预热期)
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ergodic_mean_Y = calculate_ergodic_mean(samples_Y)
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def get_log_likelihood(k, params, data):
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"""计算数据的对数似然"""
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if k == 1:
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lam = params[0]
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if lam <= 0:
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return -np.inf
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# Model 1: Poisson(λ)
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return stats.poisson.logpmf(data, lam).sum()
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elif k == 2:
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lam, r = params
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if lam <= 0 or r <= 0:
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return -np.inf
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# Model 2: Negative Binomial
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# 使用 p = r / (λ + r) 的参数化
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p = r / (lam + r)
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# 必须检查 p 是否在 (0, 1] 范围内
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if p <= 0 or p > 1:
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return -np.inf
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return stats.nbinom.logpmf(data, n=r, p=p).sum()
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# ------------------------------------------------------------------
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# 6. 绘制逐步平均值图 (实现您图片中的效果)
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# ------------------------------------------------------------------
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print("正在绘制逐步平均值图...")
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def get_log_posterior(k, params, data):
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"""计算完整的对数后验（正比于）"""
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log_prior = get_log_prior(k, params)
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if log_prior == -np.inf:
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return -np.inf
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log_lik = get_log_likelihood(k, params, data)
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if log_lik == -np.inf:
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return -np.inf
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log_model_prior = LOG_PRIOR_K1 if k == 1 else LOG_PRIOR_K2
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return log_lik + log_prior + log_model_prior
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plt.figure(figsize=(10, 6))
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plt.plot(np.arange(1, N_samples + 1), ergodic_mean_Y, label=r"逐步平均值 $\bar{y}_k$")
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plt.axhline(theoretical_mean, color='red', linestyle='--', label=f"理论均值: {theoretical_mean:.4f}")
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# --- 2. 生成模拟数据 ---
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# 添加一个垂直线来标记我们估计的预热期
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plt.axvline(N_burn_in, color='gray', linestyle=':', label=f"估计的预热期 N1 = {N_burn_in}")
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# 我们故意从一个过度离散的负二项分布生成数据
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# 泊松分布：均值=方差。 负二项：方差 > 均值。
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TRUE_LAMBDA = 5.0
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TRUE_R = 10.0 # R 值变大，方差接近均值 (方差 = 5 + 25/10 = 7.5)
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TRUE_P = TRUE_R / (TRUE_LAMBDA + TRUE_R)
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np.random.seed(42)
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N_data = 5000 # <--- 之前缺失的行
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data = stats.nbinom.rvs(n=TRUE_R, p=TRUE_P, size=N_data)
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plt.title("使用逐步平均值图查看预热期")
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plt.xlabel("迭代次数 (k)")
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plt.ylabel(r"逐步平均值 $\bar{y}_k$")
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plt.legend()
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plt.grid(True)
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plt.ylim(0, 1) # Y值在0到1之间
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print(f"模拟数据均值: {data.mean():.2f} (真实均值 = {TRUE_LAMBDA})")
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print(f"模拟数据方差: {data.var():.2f} (泊松模型的方差应为 {data.mean():.2f})")
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# --- 3. RJMCMC 主函数 ---
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def run_rjmcmc(data, n_iter=50000, burn_in=10000):
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# 初始化
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# 从模型1开始，λ 使用数据的均值
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current_k = 1
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current_lambda = data.mean()
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current_params = [current_lambda]
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# 存储轨迹
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trace_k = np.zeros(n_iter, dtype=int)
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trace_lambda = np.zeros(n_iter)
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trace_r = np.full(n_iter, np.nan) # 仅当 k=2 时有值
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# 接受计数器
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acceptance = {
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"1_to_1": 0, "2_to_2": 0, "1_to_2": 0, "2_to_1": 0
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}
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attempts = {
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"1_to_1": 0, "2_to_2": 0, "1_to_2": 0, "2_to_1": 0
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}
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for i in range(n_iter):
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# 1. 提议一个目标模型 k_prop
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# 无论当前 k 是多少，都以 50/50 的概率提议 k=1 或 k=2
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k_prop = np.random.choice([1, 2])
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# 获取当前的对数后验
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current_log_post = get_log_posterior(current_k, current_params, data)
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# ----------------------------------
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# 情况 A: 模型内移动 (k_prop == current_k)
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# ----------------------------------
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if k_prop == current_k:
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if current_k == 1:
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# --- Model 1 -> Model 1 ---
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attempts["1_to_1"] += 1
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# 提议一个新的 λ (使用正态分布随机游走)
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lambda_prop = current_params[0] + np.random.normal(0, 0.5)
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prop_params = [lambda_prop]
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# 计算接受率
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prop_log_post = get_log_posterior(1, prop_params, data)
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log_alpha = prop_log_post - current_log_post
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# (提议分布是对称的, q(λ'|λ) = q(λ|λ'))
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if np.log(np.random.rand()) < log_alpha:
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current_params = prop_params
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acceptance["1_to_1"] += 1
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elif current_k == 2:
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# --- Model 2 -> Model 2 ---
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attempts["2_to_2"] += 1
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# 提议新的 (λ, r)
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lambda_prop = current_params[0] + np.random.normal(0, 0.5)
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r_prop = current_params[1] + np.random.normal(0, 0.5)
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prop_params = [lambda_prop, r_prop]
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# 计算接受率
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prop_log_post = get_log_posterior(2, prop_params, data)
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log_alpha = prop_log_post - current_log_post
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if np.log(np.random.rand()) < log_alpha:
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current_params = prop_params
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acceptance["2_to_2"] += 1
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# ----------------------------------
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# 情况 B: 跨模型移动 (k_prop != current_k)
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# ----------------------------------
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else:
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if current_k == 1 and k_prop == 2:
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# --- Model 1 -> Model 2 (诞生) ---
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attempts["1_to_2"] += 1
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# 1. 抽取辅助变量 w
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w = np.random.uniform(0, 1)
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log_g_w = stats.uniform.logpdf(w, 0, 1) # 这是 log(1) = 0
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# 2. 应用映射
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lambda_prop = current_params[0]
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r_prop = -np.log(w)
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prop_params = [lambda_prop, r_prop]
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# 3. 计算雅可比项 |J| = 1/w
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log_jacobian = np.log(1.0 / w)
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# 4. 计算接受率
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prop_log_post = get_log_posterior(2, prop_params, data)
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# log_alpha = (log_post_prop + log_q_backward) - (log_post_curr + log_q_forward + log_g_w) + log_jacobian
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log_alpha = (prop_log_post + LOG_Q_1_GIVEN_2) - \
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(current_log_post + LOG_Q_2_GIVEN_1 + log_g_w) + \
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log_jacobian
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if np.log(np.random.rand()) < log_alpha:
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current_k = 2
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current_params = prop_params
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acceptance["1_to_2"] += 1
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elif current_k == 2 and k_prop == 1:
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# --- Model 2 -> Model 1 (死亡) ---
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attempts["2_to_1"] += 1
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# 1. 这是一个确定性映射（h' 的逆）
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current_lambda, current_r = current_params
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# 2. 应用逆映射
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lambda_prop = current_lambda
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w_prime = np.exp(-current_r) # 这就是辅助变量 w'
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prop_params = [lambda_prop]
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# 3. 计算雅可比项 |J'| = e^(-r)
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log_jacobian_prime = np.log(np.exp(-current_r)) # 即 -current_r
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# 4. 计算 g(w')，这是 *正向* 移动 (1->2) 中 w 的密度
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# 正向移动是 w ~ U(0, 1)，所以 g(w') = 1 (只要 0 < w' < 1)
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# 因为 r > 0, 所以 w' = e^(-r) 总是在 (0, 1) 区间内
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log_g_w_prime = stats.uniform.logpdf(w_prime, 0, 1) # log(1) = 0
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# 5. 计算接受率
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prop_log_post = get_log_posterior(1, prop_params, data)
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# log_alpha = (log_post_prop + log_q_backward + log_g_w_prime) - (log_post_curr + log_q_forward) + log_jacobian
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log_alpha = (prop_log_post + LOG_Q_2_GIVEN_1 + log_g_w_prime) - \
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(current_log_post + LOG_Q_1_GIVEN_2) + \
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log_jacobian_prime
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if np.log(np.random.rand()) < log_alpha:
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current_k = 1
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current_params = prop_params
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acceptance["2_to_1"] += 1
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# 存储当前状态
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trace_k[i] = current_k
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trace_lambda[i] = current_params[0]
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if current_k == 2:
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trace_r[i] = current_params[1]
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else:
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trace_r[i] = np.nan
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# 打印接受率
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print("\n--- 接受率 ---")
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for move, count in attempts.items():
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if count > 0:
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rate = acceptance[move] / count
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print(f"{move}: {acceptance[move]}/{count} ({rate:.2%})")
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# 丢弃 Burn-in
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trace_k_burned = trace_k[burn_in:]
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trace_lambda_burned = trace_lambda[burn_in:]
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trace_r_burned = trace_r[burn_in:]
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return trace_k_burned, trace_lambda_burned, trace_r_burned
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# --- 4. 运行和绘图 ---
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N_ITER = 20000
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BURN_IN = 5000
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trace_k, trace_lambda, trace_r = run_rjmcmc(data, n_iter=N_ITER, burn_in=BURN_IN)
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# --- 绘图 ---
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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# 图 1: 模型后验概率
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plt.figure(figsize=(12, 10))
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prob_k1 = np.mean(trace_k == 1)
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prob_k2 = np.mean(trace_k == 2)
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ax1 = plt.subplot(3, 1, 1)
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bars = plt.bar([1, 2], [prob_k1, prob_k2], color=["#227dbe", "#f97b0df9"], tick_label=['模型 1 (泊松)', '模型 2 (负二项)'])
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plt.title('模型后验概率', fontsize=16)
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plt.ylabel('P(k | data)', fontsize=12)
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ax1.bar_label(bars, fmt='{:.2%}', fontsize=12)
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ax1.set_ylim(0, 1)
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# 图 2: 模型跳跃轨迹 (仅显示前 2000 步，看得更清楚)
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ax2 = plt.subplot(3, 1, 2)
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ax2.plot(trace_k[:2000], 'k.', markersize=2, alpha=0.5)
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ax2.set_yticks([1, 2])
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ax2.set_yticklabels(['模型 1 (泊松)', '模型 2 (负二项)'])
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ax2.set_title('模型空间轨迹 (前2000次迭代)', fontsize=16)
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ax2.set_xlabel('迭代次数', fontsize=12)
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# 图 3: 参数后验分布
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# λ (lambda) 的后验
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ax3 = plt.subplot(3, 2, 5)
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ax3.hist(trace_lambda, bins=50, density=True, color='#1f77b4', alpha=0.7, label='$\lambda$ 的后验分布')
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||||
ax3.axvline(data.mean(), color='red', linestyle='--', label=f'数据均值 ({data.mean():.2f})')
|
||||
ax3.axvline(TRUE_LAMBDA, color='black', linestyle=':', label=f'真实 $\lambda$ ({TRUE_LAMBDA})')
|
||||
ax3.set_title('参数 $\lambda$ 的后验分布', fontsize=14)
|
||||
ax3.set_xlabel('$\lambda$ 值', fontsize=12)
|
||||
ax3.set_ylabel('密度', fontsize=12)
|
||||
ax3.legend()
|
||||
|
||||
# r 的后验
|
||||
ax4 = plt.subplot(3, 2, 6)
|
||||
# 仅使用 k=2 时的 r 值
|
||||
trace_r_k2 = trace_r[~np.isnan(trace_r)]
|
||||
if len(trace_r_k2) > 0:
|
||||
ax4.hist(trace_r_k2, bins=50, density=True, color='#ff7f0e', alpha=0.7, label='$r$ 的后验分布 (当 k=2)')
|
||||
ax4.axvline(TRUE_R, color='black', linestyle=':', label=f'真实 $r$ ({TRUE_R})')
|
||||
ax4.set_title('参数 $r$ 的后验分布 (仅当k=2)', fontsize=14)
|
||||
ax4.set_xlabel('$r$ 值', fontsize=12)
|
||||
ax4.legend()
|
||||
else:
|
||||
ax4.set_title('参数 $r$ 的后验分布 (未采样到)', fontsize=14)
|
||||
ax4.text(0.5, 0.5, '从未接受过模型 2', horizontalalignment='center', verticalalignment='center', transform=ax4.transAxes)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
Reference in New Issue
Block a user