Alias采样过程分析
学习来源:
1、时间复杂度O(1)的离散采样算法——Alias method
程序实现
import numpy as np def alias_setup(probs): K = len(probs) q = np.zeros(K) J = np.zeros(K, dtype=np.int) smaller = [] larger = [] for kk, prob in enumerate(probs): q[kk] = K * prob # 保存面积块 if q[kk] < 1.0: smaller.append(kk) # 面积小于1的元素下标 else: larger.append(kk) # 面积大于1的元素下标 while len(smaller) > 0 and len(larger) > 0: small = smaller.pop() large = larger.pop() # J 记录的是填充这一列的下标 J[small] = large # q 记录的是原来事件的占比 q[large] = q[large] - (1 - q[small]) if q[large] < 1.0: smaller.append(large) else: larger.append(large) return J, q def alias_draw(J, q): K = len(J) kk = int(np.floor(np.random.rand() * K)) # 随机选一列 if np.random.rand() < q[kk]: return kk else: return J[kk] if __name__ == "__main__": J, q = alias_setup([1/2, 1/3, 1/12, 1/12]) print(J, q) # [0 0 0 1] [1. 0.66666667 0.33333333 0.33333333] ret = alias_draw(J, q)
构造表的时间复杂度:O(n)
采样的时间复杂度:O(1)