如图,刷微博时,看到一个问题,第一个想到的就是用蒙特卡洛方法求解,当时正在练python,于是尝试用python编写程序. import random # 先求s1 k=0 n=100000000 for i in range(n): x=random.uniform(0,10) y=random.uniform(0,10) if ((x-5)**2+(y-5)**2>25) and (y<-2*x+20): k=k+1 else: k=k s1=(k/n)*100 #求s2 import m
Description The map of Berland is a rectangle of the size n × m, which consists of cells of size 1 × 1. Each cell is either land or water. The map is surrounded by the ocean. Lakes are the maximal regions of water cells, connected by sides, which are
D. Lakes in Berland time limit per test 2 seconds memory limit per test 256 megabytes input standard input output standard output The map of Berland is a rectangle of the size n × m, which consists of cells of size 1 × 1. Each cell is either land or
这是本专题的第二节,在这一节我们将以David Silver等人的Natrue论文Mastering the game of Go with deep neural networks and tree search为基础讲讲AlphaGo的基本框架,力求简洁清晰,具体的算法细节参见原论文.本人水平有限,如有错误还望指正.如需转载,须征得本人同意. AlphaGo流程 以人类的棋局用监督学习训练出一个策略网络 \(p_\sigma\) 以人类的棋局用监督学习训练出一个策略网络 \(p_\pi\
