def compute_p_ge_x(neighbors_p, x): n = len(neighbors_p) total = 0.0 for mask in range(1 << n): cnt = bin(mask).count('1') if cnt >= x: prob = 1.0 for i in range(n): if mask & (1 << i): prob *= neighbors_p[i] else: prob *= (1 - neighbors_p[i]) total += prob return total def main(): import sys input = sys.stdin.read data = input().split() idx = 0 R = int(data[idx]) idx += 1 C = int(data[idx]) idx += 1 P = [] for _ in range(R): row = list(map(int, data[idx:idx+C])) P.append(row) idx += C S = [] for _ in range(R): row = list(map(int, data[idx:idx+C])) S.append(row) idx += C p = [[0.0 for _ in range(C)] for _ in range(R)] max_iterations = 100000 tolerance = 1e-10 for _ in range(max_iterations): new_p = [[0.0 for _ in range(C)] for _ in range(R)] for i in range(R): for j in range(C): neighbors = [] if i > 0: neighbors.append((i-1, j)) if j > 0: neighbors.append((i, j-1)) if j < C-1: neighbors.append((i, j+1)) neighbor_p = [p[ki][kj] for (ki, kj) in neighbors] p_zhi = P[i][j] / 100.0 x1 = 4 P1 = compute_p_ge_x(neighbor_p, x1) Sij = S[i][j] x2 = Sij P2 = compute_p_ge_x(neighbor_p, x2) new_p[i][j] = (1 - p_zhi) * P1 + p_zhi * P2 max_diff = 0.0 for i in range(R): for j in range(C): diff = abs(new_p[i][j] - p[i][j]) if diff > max_diff: max_diff = diff if max_diff < tolerance: break p = new_p expectation = 0.0 for i in range(R): for j in range(C): expectation += p[i][j] print("{0:.10f}".format(expectation)) if __name__ == "__main__": main()