习题6.6

合集 - python数学建模课后习题(41)

1.习题5.4求解非凸非线性规划2024-10-112.习题5.5求非凸函数的线性规划问题2024-10-123.习题6.42024-10-144.习题6.32024-10-145.习题6.22024-10-146.习题5.7 生产计划问题2024-10-147.习题4.3 求4.3节模型二的解2024-10-158.习题2.12024-10-159.习题2.22024-10-1510.习题2.32024-10-1511.习题2.42024-10-1512.习题2.5（1）2024-10-1513.习题2.5（2）2024-10-1514.习题2.62024-10-1515.习题2.7（1）2024-10-1516.习题2.7（2）2024-10-1517.习题2.82024-10-1518.习题2.92024-10-1519.习题2.102024-10-1520.习题2.112024-10-1521.习题2.122024-10-1522.习题2.132024-10-1523.习题3.22024-10-1524.习题3.32024-10-1525.习题6.52024-10-26

26.习题6.62024-10-26

27.习题7.12024-11-1228.习题7.32024-11-1229.习题7.42024-11-1230.习题7.72024-11-1231.习题7.102024-11-1232.习题8.42024-11-1233.习题8.52024-11-1234.8.8最优捕鱼策略2024-12-0935.8.92024-12-0936.9.2车间生产滚珠2024-12-0937.9.32024-12-0938.9.52024-12-0939.习题10.12024-12-2140.习题10.22024-12-2141.习题10.32024-12-21

收起

import numpy as np  
  
matches = np.array([  
    [0, 1, 0, 1, 1, 1],  # 1队  
    [0, 0, 0, 1, 1, 1],  # 2队  
    [1, 1, 0, 1, 0, 0],  # 3队  
    [0, 0, 0, 0, 1, 1],  # 4队  
    [0, 0, 1, 0, 0, 1],  # 5队  
    [0, 0, 1, 0, 0, 0]   # 6队  
], dtype=int)  
  
n = matches.shape[0]  
closure = matches.copy()  
for k in range(n):  
    for i in range(n):  
        for j in range(n):  
            closure[i, j] = closure[i, j] or (closure[i, k] and closure[k, j])  
  
strength = closure.sum(axis=1)  
   
ranking = np.argsort(-strength) 
  
for i, rank in enumerate(ranking):  
    print(f"{chr(65 + rank)}队 排名 {i + 1}")
    
    
import numpy as np  
from scipy.sparse import csr_matrix  
  
edges = [  
    (0, 1), (0, 3), (0, 4), (0, 5),  # 1队胜  
    (1, 3), (1, 4), (1, 5),          # 2队胜  
    (2, 0), (2, 1), (2, 3),          # 3队胜  
    (3, 4), (3, 5),                  # 4队胜  
    (4, 2), (4, 5),                  # 5队胜  
    (5, 2)                           # 6队胜  
]  
  
 
num_teams = 6  
  
 
row_ind = []  
col_ind = []  
data = []  
for u, v in edges:  
    row_ind.append(u)  
    col_ind.append(v)  
    data.append(1)  
adj_matrix = csr_matrix((data, (row_ind, col_ind)), shape=(num_teams, num_teams))  
  
 
adj_matrix_T = adj_matrix.T  
  
 
d = 0.85  
out_degree = np.array(adj_matrix_T.sum(axis=1)).flatten()  
out_degree[out_degree == 0] = 1  
M = adj_matrix_T.multiply(1.0 / out_degree).tocsr()  
M = M + (1 - d) / num_teams * csr_matrix(np.ones((num_teams, num_teams)))  
  
 
R = np.ones(num_teams) / num_teams  
  
 
num_iterations = 100  
for _ in range(num_iterations):  
    R = R.dot(M.toarray())  
  
 
pagerank_ranking = np.argsort(-R) 
  
 
for i, rank in enumerate(pagerank_ranking):  
    print(f"{chr(65 + rank)}队 PageRank排名 {i + 1}")
 
print("学号：3008")

结果如下图所示