Update matrix_running_time_for_different_num_of_cpu_cores.py

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py

@@ -10,44 +10,46 @@ n = 5000
 A = np.random.rand(n, n)
 B = np.random.rand(n, n)
 
+test_times = 20
+
 # 矩阵行列式
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     det_A = np.linalg.det(A)
-det_time = (time.time() - start_time)/20
+det_time = (time.time() - start_time)/test_times
 print(f"矩阵行列式时间: {det_time:.3f} 秒")
 
 # 矩阵乘法
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     C = np.dot(A, B)
-multiply_time = (time.time() - start_time)/20
+multiply_time = (time.time() - start_time)/test_times
 print(f"矩阵乘法时间: {multiply_time:.3f} 秒")
 
 # 矩阵求逆
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     inv_A = np.linalg.inv(A)
-inv_time = (time.time() - start_time)/20
+inv_time = (time.time() - start_time)/test_times
 print(f"矩阵求逆时间: {inv_time:.3f} 秒")
 
 # 矩阵的秩
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     rank_A = np.linalg.matrix_rank(A)
-rank_time = (time.time() - start_time)/20
+rank_time = (time.time() - start_time)/test_times
 print(f"矩阵的秩时间: {rank_time:.3f} 秒")
 
 # 矩阵的特征值
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     eigenvalues_A = np.linalg.eigvals(A)
-eigen_time = (time.time() - start_time)/20
+eigen_time = (time.time() - start_time)/test_times
 print(f"矩阵特征值时间: {eigen_time:.3f} 秒")
 
 # 矩阵的特征值和特征向量
 start_time = time.time()
-for _ in range(20):
+for _ in range(test_times):
     eigenvalues_A, eigenvector_A = np.linalg.eig(A)
-eigen_time = (time.time() - start_time)/20
+eigen_time = (time.time() - start_time)/test_times
 print(f"矩阵特征值和特征向量时间: {eigen_time:.3f} 秒")