Update matrix_running_time_for_different_num_of_cpu_cores.py

2025.03.08_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py

@@ -12,36 +12,42 @@ B = np.random.rand(n, n)
 
 # 矩阵行列式
 start_time = time.time()
-det_A = np.linalg.det(A)
+for _ in range(10):
-det_time = time.time() - start_time
+    det_A = np.linalg.det(A)
+det_time = (time.time() - start_time)/10
 print(f"矩阵行列式时间: {det_time:.3f} 秒")
 
 # 矩阵乘法
 start_time = time.time()
-C = np.dot(A, B)
+for _ in range(10):
-multiply_time = time.time() - start_time
+    C = np.dot(A, B)
+multiply_time = (time.time() - start_time)/10
 print(f"矩阵乘法时间: {multiply_time:.3f} 秒")
 
 # 矩阵求逆
 start_time = time.time()
-inv_A = np.linalg.inv(A)
+for _ in range(10):
-inv_time = time.time() - start_time
+    inv_A = np.linalg.inv(A)
+inv_time = (time.time() - start_time)/10
 print(f"矩阵求逆时间: {inv_time:.3f} 秒")
 
 # 矩阵的秩
 start_time = time.time()
-rank_A = np.linalg.matrix_rank(A)
+for _ in range(10):
-rank_time = time.time() - start_time
+    rank_A = np.linalg.matrix_rank(A)
+rank_time = (time.time() - start_time)/10
 print(f"矩阵的秩时间: {rank_time:.3f} 秒")
 
 # 矩阵的特征值
 start_time = time.time()
-eigenvalues_A = np.linalg.eigvals(A)
+for _ in range(10):
-eigen_time = time.time() - start_time
+    eigenvalues_A = np.linalg.eigvals(A)
+eigen_time = (time.time() - start_time)/10
 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()
-eigenvalues_A, eigenvector_A = np.linalg.eig(A)
+for _ in range(10):
-eigen_time = time.time() - start_time
+    eigenvalues_A, eigenvector_A = np.linalg.eig(A)
+eigen_time = (time.time() - start_time)/10
 print(f"矩阵特征值和特征向量时间: {eigen_time:.3f} 秒")