From 249d292bd8bcbbc145b986a0b5c9bf5abc24db80 Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Sat, 8 Mar 2025 20:35:02 +0800
Subject: [PATCH] Update matrix_running_time_for_different_num_of_cpu_cores.py

---
 ...ing_time_for_different_num_of_cpu_cores.py | 30 +++++++++++--------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/2025.03.08_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py b/2025.03.08_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py
index 31381eb..e8bc080 100644
--- a/2025.03.08_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py
+++ b/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)
-det_time = time.time() - start_time
+for _ in range(10):
+    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)
-multiply_time = time.time() - start_time
+for _ in range(10):
+    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)
-inv_time = time.time() - start_time
+for _ in range(10):
+    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)
-rank_time = time.time() - start_time
+for _ in range(10):
+    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)
-eigen_time = time.time() - start_time
+for _ in range(10):
+    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)
-eigen_time = time.time() - start_time
+for _ in range(10):
+    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")
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