Create matrix_running_time.py

2025.03.08_matrix_running_time/matrix_running_time.py

@@ -0,0 +1,100 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/45275
+"""
+
+import numpy as np
+import time
+import sys
+from numba import jit
+
+n_array = np.concatenate((np.arange(1000, 10000, 1000), np.arange(10000, 40000, 10000)))
+print(f'n_array={n_array}\n')
+
+@jit
+def numba_test(C, n):
+    for i0 in range(n):
+        for j0 in range(n):
+            C[i0, j0] = np.random.rand()
+    return C
+
+for n in n_array:
+    print(f'n={n}')
+
+    A = np.random.rand(n, n)
+    B = np.random.rand(n, n)
+    C = np.random.rand(n, n)
+
+    # 矩阵占用内存
+    size = sys.getsizeof(C)
+    print(f'矩阵占用内存: {size/(1024*1024):.2f} MB')
+
+    # 矩阵的迹
+    start_time = time.time()
+    trace_A = np.trace(A)
+    trace_time = time.time() - start_time
+    print(f"矩阵的迹时间: {trace_time:.3f} 秒")
+
+    # 矩阵转置
+    start_time = time.time()
+    A_T = A.T
+    transpose_time = time.time() - start_time
+    print(f"矩阵转置时间: {transpose_time:.3f} 秒")
+
+    # 矩阵加法
+    start_time = time.time()
+    C = A + B
+    add_time = time.time() - start_time
+    print(f"矩阵加法时间: {add_time:.3f} 秒")
+
+    # numba for 循环赋值
+    start_time = time.time()
+    numba_test(C, n)
+    create_time = time.time() - start_time
+    print(f"numba for 循环赋值时间: {create_time:.3f} 秒")
+
+    # 矩阵创建
+    start_time = time.time()
+    C = np.random.rand(n, n)
+    create_time = time.time() - start_time
+    print(f"矩阵创建时间: {create_time:.3f} 秒")
+
+    # for 循环赋值
+    start_time = time.time()
+    for i0 in range(n):
+        for j0 in range(n):
+            C[i0, j0] = np.random.rand()
+    create_time = time.time() - start_time
+    print(f"for 循环赋值时间: {create_time:.3f} 秒")
+
+    # 矩阵乘法
+    start_time = time.time()
+    C = np.dot(A, B)
+    multiply_time = time.time() - start_time
+    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
+    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
+    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
+    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
+    print(f"矩阵特征值和特征向量时间: {eigen_time:.3f} 秒")
+
+    print()