update

academic_codes/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py

@@ -0,0 +1,61 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+
+time_1 = np.array([])
+time_2 = np.array([])
+time_3 = np.array([])
+n_all = np.arange(2,5000,200)  # 测试的范围
+start_all = time.process_time()
+for n in n_all:
+    print(n)
+    matrix_1 = np.zeros((n,n))
+    matrix_2 = np.zeros((n,n))
+    for i0 in range(n):
+        for j0 in range(n):
+            matrix_1[i0,j0] = np.random.uniform(-10, 10)
+    for i0 in range(n):
+        for j0 in range(n):
+            matrix_2[i0,j0] = np.random.uniform(-10, 10)
+
+    start = time.process_time()
+    matrix_3 = np.dot(matrix_1, matrix_2)  # 矩阵乘积
+    end = time.process_time()
+    time_1 = np.append(time_1, [end-start], axis=0)
+
+    start = time.process_time()
+    matrix_4 = np.linalg.inv(matrix_1)  # 矩阵求逆
+    end = time.process_time()
+    time_2 = np.append(time_2, [end-start], axis=0)
+
+    start = time.process_time()
+    eigenvalue, eigenvector = np.linalg.eig(matrix_1)   # 求矩阵本征值
+    end = time.process_time()
+    time_3 = np.append(time_3, [end-start], axis=0)
+
+
+
+end_all = time.process_time()
+print('总共运行时间：', (end_all-start_all)/60, '分')
+
+plt.subplot(131)
+plt.xlabel('n^3/10^9')
+plt.ylabel('时间（秒）') 
+plt.title('矩阵乘积')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_1, 'o-')
+
+plt.subplot(132)
+plt.xlabel('n^3/10^9')
+plt.title('矩阵求逆')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_2, 'o-')
+
+plt.subplot(133)
+plt.xlabel('n^3/10^9') 
+plt.title('求矩阵本征值')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_3, 'o-')
+
+plt.rcParams['font.sans-serif'] = ['SimHei']  # 在画图中正常显示中文
+plt.rcParams['axes.unicode_minus'] = False  # 中文化后，加上这个使正常显示负号
+plt.show()