import numpy as np import numpy.matlib import scipy.linalg m=10 n=5 A = np.matlib.randn(m, n) #生成A b = np.matlib.rand(m, 1) #随机生成向量b x = scipy.linalg.lstsq(A,b)[0] r = np.linalg.norm(A*x-b,ord=np.inf) print("矩阵A:") print(A) print("矩阵B:") print(b) print("所求的x:") print(x) print("残差的无穷范数:") print(r)
10.2
参考资料:
Scipy教程 - 优化和拟合库scipy.optimize
https://blog.csdn.net/pipisorry/article/details/51106570
import numpy import scipy.optimize f = lambda x:-1*(numpy.sin(x-2)**2)*(numpy.exp(-(x**2))) ans = scipy.optimize.minimize_scalar(f) max = -1*ans['fun'] print('maximum:',max)
10.3
import numpy as np import scipy.spatial.distance as dist n, m = 5, 5 X = np.random.randint(0, 10, size=(n, m)) table = dist.cdist(X, X) print(table)