10.1
# -*- coding: UTF-8 -*- import numpy as np import scipy.optimize as opt A = np.random.rand(20, 10) b = np.random.rand(20, 1) def err(p,A,b): x = p.reshape(10,1) #没有这一步拟合结果会有问题,因为leastsq传入的p会从mat变成ndarray,之后不满足矩阵运算的操作会出现一些py自定义的结果 return (np.dot(A,x) - b).reshape(-1) p0 = np.random.rand(10,1) para = opt.leastsq(err,p0,(A,b)) x1 = para[0] print("the answer:",x1) print("the norm:",np.linalg.norm(err(x1, A, b)))
10.2
import numpy as np import scipy.optimize as opt def f(x): return - np.sin(x - 2) ** 2 * np.e ** ( - x ** 2) para = opt.minimize(f,0) print(-para.fun)
10.3
import numpy as np import scipy.spatial.distance as dis A = np.random.randint(0,2,size=(5,5)) para = dis.pdist(A) print(A) print(dis.squareform(para))