Scipy
下面为作业文档中Scipy这一章的内容
Exercise 10.1
1. 代码
import numpy as np import scipy.linalg as sl m, n = 6, 5 A = np.random.randint(1, 10, size = (m, n)) print("A is:") print(A) b = np.random.randint(1, 10, size = (m, 1)) print("b is:") print(b) x_ans = sl.lstsq(A, b)[0] print("x is:") print(x_ans) residual = np.dot(A, x_ans) - b print("The residual is:") print(np.linalg.norm(residual, 2))
2. 结果截图
Exercise 10.2
1. 代码
import numpy as np import scipy.optimize as opt def fun(x): return (-1)*((np.sin(x-2)**2) * np.exp(-(x**2))) max_x = opt.fminbound(fun, -1, 1) print('Maxmum in (-1, 1): ', -fun(max_x))
2. 结果截图
Exercise 10.3
1. 代码
import scipy.spatial import numpy as np m, n = 3, 3 X = np.random.randint(1, 10, size = (m, n)) print(X) distances = scipy.spatial.distance_matrix(X, X) print(distances)
【注意】这里的函数scipy.spatial.distance_matrix的返回值为一个矩阵,该矩阵的(m,n)个元素是X的第m列与n的第n列之间的距离
2. 结果截图