python作业：Scipy

>>> import numpy as np
>>> import scipy.linalg as sl
>>> m = 30
>>> n = 15
>>> A = np.random.random((m,n))
>>> b = np.random.random((m,1))
>>> res = sl.lstsq(A,b)
>>> x = res[0]
>>> residual = sl.norm(np.dot(A,x)-b,ord=2)
>>> print(residual)

使用scipy.linalg中的lstsq和norm函数可以实现以上功能。

>>> import numpy as np
>>> import scipy.optimize as opt
>>> def function(x):
	return (np.sin(x-2)**2)*(np.exp(-1*(x**2)))

>>> f = lambda y: -function(y)
>>> result = opt.minimize_scalar(f)
>>> print('maximum',-1*result['fun'])
maximum 0.9116854118471548

scipy.optimize的最优化方法只能求函数的最小值，因此要取反求最大之后再取反。

>>> import numpy as np
>>> import scipy.spatial.distance as dist
>>> n = 5
>>> m = 10
>>> X = np.random.randint(5,20,size=(n,m))
>>> Distance = dist.cdist(X,X)
>>> print(Distance)

扫描二维码关注公众号，回复： 1652314 查看本文章