Python scipy 部分习题

最新推荐文章于 2023-04-19 00:00:00 发布

Schnee_Cy

最新推荐文章于 2023-04-19 00:00:00 发布

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本文链接：https://blog.youkuaiyun.com/Empire_03/article/details/80565799

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本文主要探讨了Python的scipy库在数值计算、最优化、插值和积分等领域的应用，通过实例解析常见问题的解决方案，帮助读者深入理解scipy的强大功能。

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import numpy as np  
from scipy.linalg import lstsq  
  
m = 80
n = 40  
A = np.random.normal(1, 2, (m, n))  
b = np.random.normal(1, 2, (m, 1))  
  
r_norm = lstsq(A, b)[1]  
print(r_norm)

输出：

[203.82746375]

import numpy as np 
from scipy.optimize import fmin

def func(x):
    return (np.sin((x-2)*(np.exp(-x**2)))**2)*(-1)

xopt = fmin(func, 0)

print(xopt[0], -func(xopt[0]))

输出：

Optimization terminated successfully.
         Current function value: -1.000000
         Iterations: 21
         Function evaluations: 42
0.2906875000000003 0.9999999997859061

import numpy as np  
from scipy import optimize  
from scipy.spatial import distance
  
m = 30  
n = 6 
A = np.random.normal(1, 2, (n, m))  
C = distance.pdist(A)  
C = distance.squareform(C)  
  
print(C)

输出：

[[ 0.         17.16746799 15.91489311 16.8122172  19.99007586 18.61492197]
 [17.16746799  0.         14.85630827 19.14838444 18.22197593 15.23586407]
 [15.91489311 14.85630827  0.         16.3998096  12.51200226 13.12386771]
 [16.8122172  19.14838444 16.3998096   0.         16.83916137 16.806151  ]
 [19.99007586 18.22197593 12.51200226 16.83916137  0.         14.83112775]
 [18.61492197 15.23586407 13.12386771 16.806151   14.83112775  0.        ]]