scipy作业

原创于 2018-06-02 21:35:42 发布 · 326 阅读

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本文通过三个实例介绍了使用Python进行科学计算的方法。首先展示了如何利用最小二乘法求解线性方程组；接着介绍了一个寻找函数最大值的问题；最后探讨了距离矩阵的计算。这些实例覆盖了线性代数、最优化及空间距离计算等领域。

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import numpy as np
import scipy.linalg
import scipy.spatial
import scipy.optimize
import random
import math

#exercise one
m = 20
n = 10
A = np.random.randint(0, 10, (m, n))
b = np.random.randint(0, 10, (m, 1)) 
x, resid, rnk, s = scipy.linalg.lstsq(A, b)
print('A =', A)
print('b =', b)
print('x =', x)
print('Norm of residual =', resid/n)

import numpy as np
import scipy.linalg
import scipy.spatial
import scipy.optimize
import random
import math

#exercise two
def f(x):
	return (math.sin(x-2)**2*math.e**(-x*x))
answer = scipy.optimize.fmin(lambda x:-f(x), 1, full_output=True, disp = 0)
print('maximun:',-answer[1])

import numpy as np
import scipy.linalg
import scipy.spatial
import scipy.optimize
import random
import math

#exercise three
m = 20
n = 10
X = np.random.randint(0, 10, (n, m))
Y = scipy.spatial.distance.pdist(X)
print(Y)