【第十三周】Scipy作业

import numpy as np
import scipy.optimize as opt

#suppose that m=20 n=3
A = np.random.randn(20,3)
B = np.random.randn(20,1)
print("A: ",A)
print("B: ",B)

"""
#suppose that m=8 n=1
A = np.array([[5],[31],[13],[49],[20],[18],[16],[33]])
B = np.array([[33],[72],[70],[10],[21],[6],[21],[52]])
"""

def f(x,target,var):
    return sum(target - var@x)

target = B
var = A
ans=opt.leastsq(f,[[1],[1],[1]] ,args=(target,var))
print("Solution x for Ax=b:\n",ans[0])

运行结果：

import scipy.optimize as opt
import numpy as np
import math
"""
x = arrange(0,10,1000)
f = lamda x: -np.power(np.sin((x-2) * np.power(np.e,-np.power(x,2)) ) ,2)
#y = np.power(np.sin((x-2) * np.power(np.e,-np.power(x,2)) ) ,2)
"""
def f(x):
    return -math.pow(math.sin(x-2) ,2) * math.pow(math.e,-math.pow(x,2))

max_ = opt.minimize_scalar(f)

print(-max_.fun)

截图：

import numpy as np
from scipy.spatial.distance import cdist

#suppose n = 10, m = 21
X = np.random.randint(0,100,(10,21))

print("X:\n",X)
dist = []

for i in range(0,10):
    dist.append([])
    for j in range(0,10):
         ele_dist=cdist([X[i]],[X[j]],metric='euclidean')
         dist[i].append(ele_dist[0][0])
for i in range(0,10):
     print("From row",i," to rows 0-9:")
     print(dist[i])

运行截图：