【高编作业】 exercise - Scipy

题目

10.1

from scipy.optimize import leastsq  
import numpy as np  
  
def err(x, A, b):  
    return b - A * x  

def main():
	A = np.array([np.random.random()for i in range(20)]) 
	b = np.array([np.random.random()for i in range(20)])  
	x0 = 0.1
	x = leastsq(err, x0, args=(A, b))  
	print(x[0]) 


main()

10.2

from scipy.optimize import minimize_scalar  
import numpy as np
from math import *  
  
def f(x):  
    return -pow(sin(x - 2),2) * exp(-(x*x))  
  
t = minimize_scalar(f)  
print(-t.fun)  

10.3

from scipy.optimize import minimize_scalar  
from scipy.spatial.distance import pdist  
import numpy as np  
from math import * 
 
cities = ['beijing', 'shanghai', 'guangzhou', 'shenzhen']
m = 4  
n = 4
l = []
for i in range(n):
	l.append([])
	for j in range(m):
		if i!=j:
			l[i].append(np.random.randint(0, 100))
		else:
			l[i].append(0)
distance = pdist(l)  
t = 0  
for i in range(n):  
    for j in range(i+1, n):  
        print(('distance from ' + cities[i] + ' to ' + cities[j] + 
        	': ' + str(distance[t]))) 
        t += 1