首页 > 学术百科

网络传播模型Python代码实现

⽹络传播模型Python代码实现SI模型

import numpy as np

import matplotlib.pyplot as plt

import smallworld as sw

#邻接矩阵

a = sw.a

# 感染率

beta = sw.beta

#初始患者

origin = sw.origin

def si_(a, beta, origin):

#总⼈数

n = a.shape[0]

#控制符

judge = 1

#未感染⼈

s = np.arange(n)

s = np.delete(s, origin)

#上⼀轮或原先感染⼈

i = origin

#感染者记录

r = []

#感染时间记录

t = []

#感染⼈数记录

speed = []

#计时器

h = 0

#总感染者

infected = i

#开始传播

while judge == 1:

temp_i = []

#感染⼈数

m = len(infected)

for j in range(0, m):

node = int(infected[j])

asd = []

for k in range(0, n):

if a[node, k] == 1:

asd.append(k)

#感染者邻接的未感染者

asd2 = np.intersect1d(asd, s)

#随机⽣成被感染率

num = np.random.rand(len(asd2)) - beta

#该感染者传播的新感染者

asd_final =[]

for k in range(0, len(asd2)):

if num[k] <= 0:

asd_final.append(asd2[k])

#这⼀轮总的新感染者

temp_i = np.union1d(temp_i, asd_final)

s = np.setdiff1d(s, asd_final)

if len(i) > 0:

for k in range(0, len(i)):

r.append(i[k])

t.append(h)

#新⼀轮感染⼈

i = temp_i

infected = np.union1d(infected, i)

#所有⼈都被感染则跳出循环

if len(s) == 0:

judge = 0

if len(i) > 0:

for k in range(0, len(i)):

r.append(i[k])

t.append(h)

speed.append(len(r))

天下第一大火锅

h = h+1

coverage = r

for j in range(h, 2*h):

speed.append(speed[j-1])

print(speed)

plt.plot(speed, "-ro", label='Infectious')

plt.title('SI')

plt.legend()

si_(a, beta, origin)

效果图：

SIR模型

import numpy as np

import matplotlib.pyplot as plt import smallworld as sw

#邻接矩阵

a = sw.a

#感染率

浙江省湖州市织里镇beta = sw.beta

#复原率

gama = sw.gama

生产性活动#初始患者

origin = sw.origin

def sir_(a, origin, beta, gama): #总⼈数

n = a.shape[0]

#控制符

judge = 1

#未感染⼈

s = np.arange(n)

s = np.delete(s, origin)

# 未感染⼈数记录

snum = list()

snum.append(len(s))

#感染⼈

i = origin

#感染⼈数记录

inum = list()

inum.append(len(i))

#复原⼈

r = []

#复原⼈数记录

rnum = list()

rnum.append(0)

#计时器

h = 1

冠名

#开始传播

while judge ==1:

#新⼀轮感染者

temp_i = []

m = len(i)

for j in range(0, m):

#感染者

node = int(i[j])

asd = []

for k in range(0, n):

if a[node, k] == 1:

asd.append(k)

#未被感染者且不为复原者

asd2 = np.intersect1d(asd, s)

asd2 = np.setdiff1d(asd2, r)

num = np.random.rand(len(asd2)) - beta #新感染者

asd_final = []

for k in range(0, len(asd2)):

if num[k] <= 0:

asd_final.append(asd2[k])

temp_i = np.union1d(temp_i, asd_final) s = np.setdiff1d(s, asd_final)

#复原

num = np.random.rand(m) - gama

asd = []

asd_final = []

for k in range(0, m):

if num[k] <= 0:

asd.append(k)

asd_final.append(i[k])

中国青年创业国际计划

#原感染者部分复原

r = np.union1d(r, asd_final)

i = np.delete(i, asd)

#这⼀轮后总感染者

i = np.union1d(i, temp_i)

snum.append(len(s))

inum.append(len(i))

rnum.append(len(r))

h = h + 1

if len(i) == 0:

judge = 0

for j in range(h, h*2):

snum.append(snum[j-1])

inum.append(inum[j - 1])

rnum.append(rnum[j - 1])

plt.plot(snum, "-bo", label='Susceptibles')

plt.plot(inum, "-ro", label='Infectious')

plt.plot(rnum, "-go", label='Recovereds')

plt.title('SIR')

plt.legend()

plt.show()

sir_(a, origin, beta, gama)

效果图：

SIS模型

import numpy as np

import matplotlib.pyplot as plt

import BA

import random

#邻接矩阵

a = BA.a

#感染率

beta = 0.6

#复原率

gama = 0.3

#初始感染者

origin = random.sample(range(0, a.shape[0]), 5) def sis_(a, origin, beta, gama):

#总⼈数

n = a.shape[0]

#未感染⼈

s = np.arange(n)

s = np.delete(s, origin)

#感染⼈

i = origin

#感染⼈数记录

speed = []

speed.append(len(i))

#计时器

h = 1

while h < 70:

temp_i = []

m = len(i)

for j in range(0, m):

node = int(i[j])

asd = []

for k in range(0, n):

if a[node, k] == 1:

asd.append(k)

asd2 = np.intersect1d(asd, s)

num = np.random.rand(len(asd2)) - beta asd_final = []

for k in range(0, len(asd2)):

if num[k] <= 0:

asd_final.append(asd2[k])

temp_i = np.union1d(temp_i, asd_final) s = np.setdiff1d(s, asd_final)

num = np.random.rand(m) - gama

asd = []

asd_final = []

for j in range(0, m):

if num[j] <= 0:

asd.append(j)

asd_final.append(i[j])

s = np.union1d(s, asd_final)

i = np.delete(i, asd)

i = np.union1d(i, temp_i)

speed.append(len(i))

基础教育论坛h = h + 1

snum = []

for j in range(0, h):

snum.append(n - speed[j])

plt.plot(speed, "-ro", label='Infectious')

plt.plot(snum, "-bo", label='Susceptibles')

plt.title('SIS')

plt.legend()

plt.show()

sis_(a, origin, beta, gama)

效果图：

LT模型

import numpy as np

import smallworld as sw

import networkx as nx

import matplotlib.pyplot as plt

#邻接矩阵

a = sw.a

#节点度数, 1/b是其他节点对该节点的影响⼒

b = sw.b

#节点阀值

beta = sw.beta

#原激活节点

origin = sw.origin

#超过beta（如50%）的邻接节点处于激活状态，该节点才会进⼊激活状态def lt_(a, b, origin, beta):

#节点数

n = a.shape[0]

#控制符

judge = 1

#未激活节点

s = np.arange(n)

s = np.delete(s, origin)

#激活节点

i = origin

while judge == 1:

#该轮激活节点

temp_i = []

#激活节点个数

m = len(i)

for j in range(0, m):

node = int(i[j])

asd = []

for k in range(0, n):

if a[node, k] == 1:

asd.append(k)

#到相邻的未激活节点

asd2 = np.intersect1d(asd, s)

asd_final = []

for k in range(0,len(asd2)):

num = 0

#该未激活节点相邻的激活节点个数

for t in range(0, m):

if a[int(i[t]), asd2[k]] == 1:

num = num + 1

if 1 / b[asd2[k]] * num >= beta:

asd_final.append(asd2[k])

本文发布于:2024-09-23 16:23:03，感谢您对本站的认可！

本文链接：https://www.17tex.com/xueshu/369888.html

上一篇：SARS传播的数学模型数学建模全国赛优秀论文

下一篇：数学建模之传染病模型

标签：节点感染者感染记录传播

留言与评论（共有 0 条评论）