二叉树的博客

决策树线性回归

代码

1
2
3
import numpy as np
from sklearn.tree import DecisionTreeRegressor #回归树
import matplotlib.pyplot as plt
1
2
3
#生成噪声曲线
rng = np.random.RandomState(1)
rng.rand(2,3)#生成区分行列的二维数据,一维数据不分行列,生成0~1之间的数
array([[4.17022005e-01, 7.20324493e-01, 1.14374817e-04],
       [3.02332573e-01, 1.46755891e-01, 9.23385948e-02]])
1
2
3
X = np.sort(5*rng.rand(80,1),axis=0)
y = np.sin(X).ravel()#导入二维数据,ravel降维y,可将任意维度降维为一维
plt.scatter(X,y,s=20,edgecolors="black",c="darkorange",label="data")
<matplotlib.collections.PathCollection at 0x7f87d52f56a0>
1
2
y[::5] += 3 * (0.5 - rng.rand(16))#每5个数字加随机+-0.5之间的数当成噪声
plt.scatter(X,y,s=20,edgecolors="black",c="darkorange",label="data")
<matplotlib.collections.PathCollection at 0x7f87d5113e50>
1
2
3
4
5
#设定参数的模型导入并训练
regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X,y)
regr_2.fit(X,y)
DecisionTreeRegressor(max_depth=5)
1
2
X_test = np.arange(0.0,5.0,0.01)[:,np.newaxis]
#取0~5以0.01为步长的有顺序点,后面将1维升维2,跟reshape(-1,1)作用一致
1
2
y_1 = regr_1.predict(X_test)
y_2 = regr_2.predict(X_test)
1
2
3
4
5
6
7
8
9
plt.figure()
plt.scatter(X,y,s=20,edgecolors="black",c="darkorange",label="data")#边框,点,图例名称
plt.plot(X_test,y_1,color="cornflowerblue",label="max_depth=2",linewidth=2)
plt.plot(X_test,y_2,color="yellowgreen",label="max_depth=5",linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Decision Tree Regression")
plt.legend()#显示图例
plt.show()

直接通过jupyternotebook安装库

我的前提条件,实际能使用jupyternotebook都可

Ubuntu 20.0系统

miniconda3jupyter

直接通过jupyter notebook安装库

代码:

查找解释器位置

1
2
import os
os.sys.executable

示例结果:

1
'/usr/bin/python3'

安装示例模板

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
import os
libs = {
    "requests","jieba","beautifulsoup4","matplotlib","numpy","pandas","openpyxl","tensorflow","仿照格式这里填写要安装的包"
}
try:
    for lib in libs:
        #这里的地址是上一步显示的解释器位置
        os.system('/usr/bin/python3 -m pip install '+lib)
        print("Successful")
except:
    print('error')

2020新型冠状病毒(COVID-19/2019-nCoV)疫情分析(补档)

新型冠状病毒(COVID-19/2019-nCoV)疫情分析

源文档详见:博客相关资源-新冠疫情数据分析文件

重要说明

分析文档:完成度:代码质量 3:5:2

其中分析文档是指你数据分析的过程中,对各问题分析的思路、对结果的解释、说明(要求言简意赅,不要为写而写)

某区域流体数据处理(只会写操作,不知道具体意义)

前言

帮舍友整的,不知道具体实际意义。

代码

1
2
3
4
5
import numpy as np
import pandas as pd


data = pd.read_excel(r'C:\Users\祈LHL\Desktop\data.xlsx')
1
data.head()
cellnumberx-coordinatey-coordinatez-coordinatedensityz-velocityrelative-z-velocityx-coordinate.1y-coordinatez-face-areaboundary-cell-distboundary-normal-dist
01-12.597898-2.404495-6.3204971.226-22.205814-22.205814-12.597899-2.404528-0.00682410.010276
12-12.597898-2.321485-6.3204871.226-23.532957-23.532957-12.597899-2.321584-0.00682410.020553
23-12.515688-2.404495-6.3205001.226-23.167622-23.167622-12.515688-2.404528-0.00682410.020636
34-12.515688-2.321485-6.3205001.226-24.882029-24.882029-12.515688-2.321584-0.00682410.051111
45-12.433477-2.404495-6.3205001.226-23.488083-23.488083-12.433478-2.404528-0.00682410.020719
1
y = data.sort_values(by="y-coordinate" , ascending=False)
1
2
3
4
5
6

step = 4.892/6
i = 0
y_list = []
section = []
#y6  x3
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
#分y
while i < 6:
    step_y = float("%.8f"%(i*step))
    a_y = step_y-2.446
    y_min = y.loc[y['y-coordinate'] >= a_y]
    i +=1
    step_y = float("%.8f"%(i*step))
    b_y = step_y-2.446
    y_max = y_min.loc[y_min['y-coordinate'] < b_y]
    y_list.append(y_max)
    section_y = (a_y,b_y)
    section.append(section_y)
#section[5]
1
2
3
4
5
j = 0
step = 1.562/3
x_list = []
section_x_list = []
section_y_list = []
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
#在y中分x
while j <= 5:
    temp = y_list[j]
    m = 0
    while m <= 2:
        step_x = float("%.8f"%(m*step))
        a_x = step_x-12.639
        x_min = temp.loc[temp['x-coordinate'] >= a_x]
        m +=1
        step_x = float("%.8f"%(m*step))
        b_x = step_x-12.639
        x_max = x_min.loc[temp['x-coordinate'] < b_x]
        x_list.append(x_max)
        section_x = (a_x,b_x)
        section_x_list.append(section_x)
        section_y_list.append(section[j])
    j += 1
#18个块
1
2
3
section_list = []
Q_list = []
i = 0
1
2
3
4
5
while i <= 17:
    temp_l = x_list[i]
    Q = (temp_l["density"]*temp_l["z-velocity"]*temp_l["z-face-area"]).sum()
    i +=1
    Q_list.append(Q)
1
section_x_list
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
[(-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077),
 (-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077),
 (-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077),
 (-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077),
 (-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077),
 (-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077)]
1
section_y_list
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
[(-2.446, -1.63066667),
 (-2.446, -1.63066667),
 (-2.446, -1.63066667),
 (-1.63066667, -0.8153333300000001),
 (-1.63066667, -0.8153333300000001),
 (-1.63066667, -0.8153333300000001),
 (-0.8153333300000001, 0.0),
 (-0.8153333300000001, 0.0),
 (-0.8153333300000001, 0.0),
 (0.0, 0.8153333299999996),
 (0.0, 0.8153333299999996),
 (0.0, 0.8153333299999996),
 (0.8153333299999996, 1.6306666699999997),
 (0.8153333299999996, 1.6306666699999997),
 (0.8153333299999996, 1.6306666699999997),
 (1.6306666699999997, 2.446),
 (1.6306666699999997, 2.446),
 (1.6306666699999997, 2.446)]
1
2
3
4
5
6
index = []
i = 0
while i <= 2:
    index.append(section_x_list[i])
    i+=1
index
1
2
3
[(-12.639, -12.118333329999999),
 (-12.118333329999999, -11.597666669999999),
 (-11.597666669999999, -11.077)]
1
2
3
4
5
6
cols = []
i = 0
while i <= 17:
    cols.append(section_y_list[i])
    i+=3
cols
1
2
3
4
5
6
[(-2.446, -1.63066667),
 (-1.63066667, -0.8153333300000001),
 (-0.8153333300000001, 0.0),
 (0.0, 0.8153333299999996),
 (0.8153333299999996, 1.6306666699999997),
 (1.6306666699999997, 2.446)]
1
2
3
4
5
6
7
j = 0
data = {}
while j <=5:
    col = cols[j]
    data[col]= 1
    j += 1
data
1
2
3
4
5
6
{(-2.446, -1.63066667): 1,
 (-1.63066667, -0.8153333300000001): 1,
 (-0.8153333300000001, 0.0): 1,
 (0.0, 0.8153333299999996): 1,
 (0.8153333299999996, 1.6306666699999997): 1,
 (1.6306666699999997, 2.446): 1}
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
temp_list = Q_list.copy()
r = 0
l = 0
for i in data:
    content = []
    for j in range(3):
        content.append(Q_list[l])
        l+=1
    data[i] = content
data
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
{(-2.446, -1.63066667): [12.427282344087539,
  12.86438295520224,
  8.104732674789329],
 (-1.63066667, -0.8153333300000001): [14.059159859218617,
  14.0170078290342,
  6.50613023081204],
 (-0.8153333300000001, 0.0): [13.851341644545903,
  14.102576555137208,
  6.549793869809068],
 (0.0, 0.8153333299999996): [13.873905539737082,
  14.100577509379482,
  6.539991338980014],
 (0.8153333299999996, 1.6306666699999997): [14.030126072372612,
  13.966730831575635,
  6.467075694188603],
 (1.6306666699999997, 2.446): [11.644887356127308,
  12.004108151505823,
  7.422577844039829]}
1
2
df = pd.DataFrame(data=data, index=index)
df
-2.446000-1.630667-0.8153330.0000000.8153331.630667
-1.630667-0.8153330.0000000.8153331.6306672.446000
(-12.639, -12.118333329999999)12.42728214.05916013.85134213.87390614.03012611.644887
(-12.118333329999999, -11.597666669999999)12.86438314.01700814.10257714.10057813.96673112.004108
(-11.597666669999999, -11.077)8.1047336.5061306.5497946.5399916.4670767.422578

相关资料:数据集链接点此下载

多元回归和熵值评价法及多目标遗传优化算法

前言

感谢组员书写书面报告,代码部分由我书写,我写的很烂,将就看吧。

第一届长三角数学建模

B题 锅炉水冷壁温度曲线

1Stopt多元拟合

个人体会:

只能确定2个自变量1个因变量的拟合函数形式,更高维的无法寻找公式进行拟合。

改进的SIR差分模型及三个模型的应用

前言

感谢组员的共同协作,做组长的有些东西帮不上实在抱歉。

改进的SIR差分模型

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
%% SIR差分模型
frame=importdata('美国covid19疫情数据l-history改.csv');
date_row=size(frame.data,1);
data=frame.data;
date=frame.textdata(:,1);
real_date=date(2:date_row+1,:);
E=zeros(2,90);
l=0.000125;    %日接触率
m=0.01;   %日治愈率0.0005
E(1,1)=data(345,12)/300000000;
E(2,1)=1-E(1,1);
for i=1:90
    E(1,i+1)=l*E(2,i)-m*E(1,i)+E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
end
X=flip(data(311:345,12)');% 5.31  4.2
rate=flip(data(311:345,12)')%现阳性率
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
%a0=[100,10]; %初值
figure(1)
h1 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
h2 = plot(a,'r')
xlabel('日期');         %设置横坐标名
ylabel('感染人数');   %设置纵坐标名
legend([h1 h2],'3~5月感染人数','SIR模型迭代曲线','Location','NorthWest');
hold on
%%  预测
X=flip(data(255:345,12)');%311行后为第一题
rate=flip(data(255:345,12)')%现阳性率
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(2)
h3 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
h4=plot(a,'r')
xlabel('日期');
ylabel('感染人数');
legend([h3 h4],'3~6月感染人数','SIR模型迭代曲线','Location','NorthWest');
hold on
%% 接种疫苗前
frame=importdata('usc.csv');
date_row=size(frame.data,1);
data=frame.data;
date=frame.textdata(:,1);
real_date=date(2:date_row+1,:);
E=zeros(3,150);
l=0.0018;    %日接触率 0.0018
m=0.01;   %日治愈率 0.01
E(1,1)=data(320,1)/300000000;
E(3,1)=28664448/300000000%43714928  33714928  28664448
E(2,1)=1-E(1,1)-E(3,1);

for i=1:150
    E(1,i+1)=E(1,i)+l*E(2,i)-m*E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
    E(3,i+1)=1-E(1,i+1)- E(2,i+1);
end
X=data(320:486,1)';%80-482  3.1-7.1
rate=data(320:486,1)'%
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(6)
h11 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
c=E(3,:);
t=1:151;
h1=plot(t,a,'r')%感染人数
%plot(t,b,'b')%健康人数
plot(t,c,'r')%移除者
%legend('i(t)','s(t)','r(t)')
hold on
%% 接种疫苗后
E=zeros(3,150);
l=0.0018;    %日接触率 0.0018
m=0.01;   %日治愈率 0.01
E(1,1)=data(320,1)/300000000;
E(3,1)=53714928/300000000%43714928  33714928  28664448
E(2,1)=1-E(1,1)-E(3,1);
for i=1:150
    E(1,i+1)=E(1,i)+l*E(2,i)-m*E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
    E(3,i+1)=1-E(1,i+1)- E(2,i+1);
end
X=data(320:486,1)';%80-482  3.1-7.1
rate=data(320:486,1)'%
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(6)
%h11 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
c=E(3,:);
t=1:151;
h2=plot(t,a,'b')%感染人数
%plot(t,b,'b')%健康人数
plot(t,c,'b')%移除者
%legend('i(t)','s(t)','r(t)')
hold on
xlabel('日期');         %设置横坐标名
ylabel('r(t)与i(t)');   %设置纵坐标名
legend([h1 h2 h11],'接种疫苗前','接种疫苗后','感染人数原始数据','Location','NorthWest');

logistic模型预测美国人口

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
%改进的指数增长模型
x=flip([324985536 322941312 320635168 318300992 315993728 313830976 311556864 309321664 306771520 304093952 301231200 298379904 295516608 292805312 290107936 287625184 284968960 282162400 279040000 275854016 272656992])
y=flip([0.006330017,0.007192424,0.007333235,0.007301613,0.006891455,0.007299188,0.007226135,0.008312845,0.008805068,0.009503504,0.009555925,0.00968912,0.009259723,0.009297836,0.008631901,0.009321099,0.009946612,0.011189794,0.011549529,0.011725443,0.012112401])
n=length(x);
t=0:1:n-1;
rk=zeros(1,n);
rk(1)=(-3*x(1)+4*x(2)-x(3))/2;
rk(n)=(x(n-2)-4*x(n-1)+3*x(n))/2;
for i=2:n-1
rk(i)=(x(i+1)-x(i-1))/2;
end
rk=rk./x;
p=polyfit(t,rk,2);
r0=p(1);
r1=p(2);
r2=p(3);
x0=x(1);
R=r0*t.^2+r1*t+r2;
X=x0*exp((r0*t.^3)/3+(r1*t.^2)/2+r2*t);
figure(1)
hold on;
xlabel('year');         %设置横坐标名
ylabel('rate');         %设置纵坐标名
grid on                 %网格线
plot(t,y,'r*')
figure(2)
hold on;
xlabel('year');         %设置横坐标名
ylabel('population');   %设置纵坐标名
grid on                 %网格线
plot(t,x,'r*',t,X)
figure(3)
hold on;
xlabel('year');         %设置横坐标名
ylabel('rate');         %设置纵坐标名
grid on                 %网格线
plot(t,y,'r*',t,R')
%2018
t1=t(21)+1;
x2018=x0*exp((r0*t1.^3)/3+(r1*t1.^2)/2+r2*t1)
y2018=r0*t1.^2+r1*t1+r2
%2019
t1=t(21)+2;
x2019=x0*exp((r0*t1.^3)/3+(r1*t1.^2)/2+r2*t1)
y2019=r0*t1.^2+r1*t1+r2

药物中毒急救建模

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
clc;
clear all;
syms x t;
f(t)=6*(exp(-0.1155*t)-exp(-0.1386*t));
h(t)=exp(-0.1386*t);
figure(1);
fplot(f,[0 25]);
hold on;
fplot(h,[0,25]);
grid on;
xlabel('时间t/h');
ylabel('药量');
legend('血液中的药量y/mg','胃肠道中的药量x/mg');
figure(2)
fplot(f,[0,25]);
hold on;
fplot(h,[0,25]);
f(t)=-(exp(-0.693*t)-exp(-0.1386*t))/4;
fplot(f,[0,25]);
xlabel('时间t/h');
ylabel('药量');
legend('血液中的药量y/mg','胃肠道中的药量x/mg','施救后血液中的药量y/mg');

热传导差分偏微分模型应用

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
clear;
close all;
clc;
pho=[300;862;74.2;1.18];
c=[1377;2100;1726;1005];
lamda=[0.082;0.37;0.045;0.028];
a=lamda./(pho.*c);

d=[0.6;6;3.6;5]*10^-3;

TT=273.15;
T_in=37;
T_out=75;
T_s=48.08;

xmin=0;
xmax=sum(d);

N=5400;
h=0.05*10^-3;
k=1;
r=k/h^2;
I=round((xmax-xmin)/h);

A=zeros(1,I);
B=zeros(1,I+1);
C=zeros(1,I);

N1=round(d(1)/h);
N2=round(d(2)/h);
N3=round(d(3)/h);
N4=round(d(4)/h);

for i=1:N1
    A(i)=-a(1)*r;
    B(i)=2+2*r*a(1);
    C(i)=-r*a(1);
end
for i=N1+1:N1+N2
    A(i)=-a(2)*r;
    B(i)=2+2*r*a(2);
    C(i)=-r*a(2);
end
for i=N1+N2+1:N1+N2+N3
    A(i)=-a(3)*r;
    B(i)=2+2*r*a(3);
    C(i)=-r*a(3);
end
for i=N1+N2+N3+1:N1+N2+N3+N4
    A(i)=-a(4)*r;
    B(i)=2+2*r*a(4);
    C(i)=-r*a(4);
end

T=zeros(I+1,N+1);
T(:,1)=(T_in+TT)*ones(I+1,1);

T_xt=xlsread('CUMCM-2018-Problem-A-Chinese-Appendix.xlsx');

h_min=110;
h_max=120;
delta_h=0.1;
H1=h_min:delta_h:h_max;
delta=zeros(1,length(H1));

for j=1:length(H1)
    h1=h_min+(j-1)*delta_h;
    k1=lamda(1);k2=lamda(2);k3=lamda(3);k4=lamda(4);
    x1=d(1);x2=d(1)+d(2);x3=d(1)+d(2)+d(3);x4=d(1)+d(2)+d(2)+d(4);
    t1=T_out+TT;t2=T_in+TT;t3=T_s+TT;

    h5=-((h1*k2*k3*k4*t1)/(k1*k2*k3*k4-h1*k1*k2*k3*x3-h1*k1*k2*k4*x2 ...
        -h1*k1*k3*k4*x1+h1*k1*k2*k3*x4+h1*k1*k2*k4*x3+h1*k1*k3*k4*x2+h1*k2*k3*k4*x1)-(h1*k2*k3*k4*t3)...
        /(k1*k2*k3*k4-h1*k1*k2*k3*x3-h1*k1*k2*k4*x2-h1*k1*k3*k4*x1+h1*k1*k2*k3*x4+h1*k1*k2*k4*x3+h1*k1*k3*k4*x2+h1*k2*k3*k4*x1))...
        /(t2/k1-t3/k1);

    AA=diag(B)+diag(A,1)+diag(C,-1);
    AA(1,1)=lamda(1)/h+h1;
    AA(1,2)=-lamda(1)/h;
    AA(I+1,I)=-lamda(4)/h;
    AA(I+1,I+1)=lamda(4)/h+h5;

    AA(N1+1,N1)=-lamda(1);
    AA(N1+1,N1+1)=lamda(1)+lamda(2);
    AA(N1+1,N1+2)=-lamda(2);

    AA(N1+N2+1,N1+N2)=-lamda(2);
    AA(N1+N2+1,N1+N2+1)=lamda(2)+lamda(3);
    AA(N1+N2+1,N1+N2+2)=-lamda(3);

    AA(N1+N2+N3+1,N1+N2+N3)=-lamda(3);
    AA(N1+N2+N3+1,N1+N2+N3+1)=lamda(3)+lamda(4);
    AA(N1+N2+N3+1,N1+N2+N3+2)=-lamda(4);

    for n=1:k:N
        D=zeros(I+1,1);
        D(1)=h1*(T_out+TT);
        D(I+1)=h5*(T_in+TT);
        for i=2:1:N1
            D(i)=r*a(1)*T(i-1,n)+(2-2*r*a(1))*T(i,n)+r*a(1)*T(i+1,n);
        end
        for i=N1+1:1:N1+N2
            D(i)=r*a(2)*T(i-1,n)+(2-2*r*a(2))*T(i,n)+r*a(2)*T(i+1,n);
        end
        for i=N1+N2+1:1:N1+N2+N3
            D(i)=r*a(3)*T(i-1,n)+(2-2*r*a(3))*T(i,n)+r*a(3)*T(i+1,n);
        end
        for i=N1+N2+N3+1:1:N1+N2+N3+N4
            D(i)=r*a(4)*T(i-1,n)+(2-2*r*a(4))*T(i,n)+r*a(4)*T(i+1,n);
        end
        D(N1+1)=0;
        D(N1+N2+1)=0;
        D(N1+N2+N3+1)=0;
        T(:,n+1)=AA\D;
    end
   delta(j)=sqrt(sum((T_xt(:,2)-T(end,:)'+TT).^2)/length(T_xt(:,1)));
end
%图二
figure(1);
mesh(0:k:N,1000*(0:h:sum(d)),(T-TT));
%图三
T_problem1=zeros(N+1,4);
T_problem1(:,1)=T(1,:)';
T_problem1(:,2)=T(N1+1,:)';
T_problem1(:,3)=T(N2+N1+1,:)';
T_problem1(:,4)=T(N3+N2+N1+1,:)';
T_problem1=T_problem1-TT;
figure(2);
plot(0:k:N,T_problem1(:,1)',0:k:N,T_problem1(:,2)',0:k:N,T_problem1(:,3)',0:k:N,T_problem1(:,4)',0:k:N,T_xt(:,2)');

python数据处理(写的很乱)

为了写第一个模型整合各种数据。。。有点乱,将就看吧。

JD脚本仓库合集

一、使用说明

  • 更新一个整库脚本
1
ql repo <repourl> <path> <blacklist> <dependence> <branch>

  • 更新单个脚本文件
1
ql raw <fileurl>

二、拉取整库实例【以下仓库排名不分先后,纯粹随机排列】

(一)某已退圈并不愿透露姓名大佬库 的现存备份托管库`(下列方案选1个就行,排名不分先后)