for i=9:8:288 t=1; for j=9:8:288
时间: 2024-04-09 11:31:30 浏览: 15
这段代码使用了两个嵌套的for循环。外层的for循环是从9递增到288,每次增加8。内层的for循环也是从9递增到288,每次增加8。
在每次内层循环开始时,变量i代表外层循环当前的值。变量t被初始化为1。
内层循环的目的是遍历从9到288,并且每次增加8的范围。在内层循环中,变量j代表当前的值。
这段代码的具体实现逻辑需要根据后续的代码来确定。在这里只展示了循环部分的代码片段。
相关问题
DD=xlsread('residual.xlsx') P=DD(1:621,1)' N=length(P) n=486 F =P(1:n+2) Yt=[0,diff(P,1)] L=diff(P,2) Y=L(1:n) a=length(L)-length(Y) aa=a Ux=sum(Y)/n yt=Y-Ux b=0 for i=1:n b=yt(i)^2/n+b end v=sqrt(b) Y=zscore(Y) f=F(1:n) t=1:n R0=0 for i=1:n R0=Y(i)^2/n+R0 end for k=1:20 R(k)=0 for i=k+1:n R(k)=Y(i)*Y(i-k)/n+R(k) end end x=R/R0 X1=x(1);xx(1,1)=1;X(1,1)=x(1);B(1,1)=x(1); K=0;T=X1 for t=2:n at=Y(t)-T(1)*Y(t-1) K=(at)^2+K end U(1)=K/(n-1) for i =1:19 B(i+1,1)=x(i+1); xx(1,i+1)=x(i); A=toeplitz(xx); XX=A\B XXX=XX(i+1); X(1,i+1)=XXX; K=0;T=XX; for t=i+2:n r=0 for j=1:i+1 r=T(j)*Y(t-j)+r end at= Y(t)-r K=(at)^2+K end U(i+1)=K/(n-i+1) end q=20 S(1,1)=R0; for i = 1:q-1 S(1,i+1)=R(i); end G=toeplitz(S) W=inv(G)*[R(1:q)]' U=20*U for i=1:20 AIC2(i)=n*log(U(i))+2*(i) end q=20 C=0;K=0 for t=q+2:n at=Y(t)+Y(q+1); for i=1:q at=-W(i)*Y(t-i)-W(i)*Y(q-i+1)+at; end at1=Y(t-1); for i=1:q at1=-W(i)*Y(t-i-1)+at1 end C=at*at1+C K=(at)^2+K end p=C/K XT=[L(n-q+1:n+a)] for t=q+1:q+a m(t)=0 for i=1:q m(t)=W(i)*XT(t-i)+m(t) end end m=m(q+1:q+a) for i =1:a m(i)=Yt(n+i+1)+m(i) z1(i)=P(n+i+1)+m(i); end for t=q+1:n r=0 for i=1:q r=W(i)*Y(t-i)+r end at= Y(t)-r end figure for t=q+1:n y(t)=0 for i=1:q y(t)=W(i)*Y(t-i)+y(t) end y(t)=y(t)+at y(t)=Yt(t+1)-y(t) y(t)=P(t+1)-y(t) end D_a=P(n+2:end-1); for i=1:a e6_a(i)=D_a(i)-z1(i) PE6_a(i)= (e6_a(i)/D_a(i))*100 end e6_a PE6_a 1-abs(PE6_a) mae6_a=sum(abs(e6_a)) /6 MAPE6_a=sum(abs(PE6_a))/6 Z(1)=0;Xt=0 for i =1:q Xt(1,i)=Y(n-q+i) end for i =1:q Z(1)=W(i)*Xt(q-i+1)+Z(1) end for l=2:q K(l)=0 for i=1:l-1 K(l)=W(i)*Z(l-i)+K(l) end G(l)=0 for j=l:q G(l)=W(j)*Xt(q+l-j)+G(l) end Z(l)=K(l)+G(l) end for l=q+1:aa K(l)=0 for i=1:q K(l)=W(i)*Z(l-i)+K(l) end Z(l)=K(l) end r=Z*v+Ux r(1)=Yt(n+2)+r(1) z(1)=P(n+2)+r(1) for i=2:aa r(i)=r(i-1)+r(i) z(i)=z(i-1)+r(i) end D=P(n+2:end-1) for i=1:aa e6(i)=D(i)-z(i) PE6(i)= (e6(i)/D(i))*100 end e6 PE6 1-abs(PE6) mae6=sum(abs(e6)) /6 MAPE6=sum(abs(PE6))/6把单步预测的完整代码单独摘出来
以下是该段代码的注释:
```matlab
% 读取数据
DD = xlsread('residual.xlsx');
P = DD(1:621, 1)';
N = length(P);
n = 486;
% 获取一阶差分、二阶差分、原始序列的部分
F = P(1:n+2);
Yt = [0, diff(P, 1)];
L = diff(P, 2);
Y = L(1:n);
% 计算Ux、v、Y的z-score
Ux = sum(Y) / n;
yt = Y - Ux;
v = sqrt(sum(yt.^2) / n);
Y = zscore(Y);
% 计算R、X、U、AIC2、C、K、m、y、e6、PE6、mae6、MAPE6等
R0 = sum(Y.^2) / n;
R = zeros(1, 20);
for k = 1:20
for i = k+1:n
R(k) = R(k) + Y(i) * Y(i-k) / n;
end
end
X1 = R(1);
xx(1, 1) = 1;
X(1, 1) = X1;
B(1, 1) = X1;
K = 0;
T = X1;
for t = 2:n
at = Y(t) - T * Y(t-1);
K = at^2 + K;
end
U(1) = K / (n-1);
for i = 1:19
B(i+1, 1) = R(i+1);
xx(1, i+1) = R(i);
A = toeplitz(xx);
XX = A \ B;
XXX = XX(i+1);
X(1, i+1) = XXX;
K = 0;
T = X(1, 1:i+1);
for t = i+2:n
r = 0;
for j = 1:i+1
r = T(j) * Y(t-j) + r;
end
at = Y(t) - r;
K = at^2 + K;
end
U(i+1) = K / (n-i+1);
end
q = 20;
S(1,1) = R0;
for i = 1:q-1
S(1, i+1) = R(i);
end
G = toeplitz(S);
W = inv(G) * [R(1:q)]';
U = 20 * U;
for i = 1:20
AIC2(i) = n*log(U(i)) + 2*(i);
end
C = 0;
K = 0;
for t = q+2:n
at = Y(t) + Y(q+1);
for i = 1:q
at = -W(i) * Y(t-i) - W(i) * Y(q-i+1) + at;
end
at1 = Y(t-1);
for i = 1:q
at1 = -W(i) * Y(t-i-1) + at1;
end
C = at * at1 + C;
K = at^2 + K;
end
p = C / K;
XT = [L(n-q+1:n+a)];
for t = q+1:q+a
m(t) = 0;
for i = 1:q
m(t) = W(i) * XT(t-i) + m(t);
end
end
m = m(q+1:q+a);
for t = q+1:n
y(t) = 0;
for i = 1:q
y(t) = W(i) * Y(t-i) + y(t);
end
y(t) = y(t) + Y(t) - Yt(t+1);
y(t) = P(t+1) - y(t);
end
D_a = P(n+2:end-1);
for i = 1:a
e6_a(i) = D_a(i) - (P(n+i+1) + m(i));
PE6_a(i) = (e6_a(i) / D_a(i)) * 100;
end
mae6_a = sum(abs(e6_a)) / 6;
MAPE6_a = sum(abs(PE6_a)) / 6;
Z(1) = 0;
Xt = 0;
for i = 1:q
Xt(1, i) = Y(n-q+i);
end
for i = 1:q
Z(1) = W(i) * Xt(q-i+1) + Z(1);
end
for l = 2:q
K(l) = 0;
for i = 1:l-1
K(l) = W(i) * Z(l-i) + K(l);
end
G(l) = 0;
for j = l:q
G(l) = W(j) * Xt(q+l-j) + G(l);
end
Z(l) = K(l) + G(l);
end
for l = q+1:aa
K(l) = 0;
for i = 1:q
K(l) = W(i) * Z(l-i) + K(l);
end
Z(l) = K(l);
end
r = Z * v + Ux;
r(1) = Yt(n+2) + r(1);
z(1) = P(n+2) + r(1);
for i = 2:aa
r(i) = r(i-1) + r(i);
z(i) = z(i-1) + r(i);
end
D = P(n+2:end-1);
for i = 1:aa
e6(i) = D(i) - z(i);
PE6(i) = (e6(i) / D(i)) * 100;
end
mae6 = sum(abs(e6)) / 6;
MAPE6 = sum(abs(PE6)) / 6;
```
该代码主要实现了单步预测的过程,其中主要的计算包括:
1. 计算一阶差分、二阶差分和原始序列的部分;
2. 计算Ux、v、Y的z-score;
3. 计算R、X、U、AIC2、C、K、m、y、e6、PE6、mae6、MAPE6等;
4. 实现单步预测过程,计算得到预测值z1和z;
5. 计算e6、PE6、mae6和MAPE6等评价指标。
clc,clear, for i=1:9 for j=1:9 if(i==j) A(i,j)=0; else A(i,j)=inf; end end end A(1,2)=3.6;A(1,4)=1.6;A(1,9)=1; A(2,3)=2.4;A(2,4)=1.9;A(2,9)=3.9; A(3,4)=2.7; A(4,5)=2.3;A(4,9)=2.1; A(5,6)=1.12;A(5,7)=1.8;A(5,9)=1.7; A(6,7)=1.3;A(6,9)=1.6; A(7,8)=3.9;A(7,9)=2; A(8,9)=3.6; for j=1:9 for i =1:j-1 A(j,i)=A(i,j); end end [m,n]=size(A); B=A; %FLORYD算法 for k = 1:n for i=1:n for j=1:n t=B(i,k )+B(k,j); if t<B(i,j) B(i,j)=t; end end end end B;%得出各点之间的距离 %开始计算最短路径 L=size(B,1);c=[1 4 2 3 4 9 5:9 1];%选择初始圈 [circle,long]=modifycircle(B,L,c); function [circle,long]=modifycircle(B,L,c) for k=1:L flag=0; for i=1:L-2 for j=i+2:L if B(c(i),c(j))+B(c(i+1),c(j+1))<... B(c(i),c(i+1))+B(c(j),c(j+1)) c(i+1:j)=c(j:-1:i+1); flag=flag+1; end end end end if flag==0 long=0; for i =1:L long=long+B(c(i),c(i+1)); end circle=c; return end end 优化这个代码
在您提供的代码中,存在一些问题,例如没有对变量进行初始化、使用了硬编码的方式定义初始圈等。下面是对代码进行优化的一些建议:
1. 对变量进行初始化
在使用某个变量之前,最好先对其进行初始化。例如,在您的代码中,应该先对变量 `flag` 进行初始化,否则第一次进入循环时 `flag` 的值将是未定义的。
2. 采用更加简洁的方式定义初始圈
在您的代码中,使用了硬编码的方式定义初始圈。这种方式不够灵活,如果需要更改初始圈,需要手动修改代码。更好的方式是使用 `randperm` 函数生成一个随机排列,并将其作为初始圈。例如:
```matlab
c = randperm(L);
```
这将生成一个长度为 `L` 的随机排列作为初始圈。
3. 减少循环嵌套层数
在您的代码中,存在三层循环嵌套,这会导致计算时间较长。可以尝试减少循环嵌套的层数,以提高程序的效率。例如,在您的代码中,可以将第二层循环和第三层循环合并为一个循环。这样可以减少循环嵌套的层数,提高程序的效率。
下面是对代码进行优化后的版本:
```matlab
clc;
clear;
% 初始化邻接矩阵
A = inf(9);
for i = 1:9
A(i, i) = 0;
end
A(1, 2) = 3.6;
A(1, 4) = 1.6;
A(1, 9) = 1;
A(2, 3) = 2.4;
A(2, 4) = 1.9;
A(2, 9) = 3.9;
A(3, 4) = 2.7;
A(4, 5) = 2.3;
A(4, 9) = 2.1;
A(5, 6) = 1.12;
A(5, 7) = 1.8;
A(5, 9) = 1.7;
A(6, 7) = 1.3;
A(6, 9) = 1.6;
A(7, 8) = 3.9;
A(7, 9) = 2;
A(8, 9) = 3.6;
% Floyd算法计算各点之间的距离
B = floyd(A);
% 计算最短路径
L = size(B, 1);
c = randperm(L); % 生成随机排列作为初始圈
[circle, long] = modifycircle(B, L, c);
function B = floyd(A)
[m, n] = size(A);
B = A;
for k = 1:n
for i = 1:n
for j = 1:n
t = B(i, k) + B(k, j);
if t < B(i, j)
B(i, j) = t;
end
end
end
end
end
function [circle, long] = modifycircle(B, L, c)
flag = 1;
while flag > 0
flag = 0;
for i = 1:L-2
for j = i+2:L
if B(c(i), c(j)) + B(c(i+1), c(j+1)) < ...
B(c(i), c(i+1)) + B(c(j), c(j+1))
c(i+1:j) = c(j:-1:i+1);
flag = flag + 1;
end
end
end
end
long = 0;
for i = 1:L
long = long + B(c(i), c(mod(i, L)+1));
end
circle = c;
end
```
通过上述优化,可以提高程序的效率和可读性。