百看不厌-matlab入门经典教程

wings

谢谢楼主了

wyqxxg

谢谢分享，完全不会真是头疼

yomi233

66666666666

云胡不喜

回帖也是一种美德

wqmaa

% This is an example of scaling EOFs
% Start by constructing a simple data set
norm = input('If you want to normalize input data, type 1, otherwise 0: ')
xnoise=0.5
xnoise = input('Set the noise level: 0.5 is low 2.0 is high: ')
N=100
M=20
per=12.
yes='yes';
no='no '
ans='no '
if norm == 1
ans=yes;
end

% In MATLAB the second index is the row index, which goes across the columns and
% gives the row data.
for i=1:N
for j=1:M
  x(j,i)=5.0*(j/M)*(sin(2*pi*(j-1)/(M-1))*sin(2*pi*i/per)+xnoise*randn(1));
end
end
xm=x;

% remove mean and possible standard deviation from data time series
for j=1:M
  xbar=mean(x(j,:));
   if norm == 1
    sdev=std(x(j,:));
    else
    sdev=1;
   end
for i=1:N
  xm(j,i)=x(j,i)/sdev-xbar;
end
end

% calculate covariance matrix
C=xm*xm'/N;
%C

% Do eigenanalysis
[E,D]=eig(C);
E
%calculate autocorrelation
for j=1:M
alpha(j,:)=auto(xm(j,:),1);
end
alpha
% the temporal autocorrelation is different for each spatial grid point,
% a problem.  Let's kluge this by using the mean of the mean and the maximum
% autocorrelation.  We'll take the absolute value before averaging, so that
% random negative correlations can't cancel out positive ones.  This is
% conservative.
ralph=(mean(abs(alpha(:,2)))+max(alpha(:,2)))/2.
tau=-1.0/log(ralph)
Nstar=N/(2*tau)
factor=sqrt(2./Nstar)

% Plot Eigenvalues
L=diag(D,0);
LE=L*factor
for j=1:M
ind(j)=M-j+1;
end
errorbar(ind,L,LE)
title(['Eigenvalue Spectrum:  Norm=  ' ans])
xlabel('index')

pause
% Plot eigenvectors
plot(E(:,M-2:M))
MP=M+1
axis([0 MP -0.60 0.60])
%set(gca,'DataAspectRatio',[1 22 1])
title(['First Three Eigenvectors:  Norm=  ' ans])
xlabel('Structure dimension')
pause

% Project eigenvectors onto original data to get PCs
Z=E'*x;
plot(Z(M-2:M,:)')
title(['First Three Principle Components:  Norm=  ' ans])
xlabel('Sampling dimension')
pause
%Normalize principle components time series
ZM=Z;
for i=1:N
for j=1:M
   zbar=mean(Z(j,:));
   sdev=std(Z(j,:));
  ZM(j,i)=Z(j,i)/sdev-xbar;
end
end

% remove mean from original, unnormalized data time series
for i=1:N
for j=1:M
  xbar=mean(x(j,:));
  xm(j,i)=x(j,i)-xbar;
end
end

% Project data onto normalized PC time series

EM=xm*ZM'/N;

%plot first three dimensional eigenvector regressions
plot(EM(:,M-2:M))
xmax=max(abs(EM(:,M)))*1.2;
axis([0 MP -xmax xmax])

title(['Regressions of First Three Normalized PCs on Unnormalized Data:  Norm=  ' ans])
xlabel('Structure dimension')

pause

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bei156bei

谢谢楼主，拿来参考一下