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我需要一些關於AAM的建議，例如編碼，我試圖理解。結果無法完成，因爲出現錯誤說，說Matlab的內存不足：計算4D矩陣的平均值的替代方法
使用零的錯誤 內存不足。爲您的選項鍵入HELP MEMORY。
在導致錯誤的編碼是：
Error in AAM_MakeSearchModel2D (line 6)
drdp=zeros(size(ShapeAppearanceData.Evectors,2)+4,6,length(TrainingData),length(AppearanceData.g_mean));
在DRPD的實際數據是：
drdp=zeros(13,6,10,468249);
由於第四陣列是大的，這是可以理解的是，Matlab的那我使用的是32位內存不足。代碼將生成的輸出是2d。下面是以後要使用DRPD代碼：
drdpt=squeeze(mean(mean(drdp,3),2));
R=pinv(drdpt)';
，我要問的問題是，是否有可能分裂四維矩陣分解成較小的（如2D或3D），並進行正常的加法和除法（得到平均）。如果是的話，一個人會怎麼做？
編輯17/12/2013
由於4D DRPD是用於獲得存儲模型的所有加權誤差相對真正進入DRPD另一整個計算的初始化我不能使用稀疏。我複製AAM功能的一部分計算該DRPD：
function R=AAM_MakeSearchModel2D(ShapeAppearanceData,ShapeData,AppearanceData,TrainingData,options)
% Structure which will contain all weighted errors of model versus real
% intensities, by several offsets of the parameters
drdp=zeros(size(ShapeAppearanceData.Evectors,2)+4,6,length(TrainingData),length(AppearanceData.g_mean));
% We use the trainingdata images, to train the model. Because we want
% the background information to be included
% Loop through all training images
for i=1:length(TrainingData);
% Loop through all model parameters, bot the PCA parameters as pose
% parameters
for j = 1:size(ShapeAppearanceData.Evectors,2)+4
if(j<=size(ShapeAppearanceData.Evectors,2))
% Model parameters, offsets
de = [-0.5 -0.3 -0.1 0.1 0.3 0.5];
% First we calculate the real ShapeAppearance parameters of the
% training data set
c = ShapeAppearanceData.Evectors'*(ShapeAppearanceData.b(:,i) -ShapeAppearanceData.b_mean);
% Standard deviation form the eigenvalue
c_std = sqrt(ShapeAppearanceData.Evalues(j));
for k=1:length(de)
% Offset the ShapeAppearance parameters with a certain
% value times the std of the eigenvector
c_offset=c;
c_offset(j)=c_offset(j)+c_std *de(k);
% Transform back from ShapeAppearance parameters to Shape parameters
b_offset = ShapeAppearanceData.b_mean + ShapeAppearanceData.Evectors*c_offset;
b1_offset = b_offset(1:(length(ShapeAppearanceData.Ws)));
b1_offset= inv(ShapeAppearanceData.Ws)*b1_offset;
x = ShapeData.x_mean + ShapeData.Evectors*b1_offset;
pos(:,1)=x(1:end/2);
pos(:,2)=x(end/2+1:end);
% Transform the Shape back to real image coordinates
pos=AAM_align_data_inverse2D(pos,TrainingData(i).tform);
% Get the intensities in the real image. Use those
% intensities to get ShapeAppearance parameters, which
% are then used to get model intensities
[g, g_offset]=RealAndModel(TrainingData,i,pos, AppearanceData,ShapeAppearanceData,options,ShapeData);
% A weighted sum of difference between model an real
% intensities gives the "intensity/offset" ratio
w = exp ((-de(k)^2)/(2*c_std^2))/de(k);
drdp(j,k,i,:)=(g-g_offset)*w;
end
else
% Pose parameters offsets
j2=j-size(ShapeAppearanceData.Evectors,2);
switch(j2)
case 1 % Translation x
de = [-2 -1.2 -0.4 0.4 1.2 2]/2;
case 2 % Translation y
de = [-2 -1.2 -0.4 0.4 1.2 2]/2;
case 3 % Scaling & Rotation Sx
de = [-0.2 -.12 -0.04 0.04 0.12 0.2]/2;
case 4 % Scaling & Rotation Sy
de = [-0.2 -.12 -0.04 0.04 0.12 0.2]/2;
end
for k=1:length(de)
tform=TrainingData(i).tform;
switch(j2)
case 1 % Translation x
tform.offsetv(1)=tform.offsetv(1)+de(k);
case 2 % Translation y
tform.offsetv(2)=tform.offsetv(2)+de(k);
case 3 % Scaling & Rotation Sx
tform.offsetsx=tform.offsetsx+de(k);
case 4 % Scaling & Rotation Sy
tform.offsetsy=tform.offsetsy+de(k);
end
% From Shape tot real image coordinates, with a certain
% pose offset
pos=AAM_align_data_inverse2D(TrainingData(i).CVertices, tform);
% Get the intensities in the real image. Use those
% intensities to get ShapeAppearance parameters, which
% are then used to get model intensities
[g, g_offset]=RealAndModel(TrainingData,i,pos, AppearanceData,ShapeAppearanceData,options,ShapeData);
% A weighted sum of difference between model an real
% intensities gives the "intensity/offset" ratio
w =exp ((-de(k)^2)/(2*2^2))/de(k);
drdp(j,k,i,:)=(g-g_offset)*w;
end
end
end
end
% Combine the data to the intensity/parameter matrix,
% using a pseudo inverse
% for i=1:length(TrainingData);
% drdpt=squeeze(mean(drdp(:,:,i,:),2));
% R(:,:,i) = (drdpt * drdpt')\drdpt;
% end
% % Combine the data intensity/parameter matrix of all training datasets.
% %
% % In case of only a few images, it will be better to use a weighted mean
% % instead of the normal mean, depending on the probability of the trainingset
% R=mean(R,3);
drdpt=squeeze(mean(mean(drdp,3),2));
R=pinv(drdpt)';
%R = (drdpt * drdpt')\drdpt;
正如你可以在函數的最終代碼中看到的，四維DRPD是再擠，然後再計算成爲一個二維矩陣商店中的R 。由於'內存不足'問題，該函數無法初始化drpd，因爲它使用了很多空間（drdp =零（13,6,10,））。我可以以2D或3D形式存儲數據（拆分drpd部分），然後進行簡單的加法和除法以獲得均值，然後獲得'R'？
謝謝，並且對於長期的問題感到抱歉。