How to obtain mean of columns in multiple cells?

Question

MP 2022-10-9

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1820520-how-to-obtain-mean-of-columns-in-multiple-cells

评论： MP 2022-10-11

采纳的回答： Chunru

I have a cell (say "a") and it has 5X1 dimension , Class: Cell. (Shown in picture below)

Each cell contains different cells, having size 3439x72 Single.

Now, I want to obtain the mean values of

a{1,1}(1,:) , a{2,1}(1,:) , a{3,1}(1,:) , a{4,1}(1,:) , a{5,1}(1,:),...

a{1,1}(2,:) , a{2,1}(2,:) , a{3,1}(2,:) , a{4,1}(2,:) , a{5,1}(2,:),...

a{1,1}(3,:) , a{2,1}(3,:) , a{3,1}(3,:) , a{4,1}(3,:) , a{5,1}(3,:),...

...

a{1,1}(3439,:) , a{2,1}(3439,:) , a{3,1}(3439,:) , a{4,1}(3439,:) , a{5,1}(3439,:)

The final output will be one cell with size 1x1 containing mean values of matrix "a" and having dimension 3439x72 Single.

Can anyone please help. Any help will be greatly appriciated.

The picture below shows the example of matrix "a".

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Chunru 2022-10-9

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1820520-how-to-obtain-mean-of-columns-in-multiple-cells#answer_1069830

编辑：Chunru 2022-10-10

% Generate data
for i=1:5
    a{i} = randn(3439, 72);
end
% find means
s = zeros(size(a{1}));
for i=1:5
    s = s + a{i};
end
s = s/5;
m{1} = s  % if cell output is needed
m = 1×1 cell array
    {3439×72 double}
% Another way
% convert the cell array to n-d arraym
b = cell2mat(a);
b = reshape(b, 3439, 72, []);
bmean = mean(b, 3, 'omitnan')
bmean = 3439×72
   -0.1072    0.7186   -1.2518    0.6538    0.0450    0.2487   -0.0131   -0.5979    0.0913    0.2459    0.2499   -0.4384    0.2268    1.2775    0.4260    0.3953    1.1213    0.4292   -0.5834   -0.0036   -0.5446   -0.2942   -0.3175   -0.0058    0.3379    0.0833   -0.1570   -0.4981    0.6502   -0.3571
    0.3296   -0.3576   -0.0878    0.7029   -0.3468   -0.3598    0.3431    0.2771   -0.3125    0.4491    0.2682    0.3570    0.3437    1.4581   -0.1591    0.2845   -0.0704    0.4437    0.1149   -0.5652    0.3740    0.5756    0.3369    0.2193   -0.0896    0.7258    0.1833   -0.2490    0.6524    0.3205
    0.7501   -0.7461    0.0481   -0.6806   -0.0029   -0.4085    0.1619    0.3956    0.1897   -0.9844    0.3036    0.5573    0.1616   -0.5567   -0.2975    0.3156   -0.4230   -0.3315    0.2060    0.4804   -0.2614    0.3183   -0.3542    1.0261    0.2275    0.3702    0.2705   -0.1034    0.9476    0.4038
    0.5143   -0.6436    0.4221    0.0232    0.6096    0.6368   -0.0515    0.6973   -0.6362   -0.5205    0.0560   -0.0415   -0.7558    0.6641    0.6799   -0.3440    0.0877    0.3927   -0.0997   -0.9058    0.4118   -0.0052   -0.1239    0.7165   -0.0513   -0.4080    0.0001   -0.0915   -0.3898    0.3817
   -0.2457   -0.3139   -0.0633   -0.2184    0.3175    0.6855    0.1546    0.1740    0.3062    0.2652    0.0356    0.4685   -0.6416    0.0628   -0.0215    0.1770   -0.1564   -0.1714    0.5383   -0.0258    1.0216   -0.2419    0.0912   -0.6164    0.7431   -0.5076    0.2734    0.4250    0.2200    0.7192
    0.4551   -0.2876    0.3299   -0.6889   -0.2404   -0.8372    0.0219   -0.0181    0.1463   -0.5245    0.2721    0.1144    0.1633   -0.3012    0.2346    0.4896    0.0539   -0.4367   -0.4442   -0.1432    0.3200   -0.3041    0.4268   -0.1290   -0.3429    0.5477   -0.0069   -0.1900   -0.3022    0.6622
   -0.0253    0.3395    0.0301   -0.2680   -0.0379    0.1428    0.3302    0.0596    0.5808   -0.0830   -0.2721    0.6924    0.0888    0.8348    0.1913    0.0173   -0.1094    0.3777    0.2724   -0.4384   -0.0410    0.3503    0.5023    0.2206   -0.5890    0.0367   -0.3338    0.9258   -0.4508   -0.2004
    0.0719    1.0938    0.8232   -0.2318    0.1890    0.1043   -0.4000    0.2134   -0.2826   -0.1097    0.6548    0.4961    0.3926   -0.5436   -0.1727   -0.5459    0.1039    0.0243    0.1120   -0.1421   -0.3257    0.8620   -0.3420    0.0868   -0.4102   -0.3604   -0.1350    0.4695    0.0834    0.3250
   -0.3574   -0.1595    0.1768   -0.0326   -0.2493    0.1360    0.2644    0.3307   -0.0123    0.0974    0.2792   -0.6300    0.5049   -0.0481   -0.0354   -0.8216   -0.1767   -0.3365    0.1326    0.4728   -0.3519   -0.2839    0.4667    0.1003    0.1995   -0.4195   -0.4603    1.2199   -0.0732   -0.5929
   -0.5021   -0.7128   -0.6214    0.3192    0.1684   -0.2102    0.4546    0.3216    0.6002    0.0061    0.2754    0.0905   -0.4532   -0.4504    0.4331    0.2006   -0.0750   -0.7206   -0.8921    0.5873   -0.0347   -0.1797   -1.4079   -0.2568    0.0494   -0.0938    0.9003    0.1116   -0.6840   -0.5167
whos
  Name           Size                Bytes  Class     Attributes

  a              1x5               9904840  cell                
  b           3439x72x5            9904320  double              
  bmean       3439x72              1980864  double              
  cmdout         1x33                   66  char                
  i              1x1                     8  double              
  m              1x1               1980968  cell                
  s           3439x72              1980864  double              

6 个评论
显示 4更早的评论隐藏 4更早的评论

Chunru 2022-10-10

%load(websave("bmean.mat", "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1150810/bmean.mat"))
%load(websave("b.mat", "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1150815/b.mat"))
load(websave("a.mat", "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1150820/a.mat"))
FODU4   % This is a 5x1 cell array instead of 1x5 array
FODU4 = 5×1 cell array
    {3439×72 single}
    {3439×72 single}
    {3439×72 single}
    {3439×72 single}
    {3439×72 single}
FODU5 = cell2mat(FODU4');           % tranpose the cell array so that cell2mat works
FODU5 = reshape(FODU5, 3439, 72, []); 
FODU6 = mean(FODU5, 3, 'omitnan'); 
whos
  Name           Size                Bytes  Class     Attributes

  FODU4          5x1               4952680  cell                
  FODU5       3439x72x5            4952160  single              
  FODU6       3439x72               990432  single              
  cmdout         1x33                   66  char                