neural network input target data format, Vertical or Horizontal vector within cell array?
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I have a set of vectors measured dx dy on a given set of coordinates over a time period i=1:5;
If I make two neural networks, one for x and one for y, does my input for “error” and “input” (see below example code) have to be vertical or horizontal vector within the cell array?
%%%%%%%%%
example code
%%%%%%%%%
% if I have a set of vertical vectors coordinates with X position defined by Column 1 = cord(:,1) and Y position defines by Column 2 = (cord(:,2))
cord=[-2 -2;-2 -1;-2 0;-2 1;-2 2;-1 -2;-1 -1;-1 0;-1 1;-1 2;0 -2;0 -1;0 0;0 1;0 2; 1 -2;1 -1;1 0;1 1;1 2;2 -2;2 -1;2 0;2 1;2 2];
% I have a set of target vectors per point defined by dx = tmpError(:,1) & dy = tmpError(:,2)
tmpError=[0.3 0.2;0.1 0.2;0.3 0.4;0.1 0.2;0.4 0.5;...
0.1 0.4;0.2 0.1;0.3 0.2;0.1 0.2;0.4 0.5;...
0.2 0.2;0.4 0.4;0.2 0.1;0.3 0.2;0.1 0.5;...
0.4 0.3;0.3 0.3;0.3 0.2;0.2 0.2;0.3 0.5;...
0.3 0.2;0.4 0.2;0.1 0.4;0.4 0.2;0.4 0.5];
for i=1:5
error{i}=tmpError*i;
errorDx{i}=tmpError*i;
errorDy{i}=tmpError*i;
end
% Input would be same dxInput = input(:,1) & dyInput = input(:,2)
tmpInput=[1.3 1.2;1.1 1.2;1.3 1.4;1.1 1.2;1.4 1.5; 2.1 2.4;2.2 2.1;2.3 2.2;2.1 2.2;2.4 2.5; 3.2 3.2;3.4 3.4;3.2 3.1;3.3 3.2;3.1 3.5; 4.4 4.3;4.3 4.3;4.3 4.2;4.2 4.2;4.3 4.5; 5.3 5.2;5.4 5.2;5.1 5.4;5.4 5.2;5.4 5.5];
for i=1:5
input{i}=tmpInput*i;
inputDx{i}=tmpInput(:,1)*i;
inputDy{i}=tmpInput(:,2)*i;
end
%% OR should they be transposed....
cord=cord';
tmpError=tmpError';
for i=1:5
error{i}=tmpError*i;
errorDx{i}=tmpError(1,:)*i;
errorDy{i}=tmpError(2,:)*i;
end
tmpInputDx=repmat(tmpInput(:,1),1,5);
tmpInputDy=repmat(tmpInput(:,2),1,5);
2 commentaires
Greg Heath
le 15 Fév 2013
AAARRRGH ... Please use the ANSWERS formatting rules. See the blocks B,I,Aa,...{} Code and Help above the reply box? Even if you just use 1 indented line per command, it would help immensely. Also try putting braces {} around your code.
Greg
Réponses (2)
Greg Heath
le 15 Fév 2013
It would be helpful to the reader (especially the older ones) if you would use the notation 0.69 instead of just .69.
All inputs and outputs to the NN functions are matrices or cells of column vectors.
For a data set of N I-dimensional input vectors and N corresponding O-dimensional target vectors
[ I N ] = size(input)
[ O N ] = size(target)
or the cell equivalents
Inputs = { inputs };
Targets = { targets };
Hope this helps.
Thank you for formally accepting my answer
Greg
3 commentaires
Greg Heath
le 16 Fév 2013
Modifié(e) : Greg Heath
le 16 Fév 2013
In the general case, they are columns. You may be getting fooled by looking at one-dimensional timeseries. Each measurement is a one-dimensional column vector. The fact that you see the total data as a row instead of a sequence of one-dimensional columns is irrelevant.
Hope this helps.
Greg
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