I understand that you have a neural network with inputs of size 20x3 and a target matrix of size 20x900. You want to predict each column of the target individually using the same inputs and store the results in a matrix A of the same size (20x900).
To achieve this in you can loop through each column of the target matrix, perform predictions using your neural network, and store the results in 'matrix A'. Here is an example implementation for the approach:
% Assuming 'net' is your trained neural network
% 'inputs' is your input matrix (20x3)
% 'target' is your target matrix (20x900)
% Initialize matrix A
A = zeros(size(target));
% Iterate over each column of the target
for i = 1:size(target, 2)
% Extract the current column of the target
current_target = target(:, i);
% Run the neural network with the inputs
% Note: Ensure your neural network function is set up to handle
% input and output dimensions correctly.
predicted_output = net(inputs')'; % Transpose if net requires column vectors
% Store the predicted output in the corresponding column of A
A(:, i) = predicted_output;
end
I hope this helps!
