I understand that you are trying to code Garson's Algorithm in MATLAB. I have implemented the function below for your reference. Please note that this implementation assumes a single output neuron. If your network has multiple output neurons, you'll need to adjust the algorithm accordingly. The importance values are printed as percentages, indicating the relative importance of each input variable to the network's output.
function garsonsAlgorithm(net)
IW = net.IW{1}; % Input-to-hidden weights
LW = net.LW{2,1}; % Hidden-to-output weights
% Number of input and hidden neurons
numInputs = size(IW, 2);
numHidden = size(IW, 1);
% Calculate the connection strengths
hiddenLayerWeights = abs(IW);
outputLayerWeights = abs(LW);
% Calculate the relative importance (RI) for each input neuron to each hidden neuron
RI = hiddenLayerWeights ./ sum(hiddenLayerWeights, 2);
% Calculate the contribution of each hidden neuron to the output neuron
contribution = RI .* outputLayerWeights';
% Normalize contribution for each input to sum to 1
inputImportance = contribution ./ sum(contribution, 1);
% Display the importance of each input variable
for i = 1:numInputs
fprintf('Input %d importance: %.2f%%\n', i, 100 * inputImportance(i));
end
end
Given below is the code I used to test the function created above:
X = rand(10, 100); % 10 input features, 100 samples
T = rand(1, 100); % 1 output, 100 samples
% Create and train a feedforward neural network with one hidden layer
net = feedforwardnet(5); % 5 hidden neurons
net = train(net, X, T);
% Apply Garson's algorithm to the trained network
garsonsAlgorithm(net);
The results so obtained were as follows:
Secondly, changing the number of inputs to a neural network can affect the relative importance scores calculated by Garson's algorithm due to weight redistribution.
I hope this helps,
With regards,
Debraj.
