Hi Pat,
To automate the process of testing multiple input combinations and displaying the accuracy and error for each combination in MATLAB, these following steps might help:
- Load the Trained Neural Network
- Create a list of all the input combinations you wish to test.
- Use a loop to feed each combination into the neural network and collect the results.
- Display the accuracy and error for each combination.
Below is the sample of code that can help for better understanding:
% Load the trained neural network
% Assuming the trained network is saved as 'trainedNet.mat'
load('trainedNet.mat'); % Replace 'trainedNet.mat' with your actual file
% Define the input combinations
% Example: combination of inputs from 1 to 100
inputCombinations = nchoosek(1:100, 2); % Generate all 2-combinations of numbers 1 to 100
% Initialize arrays to store results
numCombinations = size(inputCombinations, 1);
accuracyResults = zeros(numCombinations, 1);
errorResults = zeros(numCombinations, 1);
% Loop through each combination
for i = 1:numCombinations
input = inputCombinations(i, :);
% Feed the input to the neural network
output = predict(trainedNet, input);
% Calculate accuracy and error
% Assuming you have a function calculateAccuracyAndError to compute these
[accuracy, error] = calculateAccuracyAndError(output, input);
% Store the results
accuracyResults(i) = accuracy;
errorResults(i) = error;
% Display the results
fprintf('Combination: %d, %d - Accuracy: %.2f%%, Error: %.2f\n', ...
input(1), input(2), accuracy * 100, error);
end
% Function to calculate accuracy and error
% Replace this with your actual accuracy and error calculation logic
function [accuracy, error] = calculateAccuracyAndError(output, input)
% Placeholder logic for calculating accuracy and error
% This should be replaced with your actual calculation logic
% For example, comparing output with expected values
expectedOutput = someExpectedOutputFunction(input); % Define this function as needed
accuracy = sum(output == expectedOutput) / length(expectedOutput);
error = sum((output - expectedOutput).^2) / length(expectedOutput);
end
Hope that helps!
