Error using confusionmat command

Hi! I am trying to do this question:

a). Use fitcknn() with default options to create a k-nearest neighbour classifier for the data. Use the classifier to predict the classes for the test set in sat.tst and compare the predictions to the actual classes. Finally, draw a confusion plot of the test set results.

b). Use patternnet() to create and train a pattern recognition neural network using the training data. Compare the test set confusion plot to part a).

I have managed to ge the code working for part a but when I try to do part b i get this error "Error using confusionmat

G and GHAT need to have same number of rows

Error in exercise3 (line 32)

C_nn = confusionmat(Y_test, Y_pred_nn);"

This is the code i have

trainData = load('sat.trn');
X_train = trainData(:, 1:36);
Y_train = trainData(:, 37);
% Load test data
testData = load('sat.tst');
X_test = testData(:, 1:36);
Y_test = testData(:, 37);
% Train a k-NN classifier
knnModel = fitcknn(X_train, Y_train);
Y_pred = predict(knnModel, X_test);
C = confusionmat(Y_test, Y_pred);
confusionchart(C, unique(Y_test));
%% Exercise 3b
net = patternnet(10); % Adjust the number of output neurons to match the number of classes (7 in your case)
% Train the network
net = train(net, X_train', Y_train');
% Use the trained network to make predictions on the test data
Y_pred_nn = net(X_test');
% Convert the network's output to class labels
[~, Y_pred_nn] = max(Y_pred_nn);
% Calculate the confusion matrix using confusionmat
C_nn = confusionmat(Y_test, Y_pred_nn);
% Plot the confusion matrix
confusionchart(C_nn, unique(Y_test));

does anyone know what the error means and how do i fix it? I can't seem to find it online.

[the cyclist]

Can you upload the data, so that we can run your code? You can use the paper clip icon in the INSERT section of the toolbar.

Answer 1

[the cyclist]

I'd like to see the data, but in this line

C_nn = confusionmat(Y_test, Y_pred_nn);

isn't Y_test a Nx1 vector (where N is the number of observations in the test set), and Y_pred_nn is just a scalar (the index to the max), because you redefined Y_pred_nn in the line

[~, Y_pred_nn] = max(Y_pred_nn);

I think maybe you re-used a variable to mean something else, in a way you did not intend.