how to fix NaN value?

% Import Data
data = readmatrix('matrix 1.csv');
x = data(:,1:3);
y = data(:,4);
m = length(y);
% Visualization of data
histogram(x(:,3),10);
plot(x(:,3),y,'o');
% Normilize the features and transform the output
y2 = log(1+y);
for i = 1:3
x2(:,i) = (x(:,i)-min(x(:,i)))/(max(x(:,i))-min(x(:,i)));
end
histogram(x2(:,1),10);
% Train the Artificial Neural Network (ANN)
xt = x2';
yt = y2';
HiddenLayerSize = 10;
net = fitnet(HiddenLayerSize);
net.divideParam.trainRatio = 90/100;
net.divideParam.valRatio = 50/100;
net.divideParam.testRatio = 0/100;
[net,tr] = train(net, xt, yt);
% Preformance of the ANN network
yTrain = exp(net(xt(:,tr.trainInd)))-1; % GIVES NaN value
yTrainTrue = exp(yt(tr.trainInd))-1;
MSE = mean((yTrain - yTrainTrue).^2); %MSE gives NaN value
RMSE = sqrt(mean((yTrain - yTrainTrue).^2)); %RMSE gives NaN value
RealPercentageOfDestruction = exp(net(xt(:,tr.trainInd)))-1;%Real Percentage Of Destruction gives NaN value
yVal = exp(net(xt(:,tr.valInd)))-1; GIVES NaN value
yValTrue = exp(yt(tr.valInd))-1;
MSE1 = mean((yVal - yValTrue).^2); %MSE gives NaN value
RMSE1 = sqrt(mean((yVal - yValTrue).^2)); %RMSE gives NaN value

6 Comments

KSSV
KSSV on 8 Jun 2022
Check the values of yVal, do they have NaN? We cannot test as the input data file is not given.
here is my input data file
KSSV
KSSV on 8 Jun 2022
Seriously? You got only data of size 5x4 and you want to run ML?
Is that the issue? I'm sorry, I'm not a coder, can you please help me understand?
The question is vague. Wheher do you observe NaN values? What does "fixing" mean?
data = [30, 9.6, 0, 61.7; ...
40, 9.6, 53.885, 58.6; ...
50, 9.6, 61.725, 55.7; ...
60, 9.6, 62.555, 60.4; ...
70, 9.6, 63.415, 54.4];
x = data(:,1:3);
for i = 1:3
x2(:,i) = (x(:,i)-min(x(:,i)))/(max(x(:,i))-min(x(:,i)));
end
The first NaNs appear here: for i=2, the max and min values are the same, so you divide by zero. How do you want to "fix" this? It is the correct result in a mathematical sense.
I got it now, thanks for helping me out Jan, I know I'm dumb sorry about that.

Sign in to comment.

Answers (0)

Categories

Find more on Deep Learning Toolbox in Help Center and File Exchange

Products

Release

R2022a

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!