Extracted Features in .MAT files
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Hi.
I want to save the extracted features after signal processing to feed into classifier. For example, I have mean, standard deviation and one other coefficient as extracted features. How can I save them in .mat files? I should save it together or independently?
Thank you.
4 个评论
Guillaume
2019-4-1
To me it makes more sense to keep them together in the same file, but it's really a matter of preference. Whichever way you choose, you'll have to write the code to load them to match.
David Lee
2019-4-1
I am having a total of 128 samples of data and for each sample, three different features have been extracted from each sample
Presumably, you end up with just one variable, an array of 128 arrays of 3 features. So you would just save that in a mat file. If you end up with 128 different variables, then you've gone wrong, and we need to fix that part of your code.
David Lee
2019-4-1
采纳的回答
Guillaume
2019-4-1
It's hard to give you proper code without knowing the exact code you're using. It would go something like this:
samplesignals = {???}; %No idea what you're starting with. Here I am assuming a cell array of signals
featurevectors = cell(size(samplesignals)); %could also use a 2d matrix instead of a cell array
for sampleidx = 1:numel(samplesignals) % iterate over the signals
featurevectors{sampleidx} = yourfeaturefunction(samplesignals{sampleidx});
end
save('somefile.mat', 'featurevectors');
2 个评论
David Lee
2019-4-1
Basically, I want to save the extracted features to something like this. 
Guillaume
2019-4-1
Well, it'd be more or less the same code. Again, rough outline since you haven't given specifics.
Here I'm storing your feature vectors in a 128x3 matrix, which I then convert into a table.
samplesignals = {???}; %No idea what you're starting with. Here I am assuming a cell array of signals
numfeatures = 3;
featurevectors = zeros(numel(samplesginals), numfeatures);
for sampleidx = 1:numel(samplesignals) % iterate over the signals
featurevectors(sampleidx, :) = yourfeaturefunction(samplesignals{sampleidx}); %assuming it returns a 3 element vector
end
featurevectors = array2table(featurevectors, 'VariableNames', {'mean', 'std', 'corrcoeff'});
save('somefile.mat', 'featurevectors');
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