Matlab Covariance Matrix Computation for Different Classes - matlab

Question

I've got 2 different files, one of them is an input matrix (X) which has 3823*63 elements (3823 input and 63 features), the other one is a class vector (R) which has 3823*1 elements; those elements have values from 0 to 9 (there are 10 classes).
I have to compute covariance matrices for every classes. So far, i could only compute mean vectors for every classes with so many nested loops. However, it leads me to brain dead.
Is there any other easy way?
There is the code for my purpose (thanks to Sam Roberts):
xTra = importdata('optdigits.tra');
xTra = xTra(:,2:64); % first column's inputs are all zero
rTra = importdata('optdigits.tra');
rTra = rTra(:,65); % classes of the data
c = numel(unique(rTra));
for i = 1:c
rTrai = (rTra==i-1); % Get indices of the elements from the ith class
meanvect{i} = mean(xTra(rTrai,:)); % Calculate their mean
covmat{i} = cov(xTra(rTrai,:)); % Calculate their covariance
end

Answer 1

Does this do what you need?
X = rand(3263,63);
R = randi(10,3263,1)-1;
numClasses = numel(unique(R));
for i = 1:numClasses
Ri = (R==i); % Get indices of the elements from the ith class
meanvect{i} = mean(X(Ri,:)); % Calculate their mean
covmat{i} = cov(X(Ri,:)); % Calculate their covariance
end
This code loops through each of the classes, selects the rows of R that correspond to observations from that class, and then gets the same rows from X and calculates their mean and covariance. It stores them in a cell array, so you can access the results like this:
% Display the mean vector of class 1
meanvect{1}
% Display the covariance matrix of class 2
covmat{2}
Hope that helps!

Answer 2

Don't use mean and sum as a variable names because they are names of useful Matlab built-in functions. (Type doc mean or doc sum for usage help)
Also cov will calculate the covariance matrix for you.
You can use logical indexing to pull out the examples.
covarianceMatrices = cell(m,1);
for k=0:m-1
covarianceMatrices{k} = cov(xTra(rTra==k,:));
end
One-liner
covarianceMatrices = arrayfun(#(k) cov(xTra(rTra==k,:)), 0:m-1, 'UniformOutput', false);

Answer 3

First construct the data matrix for each class.
Second compute the covariance for each data matrix.
The code below does this.
% assume allData contains all the data you've read in, each row is one data point
% assume classVector contains the class of each data point
numClasses = 10;
data = cell(10,1); %use cells to store each of the data matrices
covariance = cell(10,1); %store each of the class covariance matrices
[numData dummy] = size(allData);
%get the data out of allData and into each class' data matrix
%there is probably a nice matrix way to do this, but this is hopefully clearer
for i = 1:numData
currentClass = classVector(i) + 1; %Matlab indexes from 1
currentData = allData(i,:);
data{currentClass} = [data{currentClass}; currentData];
end
%calculate the covariance matrix for each class
for i = 1:numClasses
covariance{i} = cov(data{i});
end