X(100,371)
%% contains 100 datapoints for 371 variables
I want to keep only the data which are within mean+standard deviation:mean-standard deviation.
This is how I am proceeding:
mx=mean(X);sx=std(X);
%%generate mean, std
%%this generates mx(1,371) and sx(1,371)
mx=double(repmat(mx,100,1));
%%this fills a matrix with the same datapoints,
%%100 times
sx=double(repmat(sx,100,1));
%% this gives mx(100,371) and sx(100,371)
g=X>mx-sx & X<mx+sx;
%%this creates a logical mask g(100,371)
%%filled with 1s and 0s
test(g)=X(g);
%%this should give me test(100,371), but I get
%%test(37100), which is wrong as it doesnt maintain
%%the shape of X
test=reshape(test,100,371)
%% but when I compare this to the my original matrix
%% X(100,371) I hardly see a difference (datapoints
%% in test are still outside the range I want.
What am I doing wrong?
There is just a little bit of syntax issue with the line
test(g) = X(g);
When the compiler executes X(g)
it returns all the elements in X
for which g
indicates 1, and at the assignment when it executes test(g)
it creates a test
variable good enough in size to be indexed by g
which is 1x37100 and then assigns all the elements at the right places. Long story short before the assignment you can add something like:
test = zeros(size(X));
While we are at this, you could use bsxfun to get the logical indexing without having to do repmat
g = bsxfun(@gt,X,mx - sx) & bsxfun(@lt,X,mx + sx)
In R2016b or recent there is implicit bsxfun expansion
g = X > mx - sx & X < mx + sx