I would like to include a loop in my script which finds the correlation of every possible combination of the data. This can be done manually by the following code:
clear all
%generate fake data
LName={'Name1','Name2','Name3'};
Data={rand(12,1),rand(12,1),rand(12,1)};
%place in a structure
d = [LName;Data];
Data = struct(d{:});
%find the correlation
[R,P] = corrcoef(Data.Name1,Data.Name2);
[R2,P2] = corrcoef(Data.Name1,Data.Name3);
[R3,P3] = corrcoef(Data.Name2,Data.Name3);
However, I would like to do this in a loop, I have started but have failed at the first hurdle. My attempted loop, which doesn't work is shown below:
SNames=fieldnames(Data);
for i=1:numel(SNames);
[R{i},P{i}] = corrcoef(Data.(SNames{i}),Data.(SNames{i+1}));
end
I'm struggling on knowing how to tell matlab to loop over a different combination of values with every iteration.
Any help provided would be much appreciated.
Try something like this:
pairs = combnk (1:3,2) % all combinations of 2 elements taken out of the vector [1,2,3]
for i = 1 : size (pairs,1)
[R{i},P{i}] = corrcoef(Data.(SNames{pairs(i,1)}),Data.(SNames{pairs(i,2)}));
end
#ItamarKatz answer is a good one. However, if you don't have the statistics toolbox, you can not use the combnk command.
In that case, you can download combinations generator from here.
Related
I have a huge matrix (5000x5000x100) and I'm trying to smooth each index along the third dimension, but it takes HOURS. I am doing something inefficiently.
new_mat=zeros(size(my_mat));
for i = 1:length(mymat)
for j = 1:length(mymat)
new_mat(i,j,:) = wdenoise(squeeze(mymat(i,j,:)));
end
end
I know arrays and indexing would help but I'm not sure how to apply them here. Thanks for any help.
I don't have access to the newer Wavelet Toolbox needed for wdenoise, but since the function will operate across columns if you provide a matrix you should be able to remove the inner loop which may increase the speed a bit:
new_mat=zeros(my_mat)
for i = 1:length(mymat)
new_mat(i,:,:) = wdenoise(squeeze(mymat(i,:,:)));
end
I have a dataset of n nifti (.nii) images. Ideally, I'd like to be able to get the value of the same voxel/element from each image, and apply a function to the n data points. I'd like to do this for each voxel/element across the whole image, so that I can reconvert the result back into .nii format.
I've used the Tools for NIfTI and ANALYZE image toolbox to load my images:
data(1)=load_nii('C:\file1.nii');
data(2)=load_nii('C:\file2.nii');
...
data(n)=load_nii('C:\filen.nii');
From which I obtain a struct object with each sub-field containing one loaded nifti. Each of these has a subfield 'img' corresponding to the image data I want to work on. The problem comes from trying to select a given xyz within each img field of data(1) to data(n). As I discovered, it isn't possible to select in this way:
data(:).img(x,y,z)
or
data(1:n).img(x,y,z)
because matlab doesn't support it. The contents of the first brackets have to be scalar for the call to work. The solution from googling around seems to be a loop that creates a temporary variable:
for z = 1:nz
for x = 1:nx
for y = 1:ny
for i=1:n;
points(i)=data(i).img(x,y,z);
end
[p1(x,y,z,:),~,p2(x,y,z)] = fit_data(a,points,b);
end
end
end
which works, but takes too long (several days) for a single set of images given the size of nx, ny, nz (several hundred each).
I've been looking for a solution to speed up the code, which I believe depends on removing those loops by vectorisation, preselecting the img fields (via getfield ?)and concatenating them, and applying something like arrayfun/cellfun/structfun, but i'm frankly a bit lost on how to do it. I can only think of ways to pre-select which themselves require loops, which seems to defeat the purpose of the exercise (though a solution with fewer loops, or fewer nested loops at least, might do it), or fun into the same problem that calls like data(:).img(x,y,z) dont work. googling around again is throwing up ways to select and concatenate fields within a struct, or a given field across multiple structs. But I can't find anything for my problem: select an element from a non-scalar sub-field in a sub-struct of a struct object (with the minimum of loops). Finally I need the output to be in the form of a matrix that the toolbox above can turn back into a nifti.
Any and all suggestions, clues, hints and help greatly appreciated!
You can concatenate images as a 4D array and use linear indexes to speed up calculations:
img = cat(4,data.img);
p1 = zeros(nx,ny,nz,n);
p2 = zeros(nx,ny,nz);
sz = ny*nx*nz;
for k = 1 : sz
points = img(k:sz:end);
[p1(k:sz:end),~,p2(k)] = fit_data(a,points,b);
end
I have a large matrix, and I need to extract a small matrix taken from a sliding window which runs all over the large matrix, but during the operations the content of the extracted matrix does not change, so I'd like to extract the submatrix without creating a new copy but instead just acts like a C pointer that points to a portion of the large matrix. How can I do this? Please help me, thank you very much :)
I did some benchmarking to test if not using an explicit temporary matrix is faster, and it's probably not:
function move_mean(N)
M = randi(100,N);
window_size = [50 50];
dir_time = timeit(#() direct(M,window_size))
tmp_time = timeit(#() with_tmp(M,window_size))
end
function direct(M,window_size)
m = zeros(size(M)./2);
for r = 1:size(M,1)-window_size(1)
for c = 1:size(M,2)-window_size(2)
m(r,c) = mean(mean(M(r:r+window_size(1),c:c+window_size(2))));
end
end
end
function with_tmp(M,window_size)
m = zeros(size(M)./2);
for r = 1:size(M,1)-window_size(1)
for c = 1:size(M,2)-window_size(2)
tmp = M(r:r+window_size(1),c:c+window_size(2));
m(r,c) = mean(mean(tmp));
end
end
end
for M at size 100*100:
dir_time =
0.22739
tmp_time =
0.22339
So it's seems like using a temporary variable only makes your code readable, not slower.
In this answer I describe what is the 'best' solution in general. For this answer I define 'best' as most readable without a significant performance hit. (Partially shown by the existing answer).
Basically there are 2 situations that you may be in.
1. You use your submatrix several times
In this situation the best solution in general is to create a temporary variable containing the submatrix.
A = M(rmin:rmax, cmin:cmax)
There may be ways around it (defining a function/anonymous function that indexes into the matrix for you), but in general that won't make you happy.
2. You use your submatrix only 1 time
In this case the best solution is typically exactly what you referred to in the comments:
M(rmin:rmax, cmin:cmax)
A specific case of using the submatrix only 1 time, is when it is passed once to a function. Of course the contents of the submatrix may be used in that function several times, but that is irrelevant.
I have the following matlab code:
messages_llr_matrix = sparse(ROW_NUM, COL_NUM);
for index = 1 : length(II)
messages_llr_matrix(II(index), JJ(index)) = ...
code_word_aprior_prob(JJ(index));
end
This code takes a vector and copies it, to a sparse matrix rows if the element in the matrix is not zero.
matlab tells me not to use indexing in the previous matlab code, because it makes it very slow.
I want to build the messages_llr_matrix matrix in the following manner:
messages_llr_matrix = sparse(II,JJ,code_word_aprior_prob,ROW_NUM,COL_NUM);
This is not a solution that works.
The problem seems to be that in your latter code II(ind), JJ(ind) and code_word_aprior_prob(ind) should go together, but in your convention you need code_word_aprior_prob(JJ(ind)).
Try calling
messages_llr_matrix = sparse(II,JJ,code_word_aprior_prob(JJ),ROW_NUM,COL_NUM);
My data is x,y co-ordinates in multiple files
a=dir('*.mat')
b={a(:).name}
to load the filenames in a cell array
How do I use a loop to sequentially load one column of data from each file into consecutive rows of a new/separate array......?
I've been doing it individually using e.g.
Load(example1.mat)
A(:,1)=AB(:,1)
Load(example2.mat)
A(:,2)=AB(:,1)
Load(example3.mat)
A(:,3)=AB(:,1)
Obviously very primitive and time consuming!!
My Matlab skills are weak so any advice gratefully received
Cheers
Many thanks again, I'm still figuring out how to read the code but I used it like this;
a=dir('*.mat');
b={a(:).name};
test1=zeros(numel(b),1765);
for k=1:numel(b) S=load(b{k});
I then used the following code to create a PCA cluster plot
test1(k,:)=S.AB(:,2); end [wcoeff,score,latent,tsquared,explained] = pca(test1,... 'VariableWeights','variance');
c3 = wcoeff(:,1:3) coefforth = inv(diag(std(test1)))*wcoeff; I = c3'*c3 cscores = zscore(test1)*coefforth;
figure() plot(score(:,1),score(:,2),'+') xlabel('1st Principal Component') ylabel('2nd Principal Component') –
I was using 'gname' to label the points on the cluster plot but found that the point were simply labelled from 1 to the number of rows in the array.....I was going to ask you about this but I found out simply through trial and error if I used 'gname(b)' this labels the points with the .names listed in b.....
However the clusterplot starts to look very busy/messy once I have labelled quite a few points so now I am wondering is is possible to extract the filenames into a list by dragging round or selecting a few points, I think it is possible as I have read a few related topics.....but any tips/advice around gname or labelled/extracting labels from clusterplots would be greatly appreciated. Apologies again for my formatting I'm still getting used to this website!!!
Here is a way to do it. Hopefully I got what you wanted correctly :)
The code is commented but please ask any questions if something is unclear.
a=dir('*.mat');
b={a(:).name};
%// Initialize the output array. Here SomeNumber depends on the size of your data in AB.
A = zeros(numel(b),SomeNumber);
%// Loop through each 'example.mat' file
for k = 1:numel(b)
%// ===========
%// Here you could do either of the following:
1)
%// Create a name to load with sprintf. It does not require a or b.
NameToLoad = sprintf('example%i.mat',k);
%// Load the data
S = load(NameToLoad);
2)
%// Load directly from b:
S = load(b{k});
%// ===========
%// Now S is a structure containing every variable from the exampleX.mat file.
%// You can access the data using dot notation.
%// Store the data into rows of A
A(k,:) = S.AB(:,1);
end
Hope that is what you meant!