CODEWITHSUNDEEP
matlabgroupingmatrix-indexing
matlabit
matlabit

Reputation: 858

Compute mean on matrix by groups of indices matlab

I have the following data:

A = [1 2 ; 3 2; 4 7; 10 2; 6 7; 10 9]
B = [1 2 3; 4 4 9; 1 8 0; 3 7 9; 3 6 8]
C = [4; 10; 6; 3; 1]

A =
    1     2
    3     2
    4     7
   10     2
    6     7
   10     9

B =
    1     2     3
    4     4     9
    1     8     0
    3     7     9
    3     6     8

C.' =
    4    10     6     3     1

For each unique value in A(:,2) I need to take the corresponding values in A(:,1), look for their value in C, then take the relevant rows in B and compute their mean. The result should be length(unique(A(:,2)) x size(B,2);

The expected result for this example:

Correspondingly:

I compute it now by applying unique on A and iterating each value, searching the right indices. My data set is quite large, so it takes quite a time. How can I avoid the loops?

Upvotes: 1

Views: 1026

Answers (3)

Mohsen Nosratinia
Mohsen Nosratinia

Reputation: 9864

Here is another solution without arrayfun and accumarray using good old-fashion matrix multiplication:

r = bsxfun(@eq, A(:,1), C')*(1:numel(C))'; 
[~,m,n] = unique(A(:,2));
f=histc(n, 1:numel(m));
result = diag(1./f)*bsxfun(@eq, 1:numel(m), n).'*B(r,:);

I ran a benchmark against other two solutions and it appears to be faster than both. For 1000 repetitions:

  • This method takes 0.205650 seconds.
  • Eitan T's solution takes 0.546976 seconds.
  • matlabit's solution takes 1.619039 seconds.

Here is the benchmark code:

N = 1e3;

tic
for k=1:N,
    r = bsxfun(@eq, A(:,1), C')*(1:numel(C))'; % faster than [~,r] = ismember(A(:,1), C)
    [~,m,n] = unique(A(:,2));
    f=histc(n, 1:numel(m));
    result2 = diag(1./f)*bsxfun(@eq, 1:numel(m), n).'*B(r,:);
end
toc

tic
for k=1:N,
    [U, ia, iu] = unique(A(:, 2));
    [tf, loc] = ismember(A(:, 1), C);
    [X, Y] = meshgrid(1:size(B, 2), iu);
    result1 = accumarray([Y(:), X(:)], reshape(B(loc, :), [], 1), [], @mean);
end
toc

tic
for k=1:N,
    D = [arrayfun(@(x) find(C == x,1,'first'), A(:,1) ), A(:,2)];
    data = [B(D(:,1),:), D(:,2)];
    st = grpstats(data(:,1:3),data(:,4:4),{'mean'});
end
toc

Upvotes: 3

Eitan T
Eitan T

Reputation: 32920

Let's do what you say in the question step by step:

  1. For each unique value in A(:, 2):

    [U, ia, iu] = unique(A(:, 2));

  2. Take the corresponding values in A(:, 1) and look for their value in C:

    [tf, loc] = ismember(A(:, 1), C);

    It's also recommended to make sure, just in case, that all values are actually found in C:

    assert(all(tf))

  3. Then take the relevant rows in B and compute their mean:

    [X, Y] = meshgrid(1:size(B, 2), iu);
result = accumarray([Y(:), X(:)], reshape(B(loc, :), 1, []), [], @mean);

Hope this helps! :)

Example

%// Sample input
A = [1 2 ; 3 2; 4 7; 10 2; 6 7; 10 9];
B = [1 2 3; 4 4 9; 1 8 0; 3 7 9; 3 6 8];
C = [4; 10; 6; 3; 1];

%// Compute means
[U, ia, iu] = unique(A(:, 2));
[tf, loc] = ismember(A(:, 1), C);
[X, Y] = meshgrid(1:size(B, 2), iu);
result = accumarray([Y(:), X(:)], reshape(B(loc, :), [], 1), [], @mean);

The result is:

result = 
   3.3333   5.6667   8.6667
   1.0000   5.0000   1.5000
   4.0000   4.0000   9.0000

Upvotes: 6

matlabit
matlabit

Reputation: 858

Thanks, I also thought of:

D = [arrayfun(@(x) find(C == x,1,'first'), A(:,1) ), A(:,2)];
data = [B(D(:,1),:), D(:,2)];
st = grpstats(data(:,1:3),data(:,4:4),{'mean'});

Upvotes: 1

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