If B=[1; 2] and A=[B B B...(n times B)], how to obtain the matrix C corresponding to all the possible combinations between the column vectors of A .i.e. I want to get the combinations between n copies of the same vector.
For example, for n=3:
A =
1 1 1
2 2 2
So, C can be obtained using the function from File Exchange 'allcomb(varargin)':
C=allcomb(A(:,1),A(:,2),A(:,3))
C =
1 1 1
1 1 2
1 2 1
1 2 2
2 1 1
2 1 2
2 2 1
2 2 2
In my case n is variable. How to obtain C for any value of n?
You can put the repetitions in a cell, and use the {:} syntax to put all cell elements as inputs to allcomb
n = 3;
B = [1,2];
A = repmat( {B}, n, 1 );
C = allcomb( A{:} ); % allcomb is FileExchange.
% combvec is a documented alternative.
Output:
C =
1 1 1
1 1 2
1 2 1
1 2 2
2 1 1
2 1 2
2 2 1
2 2 2
Since the alphabets for each of the places is the same, this is really a base conversion. MATLAB only accepts integer bases, but we can use that integer as an index into the alphabet B:
B=[1; 2];
n = 3;
b = numel(B);
for k = 0:(b^n-1) % loop over all possible combinations
C(k+1,:) = dec2base(k, b, n);
end
C = C - '0' + 1; % convert 0..b-1 (in chars) into 1..b (in ints) for indexing
C = B(C); % index into alphabet B
Results:
>> C
C =
1 1 1
1 1 2
1 2 1
1 2 2
2 1 1
2 1 2
2 2 1
2 2 2
The last line of the script doesn't appear to do much in this case because the alphabet happens to be the same range as our indices, but changing the alphabet to B = [7; 14] will correctly result in:
C =
7 7 7
7 7 14
7 14 7
7 14 14
14 7 7
14 7 14
14 14 7
14 14 14
Funnily enough, allcomb from MATLAB File Exchange seems to be what you want.
allcomb([1; 2],[1; 2], [1; 2])
ans =
1 1 1
1 1 2
1 2 1
1 2 2
2 1 1
2 1 2
2 2 1
2 2 2
To do it for any n, simply construct the matrix with:
>> n = 3;
>> repmat(B, 1, n)
ans =
1 1 1
2 2 2
Related
If I have this vector:
x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6]
I would like to get the position of each unique number according to itself.
y = [1 2 3 4 5 1 2 3 1 1 2 1 2 3 4]
At the moment I'm using:
y = sum(triu(x==x.')) % MATLAB 2016b and above
It's compact but obviously not memory efficient.
For the pure beauty of MATLAB programming I would avoid using a loop. Do you have a better simple implementation ?
Context:
My final goal is to sort the vector x but with the constraint that a number that appear N times has the priority over another number that has appeared more than N times:
[~,ind] = sort(y);
x_relative_sort = x(ind);
% x_relative_sort = 1 2 3 4 6 1 2 4 6 1 2 6 1 6 1
Assuming x is sorted, here's one vectorized alternative using unique, diff, and cumsum:
[~, index] = unique(x);
y = ones(size(x));
y(index(2:end)) = y(index(2:end))-diff(index).';
y = cumsum(y);
And now you can apply your final sorting:
>> [~, ind] = sort(y);
>> x_relative_sort = x(ind)
x_relative_sort =
1 2 3 4 6 1 2 4 6 1 2 6 1 6 1
If you have positive integers you can use sparse matrix:
[y ,~] = find(sort(sparse(1:numel(x), x, true), 1, 'descend'));
Likewise x_relative_sort can directly be computed:
[x_relative_sort ,~] = find(sort(sparse(x ,1:numel(x),true), 2, 'descend'));
Just for variety, here's a solution based on accumarray. It works for x sorted and containing positive integers, as in the question:
y = cell2mat(accumarray(x(:), x(:), [], #(t){1:numel(t)}).');
You can be more memory efficient by only comparing to unique(x), so you don't have a large N*N matrix but rather N*M, where N=numel(x), M=numel(unique(x)).
I've used an anonymous function syntax to avoid declaring an intermediate matrix variable, needed as it's used twice - this can probably be improved.
f = #(X) sum(cumsum(X,2).*X); y = f(unique(x).'==x);
Here's my solution that doesn't require sorting:
x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
y = cell2mat( splitapply(#(v){cumsum(v)},x,cumsum(logical([1 diff(x)]))) ) ./ x;
Explanation:
% Turn each group new into a unique number:
t1 = cumsum(logical([1 diff(x)]));
% x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
% t1 = [1 1 1 1 1 2 2 2 3 4 4 5 5 5 5 6 6 6];
% Apply cumsum separately to each group:
t2 = cell2mat( splitapply(#(v){cumsum(v)},x,t1) );
% t1 = [1 1 1 1 1 2 2 2 3 4 4 5 5 5 5 6 6 6];
% t2 = [1 2 3 4 5 2 4 6 3 4 8 6 12 18 24 1 2 3];
% Finally, divide by x to get the increasing values:
y = t2 ./ x;
% x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
% t2 = [1 2 3 4 5 2 4 6 3 4 8 6 12 18 24 1 2 3];
So i have two very long matrices. A sample is given below:
First_Matrix:
A = [...
1 1 1;
1 1 2;
1 1 3;
1 2 1;
1 2 2;
1 2 3;
1 3 1;
1 3 2;
1 3 3];
Second Matrix
B = [...
1 1 916;
1 2 653;
1 3 114];
And I would like a thirds matrix that would combine the first matrix with the third column of the second matrix, based on the values in the first two column of the 2 matrices matching (being the same).
So Ouput_Matrix:
C = [...
1 1 1 916;
1 1 2 916;
1 1 3 916;
1 2 1 653;
1 2 2 653;
1 2 3 653;
1 3 1 114;
1 3 2 114;
1 3 3 11];
What would be the best way to do this?
Thanks in advance
Use the second output of ismember with the 'rows' option to get the indices of the matching, from which you can easily build the result:
[~, ind] = ismember(A(:, [1 2]), B(:, [1 2]), 'rows');
C = [A B(ind, 3)];
The for loop isn't pretty and might slow you down if B is very long. But I don't think it's possible to avoid (edit: it seems it is).
A = [1 1 1
1 1 2
1 1 3
1 2 1
1 2 2
1 2 3
1 3 1
1 3 2
1 3 3];
B = [1 1 916
1 2 653
1 3 114];
C = [A zeros(size(A,1),1)];
for i = 1:size(B,1)
C(all(B(i,1:2)==A(:,1:2),2),4) = B(i,3);
end
C =
1 1 1 916
1 1 2 916
1 1 3 916
1 2 1 653
1 2 2 653
1 2 3 653
1 3 1 114
1 3 2 114
1 3 3 114
It is possible to achieve what you want without a for loop, but it may not be the most optimal implementation:
n = size(B, 1); % number of rows in B
B_(1, :, :) = B'; % convert to 3D matrix to be able to use elementwise comparision
x = squeeze(all(bsxfun(#eq, A(:, 1:2), B_(1, 1:2,:)), 2)); % x(i, j) == 1 if row A(i, :) matches B(j, :)
index = x * (1:n)'; % row B(index(i), :) corresponds with row A(i, :)
A(:, 4) = B(index, 3); % add data to A
An alternative formulation for x, without conversion to 3D, is:
x = bsxfun(#eq, A(:, 1), B(:,1)') & bsxfun(#eq, A(:, 2), B(:,2)');
The disadvantage of this method is that it less extendable to more matching columns.
given matrix A of size 6 by 6 contain blocks of numbers,each block of size 2 by 2, and outher reference matrix R of size 2 by 12 also contain blocks of numbers, each block of size 2 by 2. the perpse of the whole process is to form a new matrix, called the Index matrix, contain index's that refer to the position of the blocks within the matrix A based on the order of the blocks within the reference matrix R. and here is an exemple
matrix A:
A =[1 1 2 2 3 3;
1 1 2 2 3 3;
1 1 3 3 4 4;
1 1 3 3 4 4;
4 4 5 5 6 6;
4 4 5 5 6 6 ]
matrix R:
R=[1 1 2 2 3 3 4 4 5 5 6 6;
1 1 2 2 3 3 4 4 5 5 6 6 ]
the new matrix is:
Index =[1 2 3;
1 3 4;
4 5 6]
any ideas ?
With my favourite three guys - bsxfun, permute, reshape for an efficient and generic solution -
blksz = 2; %// blocksize
num_rowblksA = size(A,1)/blksz; %// number of blocks along rows in A
%// Create blksz x blksz sized blocks for A and B
A1 = reshape(permute(reshape(A,blksz,num_rowblksA,[]),[1 3 2]),blksz^2,[])
R1 = reshape(R,blksz^2,1,[])
%// Find the matches with "bsxfun(#eq" and corresponding indices
[valid,idx] = max(all(bsxfun(#eq,A1,R1),1),[],3)
%// Or with PDIST2:
%// [valid,idx] = max(pdist2(A1.',reshape(R,blksz^2,[]).')==0,[],2)
idx(~valid) = 0
%// Reshape the indices to the shapes of blocked shapes in A
Index = reshape(idx,[],num_rowblksA).'
Sample run with more random inputs -
>> A
A =
2 1 1 2
1 2 2 1
1 1 1 1
2 2 2 2
1 2 2 1
1 2 1 1
>> R
R =
2 1 1 1 1 2 2 2 1 1 1 1
2 1 2 1 1 2 2 1 2 2 2 1
>> Index
Index =
0 0
5 5
3 0
So here's my problem i want to count the numbers of same values in a column, this my data:
a b d
2 1 5
1 3 10
1 -2 5
0 5 25
5 0 25
1 1 2
-1 -1 2
i want to count the same values of d (where d = a^2 + b^2), so this is the output i want:
a b d count
2 1 5 2
1 3 10 1
0 5 25 2
1 1 2 2
so as you can see, only positive combinations of a and b displayed. so how can i do that? thanks.
Assuming your data is a matrix, here's an accumarray-based approach. Note this doesn't address the requirement "only positive combinations of a and b displayed".
M = [2 1 5
1 3 10
1 -2 5
0 5 25
5 0 25
1 1 2
-1 -1 2]; %// data
[~, jj, kk] = unique(M(:,3),'stable');
s = accumarray(kk,1);
result = [M(jj,:) s];
Assuming your input data to be stored in a 2D array, this could be one approach -
%// Input
A =[
2 1 5
1 3 10
1 -2 5
0 5 25
5 0 25
1 1 2
-1 -1 2]
[unqcol3,unqidx,allidx] = unique(A(:,3),'stable')
counts = sum(bsxfun(#eq,A(:,3),unqcol3.'),1) %//'
out =[A(unqidx,:) counts(:)]
You can also get the counts with histc -
counts = histc(allidx,1:max(allidx))
Note on positive combinations of a and b: If you are looking to have positive combinations of A and B, you can select only those rows from A that fulfill this requirement and save back into A as a pre-processing step -
A = A(all(A(:,1:2)>=0,2),:)
Please allow me to post this admin:
ok so this is my problem, i want to generate all combination of a, and b, for example 1 and 2, having a combinations of (1,2), (2,1),(-1,2), and (2,-1), so 4 combination, but i want only one combination as representative of all 4 combination to be display in output for example only (1,2). so this is my draft code:
fprintf(' a b z \n _ _ _ \n');
for a= -1:3
for b=-1:3
z=a^2 + b^2
end
end
ctr=1;
i(:,3) %the position of z in array
for x =1:length(z) %the length of z array
if z = i(1,1)
ctr = ctr +1;
else
fprintf(' %d %d %d\n',a,b,z);
end
end
so this the output i want:
a b z no. of repetitions
1 1 2 4
1 0 1 4
1 2 5 4
1 3 10 4
0 2 4 2
0 3 9 2
2 2 8 1
2 3 13 2
3 3 18 1
0 0 0 1
no. of repetition means how many possible combination of a and b can generate
in=-1:3
%calculate z
[a,b]=meshgrid(in);
z=a.^2+b.^2;
%sort absolute values ascending, which allows to use unique
ac=sort(abs([a(:) b(:)]),2);
%use unique to identify duplicates
[f,g,h]=unique(ac,'rows');
%count
cnt=histc(h,1:max(h));
disp([a(g),b(g),z(g),cnt])
The output is:
0 0 0 1
1 0 1 4
2 0 4 2
3 0 9 2
1 1 2 4
2 1 5 4
3 1 10 4
2 2 8 1
3 2 13 2
3 3 18 1