I have two values (k and j) which I know are within an nx3 matrix (M). I know that they're and on the same row and that j is always to the right of k, so if k is in M(2,1), then j will be in M(2,2). I tested for this earlier in the function, but now I want to know which row that is for a given k and j. I need the row number of their location to proceed. There are no duplicate combinations of k and j in the matrix.
So if I have the matrix
M=
1 4 5
1 5 7
k j 5
4 5 6
2 3 1
Then I want to know that they're in row 3. None of the columns are ordered.
What I have tried:
I used the code below
[row,~] = find(M==k);
I'm not sure how to look for a combination of them. I want to avoid using the find function. I hope to potentially use logical indexing.
How do I go about doing this? I hope this question makes sense.
You can use bsxfun -
find(all(bsxfun(#eq,A(:,1:2),[k,j]),2) | all(bsxfun(#eq,A(:,2:3),[k,j]),2))
Being a relational operation with bsxfun, according to this post on benchmarked results, this should be pretty efficient.
Sample runs
Case #1 :
A =
1 4 5
1 5 7
6 7 1
4 5 6
2 3 1
k =
6
j =
7
>> find(all(bsxfun(#eq,A(:,1:2),[k,j]),2) | all(bsxfun(#eq,A(:,2:3),[k,j]),2))
ans =
3
Case #2 :
A =
1 4 5
1 5 7
1 6 7
4 5 6
2 3 1
k =
6
j =
7
>> find(all(bsxfun(#eq,A(:,1:2),[k,j]),2) | all(bsxfun(#eq,A(:,2:3),[k,j]),2))
ans =
3
Slightly different version on bsxfun. This one doesn't limit the matrix to three columns.
find(sum(((bsxfun(#eq,M,j) + bsxfun(#eq,M,k)) .* M).' ) == j+k >0)
Case 1:
M = [
1 4 5
1 5 7
6 7 1
4 5 6
2 3 1]
k=6;j=7;
ans = 3
Case 2:
M=[
1 4 5
1 5 7
1 6 7
4 5 6
2 3 1
];
k=6;j=7;
ans = 3
Use this:
row = find(((M(:,1) == k ) & ( M(:,2) == j)) | ((M(:,1) == k ) & ( M(:,3) == j)) | ((M(:,2) == k ) & ( M(:,3) == j)) )
Also, logical indexing can only give you a matrix with zeros at all other positions and one at your required position. But to get the index of that position, you will have to use find.
Related
Can I shift rows in matrix A with respect to values in vector v?
For instance A and v specified as follows:
A =
1 0 0
1 0 0
1 0 0
v =
0 1 2
In this case I want to get this matrix from A:
A =
1 0 0
0 1 0
0 0 1
Every i-th row in A has been shifted by i-th value in v
Can I do this operation with native functions?
Or should I write it by myself?
I've tried circshift function, but I couldn't figure out how to shift rows separately.
The function circshift does not work as you want and even if you use a vector for the amount of shift, that is interpreted as the amount of shift for each dimension. While it is possible to loop over the rows of your matrix, that will not be very efficient.
More efficient is if you compute the indexing for each row which is actually quite simple:
## First, prepare all your input
octave> A = randi (9, 4, 6)
A =
8 3 2 7 4 5
4 4 7 3 9 1
1 6 3 9 2 3
7 4 1 9 5 5
octave> v = [0 2 0 1];
octave> sz = size (A);
## Compute how much shift per row, the column index (this will not work in Matlab)
octave> c_idx = mod ((0:(sz(2) -1)) .- v(:), sz(2)) +1
c_idx =
1 2 3 4 5 6
5 6 1 2 3 4
1 2 3 4 5 6
6 1 2 3 4 5
## Convert it to linear index
octave> idx = sub2ind (sz, repmat ((1:sz(1))(:), 1, sz(2)) , c_idx);
## All you need is to index
octave> A = A(idx)
A =
8 3 2 7 4 5
9 1 4 4 7 3
1 6 3 9 2 3
5 7 4 1 9 5
% A and v as above. These could be function input arguments
A = [1 0 0; 1 0 0; 1 0 0];
v = [0 1 2];
assert (all (size (v) == [1, size(A, 1)]), ...
'v needs to be a horizontal vector with as many elements as rows of A');
% Calculate shifted indices
[r, c] = size (A);
tmp = mod (repmat (0 : c-1, r, 1) - repmat (v.', 1, c), c) + 1;
Out = A(sub2ind ([r, c], repmat ([1 : r].', 1, c), tmp))
Out =
1 0 0
0 1 0
0 0 1
If performance is an issue, you can replace repmat with an equivalent bsxfun call which is more efficient (I use repmat here for simplicity to demonstrate the approach).
With focus on performance, here's one approach using bsxfun/broadcasting -
[m,n] = size(A);
idx0 = mod(bsxfun(#plus,n-v(:),1:n)-1,n);
out = A(bsxfun(#plus,(idx0*m),(1:m)'))
Sample run -
A =
1 7 5 7 7
4 8 5 7 6
4 2 6 3 2
v =
3 1 2
out =
5 7 7 1 7
6 4 8 5 7
3 2 4 2 6
Equivalent Octave version to use automatic broadcasting would look something like this -
[m,n] = size(A);
idx0 = mod( ((n-v(:)) + (1:n)) -1 ,n);
out = A((idx0*m)+(1:m)')
Shift vector with circshift in loop, iterating row index.
I would like to align and count vectors with different time stamps to count the corresponding bins.
Let's assume I have 3 matrix from [N,edges] = histcounts in the following structure. The first row represents the edges, so the bins. The second row represents the values. I would like to sum all values with the same bin.
A = [0 1 2 3 4 5;
5 5 6 7 8 5]
B = [1 2 3 4 5 6;
2 5 7 8 5 4]
C = [2 3 4 5 6 7 8;
1 2 6 7 4 3 2]
Now I want to sum all the same bins. My final result should be:
result = [0 1 2 3 4 5 6 7 8;
5 7 12 16 ...]
I could loop over all numbers, but I would like to have it fast.
You can use accumarray:
H = [A B C].'; %//' Concatenate the histograms and make them column vectors
V = [unique(H(:,1)) accumarray(H(:,1)+1, H(:,2))].'; %//' Find unique values and accumulate
V =
0 1 2 3 4 5 6 7 8
5 7 12 16 22 17 8 3 2
Note: The H(:,1)+1 is to force the bin values to be positive, otherwise MATLAB will complain. We still use the actual bins in the output V. To avoid this, as #Daniel says in the comments, use the third output of unique (See: https://stackoverflow.com/a/27783568/2732801):
H = [A B C].'; %//' stupid syntax highlighting :/
[U, ~, IU] = unique(H(:,1));
V = [U accumarray(IU, H(:,2))].';
If you're only doing it with 3 variables as you've shown then there likely aren't going to be any performance hits with looping it.
But if you are really averse to the looping idea, then you can do it using arrayfun.
rng = 0:8;
output = arrayfun(#(x)sum([A(2,A(1,:) == x), B(2,B(1,:) == x), C(2,C(1,:) == x)]), rng);
output = cat(1, rng, output);
output =
0 1 2 3 4 5 6 7 8
5 7 12 16 22 17 8 3 2
This can be beneficial for particularly large A, B, and C variables as there is no copying of data.
Supose there is a Matrix
A =
1 3 2 4
4 2 5 8
6 1 4 9
and I have a Vector containing the "class" of each column of this matrix for example
v = [1 , 1 , 2 , 3]
How can I sum the columns of the matrix to a new matrix as column vectors each to the column of their class? In this example columns 1 and 2 of A would added to the first column of the new matrix, column 2 to the 3 to the 2nd, column 4 the the 3rd.
Like
SUM =
4 2 4
6 5 8
7 4 9
Is this possible without loops?
One of the perfect scenarios to combine the powers of accumarray and bsxfun -
%// Since we are to accumulate columns, first step would be to transpose A
At = A.' %//'
%// Create a vector of linear IDs for use with ACCUMARRAY later on
idx = bsxfun(#plus,v(:),[0:size(A,1)-1]*max(v))
%// Use ACCUMARRAY to accumulate rows from At, i.e. columns from A based on the IDs
out = reshape(accumarray(idx(:),At(:)),[],size(A,1)).'
Sample run -
A =
1 3 2 4 6 0
4 2 5 8 9 2
6 1 4 9 8 9
v =
1 1 2 3 3 2
out =
4 2 10
6 7 17
7 13 17
An alternative with accumarray in 2D. Generate a grid with the vector v and then apply accumarray:
A = A.';
v = [1 1 2 3];
[X, Y] = ndgrid(v,1:size(A,2));
Here X and Y look like this:
X =
1 1 1
1 1 1
2 2 2
3 3 3
Y =
1 2 3
1 2 3
1 2 3
1 2 3
Then apply accumarray:
B=accumarray([X(:) Y(:)],A(:)),
SUM = B.'
SUM =
4 2 4
6 5 8
7 4 9
As you see, using [X(:) Y(:)] create the following array:
ans =
1 1
1 1
2 1
3 1
1 2
1 2
2 2
3 2
1 3
1 3
2 3
3 3
in which the vector v containing the "class" is replicated 3 times since there are 3 unique classes that are to be summed up together.
EDIT:
As pointed out by knedlsepp you can get rid of the transpose to A and B like so:
[X2, Y2] = ndgrid(1:size(A,1),v);
B = accumarray([X2(:) Y2(:)],A(:))
which ends up doing the same. I find it a bit more easier to visualize with the transposes but that gives the same result.
How about a one-liner?
result = full(sparse(repmat(v,size(A,1),1), repmat((1:size(A,1)).',1,size(A,2)), A));
Don't optimize prematurely!
The for loop performs fine for your problem:
out = zeros(size(A,1), max(v));
for i = 1:numel(v)
out(:,v(i)) = out(:,v(i)) + A(:,i);
end
BTW: With fine, I mean: fast, fast, fast!
i want to control the creation of random numbers in this matrix :
Mp = floor(1+(10*rand(2,20)));
mp1 = sort(Mp,2);
i want to modify this code in order to have an output like this :
1 1 2 2 3 3 3 4 5 5 6 7 7 8 9 9 10 10 10 10
1 2 3 3 3 3 3 3 4 5 6 6 6 6 7 8 9 9 9 10
i have to fill each row with all the numbers going from 1 to 10 in an increasing order and the second matrix that counts the occurences of each number should be like this :
1 2 1 2 1 2 3 1 1 2 1 1 2 1 1 2 1 2 3 4
1 1 1 2 3 4 5 6 1 1 1 2 3 4 1 1 1 2 3 1
and the most tricky matrix that i'v been looking for since the last week is the third matrix that should skim through each row of the first matrix and returns the numbers of occurences of each number and the position of the last occcurence.here is an example of how the code should work. this example show the intended result after running through the first row of the first matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 (positions)
1 2
2 2
3 3
4 1
5 2
6 1
7 2
8 1
9 2
10 4
(numbers)
this example show the intended result after running through the second row of the first matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 (positions)
1 1 2
2 1 2
3 3 6
4 1 1
5 3
6 1 4
7 2 1
8 1 1
9 2 3
10 4
(numbers)
so the wanted matrix must be filled up with zeros from the beginning and each time after running through each row of the first matrix, we add the new result to the previous one...
I believe the following code does everything you asked for. If I didn't understand, you need to get a lot clearer in how you pose your question...
Note - I hard coded some values / sizes. In "real code" you would never do that, obviously.
% the bit of code that generates and sorts the initial matrix:
Mp = floor(1+(10*rand(2,20)));
mp1 = sort(Mp, 2);
clc
disp(mp1)
occCount = zeros(size(mp1));
for ii = 1:size(mp1,1)
for jj = 1:size(mp1,2)
if (jj == 1)
occCount(ii,jj) = 1;
else
if (mp1(ii,jj) == mp1(ii,jj-1))
occCount(ii,jj) = occCount(ii, jj-1) + 1;
else
occCount(ii,jj) = 1;
end
end
end
end
% this is the second matrix you asked for
disp(occCount)
% now the third:
big = zeros(10, 20);
for ii = 1:size(mp1,1)
for jj = 1:10
f = find(mp1(ii,:) == jj); % index of all of them
if numel(f) > 0
last = f(end);
n = numel(f);
big(jj, last) = big(jj, last) + n;
end
end
end
disp(big)
Please see if this is indeed what you had in mind.
The following code solves both the second and third matrix generation problems with a single loop. For clarity, the second matrix M2 is the 2-by-20 array in the example containing the cumulative occurrence count. The third matrix M3 is the sparse matrix of size 10-by-20 in the example that encodes the number and position of the last occurrence of each unique value. The code only loops over the rows, using accumarray to do most of the work. It is generalized to any size and content of mp1, as long as the rows are sorted first.
% data
mp1 = [1 1 2 2 3 3 3 4 5 5 6 7 7 8 9 9 10 10 10 10;
1 2 3 3 3 3 3 3 4 5 6 6 6 6 7 8 9 9 9 10]; % the example first matrix
nuniq = max(mp1(:));
% accumulate
M2 = zeros(size(mp1));
M3 = zeros(nuniq,size(mp1,2));
for ir=1:size(mp1,1),
cumSums = accumarray(mp1(ir,:)',1:size(mp1,2),[],#numel,[],true)';
segments = arrayfun(#(x)1:x,nonzeros(cumSums),'uni',false);
M2(ir,:) = [segments{:}];
countCoords = accumarray(mp1(ir,:)',1:size(mp1,2),[],#max,[],true);
[ii,jj] = find(countCoords);
nzinds = sub2ind(size(M3),ii,nonzeros(countCoords));
M3(nzinds) = M3(nzinds) + nonzeros(cumSums);
end
I won't print the outputs because they are a bit big for the answer, and the code is runnable as is.
NOTE: For new test data, I suggest using the commands Mp = randi(10,[2,20]); mp1 = sort(Mp,2);. Or based on your request to user2875617 and his response, ensure all numbers with mp1 = sort([repmat(1:10,2,1) randi(10,[2,10])],2); but that isn't really random...
EDIT: Error in code fixed.
I am editing the previous answer to check if it is fast when mp1 is large, and apparently it is:
N = 20000; M = 200; P = 100;
mp1 = sort([repmat(1:P, M, 1), ceil(P*rand(M,N-P))], 2);
tic
% Initialise output matrices
out1 = zeros(M, N); out2 = zeros(P, N);
for gg = 1:M
% Frequencies of each row
freqs(:, 1) = mp1(gg, [find(diff(mp1(gg, :))), end]);
freqs(:, 2) = histc(mp1(gg, :), freqs(:, 1));
cumfreqs = cumsum(freqs(:, 2));
k = 1;
for hh = 1:numel(freqs(:, 1))
out1(gg, k:cumfreqs(hh)) = 1:freqs(hh, 2);
out2(freqs(hh, 1), cumfreqs(hh)) = out2(freqs(hh, 1), cumfreqs(hh)) + freqs(hh, 2);
k = cumfreqs(hh) + 1;
end
end
toc
Hi I need to sort a vector and assign a ranking for the corresponding sorting order. I'm using sort function [sortedValue_X , X_Ranked] = sort(X,'descend');
but the problem is it assigns different ranks for the same values (zeros).
i.e. x = [ 13 15 5 5 0 0 0 1 0 3] and I want zeros to take the same last rank which is 6 and fives needs to share the 3rd rank etc..
any suggestions?
The syntax [sortedValues, sortedIndexes] = sort(x, 'descend') does not return rank as you describe it. It returns the indexes of the sorted values. This is really useful if you want to use the sort order from one array to rearrange another array.
As suggested by #user1860611, unique seems to do what you want, using the third output as follows:
x = [ 13 15 5 5 0 0 0 1 0 3];
[~, ~, forwardRank] = unique(x);
%Returns
%forwardRank =
% 5 6 4 4 1 1 1 2 1 3
To get the order you want (decending) you'll need to reverse the order, like this:
reverseRank = max(forwardRank) - forwardRank + 1
%Returns
%reverseRank =
% 2 1 3 3 6 6 6 5 6 4
You may be done at this point. But you may want to sort these into the into an acsending order. This is a reorder of the reverseRank vector which keeping it in sync with the original x vector, which is exactly what the 2nd argument of sort is desined to help with. So we can do something like this:
[xSorted, ixsSort] = sort(x, 'descend'); %Perform a sort on x
reverseRankSorted = reverseRank(ixsSort); %Apply that sort to reverseRank
Which generates:
xSorted = 15 13 5 5 3 1 0 0 0 0
reverseRankSorted = 1 2 3 3 4 5 6 6 6 6
tiedrank.m might be the thing you are looking for.
>> x = round(rand(1,5)*10)
x =
8 7 3 10 0
>> tiedrank(x)
ans =
4 3 2 5 1