I want to repeat a row vector to create a matrix, in which every row is a slightly modified version of the original vector.
For example, if I have a vector v = [10 20 30 40 50], I want every row of my matrix to be that vector, but with a random number added to every element of the vector to add some fluctuations.
My matrix should look like this:
M = [10+a 20+a 30+a 40+a 50+a;
10+b 20+b 30+b 40+b 50+b;
... ]
Where a, b, ... are random numbers between 0 and 2, for an arbitrary number of matrix rows.
Any ideas?
In Matlab, you can add a column vector to a matrix. This will add the vector elements to each of the row values accordingly.
Example:
>> M = [1 2 3; 4 5 6; 7 8 9];
>> v = [1; 2; 3];
>> v + M
ans =
2 3 4
6 7 8
10 11 12
Note that in your case v is a row vector, so you should transpose it first (using v.').
As Sardar Usama and Wolfie note, this method of adding is only possible since MATLAB version R2016b, for earlier versions you will need to use bsxfun:
>> % instead of `v + M`
>> bsxfun(#plus, v, M)
ans =
2 4 6
5 7 9
8 10 12
If you have a MATLAB version earlier than 2016b (when implicit expansion was introduced, as demonstrated in Daan's answer) then you should use bsxfun.
v = [10 20 30 40 50]; % initial row vector
offsets = rand(3,1); % random values, add one per row (this should be a column vector)
output = bsxfun(#plus,offsets,v);
Result:
>> output =
10.643 20.643 30.643 40.643 50.643
10.704 20.704 30.704 40.704 50.704
10.393 20.393 30.393 40.393 50.393
This can be more easily understood with less random inputs!
v = [10 20 30 40 50];
offsets = [1; 2; 3];
output = bsxfun(#plus,offsets,v);
>> output =
11 21 31 41 51
12 22 32 42 52
13 23 33 43 53
Side note: to get an nx1 vector of random numbers between 0 and 2, use
offsets = rand(n,1)*2
Related
I have a 4x5 matrix called A from which I want to select randomly 3 rows, then 4 random columns and then select those elements which coincide in those selected rows and columns so that I have 12 selected elements.Then I want to create a diagonal matrix called B which will have entries either 1 or 0 so that multiplication of that B matrix with reshaped A matrix (20x1) will give me those selected 12 elements of A.
How can I create that B matrix? Here is my code:
A=1:20;
A=reshape(A,4,5);
Mr=4;
Ma=3;
Na=4;
Nr=5;
M=Ma*Mr;
[S1,S2]=size(A);
N=S1*S2;
y2=zeros(size(A));
k1=randperm(S1);
k1=k1(1:Ma);
k2=randperm(S2);
k2=k2(1:Mr);
y2(k1,k2)=A(k1,k2);
It's a little hard to understand what you want and your code isn't much help but I think I've a solution for you.
I create a matrix (vector) of zeros of the same size as A and then use bsxfun to determine the indexes in this vector (which will be the diagonal of B) that should be 1.
>> A = reshape(1:20, 4, 5);
>> R = [1 2 3]; % Random rows
>> C = [2 3 4 5]; % Random columns
>> B = zeros(size(A));
>> B(bsxfun(#plus, C, size(A, 1)*(R-1).')) = 1;
>> B = diag(B(:));
>> V = B*A(:);
>> V = V(V ~= 0)
V =
2
3
4
5
6
7
8
9
10
11
12
13
Note: There is no need for B = diag(B(:)); we could have simply used element by element multiplication in Matlab.
>> V = B(:).*A(:);
>> V = V(V ~= 0)
Note: This may be overly complex or very poorly put together and there is probably a better way of doing it. It's my first real attempt at using bsxfun on my own.
Here is a hack but since you are creating y2 you might as well just use it instead of creating the useless B matrix. The bsxfun answer is much better.
A=1:20;
A=reshape(A,4,5);
Mr=4;
Ma=3;
Na=4;
Nr=5;
M=Ma*Mr;
[S1,S2]=size(A);
N=S1*S2;
y2=zeros(size(A));
k1=randperm(S1);
k1=k1(1:Ma);
k2=randperm(S2);
k2=k2(1:Mr);
y2(k1,k2)=A(k1,k2);
idx = reshape(y2 ~= 0, numel(y2), []);
B = diag(idx);
% "diagonal" matrix 12x20
B = B(all(B==0,2),:) = [];
output = B * A(:)
output =
1
3
4
9
11
12
13
15
16
17
19
20
y2 from example.
y2 =
1 0 9 13 17
0 0 0 0 0
3 0 11 15 19
4 0 12 16 20
Given an mxn matrix e.g.
M= [ 10 20 30 40; 11 12 13 14; 19 18 17 16];
and a 1xn 'selector'
S = [1 2 3 1];
where all elements of S are in the range 1..m, I want the output vector O with size 1xn s.t. O[1, i] = M[ S[i], i]. In this example
O = [10 12 17 40];
Clearly I can do this using a loop. Is there a way to vectorize it which is more cost effective than a loop assuming that m and n are in the hundreds ?
You are looking for sub2ind. So the desired output could be achieved with -
O = M(sub2ind(size(M),S,1:numel(S)))
Or for performance, you can use a raw version of sub2ind -
O = M([0:numel(S)-1]*size(M,1) + S)
How to select a submatrix (not in any pattern) in Matlab? For example, for a matrix of size 10 by 10, how to select the submatrix consisting of intersection of the 1st 2nd and 9th rows and the 4th and 6th columns?
Thanks for any helpful answers!
TLDR: Short Answer
As for your question, suppose you have an arbitrary 10-by-10 matrix A. The simplest way to extract the desired sub-matrix would be with an index vector:
B = A([1 2 9], [4 6]);
Indexing in MATLAB
There's an interesting article in the official documentation that comprehensively explains indexing in MATLAB.
Basically, there are several ways to extract a subset of values, I'll summarize them for you:
1. Indexing Vectors
Indexing vectors indicate the indices of the element to be extracted. They can either contain a single index or several, like so:
A = [10 20 30 40 50 60 70 80 90]
%# Extracts the third and the ninth element
B = A([3 9]) %# B = [30 90]
Indexing vectors can be specified for each dimension separately, for instance:
A = [10 20 30; 40 50 60; 70 80 90];
%# Extract the first and third rows, and the first and second columns
B = A([1 3], [1 2]) %# B = [10 30; 40 60]
There are also two special subscripts: end and the colon (:):
end simply indicates the last index in that dimension.
The colon is just a short-hand notation for "1:end".
For example, instead of writing A([1 2 3], [2 3]), you can write A(:, 2:end). This is especially useful for large matrices.
2. Linear Indexing
Linear indexing treats any matrix as if it were a column vector by concatenating the columns into one column vector and assigning indices to the elements respectively. For instance, we have:
A = [10 20 30; 40 50 60; 70 80 90];
and we want to compute b = A(2). The equivalent column vector is:
A = [10;
40;
70;
20;
50;
80;
30;
60;
90]
and thus b equals 40.
The special colon and end subscripts are also allowed, of course. For that reason, A(:) converts any matrix A into a column vector.
Linear indexing with matrix subscripts:
It is also possible to use another matrix for linear indexing. The subscript matrix is simply converted into a column vector, and used for linear indexing. The resulting matrix is, however always of the same dimensions as the subscript matrix.
For instance, if I = [1 3; 1 2], then A(I) is the same as writing reshape(A(I(:)), size(I)).
Converting from matrix subscripts to linear indices and vice versa:
For that you have sub2ind and ind2sub, respectively. For example, if you want to convert the subscripts [1, 3] in matrix A (corresponding to element 30) into a linear index, you can write sub2ind(size(A), 1, 3) (the result in this case should be 7, of course).
3. Logical Indexing
In logical indexing the subscripts are binary, where a logical 1 indicates that the corresponding element is selected, and 0 means it is not. The subscript vector must be either of the same dimensions as the original matrix or a vector with the same number of elements. For instance, if we have:
A = [10 20 30; 40 50 60; 70 80 90];
and we want to extract A([1 3], [1 2]) using logical indexing, we can do either this:
Ir = logical([1 1 0]);
Ic = logical([1 0 1]);
B = A(Ir, Ic)
or this:
I = logical([1 0 1; 1 0 1; 0 0 0]);
B = A(I)
or this:
I = logical([1 1 0 0 0 0 1 1 0]);
B = A(I)
Note that in the latter two cases is a one-dimensional vector, and should be reshaped back into a matrix if necessary (for example, using reshape).
Let me explain with an example:
Let's define a 6x6 matrix
A = magic(6)
A =
35 1 6 26 19 24
3 32 7 21 23 25
31 9 2 22 27 20
8 28 33 17 10 15
30 5 34 12 14 16
4 36 29 13 18 11
From this matrix you want the elements in rows 1, 2 and 5, and in the columns 4 and 6
B = A([1 2 5],[4 6])
B =
26 24
21 25
12 16
Hope this helps.
function f = sub(A,i,j)
[m,n] = size(A);
row = 1:m;
col = 1:n;
x = row;
x(i) = [];
y=col;
y(j) = [];
f= A(x,y);
Returns the matrix A, with the ith row and jth column removed.
How to select a submatrix (not in any pattern) in Matlab? For example, for a matrix of size 10 by 10, how to select the submatrix consisting of intersection of the 1st 2nd and 9th rows and the 4th and 6th columns?
Thanks for any helpful answers!
TLDR: Short Answer
As for your question, suppose you have an arbitrary 10-by-10 matrix A. The simplest way to extract the desired sub-matrix would be with an index vector:
B = A([1 2 9], [4 6]);
Indexing in MATLAB
There's an interesting article in the official documentation that comprehensively explains indexing in MATLAB.
Basically, there are several ways to extract a subset of values, I'll summarize them for you:
1. Indexing Vectors
Indexing vectors indicate the indices of the element to be extracted. They can either contain a single index or several, like so:
A = [10 20 30 40 50 60 70 80 90]
%# Extracts the third and the ninth element
B = A([3 9]) %# B = [30 90]
Indexing vectors can be specified for each dimension separately, for instance:
A = [10 20 30; 40 50 60; 70 80 90];
%# Extract the first and third rows, and the first and second columns
B = A([1 3], [1 2]) %# B = [10 30; 40 60]
There are also two special subscripts: end and the colon (:):
end simply indicates the last index in that dimension.
The colon is just a short-hand notation for "1:end".
For example, instead of writing A([1 2 3], [2 3]), you can write A(:, 2:end). This is especially useful for large matrices.
2. Linear Indexing
Linear indexing treats any matrix as if it were a column vector by concatenating the columns into one column vector and assigning indices to the elements respectively. For instance, we have:
A = [10 20 30; 40 50 60; 70 80 90];
and we want to compute b = A(2). The equivalent column vector is:
A = [10;
40;
70;
20;
50;
80;
30;
60;
90]
and thus b equals 40.
The special colon and end subscripts are also allowed, of course. For that reason, A(:) converts any matrix A into a column vector.
Linear indexing with matrix subscripts:
It is also possible to use another matrix for linear indexing. The subscript matrix is simply converted into a column vector, and used for linear indexing. The resulting matrix is, however always of the same dimensions as the subscript matrix.
For instance, if I = [1 3; 1 2], then A(I) is the same as writing reshape(A(I(:)), size(I)).
Converting from matrix subscripts to linear indices and vice versa:
For that you have sub2ind and ind2sub, respectively. For example, if you want to convert the subscripts [1, 3] in matrix A (corresponding to element 30) into a linear index, you can write sub2ind(size(A), 1, 3) (the result in this case should be 7, of course).
3. Logical Indexing
In logical indexing the subscripts are binary, where a logical 1 indicates that the corresponding element is selected, and 0 means it is not. The subscript vector must be either of the same dimensions as the original matrix or a vector with the same number of elements. For instance, if we have:
A = [10 20 30; 40 50 60; 70 80 90];
and we want to extract A([1 3], [1 2]) using logical indexing, we can do either this:
Ir = logical([1 1 0]);
Ic = logical([1 0 1]);
B = A(Ir, Ic)
or this:
I = logical([1 0 1; 1 0 1; 0 0 0]);
B = A(I)
or this:
I = logical([1 1 0 0 0 0 1 1 0]);
B = A(I)
Note that in the latter two cases is a one-dimensional vector, and should be reshaped back into a matrix if necessary (for example, using reshape).
Let me explain with an example:
Let's define a 6x6 matrix
A = magic(6)
A =
35 1 6 26 19 24
3 32 7 21 23 25
31 9 2 22 27 20
8 28 33 17 10 15
30 5 34 12 14 16
4 36 29 13 18 11
From this matrix you want the elements in rows 1, 2 and 5, and in the columns 4 and 6
B = A([1 2 5],[4 6])
B =
26 24
21 25
12 16
Hope this helps.
function f = sub(A,i,j)
[m,n] = size(A);
row = 1:m;
col = 1:n;
x = row;
x(i) = [];
y=col;
y(j) = [];
f= A(x,y);
Returns the matrix A, with the ith row and jth column removed.
I have an NxM matrix in MATLAB that I would like to reorder in similar fashion to the way JPEG reorders its subblock pixels:
(image from Wikipedia)
I would like the algorithm to be generic such that I can pass in a 2D matrix with any dimensions. I am a C++ programmer by trade and am very tempted to write an old school loop to accomplish this, but I suspect there is a better way to do it in MATLAB.
I'd be rather want an algorithm that worked on an NxN matrix and go from there.
Example:
1 2 3
4 5 6 --> 1 2 4 7 5 3 6 8 9
7 8 9
Consider the code:
M = randi(100, [3 4]); %# input matrix
ind = reshape(1:numel(M), size(M)); %# indices of elements
ind = fliplr( spdiags( fliplr(ind) ) ); %# get the anti-diagonals
ind(:,1:2:end) = flipud( ind(:,1:2:end) ); %# reverse order of odd columns
ind(ind==0) = []; %# keep non-zero indices
M(ind) %# get elements in zigzag order
An example with a 4x4 matrix:
» M
M =
17 35 26 96
12 59 51 55
50 23 70 14
96 76 90 15
» M(ind)
ans =
17 35 12 50 59 26 96 51 23 96 76 70 55 14 90 15
and an example with a non-square matrix:
M =
69 9 16 100
75 23 83 8
46 92 54 45
ans =
69 9 75 46 23 16 100 83 92 54 8 45
This approach is pretty fast:
X = randn(500,2000); %// example input matrix
[r, c] = size(X);
M = bsxfun(#plus, (1:r).', 0:c-1);
M = M + bsxfun(#times, (1:r).'/(r+c), (-1).^M);
[~, ind] = sort(M(:));
y = X(ind).'; %'// output row vector
Benchmarking
The following code compares running time with that of Amro's excellent answer, using timeit. It tests different combinations of matrix size (number of entries) and matrix shape (number of rows to number of columns ratio).
%// Amro's approach
function y = zigzag_Amro(M)
ind = reshape(1:numel(M), size(M));
ind = fliplr( spdiags( fliplr(ind) ) );
ind(:,1:2:end) = flipud( ind(:,1:2:end) );
ind(ind==0) = [];
y = M(ind);
%// Luis' approach
function y = zigzag_Luis(X)
[r, c] = size(X);
M = bsxfun(#plus, (1:r).', 0:c-1);
M = M + bsxfun(#times, (1:r).'/(r+c), (-1).^M);
[~, ind] = sort(M(:));
y = X(ind).';
%// Benchmarking code:
S = [10 30 100 300 1000 3000]; %// reference to generate matrix size
f = [1 1]; %// number of cols is S*f(1); number of rows is S*f(2)
%// f = [0.5 2]; %// plotted with '--'
%// f = [2 0.5]; %// plotted with ':'
t_Amro = NaN(size(S));
t_Luis = NaN(size(S));
for n = 1:numel(S)
X = rand(f(1)*S(n), f(2)*S(n));
f_Amro = #() zigzag_Amro(X);
f_Luis = #() zigzag_Luis(X);
t_Amro(n) = timeit(f_Amro);
t_Luis(n) = timeit(f_Luis);
end
loglog(S.^2*prod(f), t_Amro, '.b-');
hold on
loglog(S.^2*prod(f), t_Luis, '.r-');
xlabel('number of matrix entries')
ylabel('time')
The figure below has been obtained with Matlab R2014b on Windows 7 64 bits. Results in R2010b are very similar. It is seen that the new approach reduces running time by a factor between 2.5 (for small matrices) and 1.4 (for large matrices). Results are seen to be almost insensitive to matrix shape, given a total number of entries.
Here's a non-loop solution zig_zag.m. It looks ugly but it works!:
function [M,index] = zig_zag(M)
[r,c] = size(M);
checker = rem(hankel(1:r,r-1+(1:c)),2);
[rEven,cEven] = find(checker);
[cOdd,rOdd] = find(~checker.'); %'#
rTotal = [rEven; rOdd];
cTotal = [cEven; cOdd];
[junk,sortIndex] = sort(rTotal+cTotal);
rSort = rTotal(sortIndex);
cSort = cTotal(sortIndex);
index = sub2ind([r c],rSort,cSort);
M = M(index);
end
And a test matrix:
>> M = [magic(4) zeros(4,1)];
M =
16 2 3 13 0
5 11 10 8 0
9 7 6 12 0
4 14 15 1 0
>> newM = zig_zag(M) %# Zig-zag sampled elements
newM =
16
2
5
9
11
3
13
10
7
4
14
6
8
0
0
12
15
1
0
0
Here's a way how to do this. Basically, your array is a hankel matrix plus vectors of 1:m, where m is the number of elements in each diagonal. Maybe someone else has a neat idea on how to create the diagonal arrays that have to be added to the flipped hankel array without a loop.
I think this should be generalizeable to a non-square array.
% for a 3x3 array
n=3;
numElementsPerDiagonal = [1:n,n-1:-1:1];
hadaRC = cumsum([0,numElementsPerDiagonal(1:end-1)]);
array2add = fliplr(hankel(hadaRC(1:n),hadaRC(end-n+1:n)));
% loop through the hankel array and add numbers counting either up or down
% if they are even or odd
for d = 1:(2*n-1)
if floor(d/2)==d/2
% even, count down
array2add = array2add + diag(1:numElementsPerDiagonal(d),d-n);
else
% odd, count up
array2add = array2add + diag(numElementsPerDiagonal(d):-1:1,d-n);
end
end
% now flip to get the result
indexMatrix = fliplr(array2add)
result =
1 2 6
3 5 7
4 8 9
Afterward, you just call reshape(image(indexMatrix),[],1) to get the vector of reordered elements.
EDIT
Ok, from your comment it looks like you need to use sort like Marc suggested.
indexMatrixT = indexMatrix'; % ' SO formatting
[dummy,sortedIdx] = sort(indexMatrixT(:));
sortedIdx =
1 2 4 7 5 3 6 8 9
Note that you'd need to transpose your input matrix first before you index, because Matlab counts first down, then right.
Assuming X to be the input 2D matrix and that is square or landscape-shaped, this seems to be pretty efficient -
[m,n] = size(X);
nlim = m*n;
n = n+mod(n-m,2);
mask = bsxfun(#le,[1:m]',[n:-1:1]);
start_vec = m:m-1:m*(m-1)+1;
a = bsxfun(#plus,start_vec',[0:n-1]*m);
offset_startcol = 2- mod(m+1,2);
[~,idx] = min(mask,[],1);
idx = idx - 1;
idx(idx==0) = m;
end_ind = a([0:n-1]*m + idx);
offsets = a(1,offset_startcol:2:end) + end_ind(offset_startcol:2:end);
a(:,offset_startcol:2:end) = bsxfun(#minus,offsets,a(:,offset_startcol:2:end));
out = a(mask);
out2 = m*n+1 - out(end:-1:1+m*(n-m+1));
result = X([out2 ; out(out<=nlim)]);
Quick runtime tests against Luis's approach -
Datasize: 500 x 2000
------------------------------------- With Proposed Approach
Elapsed time is 0.037145 seconds.
------------------------------------- With Luis Approach
Elapsed time is 0.045900 seconds.
Datasize: 5000 x 20000
------------------------------------- With Proposed Approach
Elapsed time is 3.947325 seconds.
------------------------------------- With Luis Approach
Elapsed time is 6.370463 seconds.
Let's assume for a moment that you have a 2-D matrix that's the same size as your image specifying the correct index. Call this array idx; then the matlab commands to reorder your image would be
[~,I] = sort (idx(:)); %sort the 1D indices of the image into ascending order according to idx
reorderedim = im(I);
I don't see an obvious solution to generate idx without using for loops or recursion, but I'll think some more.