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
Related
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
I am looking for a matrix operation of the form: B = M*A*N where A is some general square matrix and M and N are the matrices I want to find.
Such that the columns of B are the diagonals of A. The first column the main diagonal, the second the diagonal shifted by 1 from the main and so on.
e.g. In MATLAB syntax:
A = [1, 2, 3
4, 5, 6
7, 8, 9]
and
B = [1, 2, 3
5, 6, 4
9, 7, 8]
Edit:
It seems a pure linear algebra solution doesn't exist. So I'll be more precise about what I was trying to do:
For some vector v of size 1 x m. Then define C = repmat(v,m,1). My matrix is A = C-C.';.
Therefore, A is essentially all differences of values in v but I'm only interested in the difference up to some distance between values.
Those are the diagonals of A; but m is so large that the construction of such m x m matrices causes out-of-memory issues.
I'm looking for a way to extract those diagonals in a way that is as efficient as possible (in MATLAB).
Thanks!
If you're not actually looking for a linear algebra solution, then I would argue that constructing three additional matrices the same size as A using two matrix multiplications is very inefficient in both time and space complexity. I'm not sure it's even possible to find a matrix solution, given my limited knowledge of linear algebra, but even if it is it's sure to be messy.
Since you say you only need the values along some diagonals, I'd construct only those diagonals using diag:
A = [1 2 3;
4 5 6;
7 8 9];
m = size(A, 1); % assume A is square
k = 1; % let's get the k'th diagonal
kdiag = [diag(A, k); diag(A, k-m)];
kdiag =
2
6
7
Diagonal 0 is the main diagonal, diagonal m-1 (for an mxm matrix) is the last. So if you wanted all of B you could easily loop:
B = zeros(size(A));
for k = 0:m-1
B(:,k+1) = [diag(A, k); diag(A, k-m)];
end
B =
1 2 3
5 6 4
9 7 8
From the comments:
For v some vector of size 1xm. Then B=repmat(v,m,1). My matrix is A=B-B.'; A is essentially all differences of values in v but I'm only interested in the difference up to some distance between values.
Let's say
m = 4;
v = [1 3 7 11];
If you construct the entire matrix,
B = repmat(v, m, 1);
A = B - B.';
A =
0 2 6 10
-2 0 4 8
-6 -4 0 4
-10 -8 -4 0
The main diagonal is zeros, so that's not very interesting. The next diagonal, which I'll call k = 1 is
[2 4 4 -10].'
You can construct this diagonal without constructing A or even B by shifting the elements of v:
k = 1;
diag1 = circshift(v, m-k, 2) - v;
diag1 =
2 4 4 -10
The main diagonal is given by k = 0, the last diagonal by k = m-1.
You can do this using the function toeplitz to create column indices for the reshuffling, then convert those to a linear index to use for reordering A, like so:
>> A = [1 2 3; 4 5 6; 7 8 9]
A =
1 2 3
4 5 6
7 8 9
>> n = size(A, 1);
>> index = repmat((1:n).', 1, n)+n*(toeplitz([1 n:-1:2], 1:n)-1);
>> B = zeros(n);
>> B(index) = A
B =
1 2 3
5 6 4
9 7 8
This will generalize to any size square matrix A.
I have been trying to use the multidimensional array function to store NxN vectors of length n in each (i,j) entry of a 3d matrix of NxNxn dimensions.
My code looks like:
a=zeros(N,N,n);
a(i,j,:)=v_ij; %here v is a vector of length n, which differs for each i,j
However when I try to extract each vector by typing e.g. a(1,1,:) I don't get a nice vector. Rather I get something like:
ans(:,:,1) = ..
ans(:,:,2) = ..
...
I don't understand why it says ans(:,:)...
That's because each vector v_ij is stored along the 3rd dimension of your matrix, so when you access a(1,1,:), you are looking for a multidimensional array consisting of every value at the location (1,1) along the 3rd dimension of a.
Let's consider a simple example:
N = 2;
n = 3;
a = zeros(N,N,n);
for k = 1:N
for l = 1:N
v_kl = randi([0 10],1,n);
a(k,l,:) = v_kl;
end
end
The randomly-generated matrix a is a 2x2x3 matrix that looks like this:
a(:,:,1) =
4 1
4 10
a(:,:,2) =
0 2
0 5
a(:,:,3) =
2 2
9 5
Therefore, using a(1,1,:) is equivalent to getting the element located at (1,1) for all 3 dimensions, here it would be 4,0 and 2.
Indeed, calling a(1,1,:) yields:
ans(:,:,1) =
4
ans(:,:,2) =
0
ans(:,:,3) =
2
Benoit_11's answer plus squeeze should work, but I would like to propose a different solution.
Rather than creating a NxNxn array, why not make it nxNxN?
N = 2;
n = 3;
a = zeros(n,N,N);
for p = 1:N
for q = 1:N
v_pq = randi([0 10],1,n);
a(:,p,q) = v_pq;
if (p == 1) && (q == 1)
v_pq %// display vector at a(:,1,1)
end
end
end
a
v_pq =
3 4 8
a =
ans(:,:,1) =
3 3
4 9
8 7
ans(:,:,2) =
5 6
10 1
9 5
Now the vectors are stored along the first dimension, so [3 4 8] shows up as the first column of ans(:,:,1). To access it you would use a(:,p,q) rather than a(p,q,:):
a(:,1,1)
ans =
3
4
8
just lets make it simple, assume that I have a 10x3 matrix in matlab. The numbers in the first two columns in each row represent the x and y (position) and the number in 3rd columns show the corresponding value. For instance, [1 4 12] shows that the value of function in x=1 and y=4 is equal to 12. I also have same x, and y in different rows, and I want to average the values with same x,y. and replace all of them with averaged one.
For example :
A = [1 4 12
1 4 14
1 4 10
1 5 5
1 5 7];
I want to have
B = [1 4 12
1 5 6]
I really appreciate your help
Thanks
Ali
Like this?
A = [1 4 12;1 4 14;1 4 10; 1 5 5;1 5 7];
[x,y] = consolidator(A(:,1:2),A(:,3),#mean);
B = [x,y]
B =
1 4 12
1 5 6
Consolidator is on the File Exchange.
Using built-in functions:
sparsemean = accumarray(A(:,1:2), A(:,3).', [], #mean, 0, true);
[i,j,v] = find(sparsemean);
B = [i.' j.' v.'];
A = [1 4 12;1 4 14;1 4 10; 1 5 5;1 5 7]; %your example data
B = unique(A(:, 1:2), 'rows'); %find the unique xy pairs
C = nan(length(B), 1);
% calculate means
for ii = 1:length(B)
C(ii) = mean(A(A(:, 1) == B(ii, 1) & A(:, 2) == B(ii, 2), 3));
end
C =
12
6
The step inside the for loop uses logical indexing to find the mean of rows that match the current xy pair in the loop.
Use unique to get the unique rows and use the returned indexing array to find the ones that should be averaged and ask accumarray to do the averaging part:
[C,~,J]=unique(A(:,1:2), 'rows');
B=[C, accumarray(J,A(:,3),[],#mean)];
For your example
>> [C,~,J]=unique(A(:,1:2), 'rows')
C =
1 4
1 5
J =
1
1
1
2
2
C contains the unique rows and J shows which rows in the original matrix correspond to the rows in C then
>> accumarray(J,A(:,3),[],#mean)
ans =
12
6
returns the desired averages and
>> B=[C, accumarray(J,A(:,3),[],#mean)]
B =
1 4 12
1 5 6
is the answer.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Sort a matrix with another matrix
The Matrix A ('10 x 1000' all numbers) look like this:
score(1.1) score(1.2) score(1.3)....score(1.1000)
score(2.1) score(2.2) score(2.3)....score(1.1000)
...
The Matrix B ('1 x 1000' all numbers):
Return(1) Return(2) Return(3) .....Return(1.1000)
Every time I sort a row of Matrix A, I want to sort Matrix B based on the order of the sorted row in Matrix A. Because there are 10 rows in Matrix A, Matrix B will be sorted 10 times and generate a new Matrix C ('10 x 1000') like this: (I am looking for a script to generate this Matrix C)
Return(3) Return(25) Return(600) .......Return(1000)
Return(36)Return(123) Return(2)........Return(212)
....
....
This should do what you want:
A = randn(10,1000);
B = randn(1,1000);
C = zeros(size(A));
for i = 1:10
[a idx] = sort(A(1,:));
A(i,:) = a;
C(i,:) = B(idx);
end
Now the rows of A are sorted, and the rows of C contain the corresponding sorted B.
This solution is a bit more compact, and it's also good to get used to doing this kind of solution for efficiency when your matrices get big. You can solve your problem with two ideas:
In [a, ix] = sort(X), a is a column-sorted version of X, and ix stores which rows moved where in each column. Thus if we do [a, ix] = sort(X.').'; (where the dot-apostrophe is the transpose) we can sort the rows.
B(ix) where ix is a bunch of indeces will make a matrix the same size as ix with the i-jth element being B at ix(i,j)
Then you just need to reshape it. So you can do:
A = rand(4,8);
B = rand(1,8);
n = size(A,1);
m = size(A,2);
[~,ix] = sort(A.');
C = reshape(B(ix'),n,m);
If I understand your question correctly the following should work. Using some sample scores:
>> score = [1 4 7 9; 3 5 1 7; 9 3 1 6]
score =
1 4 7 9
3 5 1 7
9 3 1 6
and sample return vector:
>> r = [10 20 30 40]
r =
10 20 30 40
Transpose the scores and sort since the SORT command works on columns of a matrix. We're only interested in the indices of the sorted values:
>> [~, ix] = sort(score')
ix =
1 3 3
2 1 2
3 2 4
4 4 1
Now transpose these indices and use them to reference the return values:
>> answer = r(ix)'
answer =
10 20 30 40
30 10 20 40
30 20 40 10