I have to two evenly sized very large vectors (columns) A and B. I would like to divide vector A by vector B. This will give me a large matrix AxB filled with zeros, except the last column. This column contains the values I'm interested in. When I simple divide the vectors in a Matlab script, I run out of memory. Probably because the matrix AxB becomes very large. Probably I can prevent this from happening by repeating the following:
calculating the first row of matrix AxB
filter the last value and put it into another vector C.
delete the used row of matrix AxB
redo step 1-4 for all rows in vector A
How can I make a loop which does this?
You're question doesn't make it clear what you are trying to do, although it sounds like you want to do an element wise division.
Try:
C = A./B
"Matrix product AxB" and "dividing vectors" are distinct operations.
If we understood this correctly, what you do want to calculate is "C = last column from AxB", such that:
lastcolsel=zeros(size(B,2),1)
C=(A*B)*lastcolsel
If that code breaks your memory limit, recall that matrix product is associative (MxN)xP = Mx(NxP). Simplifying your example, we get:
lastcolsel=zeros(size(B,2),1)
simplifier=B*lastcolsel
C=A*simplifier
Related
I am generating a random binary matrix with a specific number of ones in each row. Now, I want to take each row in the matrix and multiply it by its transpose (i.e row1'*row1).
So, I am using row1=rnd_mat(1,:) to get the first row. However, in the multiplication step I get this error
"Both logical inputs must be scalar. To compute elementwise TIMES, use TIMES (.*) instead."
Knowing that I don't want to compute element-wise, I want to generate a matrix using the outer product. I tried to write row1 manually using [0 0 1 ...], and tried to find the outer product. I managed to get the matrix I wanted.
So, does anyone have some ideas on how I can do this?
Matrix multiplication of logical matrices or vectors is not supported in MATLAB. That is the reason why you are getting that error. You need to convert your matrix into double or another valid numeric input before attempting to do that operation. Therefore, do something like this:
rnd_mat = double(rnd_mat); %// Cast to double
row1 = rnd_mat(1,:);
result = row1.'*row1;
What you are essentially computing is the outer product of two vectors. If you want to avoid casting to double, consider using bsxfun to do the job for you instead:
result = bsxfun(#times, row1.', row1);
This way, you don't need to cast your matrix before doing the outer product. Remember, the outer product of two vectors is simply an element-wise multiplication of two matrices where one matrix is consists of a row vector where each row is a copy of the row vector while the other matrix is a column vector, where each column is a copy of the column vector.
bsxfun automatically broadcasts each row vector and column vector so that we produce two matrices of compatible dimensions, and performs an element by element multiplication, thus producing the outer product.
I'm currently attempting to select a subset of 0's in a very large matrix (about 400x300 elements) and change their value to 1. I am able to do this, but it requires using a loop where each instance it selects the next value in a randperm vector. In other words, 50% of the 0's in the matrix are randomly selected, one-at-a-time, and changed to 1:
z=1;
for z=1:(.5*numberofzeroes)
A(zeroposition(rpnumberofzeroes(z),1),zeroposition(rpnumberofzeroes(z),2))=1;
z=z+1;
end
Where 'A' is the matrix, 'zeroposition' is a 2-column-wide matrix with the positions of the 0's in the matrix (the "coordinates" if you like), and 'rpnumberofzeros' is a randperm vector from 1 to the number of zeroes in the matrix.
So for example, for z=20, the code might be something like this:
A(3557,2684)=1;
...so that the 0 which appears in this location within A will now be a 1.
It performs this loop thousands of times, because .5*numberofzeroes is a very big number. This inevitably takes a long time, so my question is can this be done without using a loop? Or at least, in some way that takes less processing resources/time?
As I said, the only thing that needs to be done is an entirely random selection of 50% (or whatever proportion) of the 0's changed to 1.
Thanks in advance for the help, and let me know if I can clear anything up! I'm new here, so apologies in advance if I've made any faux pa's.
That's very easy. I'd like to introduce you to my friend sub2ind. sub2ind allows you to take row and column coordinates of a matrix and convert them into linear column-major indices so that you can access multiple values in a matrix simultaneously in a single call. As such, the equivalent code you want is:
%// First access the values in rpnumberofzeroes
vals = rpnumberofzeroes(1:0.5*numberofzeroes, :);
%// Now, use the columns of these to determine which rows and columns we want
%// to access A
rows = zeroposition(vals(:,1), 1);
cols = zeroposition(vals(:,2), 2);
%// Get linear indices via sub2ind
ind1 = sub2ind(size(A), rows, cols);
%// Now set these locations to 1
A(ind1) = 1;
The first statement gets the first half of your matrix of coordinates stored in rpnumberofzeroes. The first column is the row coordinates, the second column is the column coordinates. Notice that in your code, you wish to use the values in zeroposition to access the locations in A. As such, extract out the corresponding rows and columns from rpnumberofzeroes to figure out the right rows and columns from zeroposition. Once that's done, we wish to use these new rows and columns from zeroposition and index into A. sub2ind requires three inputs - the size of the matrix you are trying to access... so in our case, that's A, the row coordinates and the column coordinates. The output is a set of column major indices that are computed for each row and column pair.
The last piece of the puzzle is to use these to index into A and set the locations to 1.
This can be accomplished with linear indexing as well:
% find linear position of all zeros in matrix
ix=find(abs(A)<eps);
% set one half of those, selected at random, to one.
A(ix(randperm(round(numel(ix)*.5)))=1;
I'm looking for a way to select the vector that has the largest sum. Is there a simple way of doing this? I was thinking of writing a loop, but I'm not sure how to loop over a set of vectors.
Thanks for your help!
For the case in which the vectors have the same length (as stated in the comment), I think a simple loop-free way would be to build a matrix from each vector and fetch directly the row (or column) with the largest sum:
clear
clc
RandMat = rand(8,10);
[~,Ind] = max(sum(RandMat,2)); %// Get row index for largest sum. If you want the column, use 1 instead.
MaxRow = RandMat(Ind,:); %// Index in original matrix to get the vector. If you want the column, use RandMat(:,Ind);
If vectors don't have the same length then you would need to pad the missing values with NaN for example to use a regular matrix, otherwise you would need a cell array.
If you prefer a solution in which you don't have to build a matrix then you could loop through each individual vector and store the sum in a variable, then compare the sums at the end. If you would like such a solution please ask!
preface: As the matlab guiderules state, Usually, when one wants to efficiently populate a sparse matrix in matlab, he should create a vector of indexes into the matrix and a vector of values he wants to assign, and then concentrate all the assignments into one atomic operation, so as to allow matlab to "prepare" the matrix in advance and optimize the assignment speed. A simple example:
A=sparse([]);
inds=some_index_generating_method();
vals=some_value_generating_method();
A(inds)=vals;
My question: what can I do in the case where inds contain overlapping indexes, i.e inds=[4 17 8 17 9] where 17 repeats twice.
In this case, what I would want to happen is that the matrix would be assigned the addition of all the values that are mapped to the same index, i.e for the previous example
A(17)=vals(2)+vals(4) %as inds(2)==inds(4)
Is there any straightforward and, most importantly, fast way to achieve this? I have no way of generating the indexes and values in a "smarter" way.
This might help:
S = sparse(i,j,s,m,n,nzmax) uses vectors i, j, and s to generate an m-by-n sparse matrix such that S(i(k),j(k)) = s(k), with space allocated for nzmax nonzeros. Vectors i, j, and s are all the same length. Any elements of s that are zero are ignored, along with the corresponding values of i and j. Any elements of s that have duplicate values of i and j are added together.
See more at MATLAB documentation for sparse function
Is there a way in Octave to compute and store only the diagonal of a matrix product?
Basically like doing: vector = diag(A*B);
I don't care about any of the values of A*B except those on the diagonal. The matrix sizes are around 80k x 12 and 12 x 80k, so even if I didn't care about the speed/extra memory it simply wont fit in RAM.
Strange, since Octave is a package for huge data sets and diagonals are very important, so it should be possible.
The first element in the diagonal is the scalar product of the first row of A with the first column of B. The second element in the diagonal is the scalar product of the second row of A with the second column of B.
In other words:
vector = sum(A.*B',2);
This is how you could do it in MATLAB (probably similar to Octave syntax):
vector = sum(A.*B',2);
This will compute only the resulting diagonal of the operation A*B as a column vector vector.
actually I think it's the dot product of the first row of A with the first column of B... the second diagonal element is the dot product of the second row and the second column... etc