I have a 1-by-4 cell array, D. Each of the cell elements contains 2-by-2 double matrices. I want to do random permutation over each matrix independently which in result I will have the same size cell array as D but its matrices' elements will be permuted and then the inverse in order to obtain the original D again.
for a single matrix case I have the code and it works well as follows:
A=rand(3,3)
p=randperm(numel(A));
A(:)=A(p)
[p1,ind]=sort(p);
A(:)=A(ind)
but it doesn't work for a cell array.
The simplest solution for you is to use a loop:
nd = numel(D);
D_permuted{1,nd} = [];
D_ind{1,nd} = [];
for d = 1:nd)
A=D{d};
p=randperm(numel(A));
A(:)=A(p)
[~,ind]=sort(p);
D_permuted{d} = A;
D_ind{d} = ind;
end
Assuming your D matrix is just a list of identically sized (e.g. 2-by-2) matrices, then you could avoid the loop by using a 3D double matrix instead of the cell-array.
For example if you hade a D like this:
n = 5;
D = repmat([1,3;2,4],1,1,n)*10 %// Example data
Then you can do the permutation like this
m = 2*2; %// Here m is the product of the dimensions of each matrix you want to shuffle
[~,I] = sort(rand(m,n)); %// This is just a trick to get the equivalent of a vectorized form of randperm as unfortunately randperm only accepts scalars
idx = reshape(I,2,2,n);
D_shuffled = D(idx);
Related
The result of solving a polynom equation is a 1x2 vector or a 1x1 in some instances. I am trying to store all solutions for equations with different coefficients. so some solutions are just 1x1 vectors. how can i store these efficiently?
n = 1;
%sol = zeros(size(coef)); %create solution matrix in memory
sol = {};
while n < size(coef,2)
sol(n) = roots(coef(:,n));
end
"Conversion to cell from double is not possible."
error.
coef is coefficient matrix
You're almost there!
In order to store the vectors as cells in the cell array, use curly braces {} during their assignment:
sol(n) = {roots(coef(:,n))};
or alternatively:
sol{n} = roots(coef(:,n));
That way, the vectors/arrays can be of any size. Check this link for more info about accessing data in cell arrays.
Also, don't forget to increment n otherwise you will get an infinite loop.
Whole code:
n = 1;
%sol = zeros(size(coef)); %create solution matrix in memory
sol = {};
while n <= size(coef,2)
sol(n) = {roots(coef(:,n))};
n = n+1
end
I have a cell array with x columns, each with a yx1 cell. I would like to randomize the "rows" within the columns. That is, for each yx1 cell with elements a_1, a_2, ... a_y, I would like to apply the same permutation to the indices of a_i.
I've got a function that does this,
function[Oarray] = shuffleCellArray(Iarray);
len = length(Iarray{1});
width = length(Iarray);
perm = randperm(len);
Oarray=cell(width, 0);
for i=1:width;
for j=1:len;
Oarray{i}{j}=Iarray{i}{perm(j)};
end;
end;
but as you can see it's a bit ugly. Is there a more natural way to do this?
I realize that I'm probably using the wrong data type, but for legacy reasons I'd like to avoid switching. But, if the answer is "switch" then I guess that's the answer.
I'm assuming you have a cell array of column vectors, such as
Iarray = {(1:5).' (10:10:50).' (100:100:500).'};
In that case, you could do it this way:
ind = randperm(numel(Iarray{1})); %// random permutation
Oarray = cellfun(#(x) x(ind), Iarray, 'UniformOutput', 0); %// apply that permutation
%// to each "column"
Or converting to an intermediate matrix and then back to a cell array:
ind = randperm(numel(Iarray{1})); %// random permutation
x = cat(2,Iarray{:}); %// convert to matrix
Oarray = mat2cell(x(ind,:), size(x,1), ones(1,size(x,2))); %// apply permutation to rows
%// and convert back
Say I have a nxm matrix and want to treat each row as vectors in a function. So, if I have a function that adds vectors, finds the Cartesian product of vectors or for some reason takes the input of several vectors, I want that function to treat each row in a matrix as a vector.
This sounds like a very operation in Matlab. You can access the ith row of a matrix A using A(i, :). For example, to add rows i and j, you would do A(i, :) + A(j, :).
Given an nxm matrix A:
If you want to edit a single column/row you could use the following syntax: A(:, i) for the ith-column and A(i, :) for ith-row.
If you want to edit from a column/row i to a column/row j, you could use that syntax: A(:, i:j) or A(i:j, :)
If you want to edit (i.e.) from the penultimate column/row to the last one, you could you: A(:, end-1:end) or A(end-1:end, :)
EDIT:
I can't add a comment above because I don't have 50 points, but you should post the function setprod. I think you should be able to do what you want to do, by iterating the matrix you're passing as an argument, with a for-next statement.
I think you're going to have to loop:
Input
M = [1 2;
3 4;
5 6];
Step 1: Generate a list of all possible row pairs (row index numbers)
n = size(M,1);
row_ind = nchoosek(1:n,2)
Step 2: Loop through these indices and generate the product set:
S{n,n} = []; //% Preallocation of cell matrix
for pair = 1:size(row_ind,1)
p1 = row_ind(pair,1);
p2 = row_ind(pair,2);
S{p1,p2} = setprod(M(p1,:), M(p2,:))
end
Transform the matrix into a list of row vectors using these two steps:
Convert the matrix into a cell array of the matrix rows, using mat2cell.
Generate a comma-separated list from the cell array, using linear indexing of the cell contents.
Example: let
v1 = [1 2];
v2 = [10 20];
v3 = [11 12];
M = [v1; v2; v3];
and let fun be a function that accepts an arbitrary number of vectors as its input. Then
C = mat2cell(M, ones(1,size(M,1)));
result = fun(C{:});
is the same as result = fun(v1, v2, v3).
I have a cell array of 53 different (40,000 x 2000) sparse matrices. I need to take the mean over the third dimension, so that for example element (2,5) is averaged across the 53 cells. This should yield a single (33,000 x 2016) output. I think there ought to be a way to do this with cellfun(), but I am not able to write a function that works across cells on the same within-cell indices.
You can convert from sparse matrix to indices and values of nonzeros entries, and then use sparse to automatically obtain the sum in sparse form:
myCell = {sparse([0 1; 2 0]), sparse([3 0; 4 0])}; %// example
C = numel(myCell);
M = cell(1,C); %// preallocate
N = cell(1,C);
V = cell(1,C);
for c = 1:C
[m n v] = find(myCell{c}); %// rows, columns and values of nonzero entries
M{c} = m.';
N{c} = n.';
V{c} = v.';
end
result = sparse([M{:}],[N{:}],[V{:}])/C; %'// "sparse" sums over repeated indices
This should do the trick, just initialize an empty array and sum over each element of the cell array. I don't see any way around using a for loop without concatenating it into one giant 3D array (which will almost definitely run out of memory)
running_sum=zeros(size(cell_arr{1}))
for i=1:length(cell_arr)
running_sum=running_sum+cell_arr{i};
end
means = running_sum./length(cell_arr);
Say I have a cell array of (n X 1) vectors, A, and a cell array of vectors containing indices into A, called B. I wish to extract a cell array, C, such that C{i} = [A{B{i}}].
In other words, I have a cell array of arrays of indices, and I want to pull out the matrices corresponding to the concatenations of the vectors in A indexed by each of those arrays of indices.
for i = 1:length(B)
%# B{i} is an array of indices, C{i} is a matrix
C{i} = [ A{ B{i} } ];
end
The loop is equivalent to:
C = cellfun(#(x)[A{x}],B,'UniformOutput',false); %# implicit for loop w/ closure
Can I do that using an indexing expression alone? Or at least without the loop?
I think deal() might have to be involved but can't figure it out.
Here are two alternative solutions:
Collect all the indices of B together with the function cell2mat, index the contents of A to make one large matrix, then divide that matrix up using the function mat2cell and the sizes of the index arrays in B:
N = size(A{1}); % Size of an array in A
M = cellfun('prodofsize', B); % Array of sizes of elements in B
C = mat2cell([A{cell2mat(B)}], N, M);
Here's a more compact version of your cellfun-based solution:
C = cellfun(#(x) {[A{x}]}, B);
Ultimately, I would decide what solution to use based on speed and readability, which may actually turn out to be your for-loop-based solution.
Try the following expression:
C = A(cell2mat(B))
You may have a look at Loren's blog post about Cell Arrays and Their Contents