I have multiple variables, which are 2x2 arrays. I would like to stored all these into one cell array is there a for loop that could be used for this
I do not recommend the following solutions because of the reasons summarised here: https://au.mathworks.com/help/matlab/matlab_prog/string-evaluation.html.
I'll leave it to you to decide whether it's worth using eval or avoiding it.
(1. Option) If you have A, B, and C representing your 2x2 arrays, then:
A = rand(2);
B = rand(2);
C = rand(2);
array_names = ["A", "B", "C"];
D = cell(1, numel(array_names));
for ii = 1:numel(D)
D{ii} = eval(array_names(ii));
end
Should you have more than 3 arrays, just extend array_names accordingly.
(2. Option) If you have A1, A2, and A3 representing your 2x2 arrays, then:
A1 = rand(2);
A2 = rand(2);
A3 = rand(2);
D = cell(1, 3);
for ii = 1:numel(D)
D{ii} = eval("A"+ii);
end
Should you have more than 3 arrays, just change the size of the allocated cell D accordingly.
Related
If I have two sequences A and B containing the elements a1, a2, ... and b1,b2, ... where a_i and b_i are of dimension 1xn and 1xm, respectively, then I want to make a new sequence C which contains: a_i(1)*b_i, a_i(2)*b_i, ... , a_i(n)*b_i. So for the ith element of C, I want to have the Kronecker product of the elements a_i and b_i. I want to code this in Matlab, but without a for-loop. For the case when b_i are scalars, the desired result is achieved with
C = A.*B
However, this does not work for non-scalar b_i's. So what I do now is for sequences of length L:
C = [];
for ii = 1:L
C = [C; kron(A(ii,:),B(ii,:))];
end
But I have the idea that this must be possible without a for loop. Here is my code using the symbolic toolbox:
clc; clear;
L = 5;
syms('a1',[L,1]);
syms('a2',[L,1]);
syms('b1',[L,1]);
syms('b2',[L,1]);
A = [a1,a2];
B = [b1,b2];
C1 = A.*B % only for size(B,2)=1
C2 = [];
for ii = 1:L
C2 = [C2;kron(A(ii,:),B(ii,:))];
end
C2
C3 = kron(A,B) % does not work
Here reshape and implicit expansion are used to compute the result:
C = reshape(B .* reshape (A, L, 1, []), L, []);
I have a matrix A=[1,2,3] and a cell B={[1,2,3],[1,2,5],[1,2,6]}.
I would like the product of the matrix elements of a similar cell
that's mean A * B ={1*[1,2,3],2*[1,2,5],3*[1,2,6]};
Without loop in matlab
Here a few ways to multiply as you described:
% inputs
A = [1,2,3];
B = {[1,2,3],[1,2,5],[1,2,6]};
using for-loop:
C1 = cell(size(B));
for i=1:numel(C1)
C1{i} = A(i) * B{i};
end
using cellfun:
C2 = cellfun(#(a,b)a*b, num2cell(A), B, 'Uniform',false);
using bsxfun:
C3 = bsxfun(#times, A(:), cat(1,B{:}));
C3 = num2cell(C3,2)';
All results should be equal (output being a cell array):
assert(isequal(C1,C2,C3))
If I were to choose, I would stick with the for-loop. In this case it's likely faster and easiest to read.
I have a series of arrays of equal length, and want to make a matrix for each data point of these, and perform some sort of operation such a multiplying the matrices.
a=ones(1,10);
b=3*ones(1,10);
c=zeros(1,10);
for i=1:10
A(i)=[a(i) a(i);
b(i) b(i)];
B(i)=[c(i) c(i)];
C(i)=B(i)*A(i);
end
Is this possible without using cells?
A = zeros(2,2,length(a));
B = zeros(length(a),:);
C = zeros(size(B));
for i=1:10
A(:,:,i)=[a(i) a(i);
b(i) b(i)];
B(i,:)=[c(i) c(i)];
C(i,:)=B(i,:)*A(:,:,i);
end
Note you can make A and B without loops:
aa = permute(A, [3,2,1]);
bb = permute(B, [3,2,1]);
A = [aa,aa;bb,bb];
B = [c.', c.'];
I have been looking for a way to use boxplot for different length vectors. thanx for stackoverflow helpers, they give this solution:
A = randn(10, 1); B = randn(12, 1); C = randn(4, 1);
g = [repmat(1, [10, 1]) ; repmat(2, [12, 1]); repmat(3, [4, 1])];
figure; boxplot([A; B; C], g);
unfortunately, my data contains over 100 vectors with different lengths, I wonder if it can be done without repeating the repmat for over 100 times.
As long as your vectors have different lengths, store it in a cell array.
There are plenty was of doing it, here are 3 examples
1) "Naive" for loop
g = [];
vars_cell = {A, B, C, ....};
for it = 1 : length(vars_cell)
g = [g; repmat(it,size(vars_cell{it}))];
end
This way of doing it works but is very slow with big quantites of vectors or big vectors! It comes from the fact that you are re-defining g at each iteration, changing its size each time.
2) Not-naive for loop
vars_cell = {A, B, C, ....};
%find the sum of the length of all the vectors
total_l = sum(cellfun(#(x) length(x),vars_cell));
g = zeros(total_l,1);
acc = 1;
for it = 1 : length(vars_cell)
l = size(vars_cell{it});
g(acc:acc+l-1) = repmat(it,l);
acc = acc+l;
end
This method will be much faster than the 1st one because it defines g only once
3) The "one-liner"
vars_cell = {A, B, C, ....};
g = cell2mat(arrayfun(#(it) repmat(it, size(vars_cell{it})),1:length(vars_cell),'UniformOutput',0)');
This is qute equivalent to the 2nd solution, but if you like one line answers this is what you are looking for!
The code below is only for 2 cell arrays, named B and C
A=cellfun(#minus, B, C, 'UniformOutput', false)
I want to perform a loop to be able to perform substraction for all my cell arrays.
Example of B{i} and C{i} are below:
B{1}=[0.435]
B{2}=[0.333] [0.532]
B{3}=[0.021] [0.432] [0.312] //command window output
C{1}=[0.211]
C{2}=[0.243] [0.116]
C{3}=[0.553] [0.212] [0.375] //command window output
B{1}-C{1}
B{2}-C{2}
B{3}-C{3}
I tried to include {i} behind A , B and C to become something like:
A{i}=cellfun(#minus, B{i}, C{i}, 'UniformOutput' , false)
However, it seems like it's not working. Is there any solution for this? Thanks
EDIT:
You have unnecessary nested cell-arrays, i.e B is a cell-array of cell-arrays, and B{i} is a cell-array of numbers.
If you want to keep that format, here is one way to compute the result using cellfun (A will also be a cell-array of cell-arrays of numbers):
% exiting data
B = cell(3,1);
B{1} = {0.435};
B{2} = {0.333, 0.532};
B{3} = {0.021, 0.432, 0.312};
C = cell(3,1);
C{1} = {0.211};
C{2} = {0.243, 0.116};
C{3} = {0.553, 0.212, 0.375};
A = cellfun(#(b,c)cellfun(#minus, b, c, 'Uniform',false), B, C, 'Uniform',false);
Otherwise I suggest you cut down on the level of nesting, and use this instead:
% note the difference between square-brackets and curly-brackets
B = cell(3,1);
B{1} = [0.435];
B{2} = [0.333, 0.532];
B{3} = [0.021, 0.432, 0.312];
C = cell(3,1);
C{1} = [0.211];
C{2} = [0.243, 0.116];
C{3} = [0.553, 0.212, 0.375];
Now you can compute the result using a single cellfun (no nesting):
A = cellfun(#minus, B, C, 'Uniform',false)