I have a MATLAB program which uses two for loops to iterate up to 5 times. However, I want MATLAB to use only (1 1), (2 2), (3 3) and so on.
Here is the program:
syms l
a = [0 1 0 0 1 0;1 1 1 0 1 1;1 0 0 0 1 1;1 1 1 0 0 1;0 1 1 0 1 1];
n = [2 1;1 1;1 1;1 1;2 1];
for l = 1:5
for i = 1:5
j = n(l,1);
if a(i,j) == 0
a(i,j:end) = circshift(a(i,j:end),[n(l,2) n(l,2)]);
for j = n(l,1):n(l,1)+n(l,2)
a(i,n(l,1)) = 1;
end
else
a(i,j:end) = circshift(a(i,j:end),[n(l,2) n(l,2)]);
for j = n(l,1):n(l,1)+n(l,2)
a(i,n(l,1)) = 0;
end
end
break;
end
break;
end
I want MATLAB my program to work like this:
first l = 1 and i = 1;
second l = 2 and i = 2;
third l = 3 and i = 3;
fourth l = 4 and i = 4;
and so on...
#RodyOldenhuis basically answered your question, and I'm flattered he hasn't made this into an actual answer!
Simply take your code and change the inner for loop index so that it matches the outer loop index. In other words, do this:
syms l %// Why are you doing this?
a = [0 1 0 0 1 0;1 1 1 0 1 1;1 0 0 0 1 1;1 1 1 0 0 1;0 1 1 0 1 1];
n = [2 1;1 1;1 1;1 1;2 1];
for l = 1:5
i = l; %// Change here - Remove second for loop
%// ... [rest of your code here]
%// Get rid of the break in this loop
end
%// Removed break here too
end
Related
I have a 3D array. I need to remove any elements that are in the same row, column position but on the next page (3rd dimension), and only use the first occurrence at that position. So if all pages were to multiply the result would be 0.
Since the 3D array may be of any size, I can't hard code solutions like isMember. I also can't use unique because elements can be the same, just not share the same position.
For example, input:
A(:,:,1) = [ 1 0 2];
A(:,:,2) = [ 1 1 0];
A(:,:,3) = [ 0 1 0];
the desired output is:
A(:,:,1) = [ 1 0 2];
A(:,:,2) = [ 0 1 0];
A(:,:,3) = [ 0 0 0];
How can I accomplish this?
Not the most elegant, but at least it works.
A(:,:,1) = [ 1 0 2 ];
A(:,:,2) = [ 1 1 0 ];
A(:,:,3) = [ 0 1 0 ];
for ii = 1:size(A,1)
for jj = 1:size(A,2)
unique_el = unique(A(ii, jj, :)); % Grab unique elements
for kk = 1:numel(unique_el)
idx = find(A(ii,jj,:) == kk); % Contains indices of unique elements
if numel(idx) > 1 % If an element occurs more than once
A(ii, jj, idx(2:end)) = 0; % Set to 0
end
end
end
end
A
A(:,:,1) =
1 0 2
A(:,:,2) =
0 1 0
A(:,:,3) =
0 0 0
I loop over the first two dimensions of A (rows and columns), find any unique elements which occur on a certain row and column location through the third dimensions (pages). Then set all occurrences of a unique element after the first to 0.
Given a more elaborate 3D matrix this still works:
A(:,:,1) = [1 0 2 0; 2 1 3 0];
A(:,:,2) = [1 1 0 0; 2 2 1 0];
A(:,:,3) = [0 1 1 3; 1 2 2 4];
A(:,:,1) =
1 0 2 0
2 1 3 0
A(:,:,2) =
0 1 0 0
0 2 1 0
A(:,:,3) =
0 0 1 3
1 0 2 4
If you want the first non-zero element and discard any element occurring afterwards, simply get rid of the unique() call:
A(:,:,1) = [1 0 2 0; 2 1 3 0];
A(:,:,2) = [1 1 0 0; 2 2 1 0];
A(:,:,3) = [0 1 1 3; 1 2 2 4];
for ii = 1:size(A,1)
for jj = 1:size(A,2)
idx = find(A(ii,jj,:) ~= 0); % Contains indices of nonzero elements
if numel(idx) > 1 % If more than one element
A(ii, jj, idx(2:end)) = 0; % Set rest to 0
end
end
end
A(:,:,1) =
1 0 2 0
2 1 3 0
A(:,:,2) =
0 1 0 0
0 0 0 0
A(:,:,3) =
0 0 0 3
0 0 0 4
My solution assumes, that, for a given "position", EVERY value after the first occurence of any value is cleared. Some of the MATLAB regulars around here had some discussions on that, from there comes the "extended" example as also used in Adriaan's answer.
I use permute and reshape to rearrange the input, so that we have all "positions" as "page" columns in a 2D array. Then, we can use arrayfun to find the proper indices of the first occurence of a non-zero value (kudos to LuisMendo's answer here). Using this approach again, we find all indices to be set to 0.
Let's have a look at the following code:
A(:,:,1) = [1 0 2 0; 2 1 3 0];
A(:,:,2) = [1 1 0 0; 2 2 1 0];
A(:,:,3) = [0 1 1 3; 1 2 2 4]
[m, n, o] = size(A);
B = reshape(permute(A, [3 1 2]), o, m*n);
idx = arrayfun(#(x) find(B(:, x), 1, 'first'), 1:size(B, 2));
idx = arrayfun(#(x) find(B(idx(x)+1:end, x)) + idx(x) + 3*(x-1), 1:size(B, 2), 'UniformOutput', false);
idx = vertcat(idx{:});
B(idx) = 0;
B = permute(reshape(B, o, m , n), [2, 3, 1])
Definitely, it makes sense to have a look at the intermediate outputs to understand the functioning of my approach. (Of course, some lines can be combined, but I wanted to keep a certain degree of readability.)
And, here's the output:
A =
ans(:,:,1) =
1 0 2 0
2 1 3 0
ans(:,:,2) =
1 1 0 0
2 2 1 0
ans(:,:,3) =
0 1 1 3
1 2 2 4
B =
ans(:,:,1) =
1 0 2 0
2 1 3 0
ans(:,:,2) =
0 1 0 0
0 0 0 0
ans(:,:,3) =
0 0 0 3
0 0 0 4
As you can see, it's identical to Adriaan's second version.
Hope that helps!
A vectorized solution. You can use the second output of max to find the index of the first occurence of a nonzero value along the third dimension and then use sub2ind to convert that to linear index.
A(:,:,1) = [ 1 0 2];
A(:,:,2) = [ 1 1 0];
A(:,:,3) = [ 0 1 0];
[~, mi] =max(logical(A) ,[], 3);
sz=size(A) ;
[x, y] =ndgrid(1:sz(1),1:sz(2));
idx=sub2ind( sz, x,y,mi);
result=zeros(sz) ;
result(idx) =A(idx);
I have a code for a 1D heat equation. Im trying to format a for loop so that the A matrix will follow a certain pattern of 1 -2 1 down the entire diagonal of a matrix that could be infinite. The pattern starts to take shape when I mess around with the initialized count at the beginning of the for loop but this changes the size of the matrix which fails the rest of the code.
My current code is below. The commented A matrix edits are what it should be.
N = 5;
%A(2,1:3) = [1 -2 1];
%A(3,2:4) = [1 -2 1];
%A(4,3:5) = [1 -2 1];
%A(5,4:6) = [1 -2 1];
A = zeros(N+1,N+1);
A(1,1) = 1;
for count=N:N+1
A(count+1,count:count+2) = [1 -2 1];
end
A(N+1,N+1) = 1;
In Matlab you can often avoid loops. In this case you can get the desired result with 2D convolution:
>> N = 6;
>> A = [1 zeros(1,N-1); conv2(eye(N-2), [1 -2 1]); zeros(1,N-1) 1]
A =
1 0 0 0 0 0
1 -2 1 0 0 0
0 1 -2 1 0 0
0 0 1 -2 1 0
0 0 0 1 -2 1
0 0 0 0 0 1
Or, depending on what you want,
>> A = conv2(eye(N), [1 -2 1], 'same')
A =
-2 1 0 0 0 0
1 -2 1 0 0 0
0 1 -2 1 0 0
0 0 1 -2 1 0
0 0 0 1 -2 1
0 0 0 0 1 -2
There are many simple ways of creating this matrix.
Your loop can be amended as follows:
N = 5;
A = zeros(N+1,N+1);
A(1,1) = 1;
for row = 2:N
A(row, row-1:row+1) = [1 -2 1];
end
A(N+1,N+1) = 1;
I've renamed count to row, we're indexing each row (from 2 to N, skipping the first and last rows), then finding with row-1:row+1 the three indices for that row that you want to address.
Directly indexing the diagonal and off-diagonal elements. Diagonal elements for an NxN matrix are 1:N+1:end. This is obviously more complex, I'd prefer the loop:
N = 6;
A = zeros(N,N);
A(1:N+1:end) = -2;
A(2:N+1:end-2*N) = 1; % skip last row
A(2*N+2:N+1:end) = 1; % skip first row
A(1,1) = 1;
A(N,N) = 1;
Using diag. We need to special-case the first and last rows:
N = 6;
A = diag(-2*ones(N,1),0) + diag(ones(N-1,1),1) + diag(ones(N-1,1),-1);
A(1,1:2) = [1,0];
A(end,end-1:end) = [0,1];
I am trying to make a parity check matrix from non-systematic to systematic. Hence, I am attaching my code below. Somewhat it is correct, but there are some problems. It would be really great if someone could help me in this.
Subject: Information theory and coding. I am working on LDPC coding and decoding. Please check the code below
MATLAB CODE:
H = [1 0 1 1 0; 0 0 1 0 1; 1 0 0 1 0; 1 0 1 1 1]
[m,n] = size(H);
k = n-m;
for i = k+1:n
%H(:,i)
ind = find(H(:,i),1,'last');
% exchanging (ind)th row and (i-k)th row
if ind < i-k
continue;
end
if ind ~= i-k
temp = H(ind,:);
H(ind,:) = H(i-k,:);
H(i-k,:) = temp;
end
I = find(H(:,i));
% Guassian elimination
for j = 1:length(I)
if I(j) ~= i-k
H(I(j),:) = mod(H(I(j),:)+H(i-k,:),2);
end
end
end
Hsys = H
For e.g.
This is my H matrix:
H =
1 0 1 1 0
0 0 1 0 1
1 0 0 1 0
1 0 1 1 1
I want to have an identity matrix inside the matrix. The dimension on H matrix here is (mxn) which is (4x5).
Generally we use Gaussian elimination method to make the Identity matrix.hence, we make operations between rows. This is how we make it systematic.
I should have matrix as this in the result:
Hsys =
0 1 0 0 0
0 0 1 0 0
1 0 0 1 0
0 0 0 0 1
I should have an identity matrix of dimension m.
Here is how I'd do it (using Gauss-Jordan elimination):
% Not your matrix since it does not have any ones in the second column.
H=[1 1 0 1 1 0 0 1 0 0;
0 1 1 0 1 1 1 0 0 0;
0 0 0 1 0 0 0 1 1 1;
1 1 0 0 0 1 1 0 1 0;
0 0 1 0 0 1 0 1 0 1];
rows = size(H, 1);
cols = size(H, 2);
r = 1;
for c = cols - rows + 1:cols
if H(r,c) == 0
% Swap needed
for r2 = r + 1:rows
if H(r2,c) ~= 0
tmp = H(r, :);
H(r, :) = H(r2, :);
H(r2, :) = tmp;
end
end
end
% Ups...
if H(r,c) == 0
error('H is singular');
end
% Forward substitute
for r2 = r + 1:rows
if H(r2, c) == 1
H(r2, :) = xor(H(r2, :), H(r, :));
end
end
% Back Substitution
for r2 = 1:r - 1
if H(r2, c) == 1
H(r2, :) = xor(H(r2, :), H(r, :));
end
end
% Next row
r = r + 1;
end
I'd like to create a 4x12 matrix which is very similar to a upper triangle matrix, it looks like this:
1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 1 1 1
So my question is. What is the most efficient way to create it? no loops, no cellfun. Thanks.
One vectorized approach -
nrows = 4;
ncols = 12;
row_idx = repmat(1:nrows,ncols/nrows,1)
out = bsxfun(#le,[1:nrows]',row_idx(:).')
The Matlab R2015a and later approach using the newly introduced repelem:
n = 4;
m = 3;
out = repelem(triu(ones(n)),1,m);
out =
1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 1 1 1
It even seems faster than the bsxfun approach, though I can't believe this ;)
Benchmark
Unfortunately I couldn't consider andrew's solution as it is not complete and I didn't got it totally.
function [t] = bench()
n = 4;
m = 12;
t = zeros(3,15);
for ii = 1:15
fcns = {
#() thewaywewalk(ii*n,ii*m);
#() Divakar(ii*n,ii*m);
#() LuisMendo(ii*n,ii*m);
};
% timeit
for jj = 1:100;
t(:,ii) = t(:,ii) + cellfun(#timeit, fcns);
end
end
plot(1:15,t(1,:)); hold on;
plot(1:15,t(2,:)); hold on;
plot(1:15,t(3,:)); hold on;
xlabel('Matrix size: n = x*4, m = x*12')
ylabel('timing')
legend({'thewaywewalk','Divakar','Luis Mendo'},'location','northwest')
end
function Z = thewaywewalk(n,m)
Z = repelem(triu(ones(n)),1,m/n);
end
function Z = Divakar(n,m)
row_idx = repmat(1:n,m/n,1);
Z = bsxfun(#le,[1:n]',row_idx(:).');
end
function Z = LuisMendo(n,m)
Z = reshape(repmat(permute(triu(ones(n,n)), [1 3 2]), [1 m/n 1]), [n m]);
end
First bottomline - small matrices:
The new repelem does a very good job, but also the reshape(repmat(permute... does not disappoint. The bsxfun approach stays a little behind for some medium-sized matrices, before it becomes the leader for large matrices:
Second bottomline - big matrices:
As predicted by Divakar, bsxfun is the fastest for larger matrices, actually as expected as bsxfun is always the fastest! Interesting that the other two align perfectly, on could guess they almost work the same internally.
Create an upper triangular matrix of ones, permute second and third dimensions, repeat along second dimension, and reshape into desired shape:
m = 4;
n = 12;
result = reshape(repmat(permute(triu(ones(m,m)), [1 3 2]), [1 n/m 1]), [m n]);
depending on your matlab version
m = 4;
n = 12;
dec2bin(bitshift(num,-1*[0:n/m:n-1])) %this prints out a string
these should be logical arrays (i don't have either of these so I cant test it)
decimalToBinaryVector(bitshift(num,-1*[0:n/m:n-1]))
de2bi(bitshift(num,-1*[0:n/m:n-1]))
Lets say I have a matrix A:
A =
0 1 0 0
0 0 0 0
0 0 0 1
0 0 0 0
And I want to create a new matrix B of the same dimension where all ones and accompanying neighbours are replaced by the following matrix:
X =
1 1 1
1 2 1
1 1 1
The 2 in matrix X should be placed 'on top' of the 1 values as to get:
B =
1 2 1 0
1 1 2 1
0 0 1 2
0 0 1 1
Values should be added up where elements overlap and matrix X should be 'cut off' in places where it extends the dimensions of matrix A/B The idea is to eventually replace X by a 2d gaussian distribution and matrix A will be large containing many more ones. So it's essential that the code is efficient and fast. This is the code i came up with:
A = [0 1 0 0;0 0 0 0;0 0 0 1;0 0 0 0]
X = [1 1 1;1 2 1;1 1 1]
B = zeros(4,4);
t=1;
indA = find(A==1);
indX = find(X==2);
all = find(X>0);
[iall jall] = ind2sub(size(X),all);
[ia ja] = ind2sub(size(A),indA)
[ix jx] = ind2sub(size(X),indX)
iv = ia-ix
jv = ja-jx
for t=1:numel(iv),
ib = iall+iv(t);
jb = jall+jv(t);
ibjb = [ib(:), jb(:)]
c1 = (ibjb(:,1)>4)|(ibjb(:,1)<1); c2 = (ibjb(:,2)>4)|(ibjb(:,1)<1);
ibjb((c1|c2),:)=[]
isel = ibjb(:,1)-iv(t)
jsel = ibjb(:,2)-jv(t)
B(ibjb(:,1), ibjb(:,2)) = B(ibjb(:,1), ibjb(:,2))+ X(isel, jsel)
t=t+1;
end
Is there a more efficient/faster way (minimizing the loops) to code this function?
What you want is a (2D) convolution. So use conv2:
B = conv2(A, X, 'same');