I run this code:
for i=1:length(matr)
...where matr is square matrix. In this loop the size of the matr changes, but it seems that the loop continues to run, until i doesn't not exceed the initial value of length(matr)
How to maintain the fresh of length(matr) in the loop's condition?
Here is my code.
for i=1:length(matr1)
for j=1:length(matr1)
if((i~=j)&&(ismember(i,ind3)==0)&&(ismember(j,ind3)==0))
if (i>length(matr1))||(j>length(matr1))
continue
end
ind1 = find_tree(matr1,i);
ind2 = find_tree(matr1,j);
b = is_isomorphic(matr1(ind1,ind1),matr1(ind2,ind2),encode(ind1),encode(ind2));
if b,
number = number + length(ind1);
matr1(ind2,:) = [];
matr1(:,ind2) = [];
ind3 = find(summ_rows==-1);
end
end
end
end
I was managed to add
`if (i>length(matr1))||(j>length(matr1))`,
...because i and j exceeded the dimensions of the matrix.
You should be using a while loop:
ii = 0;
while(ii <= length(matr))
ii = ii + 1;
%// Your loop code here: e.g. the following line that alters the size of matr
matr = rand(randi(20) + 10);
end
Related
I build some program in Matlab for "Histograma matching".
When I'm trying to implement the function "conVector" I get the error
"Index exceeds array bounds." anyone can help me with this error?
Here is my full code. Thank you!
function [newImage] = histShape (srcimg,destimg)
%find the histogram of the image
src = imgHist(srcimg);
dest = imgHist(destimg);
sna = normalizationHist(src);
dna = normalizationHist(dest);
conVector(sna,dna);
end
function [Hist] = imgHist (img)
[Rows,Cols] = size(img);
Hist = zeros(1,256);
for i=1:Rows
for j=1:Cols
Hist(img(i,j)+1)=Hist(img(i,j)+1)+1;
end
end
end
function [Ahist] = normalizationHist (hist)
[Rows,Cols] = size(hist);
Ahist = hist;
for i=2:256
Ahist(i)=Ahist(i-1)+hist(i);
end
Ahist = Ahist/(Rows*Cols);
end
function [cv] = conVector(SNA,DNA)
cv=zeros(1,257);
s = 1;
d = 1;
while s<=256
if DNA(d)<SNA(s)
d = d+1;
else
cv(s)=d;
s = s+1;
end
end
end
If all values in DNA(d:end) are smaller then the value in SNA(s) than the loop keep adding 1 to d but not to s, and finally goes out of bound because it conditioned only by s.
I guess you should either take the s = s+1 out of the inner condition, so it will be executed on every iteration, or add a condition on d to the loop, or convert it to a for loop.
The code segment I'm working on is given below:
NphaseSteps = 6;
phases = exp( 2*pi*1i * (0:(NphaseSteps-1))/NphaseSteps );
i = 1;
while i <= 10 %number of iterations
ind = randi([1 NphaseSteps],10,10);
inField{i} = phases(ind);
save('inField.mat', 'inField')
i = i + 1;
end
Now, what I want is to keep track of these randomly created matrices "inField{i}" and eliminate the ones that are equal to each other. I know that I can use "if" condition but since I'm new to programming I don't know how to use it more efficiently so that it doesn't take too much time. So, I need your help for a fast working program that does the job. Thanks in advance.
My actual code segment (after making the changes suggested by #bisherbas) is the following. Note that I actually want to use the variable "inField" inside the loop for every random created matrix and the loop advances only if the result satisfies a specific condition. So, I think the answer given by #bisherbas doesn't really eliminate the equal inField matrices before they are used in the calculation. This is, of course, my fault since I didn't declare that in the beginning.
NphaseSteps = 6;
phases = exp( 2*pi*1i * (0:(NphaseSteps-1))/NphaseSteps );
nIterations = 5;
inField = cell(1,nIterations);
i = 1;
j = 1;
while i <= nIterations % number of iterations
ind = randi([1 NphaseSteps],TMsize,TMsize);
tmp = phases(ind);
idx = cellfun(#(x) isequal(x,tmp),inField);
if ~any(idx)
inField{i} = tmp;
end
j = j+1;
outField{i} = TM * inField{i};
outI = abs(outField{i}).^2;
targetIafter{i} = abs(outField{i}(focusX,focusY)).^2;
middleI = targetIafter{i} / 2;
if (max(max(outI)) == targetIafter{i})...
&& ( sum(sum((outI > middleI).*(outI < max(max(outI))))) == 0 )
save('inFieldA.mat', 'inField')
i = i + 1;
end
if mod(j-1,10^6) == 0
fprintf('The number of random matrices tried is: %d million \n',(j-1)/10^6)
end
end
Additionally, I've written a seemingly long expression for my loop condition:
if (max(max(outI)) == targetIafter{i})...
&& ( sum(sum((outI > middleI).*(outI < max(max(outI))))) == 0 )
save('inFieldA.mat', 'inField')
i = i + 1;
end
Here I want a maximum element at some point (focusX, focusY) in the outField matrix. So the first condition decides whether the focus point has the maximum element for the matrix. But I additionally want all other elements to be smaller than a specific number (middleI) and that's why the second part of the if condition is written. However, I'm not very comfortable with this second condition and I'm open to any helps.
Try this:
NphaseSteps = 6;
phases = exp( 2*pi*1i * (0:(NphaseSteps-1))/NphaseSteps );
i = 1;
inField = cell(1,NphaseSteps);
while i <= NphaseSteps %number of iterations
ind = randi([1 NphaseSteps],NphaseSteps,NphaseSteps);
tmp = phases(ind);
idx = cellfun(#(x) isequal(x,tmp),inField);
if ~any(idx)
inField{i} = tmp;
end
save('inField.mat', 'inField')
i = i + 1;
end
Read more on cellfun here:
https://www.mathworks.com/help/matlab/ref/cellfun.html
I'm using a code that calculates expectation value of probabilities. This code contains a while-loop that finds all possible combinations and adds up products of probability combinations. However, when the number of elements becomes large(over 40) it takes too much time, and I want to make the code faster.
The code is as follow-
function pcs = combsum(N,K,prbv)
nprbv=1-prbv; %prbv: probability vector
WV = 1:K; % Working vector.
lim = K; % Sets the limit for working index.
inc = 0; % Controls which element of WV is being worked on.
pcs = 0;
stopp=0;
while stopp==0
if logical((inc+lim)-N)
stp = inc; % This is where the for loop below stops.
flg = 0; % Used for resetting inc.
else
stp = 1;
flg = 1;
end
for jj = 1:stp
WV(K + jj - inc) = lim + jj; % Faster than a vector assignment.
end
PV=nprbv;
PV(WV)=prbv(WV);
pcs=prod(PV)+pcs;
inc = inc*flg + 1; % Increment the counter.
lim = WV(K - inc + 1 ); % lim for next run.
if (inc==K)&&(lim==N-K)
stopp=1;
WV = (N-K+1):N;
PV=nprbv;
PV(WV)=prbv(WV);
pcs=prod(PV)+pcs;
end
end
Is there a way to reduce calculation time? I wonder if parallel computing using GPU would help.
I tried to remove dependent variables in a loop for parallel computing, and I made a matrix of possible combinations using 'combnk' function. This worked faster.
nprbv=1-prbv; %prbv : a probability vector
N = 40;
K = 4;
n_combnk = size(combnk(1:N,K),1);
PV_mat = repmat(nprbv,n_combnk,1);
cnt = 0;
tic;
for i = 1:N-K+1
for j = i+1:N-K+2
for k = j+1:N-K+3
for l = k+1:N-K+4
cnt = cnt+1;
PV_mat(cnt,i) = prbv(i);
PV_mat(cnt,j) = prbv(j);
PV_mat(cnt,k) = prbv(k);
PV_mat(cnt,l) = prbv(l);
end
end
end
end
toc;
tic;
pcs_rr = sum(prod(PV_mat,2));
toc;
However, when K value gets larger, an out-of-memory problem happens in building a combination matrix(PV_mat). How can I break up the big matrix into small ones to avoid memory problem?
I'm trying to iterate in MATLAB (not allowed to use in built functions) to find the maximum value of each row in a certain matrix. I've been able to find the max value of the whole matrix but am unsure about isolating the row and finding the max value (once again without using max()).
My loop currently looks like this:
for i = 1:size(A, 1)
for j = 1:size(A, 2)
if A(i, j) > matrix_max
matrix_max = A(i, j);
row = i;
column = j;
end
end
end
You need a vector of results, not a single value. Note you could initialise this to zero. Don't initialise to zero unless you know you only have positive values. Instead, initialise to -inf using -inf*ones(...), as all values are greater than negative infinity. Or (see the bottom code block) initialise to the first column of A.
% Set up results vector, same number of rows as A, start at negative infinity
rows_max = -inf*ones(size(A,1),1);
% Set up similar to track column number. No need to track row number as doing each row!
col_nums = zeros(size(A,1),1);
% Loop through. i and j = sqrt(-1) by default in MATLAB, use ii and jj instead
for ii = 1:size(A,1)
for jj = 1:size(A,2)
if A(ii,jj) > rows_max(ii)
rows_max(ii) = A(ii,jj);
col_nums(ii) = jj;
end
end
end
Note that if vectorisation doesn't violate your "no built-ins" rule (it should be fine, it's making the most of the MATLAB language), then you can remove the outer (row) loop
rows_max = -inf*ones(size(A,1),1);
col_nums = zeros(size(A,1),1);
for jj = 1:size(A,2)
% Get rows where current column is larger than current max stored in row_max
idx = A(:,jj) > rows_max;
% Store new max values
rows_max(idx) = A(idx,jj);
% Store new column indices
col_nums(idx) = jj;
end
Even better, you can cut your loop short by 1, and initialise to the first column of A.
rows_max = A(:,1); % Set current max to the first column
col_nums = ones(size(A,1),1); % ditto
% Loop from 2nd column now that we've already used the first column
for jj = 2:size(A,2)
idx = A(:,jj) > rows_max;
rows_max(idx) = A(idx,jj);
col_nums(idx) = jj;
end
You can modified it likes the following to get each max for each row:
% initialize
matrix_max = zeros(size(A,1),1);
columns = zeros(size(A,1),1);
% find max
for i = 1:size(A, 1)
matrix_max(i) = A(i,1);
columns(i) = 1;
for j = 2:size(A, 2)
if A(i, j) > matrix_max(i)
matrix_max(i) = A(i, j);
columns(i) = j;
end
end
end
I'm attempting to create a loop that reads through a matrix (A) and stores the non-zero values into a new matrix (w). I'm not sure what is wrong with my code.
function [d,w] = matrix_check(A)
[nrow ncol] = size(A);
total = 0;
for i = 1:nrow
for j = 1:ncol
if A(i,j) ~= 0
total = total + 1;
end
end
end
d = total;
w = [];
for i = 1:nrow
for j = 1:ncol
if A(i,j) ~= 0
w = [A(i,j);w];
end
end
end
The second loop is not working (at at least it is not printing out the results of w).
You can use nonzeros and nnz:
w = flipud(nonzeros(A)); %// flipud to achieve the same order as in your code
d = nnz(A);
The second loop is working. I'm guessing you're doing:
>> matrix_check(A)
And not:
>> [d, w] = matrix_check(A)
MATLAB will only return the first output unless otherwise specified.
As an aside, you can accomplish your task utilizing MATLAB's logical indexing and take advantage of the (much faster, usually) array operations rather than loops.
d = sum(sum(A ~= 0));
w = A(A ~= 0);