I have written a code that stores data in a matrix, but I want to shorten it so it iterates over itself.
The number of matrices created is the known variable. If it was 3, the code would be:
for i = 1:31
if idx(i) == 1
C1 = [C1; Output2(i,:)];
end
if idx(i) == 2
C2 = [C2; Output2(i,:)];
end
if idx(i) == 3
C3 = [C3; Output2(i,:)];
end
end
If I understand correctly, you want to extract rows from Output2 into new variables based on idx values? If so, you can do as follows:
Output2 = rand(5, 10); % example
idx = [1,1,2,2,3];
% get rows from Output which numbers correspond to those in idx with given value
C1 = Output2(find(idx==1),:);
C2 = Output2(find(idx==2),:);
C3 = Output2(find(idx==3),:);
Similar to Marcin i have another solution. Here i predefine my_C as a cell array. Output2 and idx are random generated and instead of find i just use logical adressing. You have to convert the data to type cell {}
Output2 = round(rand(31,15)*10);
idx = uint8(round(1+rand(1,31)*2));
my_C = cell(1,3);
my_C(1,1) = {Output2(idx==1,:)};
my_C(1,2) = {Output2(idx==2,:)};
my_C(1,3) = {Output2(idx==3,:)};
If you want to get your data back just use e.g. my_C{1,1} for the first group.
If you have not 3 but n resulting matrices you can use:
Output2 = round(rand(31,15)*10);
idx = uint8(round(1+rand(1,31)*(n-1)));
my_C = cell(1,n);
for k=1:n
my_C(1,k) = {Output2(idx==k,:)};
end
Where n is a positive integer number
I would recommend a slighty different approach. Except for making the rest of the code more maintainable it may also slightly speed up the execution. This due to that matlab uses a JIT compiler and eval must be recompiled every time. Try this:
nMatrices = 3
for k = 1:nMatrices
C{k} = Output2(idx==k,:);
end
As patrik said in the comments, naming variables like this is poor practice. You would be better off using cell arrays M{1}=C1, or if all the Ci are the same size, even just a 3D array M, for example, where M(:,:,1)=C1.
If you really want to use C1, C2, ... as you variable names, I think you will have to use eval, as arielnmz mentioned. One way to do this in matlab is
for i=1:3
eval(['C' num2str(idx(i)) '=[C' num2str(idx(i)) ';Output2(' num2str(i) ',:)];'])
end
Edited to add test code:
idx=[2 1 3 2 2 3];
Output2=rand(6,4);
C1a=[];
C2a=[];
C3a=[];
for i = 1:length(idx)
if idx(i) == 1
C1a = [C1a; Output2(i,:)];
end
if idx(i) == 2
C2a = [C2a; Output2(i,:)];
end
if idx(i) == 3
C3a = [C3a; Output2(i,:)];
end
end
C1=[];
C2=[];
C3=[];
for i=1:length(idx)
eval(['C' num2str(idx(i)) '=[C' num2str(idx(i)) ';Output2(' num2str(i) ',:)];'])
end
all(C1a(:)==C1(:))
all(C2a(:)==C2(:))
all(C3a(:)==C3(:))
Related
I'm writing a program that finds the indices of a matrix G where there is only a single 1 for either a column index or a row index and removes any found index if it has a 1 for both the column and row index. Then I want to take these indices and use them as indices in an array U, which is where the trouble comes. The indices do not seem to be stored as integers and I'm not sure what they are being stored as or why. I'm quite new to Matlab (but thats probably obvious) and so I don't really understand how types work for Matlab or how they're assigned. So I'm not sure why I',m getting the error message mentioned in the title and I'm not sure what to do about it. Any assistance you can provide would be greatly appreciated.
I forgot to mention this before but G is a matrix that only contains 1s or 0s and U is an array of strings (i think what would be called a cell?)
function A = ISClinks(U, G)
B = [];
[rownum,colnum] = size(G);
j = 1;
for i=1:colnum
s = sum(G(:,i));
if s == 1
B(j,:) = i;
j = j + 1;
end
end
for i=1:rownum
s = sum(G(i,:));
if s == 1
if ismember(i, B)
B(B == i) = [];
else
B(j,:) = i;
j = j+1;
end
end
end
A = [];
for i=1:size(B,1)
s = B(i,:);
A(i,:) = U(s,:);
end
end
This is the problem code, but I'm not sure what's wrong with it.
A = [];
for i=1:size(B,1)
s = B(i,:);
A(i,:) = U(s,:);
end
Your program seems to be structured as though it had been written in a language like C. In MATLAB, you can usually substitute specialized functions (e.g. any() ) for low-level loops in many cases. Your function could be written more efficiently as:
function A = ISClinks(U, G)
% Find columns and rows that are set in the input
active_columns=any(G,1);
active_rows=any(G,2).';
% (Optional) Prevent columns and rows with same index from being simultaneously set
%exclusive_active_columns = active_columns & ~active_rows; %not needed; this line is only for illustrative purposes
%exclusive_active_rows = active_rows & ~active_columns; %same as above
% Merge column state vector and row state vector by XORing them
active_indices=xor(active_columns,active_rows);
% Select appropriate rows of matrix U
A=U(active_indices,:);
end
This function does not cause errors with the example input matrices I tested. If U is a cell array (e.g. U={'Lorem','ipsum'; 'dolor','sit'; 'amet','consectetur'}), then return value A will also be a cell array.
I have a variable pth which is a cell array of dimension 1xn where n is a user input. Each of the elements in pth is itself a cell array and length(pth{k}) for k=1:n is variable (result of another function). Each element pth{k}{kk} where k=1:n and kk=1:length(pth{k}) is a 1D vector of integers/node numbers of again variable length. So to summarise, I have a variable number of variable-length vectors organised in a avriable number of cell arrays.
I would like to try and find all possible intersections when you take a vector at random from pth{1}, pth{2}, {pth{3}, etc... There are various functions on the File Exchange that seem to do that, for example this one or this one. The problem I have is you need to call the function this way:
mintersect(v1,v2,v3,...)
and I can't write all the inputs in the general case because I don't know explicitly how many there are (this would be n above). Ideally, I would like to do some thing like this;
mintersect(pth{1}{1},pth{2}{1},pth{3}{1},...,pth{n}{1})
mintersect(pth{1}{1},pth{2}{2},pth{3}{1},...,pth{n}{1})
mintersect(pth{1}{1},pth{2}{3},pth{3}{1},...,pth{n}{1})
etc...
mintersect(pth{1}{1},pth{2}{length(pth{2})},pth{3}{1},...,pth{n}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{2},...,pth{n}{1})
etc...
keep going through all the possible combinations, but I can't write this in code. This function from the File Exchange looks like a good way to find all possible combinations but again I have the same problem with the function call with the variable number of inputs:
allcomb(1:length(pth{1}),1:length(pth{2}),...,1:length(pth{n}))
Does anybody know how to work around this issue of function calls with variable number of input arguments when you can't physically specify all the input arguments because their number is variable? This applies equally to MATLAB and Octave, hence the two tags. Any other suggestion on how to find all possible combinations/intersections when taking a vector at random from each pth{k} welcome!
EDIT 27/05/20
Thanks to Mad Physicist's answer, I have ended up using the following which works:
disp('Computing intersections for all possible paths...')
grids = cellfun(#(x) 1:numel(x), pth, 'UniformOutput', false);
idx = cell(1, numel(pth));
[idx{:}] = ndgrid(grids{:});
idx = cellfun(#(x) x(:), idx, 'UniformOutput', false);
idx = cat(2, idx{:});
valid_comb = [];
k = 1;
for ii = idx'
indices = reshape(num2cell(ii), size(pth));
selection = cellfun(#(p,k) p{k}, pth, indices, 'UniformOutput', false);
if my_intersect(selection{:})
valid_comb = [valid_comb k];
endif
k = k+1;
end
My own version is similar but uses a for loop instead of the comma-separated list:
disp('Computing intersections for all possible paths...')
grids = cellfun(#(x) 1:numel(x), pth, 'UniformOutput', false);
idx = cell(1, numel(pth));
[idx{:}] = ndgrid(grids{:});
idx = cellfun(#(x) x(:), idx, 'UniformOutput', false);
idx = cat(2, idx{:});
[n_comb,~] = size(idx);
temp = cell(n_pipes,1);
valid_comb = [];
k = 1;
for k = 1:n_comb
for kk = 1:n_pipes
temp{kk} = pth{kk}{idx(k,kk)};
end
if my_intersect(temp{:})
valid_comb = [valid_comb k];
end
end
In both cases, valid_comb has the indices of the valid combinations, which I can then retrieve using something like:
valid_idx = idx(valid_comb(1),:);
for k = 1:n_pipes
pth{k}{valid_idx(k)} % do something with this
end
When I benchmarked the two approaches with some sample data (pth being 4x1 and the 4 elements of pth being 2x1, 9x1, 8x1 and 69x1), I got the following results:
>> benchmark
Elapsed time is 51.9075 seconds.
valid_comb = 7112
Elapsed time is 66.6693 seconds.
valid_comb = 7112
So Mad Physicist's approach was about 15s faster.
I also misunderstood what mintersect did, which isn't what I wanted. I wanted to find a combination where no element present in two or more vectors, so I ended writing my version of mintersect:
function valid_comb = my_intersect(varargin)
% Returns true if a valid combination i.e. no combination of any 2 vectors
% have any elements in common
comb_idx = combnk(1:nargin,2);
[nr,nc] = size(comb_idx);
valid_comb = true;
k = 1;
% Use a while loop so that as soon as an intersection is found, the execution stops
while valid_comb && (k<=nr)
temp = intersect(varargin{comb_idx(k,1)},varargin{comb_idx(k,2)});
valid_comb = isempty(temp) && valid_comb;
k = k+1;
end
end
Couple of helpful points to construct a solution:
This post shows you how to construct a Cartesian product between arbitrary arrays using ndgrid.
cellfun accepts multiple cell arrays simultaneously, which you can use to index specific elements.
You can capture a variable number of arguments from a function using cell arrays, as shown here.
So let's get the inputs to ndgrid from your outermost array:
grids = cellfun(#(x) 1:numel(x), pth, 'UniformOutput', false);
Now you can create an index that contains the product of the grids:
index = cell(1, numel(pth));
[index{:}] = ndgrid(grids{:});
You want to make all the grids into column vectors and concatenate them sideways. The rows of that matrix will represent the Cartesian indices to select the elements of pth at each iteration:
index = cellfun(#(x) x(:), index, 'UniformOutput', false);
index = cat(2, index{:});
If you turn a row of index into a cell array, you can run it in lockstep over pth to select the correct elements and call mintersect on the result.
for i = index'
indices = num2cell(i');
selection = cellfun(#(p, i) p{i}, pth, indices, 'UniformOutput', false);
mintersect(selection{:});
end
This is written under the assumption that pth is a row array. If that is not the case, you can change the first line of the loop to indices = reshape(num2cell(i), size(pth)); for the general case, and simply indices = num2cell(i); for the column case. The key is that the cell from of indices must be the same shape as pth to iterate over it in lockstep. It is already generated to have the same number of elements.
I believe this does the trick. Calls mintersect on all possible combinations of vectors in pth{k}{kk} for k=1:n and kk=1:length(pth{k}).
Using eval and messing around with sprintf/compose a bit. Note that typically the use of eval is very much discouraged. Can add more comments if this is what you need.
% generate some data
n = 5;
pth = cell(1,n);
for k = 1:n
pth{k} = cell(1,randi([1 10]));
for kk = 1:numel(pth{k})
pth{k}{kk} = randi([1 100], randi([1 10]), 1);
end
end
% get all combs
str_to_eval = compose('1:length(pth{%i})', 1:numel(pth));
str_to_eval = strjoin(str_to_eval,',');
str_to_eval = sprintf('allcomb(%s)',str_to_eval);
% use eval to get all combinations for a given pth
all_combs = eval(str_to_eval);
% and make strings to eval in intersect
comp = num2cell(1:numel(pth));
comp = [comp ;repmat({'%i'}, 1, numel(pth))];
str_pattern = sprintf('pth{%i}{%s},', comp{:});
str_pattern = str_pattern(1:end-1); % get rid of last ,
strings_to_eval = cell(length(all_combs),1);
for k = 1:size(all_combs,1)
strings_to_eval{k} = sprintf(str_pattern, all_combs(k,:));
end
% and run eval on all those strings
result = cell(length(all_combs),1);
for k = 1:size(all_combs,1)
result{k} = eval(['mintersect(' strings_to_eval{k} ')']);
%fprintf(['mintersect(' strings_to_eval{k} ')\n']); % for debugging
end
For a randomly generated pth, the code produces the following strings to evaluate (where some pth{k} have only one cell for illustration):
mintersect(pth{1}{1},pth{2}{1},pth{3}{1},pth{4}{1},pth{5}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{1},pth{4}{2},pth{5}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{1},pth{4}{3},pth{5}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{2},pth{4}{1},pth{5}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{2},pth{4}{2},pth{5}{1})
mintersect(pth{1}{1},pth{2}{1},pth{3}{2},pth{4}{3},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{1},pth{4}{1},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{1},pth{4}{2},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{1},pth{4}{3},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{2},pth{4}{1},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{2},pth{4}{2},pth{5}{1})
mintersect(pth{1}{2},pth{2}{1},pth{3}{2},pth{4}{3},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{1},pth{4}{1},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{1},pth{4}{2},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{1},pth{4}{3},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{2},pth{4}{1},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{2},pth{4}{2},pth{5}{1})
mintersect(pth{1}{3},pth{2}{1},pth{3}{2},pth{4}{3},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{1},pth{4}{1},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{1},pth{4}{2},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{1},pth{4}{3},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{2},pth{4}{1},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{2},pth{4}{2},pth{5}{1})
mintersect(pth{1}{4},pth{2}{1},pth{3}{2},pth{4}{3},pth{5}{1})
As Madphysicist pointed out, I misunderstood the initial structure of your initial cell array, however the point stands. The way to pass an unknown number of arguments to a function is via comma-separated-list generation, and your function needs to support it by being declared with varargin. Updated example below.
Create a helper function to collect a random subcell from each main cell:
% in getRandomVectors.m
function Out = getRandomVectors(C) % C: a double-jagged array, as described
N = length(C);
Out = cell(1, N);
for i = 1 : length(C)
Out{i} = C{i}{randi( length(C{i}) )};
end
end
Then assuming you already have an mintersect function defined something like this:
% in mintersect.m
function Intersections = mintersect( varargin )
Vectors = varargin;
N = length( Vectors );
for i = 1 : N; for j = 1 : N
Intersections{i,j} = intersect( Vectors{i}, Vectors{j} );
end; end
end
Then call this like so:
C = { { 1:5, 2:4, 3:7 }, {1:8}, {2:4, 3:9, 2:8} }; % example double-jagged array
In = getRandomVectors(C); % In is a cell array of randomly selected vectors
Out = mintersect( In{:} ); % Note the csl-generator syntax
PS. I note that your definition of mintersect differs from those linked. It may just be you didn't describe what you want too well, in which case my mintersect function is not what you want. What mine does is produce all possible intersections for the vectors provided. The one you linked to produces a single intersection which is common to all vectors provided. Use whichever suits you best. The underlying rationale for using it is the same though.
PS. It is also not entirely clear from your description whether what you're after is a random vector k for each n, or the entire space of possible vectors over all n and k. The above solution does the former. If you want the latter, see MadPhysicist's solution on how to create a cartesian product of all possible indices instead.
I have the following function that works perfectly, but I would like to apply vectorization to it...
for i = 1:size(centroids,1)
centroids(i, :) = mean(X(idx == i, :));
end
It checks if idx matches the current index and if it does, it calculates the mean value for all the X values that correspond to that index.
This is my attempt at vectorization, my solution does not work and I know why...
centroids = mean(X(idx == [1:size(centroids,1)], :));
The following idx == [1:size(centroids,1)] breaks the code. I have no idea how to check if idx equals to either of the numbers from 1 to size(centroids,1).
tl:dr
Get rid of the for loop through vectorization
One option is to use arrayfun;
nIdx = size(centroids,1);
centroids = arrayfun(#(ii) mean(X(idx==ii,:)),1:nIdx, 'UniformOutput', false);
centroids = vertcat(centroids{:})
Since the output of a single function call is not necessarily a scalar, the UniformOutput option has to be set to false. Thus, arrayfun returns a cell array and you need to vertcat it to get the desired double array.
you can split the matrix into cells and take the mean from each cell using cellfun (which applies a loop in its inner operation):
generate data:
dim = 10;
N = 400;
nc = 20;
idx = randi(nc,[N 1]);
X = rand(N,dim);
centroids = zeros(nc,dim);
mean using loop (the question's method)
for i = 1:size(centroids,1)
centroids(i, :) = mean(X(idx == i, :));
end
vectorizing:
% split X into cells by idx
A = accumarray(idx, (1:N)', [nc,1], #(i) {X(i,:)});
% mean of each cell
C = cell2mat(cellfun(#(x) mean(x,1),A,'UniformOutput',0));
maximum absolute error between the methods:
max(abs(C(:) - centroids(:))) % about 1e-16
I have a code with two for loops. The code is working properly. The problem is that at the end I would like to get a variable megafinal with the results for all the years. The original varaible A has 3M rows, so it gives me an error because the size of the megafinal changes with each loop iteration and matlab stops running the code. I guess it’s a problem of inefficiency. Does anyone know a way to get this final variable despite of the size?
y = 1997:2013;
for i=1:length(y)
A=b(cell2mat(b(:,1))==y(i),:);
%Obtain the absolute value of the difference
c= cellfun(#minus,A(:,3),A(:,4));
c=abs(c);
c= num2cell(c);
A(:,end+1) = c;
%Delete rows based on a condition
d = (abs(cell2mat(A(:,8)) - cell2mat(A(:,7))));
[~, ind1] = sort(d);
e= A(ind1(end:-1:1),:);
[~, ind2,~] = unique(strcat(e(:,2),e(:, 6)));
X= e(ind2,:);
(…)
for j = 2:length(X)
if strcmp(X(j,2),X(j-1,2)) == 0
lin2 = j-1;
%Sort
X(lin1:lin2,:) = sortrows(X(lin1:lin2,:),13);
%Rank
[~,~,f]=unique([X{lin1:lin2,13}].');
g=accumarray(f,(1:numel(f))',[],#mean);
X(lin1:lin2,14)=num2cell(g(f));
%Score
out1 = 100 - ((cell2mat(X(lin1:lin2,14))-1) ./ size(X(lin1:lin2,:),1))*100;
X(lin1:lin2,15) = num2cell(out1);
lin1 = j;
end
end
%megafinal(i)=X
end
Make megafinal a cell array. This will account for the varying sizes of X at each iteration. As such, simply do this:
megafinal{i} = X;
To access a cell element, you just have to do megafinal{num}, where num is any index you want.
Please help me to improve the following Matlab code to improve execution time.
Actually I want to make a random matrix (size [8,12,10]), and on every row, only have integer values between 1 and 12. I want the random matrix to have the sum of elements which has value (1,2,3,4) per column to equal 2.
The following code will make things more clear, but it is very slow.
Can anyone give me a suggestion??
clc
clear all
jum_kel=8
jum_bag=12
uk_pop=10
for ii=1:uk_pop;
for a=1:jum_kel
krom(a,:,ii)=randperm(jum_bag); %batasan tidak boleh satu kelompok melakukan lebih dari satu aktivitas dalam satu waktu
end
end
for ii=1:uk_pop;
gab1(:,:,ii) = sum(krom(:,:,ii)==1)
gab2(:,:,ii) = sum(krom(:,:,ii)==2)
gab3(:,:,ii) = sum(krom(:,:,ii)==3)
gab4(:,:,ii) = sum(krom(:,:,ii)==4)
end
for jj=1:uk_pop;
gabh1(:,:,jj)=numel(find(gab1(:,:,jj)~=2& gab1(:,:,jj)~=0))
gabh2(:,:,jj)=numel(find(gab2(:,:,jj)~=2& gab2(:,:,jj)~=0))
gabh3(:,:,jj)=numel(find(gab3(:,:,jj)~=2& gab3(:,:,jj)~=0))
gabh4(:,:,jj)=numel(find(gab4(:,:,jj)~=2& gab4(:,:,jj)~=0))
end
for ii=1:uk_pop;
tot(:,:,ii)=gabh1(:,:,ii)+gabh2(:,:,ii)+gabh3(:,:,ii)+gabh4(:,:,ii)
end
for ii=1:uk_pop;
while tot(:,:,ii)~=0;
for a=1:jum_kel
krom(a,:,ii)=randperm(jum_bag); %batasan tidak boleh satu kelompok melakukan lebih dari satu aktivitas dalam satu waktu
end
gabb1 = sum(krom(:,:,ii)==1)
gabb2 = sum(krom(:,:,ii)==2)
gabb3 = sum(krom(:,:,ii)==3)
gabb4 = sum(krom(:,:,ii)==4)
gabbh1=numel(find(gabb1~=2& gabb1~=0));
gabbh2=numel(find(gabb2~=2& gabb2~=0));
gabbh3=numel(find(gabb3~=2& gabb3~=0));
gabbh4=numel(find(gabb4~=2& gabb4~=0));
tot(:,:,ii)=gabbh1+gabbh2+gabbh3+gabbh4;
end
end
Some general suggestions:
Name variables in English. Give a short explanation if it is not immediately clear,
what they are indented for. What is jum_bag for example? For me uk_pop is music style.
Write comments in English, even if you develop source code only for yourself.
If you ever have to share your code with a foreigner, you will spend a lot of time
explaining or re-translating. I would like to know for example, what
%batasan tidak boleh means. Probably, you describe here that this is only a quick
hack but that someone should really check this again, before going into production.
Specific to your code:
Its really easy to confuse gab1 with gabh1 or gabb1.
For me, krom is too similar to the built-in function kron. In fact, I first
thought that you are computing lots of tensor products.
gab1 .. gab4 are probably best combined into an array or into a cell, e.g. you
could use
gab = cell(1, 4);
for ii = ...
gab{1}(:,:,ii) = sum(krom(:,:,ii)==1);
gab{2}(:,:,ii) = sum(krom(:,:,ii)==2);
gab{3}(:,:,ii) = sum(krom(:,:,ii)==3);
gab{4}(:,:,ii) = sum(krom(:,:,ii)==4);
end
The advantage is that you can re-write the comparsisons with another loop.
It also helps when computing gabh1, gabb1 and tot later on.
If you further introduce a variable like highestNumberToCompare, you only have to
make one change, when you certainly find out that its important to check, if the
elements are equal to 5 and 6, too.
Add a semicolon at the end of every command. Having too much output is annoying and
also slow.
The numel(find(gabb1 ~= 2 & gabb1 ~= 0)) is better expressed as
sum(gabb1(:) ~= 2 & gabb1(:) ~= 0). A find is not needed because you do not care
about the indices but only about the number of indices, which is equal to the number
of true's.
And of course: This code
for ii=1:uk_pop
gab1(:,:,ii) = sum(krom(:,:,ii)==1)
end
is really, really slow. In every iteration, you increase the size of the gab1
array, which means that you have to i) allocate more memory, ii) copy the old matrix
and iii) write the new row. This is much faster, if you set the size of the
gab1 array in front of the loop:
gab1 = zeros(... final size ...);
for ii=1:uk_pop
gab1(:,:,ii) = sum(krom(:,:,ii)==1)
end
Probably, you should also re-think the size and shape of gab1. I don't think, you
need a 3D array here, because sum() already reduces one dimension (if krom is
3D the output of sum() is at most 2D).
Probably, you can skip the loop at all and use a simple sum(krom==1, 3) instead.
However, in every case you should be really aware of the size and shape of your
results.
Edit inspired by Rody Oldenhuis:
As Rody pointed out, the 'problem' with your code is that its highly unlikely (though
not impossible) that you create a matrix which fulfills your constraints by assigning
the numbers randomly. The code below creates a matrix temp with the following characteristics:
The numbers 1 .. maxNumber appear either twice per column or not at all.
All rows are a random permutation of the numbers 1 .. B, where B is equal to
the length of a row (i.e. the number of columns).
Finally, the temp matrix is used to fill a 3D array called result. I hope, you can adapt it to your needs.
clear all;
A = 8; B = 12; C = 10;
% The numbers [1 .. maxNumber] have to appear exactly twice in a
% column or not at all.
maxNumber = 4;
result = zeros(A, B, C);
for ii = 1 : C
temp = zeros(A, B);
for number = 1 : maxNumber
forbiddenRows = zeros(1, A);
forbiddenColumns = zeros(1, A/2);
for count = 1 : A/2
illegalIndices = true;
while illegalIndices
illegalIndices = false;
% Draw a column which has not been used for this number.
randomColumn = randi(B);
while any(ismember(forbiddenColumns, randomColumn))
randomColumn = randi(B);
end
% Draw two rows which have not been used for this number.
randomRows = randi(A, 1, 2);
while randomRows(1) == randomRows(2) ...
|| any(ismember(forbiddenRows, randomRows))
randomRows = randi(A, 1, 2);
end
% Make sure not to overwrite previous non-zeros.
if any(temp(randomRows, randomColumn))
illegalIndices = true;
continue;
end
end
% Mark the rows and column as forbidden for this number.
forbiddenColumns(count) = randomColumn;
forbiddenRows((count - 1) * 2 + (1:2)) = randomRows;
temp(randomRows, randomColumn) = number;
end
end
% Now every row contains the numbers [1 .. maxNumber] by
% construction. Fill the zeros with a permutation of the
% interval [maxNumber + 1 .. B].
for count = 1 : A
mask = temp(count, :) == 0;
temp(count, mask) = maxNumber + randperm(B - maxNumber);
end
% Store this page.
result(:,:,ii) = temp;
end
OK, the code below will improve the timing significantly. It's not perfect yet, it can all be optimized a lot further.
But, before I do so: I think what you want is fundamentally impossible.
So you want
all rows contain the numbers 1 through 12, in a random permutation
any value between 1 and 4 must be present either twice or not at all in any column
I have a hunch this is impossible (that's why your code never completes), but let me think about this a bit more.
Anyway, my 5-minute-and-obvious-improvements-only-version:
clc
clear all
jum_kel = 8;
jum_bag = 12;
uk_pop = 10;
A = jum_kel; % renamed to make language independent
B = jum_bag; % and a lot shorter for readability
C = uk_pop;
krom = zeros(A, B, C);
for ii = 1:C;
for a = 1:A
krom(a,:,ii) = randperm(B);
end
end
gab1 = sum(krom == 1);
gab2 = sum(krom == 2);
gab3 = sum(krom == 3);
gab4 = sum(krom == 4);
gabh1 = sum( gab1 ~= 2 & gab1 ~= 0 );
gabh2 = sum( gab2 ~= 2 & gab2 ~= 0 );
gabh3 = sum( gab3 ~= 2 & gab3 ~= 0 );
gabh4 = sum( gab4 ~= 2 & gab4 ~= 0 );
tot = gabh1+gabh2+gabh3+gabh4;
for ii = 1:C
ii
while tot(:,:,ii) ~= 0
for a = 1:A
krom(a,:,ii) = randperm(B);
end
gabb1 = sum(krom(:,:,ii) == 1);
gabb2 = sum(krom(:,:,ii) == 2);
gabb3 = sum(krom(:,:,ii) == 3);
gabb4 = sum(krom(:,:,ii) == 4);
gabbh1 = sum(gabb1 ~= 2 & gabb1 ~= 0)
gabbh2 = sum(gabb2 ~= 2 & gabb2 ~= 0);
gabbh3 = sum(gabb3 ~= 2 & gabb3 ~= 0);
gabbh4 = sum(gabb4 ~= 2 & gabb4 ~= 0);
tot(:,:,ii) = gabbh1+gabbh2+gabbh3+gabbh4;
end
end