I have random matrix with arbitrary dimensions and I want to assign a color for each value (randomly or not) and plot the matrix with numbers like,
So far I've done this,
m = 12;
n = 8;
A = randi(5,[m n]);
Arot = flipud(A);
pcolor(Arot);figure(gcf);
for i = 1 : n -1
for j = 1 : m -1
text(i + .5 , j + .5 ,num2str(Arot(j,i)),'FontSize',18);
end
end
which gives me this,
for
A =
4 4 4 1 2 1 4 2
5 2 2 3 2 1 1 2
1 2 1 4 1 2 5 5
1 3 5 3 1 4 1 3
3 4 4 4 3 3 3 4
2 5 2 2 1 1 2 4
1 3 1 3 5 5 2 4
5 1 2 4 1 4 1 2
2 4 5 5 1 3 5 2
4 2 2 3 4 3 3 4
3 5 3 2 4 3 3 1
1 4 5 3 2 4 3 5
but as you can see I've lost first row and last column of A.
Actually the problem starts bu using pcolor, which gives an (m-1)x(n-1) plot for mxn input.
Any suggestions?
Thanks,
Using imagesc instead of pcolor solves the problem. It also brings some other benefits:
Avoids the need for flipud;
The coordinates of the text objects become integer values;
Axes are automatically set to "matrix" mode, with the origin in the upper right corner.
Code:
m = 8;
n = 6;
A = randi(5,[m n]);
imagesc(A);
for ii = 1:n
for jj = 1:m
text(ii, jj, num2str(A(jj,ii)), 'FontSize', 18);
end
end
For
A =
4 5 4 2 4 4
5 4 3 4 4 2
5 4 1 1 1 3
4 3 5 2 5 4
1 2 2 2 5 3
1 5 2 5 1 3
4 3 1 3 3 1
3 1 2 4 2 3
this produces
I just padded the matrix prior to pcolor and I think it's the effect you wanted. The reason it works comes from the help doc for pcolor, which states that
In the default shading mode, 'faceted', each cell has a constant color
and the last row and column of C are not used.
m = 12;
n = 8;
A = randi(5,[m n]);
Arot = flipud(A);
Arot = [ Arot; Arot(end,:) ];
Arot = [ Arot, Arot(:,end) ];
pcolor(Arot);figure(gcf);
for i = 1 : n
for j = 1 : m
text(i + .5 , j + .5 ,num2str(Arot(j,i)),'FontSize',18);
end
end
Related
Suppose I have a list of length 2k, say {1,2,...,2k}. The number of possible ways of grouping the 2k numbers into k (unordered) pairs is n(k) = 1*3* ... *(2k-1). So for k=2, we have the following three different ways of forming 2 pairs
(1 2)(3 4)
(1 3)(2 4)
(1 4)(2 3)
How can I use Matlab to create the above list, i.e., create a matrix of n(k)*(2k) such that each row contains a different way of grouping the list of 2k numbers into k pairs.
clear
k = 3;
set = 1: 2*k;
p = perms(set); % get all possible permutations
% sort each two column
[~, col] = size(p);
for i = 1: 2: col
p(:, i:i+1) = sort(p(:,i:i+1), 2);
end
p = unique(p, 'rows'); % remove the same row
% sort each row
[row, col] = size(p);
for i = 1: row
temp = reshape(p(i,:), 2, col/2)';
temp = sortrows(temp, 1);
p(i,:) = reshape(temp', 1, col);
end
pairs = unique(p, 'rows'); % remove the same row
pairs =
1 2 3 4 5 6
1 2 3 5 4 6
1 2 3 6 4 5
1 3 2 4 5 6
1 3 2 5 4 6
1 3 2 6 4 5
1 4 2 3 5 6
1 4 2 5 3 6
1 4 2 6 3 5
1 5 2 3 4 6
1 5 2 4 3 6
1 5 2 6 3 4
1 6 2 3 4 5
1 6 2 4 3 5
1 6 2 5 3 4
As someone think my former answer is not useful, i post this.
I have the following brute force way of enumerating the pairs. Not particularly efficient. It can also cause memory problem when k>9. In that case, I can just enumerate but not create Z and store the result in it.
function Z = pair2(k)
count = [2*k-1:-2:3];
tcount = prod(count);
Z = zeros(tcount,2*k);
x = [ones(1,k-2) 0];
z = zeros(1,2*k);
for i=1:tcount
for j=k-1:-1:1
if x(j)<count(j)
x(j) = x(j)+1;
break
end
x(j) = 1;
end
y = [1:2*k];
for j=1:k-1
z(2*j-1) = y(1);
z(2*j) = y(x(j)+1);
y([1 x(j)+1]) = [];
end
z(2*k-1:2*k) = y;
Z(i,:) = z;
end
k = 3;
set = 1: 2*k;
combos = combntns(set, k);
[len, ~] = size(combos);
pairs = [combos(1:len/2,:) flip(combos(len/2+1:end,:))];
pairs =
1 2 3 4 5 6
1 2 4 3 5 6
1 2 5 3 4 6
1 2 6 3 4 5
1 3 4 2 5 6
1 3 5 2 4 6
1 3 6 2 4 5
1 4 5 2 3 6
1 4 6 2 3 5
1 5 6 2 3 4
You can also use nchoosek instead of combntns. See more at combntns or nchoosek
If I have this vector:
x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6]
I would like to get the position of each unique number according to itself.
y = [1 2 3 4 5 1 2 3 1 1 2 1 2 3 4]
At the moment I'm using:
y = sum(triu(x==x.')) % MATLAB 2016b and above
It's compact but obviously not memory efficient.
For the pure beauty of MATLAB programming I would avoid using a loop. Do you have a better simple implementation ?
Context:
My final goal is to sort the vector x but with the constraint that a number that appear N times has the priority over another number that has appeared more than N times:
[~,ind] = sort(y);
x_relative_sort = x(ind);
% x_relative_sort = 1 2 3 4 6 1 2 4 6 1 2 6 1 6 1
Assuming x is sorted, here's one vectorized alternative using unique, diff, and cumsum:
[~, index] = unique(x);
y = ones(size(x));
y(index(2:end)) = y(index(2:end))-diff(index).';
y = cumsum(y);
And now you can apply your final sorting:
>> [~, ind] = sort(y);
>> x_relative_sort = x(ind)
x_relative_sort =
1 2 3 4 6 1 2 4 6 1 2 6 1 6 1
If you have positive integers you can use sparse matrix:
[y ,~] = find(sort(sparse(1:numel(x), x, true), 1, 'descend'));
Likewise x_relative_sort can directly be computed:
[x_relative_sort ,~] = find(sort(sparse(x ,1:numel(x),true), 2, 'descend'));
Just for variety, here's a solution based on accumarray. It works for x sorted and containing positive integers, as in the question:
y = cell2mat(accumarray(x(:), x(:), [], #(t){1:numel(t)}).');
You can be more memory efficient by only comparing to unique(x), so you don't have a large N*N matrix but rather N*M, where N=numel(x), M=numel(unique(x)).
I've used an anonymous function syntax to avoid declaring an intermediate matrix variable, needed as it's used twice - this can probably be improved.
f = #(X) sum(cumsum(X,2).*X); y = f(unique(x).'==x);
Here's my solution that doesn't require sorting:
x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
y = cell2mat( splitapply(#(v){cumsum(v)},x,cumsum(logical([1 diff(x)]))) ) ./ x;
Explanation:
% Turn each group new into a unique number:
t1 = cumsum(logical([1 diff(x)]));
% x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
% t1 = [1 1 1 1 1 2 2 2 3 4 4 5 5 5 5 6 6 6];
% Apply cumsum separately to each group:
t2 = cell2mat( splitapply(#(v){cumsum(v)},x,t1) );
% t1 = [1 1 1 1 1 2 2 2 3 4 4 5 5 5 5 6 6 6];
% t2 = [1 2 3 4 5 2 4 6 3 4 8 6 12 18 24 1 2 3];
% Finally, divide by x to get the increasing values:
y = t2 ./ x;
% x = [1 1 1 1 1 2 2 2 3 4 4 6 6 6 6 1 1 1];
% t2 = [1 2 3 4 5 2 4 6 3 4 8 6 12 18 24 1 2 3];
given matrix A of size 6 by 6 contain blocks of numbers,each block of size 2 by 2, and outher reference matrix R of size 2 by 12 also contain blocks of numbers, each block of size 2 by 2. the perpse of the whole process is to form a new matrix, called the Index matrix, contain index's that refer to the position of the blocks within the matrix A based on the order of the blocks within the reference matrix R. and here is an exemple
matrix A:
A =[1 1 2 2 3 3;
1 1 2 2 3 3;
1 1 3 3 4 4;
1 1 3 3 4 4;
4 4 5 5 6 6;
4 4 5 5 6 6 ]
matrix R:
R=[1 1 2 2 3 3 4 4 5 5 6 6;
1 1 2 2 3 3 4 4 5 5 6 6 ]
the new matrix is:
Index =[1 2 3;
1 3 4;
4 5 6]
any ideas ?
With my favourite three guys - bsxfun, permute, reshape for an efficient and generic solution -
blksz = 2; %// blocksize
num_rowblksA = size(A,1)/blksz; %// number of blocks along rows in A
%// Create blksz x blksz sized blocks for A and B
A1 = reshape(permute(reshape(A,blksz,num_rowblksA,[]),[1 3 2]),blksz^2,[])
R1 = reshape(R,blksz^2,1,[])
%// Find the matches with "bsxfun(#eq" and corresponding indices
[valid,idx] = max(all(bsxfun(#eq,A1,R1),1),[],3)
%// Or with PDIST2:
%// [valid,idx] = max(pdist2(A1.',reshape(R,blksz^2,[]).')==0,[],2)
idx(~valid) = 0
%// Reshape the indices to the shapes of blocked shapes in A
Index = reshape(idx,[],num_rowblksA).'
Sample run with more random inputs -
>> A
A =
2 1 1 2
1 2 2 1
1 1 1 1
2 2 2 2
1 2 2 1
1 2 1 1
>> R
R =
2 1 1 1 1 2 2 2 1 1 1 1
2 1 2 1 1 2 2 1 2 2 2 1
>> Index
Index =
0 0
5 5
3 0
This question already has answers here:
Generate all possible combinations of the elements of some vectors (Cartesian product)
(4 answers)
Closed 8 years ago.
So I'm writing a program to determine the unique combinations of a beaded necklace, but I can't seem to get it right. The rules are you can't have the same necklace forwards and backwards, and you can't have the same necklace with one bead being slid around to the other end. I've attached some pictures to clarify.
I wrote the code for it, and I thought I had achieved what I was trying to do, but it's not working correctly.
n = [1 2 3 4 2 4];
% green = 1
% blue = 2
% yellow = 3
% red = 4
p = perms(n);
total = max(size(p));
for i = 1:max(size(p))
q = p;
q(i) = [];
for j = 1:max(size(q))
if isequal(p(i),fliplr(q(j)))
total = total - 1;
elseif isequal(p(i),circshift(q(j),[1,1]))
total = total - 1;
elseif isequal(p(i),circshift(q(j),[length(q(j))-1,length(q(j))-1]))
total = total - 1;
end
disp(total)
end
end
Logically, this makes sense to me, but I could just be crazy.
If the problem size is small, you can vectorize all the comparisons (using bsxfun):
n = [1 2 3 4 2 4];
%// green = 1
%// blue = 2
%// yellow = 3
%// red = 4
N = numel(n);
p = perms(n).'; %'// generate all permutations
p2 = NaN([size(p) N+1]); %// this will store permutations with flips and shifts
p2(:,:,1) = p; %// original
p2(:,:,2) = flipud(p); %// flips
for k = 1:N-1
p2(:,:,2+k) = circshift(p,k); %// circular shifts
end
eqElem = bsxfun(#eq, p, permute(p2, [1 4 2 3]));
eqMat = squeeze(any(all(eqElem, 1), 4)); %// 1 if equal
remove = any(tril(eqMat, -1), 1); %// remove permutations that are "similar"
%// to a previous one, where "similar" means "equal up to circular shifts or
%// flips"
result = p(:,~remove).'; %'// all valid arrangements; one per row
resultNum = size(result, 1); %// number of arrangements
Results:
result =
1 3 2 2 4 4
1 3 2 4 4 2
1 3 2 4 2 4
1 3 4 2 2 4
1 3 4 2 4 2
1 3 4 4 2 2
1 2 3 2 4 4
1 2 3 4 2 4
1 2 3 4 4 2
1 2 2 3 4 4
1 2 2 4 4 3
1 2 2 4 3 4
1 2 4 3 2 4
1 2 4 3 4 2
1 2 4 2 3 4
1 2 4 2 4 3
1 2 4 4 2 3
1 2 4 4 3 2
1 4 4 3 2 2
1 4 4 2 2 3
1 4 4 2 3 2
1 4 3 4 2 2
1 4 3 2 2 4
1 4 3 2 4 2
1 4 2 3 2 4
1 4 2 3 4 2
1 4 2 2 3 4
1 4 2 2 4 3
1 4 2 4 2 3
1 4 2 4 3 2
resultNum =
30
You should do p = unique(p,'rows') before any loops. To see why, call perms([1 1 1]) at the command line.
There are a few issues here:
1) p, the perms, is a 2D matrix, so to get each perm you need to do p(i,:) to get the row. p(i) is just a single number.
2) You don't remove wrong answers from your list, so you will check against them twice. For example, say the first in the list is [1 2 3 4 2 4]; and the second is [4 2 4 3 2 1];. The fliplr check will compare these two combinations twice, once in the first loop around, once in the second.
3) If you want to make sure that any permutation which is a rotation is excluded (not just moving one bead around), you'll need some more circshift.
Consider using ismember with rows option again to compare a single row (e.g. a flipped version of the row you're checking) to an entire matrix.
A = [1,4,2,5,10
2,4,5,6,2
2,1,5,6,10
2,3,5,4,2]
And I want split it into two matrix by the last column
A ->B and C
B = [1,4,2,5,10
2,1,5,6,10]
C = [2,4,5,6,2
2,3,5,4,2]
Also, this method could be applied to a big matrix, like matrix 100*22 according to the last column value into 9 groups by matlab.
Use logical indexing
B=A(A(:,end)==10,:);
C=A(A(:,end)==2,:);
returns
>> B
B =
1 4 2 5 10
2 1 5 6 10
>> C
C =
2 4 5 6 2
2 3 5 4 2
EDIT: In reply to Dan's comment here is the extension for general case
e = unique(A(:,end));
B = cell(size(e));
for k = 1:numel(e)
B{k} = A(A(:,end)==e(k),:);
end
or more compact way
B=arrayfun(#(x) A(A(:,end)==x,:), unique(A(:,end)), 'UniformOutput', false);
so for
A =
1 4 2 5 10
2 4 5 6 2
2 1 5 6 10
2 3 5 4 2
0 3 1 4 9
1 3 4 5 1
1 0 4 5 9
1 2 4 3 1
you get the matrices in elements of cell array B
>> B{1}
ans =
1 3 4 5 1
1 2 4 3 1
>> B{2}
ans =
2 4 5 6 2
2 3 5 4 2
>> B{3}
ans =
0 3 1 4 9
1 0 4 5 9
>> B{4}
ans =
1 4 2 5 10
2 1 5 6 10
Here is a general approach which will work on any number of numbers in the last column on any sized matrix:
A = [1,4,2,5,10
2,4,5,6,2
1,1,1,1,1
2,1,5,6,10
2,3,5,4,2
0,0,0,0,2];
First sort by the last column (many ways to do this, don't know if this is the best or not)
[~, order] = sort(A(:,end));
As = A(order,:);
Then create a vector of how many rows of the same number appear in that last col (i.e. how many rows per group)
rowDist = diff(find([1; diff(As(:, end)); 1]));
Note that for my example data rowDist will equal [1 3 2] as there is 1 1, 3 2s and 2 10s.
Now use mat2cell to split by these row groupings:
Ac = mat2cell(As, rowDist);
If you really want to you can now split it into separate matrices (but I doubt you would)
Ac{:}
results in
ans =
1 1 1 1 1
ans =
0 0 0 0 2
2 3 5 4 2
2 4 5 6 2
ans =
1 4 2 5 10
2 1 5 6 10
But I think you would find Ac itself more useful
EDIT:
Many solutions so might as well do a time comparison:
A = [...
1 4 2 5 10
2 4 5 6 2
2 1 5 6 10
2 3 5 4 2
0 3 1 4 9
1 3 4 5 3
1 0 4 5 9
1 2 4 3 1];
A = repmat(A, 1000, 1);
tic
for l = 1:100
[~, y] = sort(A(:,end));
As = A(y,:);
rowDist = diff(find([1; diff(As(:, end)); 1]));
Ac = mat2cell(As, rowDist);
end
toc
tic
for l = 1:100
D=arrayfun(#(x) A(A(:,end)==x,:), unique(A(:,end)), 'UniformOutput', false);
end
toc
tic
for l = 1:100
for k = 1:numel(e)
B{k} = A(A(:,end)==e(k),:);
end
end
toc
tic
for l = 1:100
Bb = sort(A(:,end));
[~,b] = histc(A(:,end), Bb([diff(Bb)>0;true]));
C = accumarray(b, (1:size(A,1))', [], #(r) {A(r,:)} );
end
toc
resulted in
Elapsed time is 0.053452 seconds.
Elapsed time is 0.17017 seconds.
Elapsed time is 0.004081 seconds.
Elapsed time is 0.22069 seconds.
So for even for a large matrix the loop method is still the fastest.
Use accumarray in combination with histc:
% Example data (from Mohsen Nosratinia)
A = [...
1 4 2 5 10
2 4 5 6 2
2 1 5 6 10
2 3 5 4 2
0 3 1 4 9
1 3 4 5 1
1 0 4 5 9
1 2 4 3 1];
% Get the proper indices to the specific rows
B = sort(A(:,end));
[~,b] = histc(A(:,end), B([diff(B)>0;true]));
% Collect all specific rows in their specific groups
C = accumarray(b, (1:size(A,1))', [], #(r) {A(r,:)} );
Results:
>> C{:}
ans =
1 3 4 5 1
1 2 4 3 1
ans =
2 3 5 4 2
2 4 5 6 2
ans =
0 3 1 4 9
1 0 4 5 9
ans =
2 1 5 6 10
1 4 2 5 10
Note that
B = sort(A(:,end));
[~,b] = histc(A(:,end), B([diff(B)>0;true]));
can also be written as
[~,b] = histc(A(:,end), unique(A(:,end)));
but unique is not built-in and is therefore likely to be slower, especially when this is all used in a loop.
Note also that the order of the rows has changed w.r.t. the order they had in the original matrix. If the order matters, you'll have to throw in another sort:
C = accumarray(b, (1:size(A,1))', [], #(r) {A(sort(r),:)} );