Referring to Reshape row wise w/ different starting/ending elements number #Divakar came with a nice solution but, what if the number of columns is not always the same?
Sample run -
>> A'
ans =
4 9 8 9 6 1 8 9 7 7 7 4 6 2 7 1
>> out
out =
4 9 8 9 0 0
6 1 8 9 7 7
7 4 6 2 7 1
I took only the first 4 terms of A and put them in out, then fill the rest 2 empty cell with 0's. So the ncols = [4 6 6]. Unfortunately vet2mat doesn't allow vector as columns number.
Any suggestions?
You can employ bsxfun's masking capability here -
%// Random inputs
A = randi(9,1,15)
ncols = [4 6 5]
%// Initialize output arary of transposed size as compared to the desired
%// output arary size, as we need to insert values into it row-wise and MATLAB
%// follows column-major indexing
out = zeros(max(ncols),numel(ncols));
mask = bsxfun(#le,[1:max(ncols)]',ncols); %//'# valid positions mask for output
out(mask) = A; %// insert input array elements
out = out.' %//'# transpose output back to the desired output array size
Code run -
A =
5 3 7 2 7 2 4 6 8 1 9 7 5 4 5
ncols =
4 6 5
out =
5 3 7 2 0 0
7 2 4 6 8 1
9 7 5 4 5 0
You could use accumarray for that:
A = [4 9 8 9 6 1 8 9 7 7 7 4 6 2 7 1].'; %'// data
ncols = [4 6 6]; %// columns
n = max(ncols);
cs = cumsum(ncols);
ind = 1;
ind(cs+1) = 1;
ind = cumsum(ind(1:end-1)); %// `ind` tells the row for each element of A
result = accumarray(ind(:), A(:), [], #(x) {[x; zeros(n-numel(x),1)]}); %// split `A` as
%// dictated by `ind`, and fill with zeros. Each group is put into a cell.
result = [result{:}].'; %'// concatenate all cells
Related
In Matlab by default convert a matrix to column vector is done column-wise.
Can we do this row-wise.
A = magic(3);
A =
8 1 6
3 5 7
4 9 2
column_vector = A(:);
column_vector =
8
3
4
1
5
9
6
7
2
% This can be done rowwise indirectly by transpose or reshape
column_vector = reshape(A',1,numel(A))';
column_vector =
8
1
6
3
5
7
4
9
2
trA = A';
column_vector = trA(:);
column_vector =
8
1
6
3
5
7
4
9
2
Is there a direct way to do this row-wise like A(:)?
Thanks,
Gopi
I have a vector of 13 entities in Matlab.
a=[3 4 6 8 1 5 8 9 3 7 3 6 2]
I want to append values [1 2 3 4 5] at regular intervals at position 1 5 9 13 & 17.
The final value of a looks like this.
a=[1 3 4 6 2 8 1 5 3 8 9 3 4 7 3 6 5 2].
The values with italics show the appended values.
How can I do it?
Since you are looking for regular intervals, you can take advantage of the reshape and cat function:
a = [3 4 6 8 1 5 8 9 3 7 3 6 2];
v = [1 2 3 4 5];
l = [1 5 9 13 17];
interval = l(2)-l(1)-1; %computes the interval between inserts
amax = ceil(size(a,2)/interval) * interval; %calculating maximum size for zero padding
a(amax) = 0; %zero padding to allow `reshape`
b = reshape (a,[interval,size(v,2)]); %reshape into matrix
result = reshape(vertcat (v,b), [1,(size(b,1)+1)*size(b,2)]); %insert the values into the right position and convert back into vector
%remove padded zeros
final = result(result ~= 0) %remove the zero padding.
>>final =
Columns 1 through 16
1 3 4 6 2 8 1 5 3 8 9 3 4 7 3 6
Columns 17 through 18
5 2
Here's an approach using boolean-indexing -
% Inputs
a = [3 4 6 8 1 5 8 9 3 7 3 6 2]
append_vals = [1 2 3 4 5]
append_interval = 4 % Starting at 1st index
% Find out indices of regular intervals where new elements are to be inserted.
% This should create that array [1,5,9,13,17]
N_total = numel(a) + numel(append_vals)
append_idx = find(rem(0:N_total-1,append_interval)==0)
% Get boolean array with 1s at inserting indices, 0s elsewhere
append_mask = ismember(1:N_total,append_idx)
% Setup output array and insert new and old elements
out = zeros(1,N_total)
out(~append_mask) = a
out(append_mask) = append_vals
Alternatively, we can also use linear-indexing and avoid creating append_mask, like so -
% Setup output array and insert new and old elements
out = zeros(1,N_total)
out(append_idx) = append_vals
out(setdiff(1:numel(out),append_idx)) = a
a=[3 4 6 8 1 5 8 9 3 7 3 6 2]; % // Your original values
pos = [1 5 9 13 17]; % // The position of the values you want to insert
b=[1 2 3 4 5]; % // The values you want to insert
% // Pre-allocate a vector with the total size to hold the resulting values
r = zeros(size(a,2)+size(pos,2),1);
r(pos) = b % // Insert the appended values into the resulting vector first
r3 = r.' <1 % // Find the indices of the original values. These will be zero in the variable r but 1 in r3
ans =
0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1
ind= find(r3==1) % // Find the indices of the original values
ind =
2 3 4 6 7 8 10 11 12 14 15 16 18
r(ind) = a; % // Insert those into the resulting vector.
r.'
ans =
1 3 4 6 2 8 1 5 3 8 9 3 4 7 3 6 5 2
You can use this function to append a bunch of values to an existing vector, given their positions in the new vector:
function r=append_interval(a,v,p)
% a - vector with initial values
% v - vector containing values to be inserted
% p - positions for values in v
lv=numel(v); % number of elements in v vector
la=numel(a); % number of elements in a vector
column_a=iscolumn(a); % check if a is a column- or row- wise vector
tot_elements=la+lv;
% size of r is tha max between the total number of elements in the two vectors and the higher positin in vector p (in this case missing positions in a are filled with zeros)
lr=max([max(p) tot_elements]);
% initialize r as nan vector
r=zeros(column_a*(lr-1)+1,~column_a*(lr-1)+1)/0;
% set elements in p position to the corresponding values in v
r(p)=v;
% copy values in a in the remaining positions and fill with zeros missing entries (if any)
tot_missing_values=lr-tot_elements;
if(tot_missing_values)
remaining_values=cat(2-iscolumn(a),a,zeros(column_a*(tot_missing_values-1)+1,~column_a*(tot_missing_values-1)+1));
else
remaining_values=a;
end
% insert values
r(isnan(r))=remaining_values;
You can use row-wise or column-wise vectors; the orientation of r will be the same of that of a.
Input:
a =
3 4 6 8 1 5 8 9 3 7 3 6 2
v =
1 2 3 4 5
p =
1 5 9 13 17
Output:
>> append_interval(a,v,p)
ans =
1 3 4 6 2 8 1 5 3 8 9 3 4 7 3 6 5 2
Every sequence of positive positions is allowed and the function will pad for you with zeros the final vector, in case you indicate a position exceding the sum of the original vector and added items.
For example, if:
v3 =
1 2 3 4 5 6 90
p3 =
1 5 9 13 17 30 33
you get:
append_interval(a,v3,p3)
ans =
Columns 1 through 19
1 3 4 6 2 8 1 5 3 8 9 3 4 7 3 6 5 2 0
Columns 20 through 33
0 0 0 0 0 0 0 0 0 0 6 0 0 90
Hope this will help.
Similarly to How to combine vectors of different length in a cell array into matrix in MATLAB I would like to combine matrix having different dimension, stored in a cell array, into a matrix having zeros instead of the empty spaces. Specifically, I have a cell array {1,3} having 3 matrix of size (3,3) (4,3) (4,3):
A={[1 2 3; 4 5 6; 7 8 9] [1 2 3; 4 5 6; 7 8 9; 9 9 9] [1 2 3; 4 5 6; 7 8 9; 4 4 4]}
and I would like to obtain something like:
B =
1 2 3 1 2 3 1 2 3
4 5 6 4 5 6 4 5 6
7 8 9 7 8 9 7 8 9
0 0 0 9 9 9 4 4 4
I tried using cellfun and cell2mat but I do not figure out how to do this. Thanks.
Even if other answers are good, I'd like to submit mine, using cellfun.
l = max(cellfun(#(x) length(x),A))
B = cell2mat(cellfun(#(x) [x;zeros(l-length(x),3)], A, 'UniformOutput', 0));
Using bsxfun's masking capability -
%// Convert A to 1D array
A1d = cellfun(#(x) x(:).',A,'Uni',0) %//'
%// Get dimensions of A cells
nrows = cellfun('size', A, 1)
ncols = cellfun('size', A, 2)
%// Create a mask of valid positions in output numeric array, where each of
%// those numeric values from A would be put
max_nrows = max(nrows)
mask = bsxfun(#le,[1:max_nrows]',repelem(nrows,ncols)) %//'
%// Setup output array and put A values into its masked positions
B = zeros(max_nrows,sum(ncols))
B(mask) = [A1d{:}]
Sample run
Input -
A={[1 2 3 5 6; 7 8 9 3 8] [1 2 3; 4 5 6; 7 8 9; 9 9 9] [1 2 3; 4 5 6; 7 8 9; 4 4 4]}
Output -
B =
1 2 3 5 6 1 2 3 1 2 3
7 8 9 3 8 4 5 6 4 5 6
0 0 0 0 0 7 8 9 7 8 9
0 0 0 0 0 9 9 9 4 4 4
I would be surprised if this is possible in one or a few lines. You will probably have to do some looping yourself. The following achieves what you want in the specific case of incompatible first dimension lengths:
A={[1 2 3; 4 5 6; 7 8 9] [1 2 3; 4 5 6; 7 8 9; 9 9 9] [1 2 3; 4 5 6; 7 8 9; 4 4 4]}
maxsize = max(cellfun(#(x) size(x, 1), A));
B = A;
for k = 1:numel(B)
if size(B{k}, 1) < maxsize
tmp = B{k};
B{k} = zeros(maxsize, size(tmp,1));
B{k}(1:size(tmp,1),1:size(tmp,2)) = tmp;
end
end
B = cat(2, B{:});
Now B is:
B =
1 2 3 1 2 3 1 2 3
4 5 6 4 5 6 4 5 6
7 8 9 7 8 9 7 8 9
0 0 0 9 9 9 4 4 4
I would do it using a good-old for loop, which is quite intuitive I think.
Here is the commented code:
clc;clear var
A={[1 2 3; 4 5 6; 7 8 9] [1 2 3; 4 5 6; 7 8 9; 9 9 9] [1 2 3; 4 5 6; 7 8 9; 4 4 4]};
%// Find the maximum rows and column # to initialize the output array.
MaxRow = max(cell2mat(cellfun(#(x) size(x,1),A,'Uni',0)));
SumCol = sum(cell2mat(cellfun(#(x) size(x,2),A,'Uni',0)));
B = zeros(MaxRow,SumCol);
%// Create a counter to keep track of the current columns to fill
ColumnCounter = 1;
for k = 1:numel(A)
%// Get the # of rows and columns for each cell from A
NumRows = size(A{k},1);
NumCols = size(A{k},2);
%// Fill the array
B(1:NumRows,ColumnCounter:ColumnCounter+NumCols-1) = A{k};
%// Update the counter
ColumnCounter = ColumnCounter+NumCols;
end
disp(B)
Output:
B =
1 2 3 1 2 3 1 2 3
4 5 6 4 5 6 4 5 6
7 8 9 7 8 9 7 8 9
0 0 0 9 9 9 4 4 4
[max_row , max_col] = max( size(A{1}) , size(A{2}) , size(A{3}) );
A{1}(end:max_row , end:max_col)=0;
A{2}(end:max_row , end:max_col)=0;
A{3}(end:max_row , end:max_col)=0;
B=[A{1} A{2} A{3}];
for this specific problem, simply this will do:
B=cat(1,A{:});
or what I often just give a try for 2D cells, and works for your example as well:
B=cell2mat(A');
if you literally don't give a f* what dimension it will be cut in (and you're exceedingly lazy): put the same into a try-catch-block and loop over some dims as below.
function A=cat_any(A)
for dims=1:10% who needs more than 10 dims? ... otherwise replace 10 with: max(cellfun(#ndims,in),[],'all')
try, A=cat(dims,A{:}); end
if ~iscell(A), return A; end
end
disp('Couldn''t cat!') %if we can't cat, tell the user
end
Beware, this might lead to unexpected results ... but in most cases simply just worked for me.
so far i have done
Declare a random Matrix M of size 88 x 88
Type of M should be uint8 (all values should be between 0 to 255).
Spilt the Matrix into 4 parts: p1, p2, p3, p4
Transpose all parts
Concatenate all these four parts into new matrix N
Approach #1
If you have the Image Processing Toolbox, you can use blockproc for a pretty straight-forward solution to this -
fun = #(block_struct) transpose(block_struct.data);
N = blockproc(M, [size(M,1)/2 size(M,2)/2], fun)
Approach #2
Let's suppose you have an input matrix of size m x n and you would like to partition it into dim1p parts along the rows and dim2p parts along the columns, so that each block is of size m/dim1p x n/dim2p and you would like transpose them and finally concatenate them back to form a 2D array. This could be thought of as a general case of what you had proposed in the question.
To solve such a case with performance in mind, you can use this -
[m,n] = size(M); %// Get size
dim1p = 2; %// number of parts to be partitioned along dimension-1 (rows)
dim2p = 2; %// number of parts to be partitioned along dimension-2 (columns)
%// Split and transpose, resulting in a 3D array
A = reshape(permute(reshape(M, m, n/dim2p, []), [2 1 3]), n/dim2p, m/dim1p, []);
%// Join the 3D slices back into a 2D array for the desired output
nrows = n*dim1p/dim2p;
N = reshape(permute(reshape(permute(A,[1 3 2]),nrows,dim2p,[]),[1 3 2]),nrows,[])
Sample run (assuming M as 9 x 8 sized and partitioning it into 3 and 4 parts along the rows and columns respectively so that each block is of size 3 x 2) -
M =
5 6 2 6 4 2 1 3
2 8 8 1 3 8 3 7
5 1 6 8 4 1 6 8
6 5 7 3 3 6 7 1
4 3 9 3 2 2 5 3
4 9 5 7 6 2 2 1
7 6 2 5 9 3 5 6
8 9 5 6 9 6 7 1
1 1 3 3 4 9 1 3
dim1p =
3
dim2p =
4
N =
5 2 5 2 8 6 4 3 4 1 3 6
6 8 1 6 1 8 2 8 1 3 7 8
6 4 4 7 9 5 3 2 6 7 5 2
5 3 9 3 3 7 6 2 2 1 3 1
7 8 1 2 5 3 9 9 4 5 7 1
6 9 1 5 6 3 3 6 9 6 1 3
You are not much clear in the question, Maybe this helps,
M = uint8(randi([0 255],[88 88]));
p1 = M(1:end/2 ,1:end/2 );
p2 = M(1:end/2 ,end/2+1:end);
p3 = M(end/2+1:end,1:end/2 );
p4 = M(end/2+1:end,end/2+1:end);
N = [p1' p2';p3' p4'];
Another approach would be to use mat2cell to split up the matrix into a 2 x 2 grid of cells, transpose each of the cell's contents using cellfun, then piece them all together using cell2mat. Therefore:
[rows, cols] = size(M);
C = mat2cell(M, [rows/2, rows/2], [cols/2, cols/2]);
D = cellfun(#transpose, C, 'uni', 0);
out = cell2mat(D);
Minor note: This only works when the rows and columns are both even.
How to generate random matrix without repetition in rows and cols with specific range
example (3x3): range 1 to 3
2 1 3
3 2 1
1 3 2
example (4x4): range 1 to 4
4 1 3 2
1 3 2 4
3 2 4 1
2 4 1 3
A way of approaching this problem is to generate a circular matrix and shuffle it.
mat_size = 4
A = gallery('circul', 1:mat_size); % circular matrix
B = A( randperm(length(A)) , randperm(length(A)) ); % shuffle rows and columns with randperm
It gives
A =
1 2 3 4
4 1 2 3
3 4 1 2
2 3 4 1
B =
3 4 1 2
2 3 4 1
4 1 2 3
1 2 3 4
This method should be fast. An 11 size problem is computed in 0.047021 seconds.
This algorithm will do the trick, assuming you want to contain all elements between 1 and n
%// Elements to be contained, but no zero allowed
a = [1 2 3 4];
%// all possible permutations and its size
n = numel(a);
%// initialization
output = zeros(1,n);
ii = 1;
while ii <= n;
%// random permuation of input vector
b = a(randperm(n));
%// concatenate with already found values
temp = [output; b];
%// check if the row chosen in this iteration already exists
if ~any( arrayfun(#(x) numel(unique(temp(:,x))) < ii+1, 1:n) )
%// if not, append
output = temp;
%// increase counter
ii = ii+1;
end
end
output = output(2:end,:) %// delete first row with zeros
It definitely won't be the fastest implementation. I would be curios to see others.
The computation time increases exponentially. But everything up to 7x7 is bearable.
I wrote another code (interesting to compare timings and, if possible, to make it parallel). Also had problem with perms (needed to restart Matlab to be able to generate for 11 elements, I have x64 and 16GB of memory). Than I decided to keep characters instead of the numbers, reducing the memory occupied by the matrix. It, of course, generates all permutations, and I shuffle them in the beginning, selecting in the loop in a new random order. It runs faster this way and 'eats' less memory. Time for 11 x 11 (of course it differs from run to run) is shown in results.
clear all;
t = cputime;
sze = 11;
variations = perms(char(1 : sze)); % permutations
varN = length(variations);
variations = variations(randperm(varN)', :); % shuffle
sudoku = zeros(sze, sze);
sudoku(1, :) = variations(1, :); % set the first row
indx = 2;
for ii = 2 : varN
% take a random index
rowVal = variations(ii, :);
% check that row numbers do not present in table at
% corresponding columns
if (~isempty(find(repmat(rowVal, sze, 1) - sudoku == 0, 1)))
continue;
end;
sudoku(indx, :) = rowVal;
disp(['Found row ' num2str(indx)]);
indx = indx + 1;
if indx > sze, break; end;
end;
disp(cputime - t);
disp(sudoku);
Result
252.9712 seconds
7 11 3 9 6 2 4 1 8 10 5
1 9 6 3 10 7 11 5 2 4 8
9 6 11 8 2 10 1 7 4 5 3
4 10 7 11 1 8 5 2 6 3 9
2 5 9 1 3 6 8 4 10 7 11
10 3 5 6 7 4 2 9 11 8 1
6 4 2 10 8 5 3 11 9 1 7
3 8 10 4 11 1 7 6 5 9 2
11 1 8 5 4 9 6 3 7 2 10
5 2 4 7 9 3 10 8 1 11 6
8 7 1 2 5 11 9 10 3 6 4
Here's a memory-efficient approach. The time it takes is random, but not very large. All possible output matrices are equally likely.
This works by randomly filling the matrix until no more positions are available or until the whole matrix has been filled. The code is commented so it should be obvious how it works.
For size 11 this takes of the order of a few thousands or tens of thousands attempts. On my old laptop that means a (random) running time from a few seconds to tens of seconds.
It could perhaps be sped up using uint8 values instead of double. I don't think that brings a large gain, though.
The code:
clear all
n = 11; %// matrix size
[ ii jj ] = ndgrid(1:n); %// rows and columns of S
ii = ii(:);
jj = jj(:);
success = 0; %// ...for now
attempt = 0; %// attempt count (not really needed)
while ~success
attempt = attempt + 1;
S = NaN(n, n); %// initiallize result. NaN means position not filled yet
t = 1; %// number t is being placed within S ...
u = 1; %// ... for the u-th time
mask = true(1, numel(ii)); %// initiallize mask of available positions
while any(mask) %// while there are available positions
available = find(mask); %// find available positions
r = randi(numel(available), 1); %// pick one available position
itu = ii(available(r)); %// row of t, u-th time
jtu = jj(available(r)); %// col of t, u-th time
S(itu, jtu) = t; %// store t at that position
remove = (ii==itu) | (jj==jtu);
mask(remove) = false; %// update mask of positions available for t
u = u+1; %// next u
if u > n %// we are done with number t
t = t+1; %// let's go with new t
u = 1; %// initiallize u
mask = isnan(S(:)); %// initiallize mask for this t
end
if t > n %// we are done with all numbers
success = 1; %// exit outer loop (inner will be exited too)
end
end
end
disp(attempt) %// display number of attempts
disp(S) %// show result
An example result:
10 11 8 9 7 2 3 4 1 6 5
8 4 2 1 10 11 6 5 7 9 3
2 3 5 6 11 8 1 10 4 7 9
9 8 7 4 6 10 11 3 5 1 2
3 5 9 8 2 1 4 7 6 11 10
11 9 4 5 3 6 2 1 8 10 7
1 2 6 3 8 7 5 9 10 4 11
7 1 11 10 5 4 9 8 2 3 6
4 7 1 2 9 3 10 6 11 5 8
6 10 3 11 1 5 7 2 9 8 4
5 6 10 7 4 9 8 11 3 2 1