I want to create a square matrix that takes input n and create a matrix incrementing from 0 up to n^2
eg.
input: n = 2
output = [1 2
4 3]
input = 4
output = [1 2 3 4
8 7 6 5
9 10 11 12
16 15 14 13]
n = 4;
output = reshape(1:n^2,n,n)';
output(2:2:end,:) = fliplr(output(2:2:end,:))
Related
I have two arrays with different length, for example A =[ 2 3 11 0 8 ] and B=[ 2 6 8] ( The data are bigger in the real case) and I want to compare them and find elements that verify abs(A(i)-B(j))> 2 .
Is there any fast function that do that (such ismember but for inequalities) ?
You can create a small function that will check all the possible combinations and send you back the "valid" combination.
A = [2 3 11 0 8];
B = [2 6 8];
C = isbigger(A,B,2); %output = the element that verify abs(A-B)>2
function COMB = isbigger(A,B,val)
[X,Y] = meshgrid(A,B);
X = X(:);
Y = Y(:);
index = abs((X(:)-Y(:)))>val;
COMB = [X(index),Y(index)];
end
OUTPUT:
C =
2 6
2 8
3 6
3 8
11 2
11 6
11 8
0 6
0 8
8 2
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.
If I have matrix size(mat)= X*Y*6
let call mat(:,:,1)=A
and mat(:,:,2)=B and so on
how do i rezise mat to X*Y*12
where
mat(:,:,1)=mat(:,:,2)= A
mat(:,:,3)=mat(:,:,4)=B
and so on
You can use the following syntax:
%defines input matrix (in your case it is already defined)
m = 500;
n = 400;
z = 6;
mat = rand(m,n,z);
%initialize output matrix
newMat = zeros(m,n,z*2);
%assign old matrix values into the new matrix
newMat(:,:,1:2:end) = mat;
newMat(:,:,2:2:end) = mat;
If you have Matlab 2015a or newer you can use repelem:
N = 2; %// number of times to repeat
result = repelem(mat, 1, 1, N); %// repeat N times along 3rd dim
For older Matlab versions you can do it manually as follows:
N = 2; %// number of times to repeat
ind = ceil(1/N:1/N:size(mat,3)); %// build index with repetitions
result = mat(:,:,ind); %// apply index along desired dim
Example:
>> %// Data
>> mat = randi(9,2,4,2)
mat(:,:,1) =
5 8 9 2
7 3 1 5
mat(:,:,2) =
5 7 1 1
1 8 8 2
>> %// First approach
>> N = 2;
>> result = repelem(mat, 1, 1, N)
result(:,:,1) =
5 8 9 2
7 3 1 5
result(:,:,2) =
5 8 9 2
7 3 1 5
result(:,:,3) =
5 7 1 1
1 8 8 2
result(:,:,4) =
5 7 1 1
1 8 8 2
>> %// Second approach
>> N = 2;
>> ind = ceil(1/N:1/N:size(mat,3));
>> result = mat(:,:,ind)
result(:,:,1) =
5 8 9 2
7 3 1 5
result(:,:,2) =
5 8 9 2
7 3 1 5
result(:,:,3) =
5 7 1 1
1 8 8 2
result(:,:,4) =
5 7 1 1
1 8 8 2
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
We have p.e. i = 1:25 iterations.
Each iteration result is a 1xlength(N) cell array, where 0<=N<=25.
iteration 1: 4 5 9 10 20
iteration 2: 3 8 9 13 14 6
...
iteration 25: 1 2 3
We evaluate the results of all iterations to one matrix sorted according to frequency each value is repeated in descending order like this example:
Matrix=
Columns 1 through 13
16 22 19 25 2 5 8 14 17 21 3 12 13
6 5 4 4 3 3 3 3 3 3 2 2 2
Columns 14 through 23
18 20 1 6 7 9 10 11 15 23
2 2 1 1 1 1 1 1 1 1
Result explanation: Column 1: N == 16 is present in 6 iterations, column 2: N == 22 is present in 5 iterations etc.
If a number N isn't displayed (in that paradigm N == 4, N == 24) in any iteration, is not listed with frequency index of zero either.
I want to associate each iteration (i) to the first N it is displayed p.e. N == 9 to be present only in first iteration i = 1 and not in i = 2 too, N == 3 only to i = 2 and not in i = 25 too etc until all i's to be unique associated to N's.
Thank you in advance.
Here's a way that uses a feature of unique (i.e. that it returns the index to the first value) that was introduced in R2012a
%# make some sample data
iteration{1} = [1 2 4 6];
iteration{2} = [1 3 6];
iteration{3} = [1 2 3 4 5 6];
nIter= length(iteration);
%# create an index vector so we can associate N's with iterations
nn = cellfun(#numel,iteration);
idx = zeros(1,sum(nn));
idx([1,cumsum(nn(1:end-1))+1]) = 1;
idx = cumsum(idx); %# has 4 ones, 3 twos, 6 threes
%# create a vector of the same length as idx with all the N's
nVec = cat(2,iteration{:});
%# run `unique` on the vector to identify the first occurrence of each N
[~,firstIdx] = unique(nVec,'first');
%# create a "cleanIteration" array, where each N only appears once
cleanIter = accumarray(idx(firstIdx)',firstIdx',[nIter,1],#(x){sort(nVec(x))},{});
cleanIter =
[1x4 double]
[ 3]
[ 5]
>> cleanIter{1}
ans =
1 2 4 6
Here is another solution using accumarray. Explanations in the comments
% example data (from your question)
iteration{1} = [4 5 9 10 20 ];
iteration{2} = [3 8 9 13 14 6];
iteration{3} = [1 2 3];
niterations = length(iteration);
% create iteration numbers
% same as Jonas did in the first part of his code, but using a short loop
for i=1:niterations
idx{i} = i*ones(size(iteration{i}));
end
% count occurences of values from all iterations
% sort them in descending order
occurences = accumarray([iteration{:}]', 1);
[occ val] = sort(occurences, 1, 'descend');
% remove zero occurences and create the Matrix
nonzero = find(occ);
Matrix = [val(nonzero) occ(nonzero)]'
Matrix =
3 9 1 2 4 5 6 8 10 13 14 20
2 2 1 1 1 1 1 1 1 1 1 1
% find minimum iteration number for all occurences
% again, using accumarray with #min function
assoc = accumarray([iteration{:}]', [idx{:}]', [], #min);
nonzero = find(assoc);
result = [nonzero assoc(nonzero)]'
result =
1 2 3 4 5 6 8 9 10 13 14 20
3 3 2 1 1 2 2 1 1 2 2 1