I have sum of 3 cell arrays
A=72x1
B=72x720
C=72x90
resultant=A+B+C
size of resultant=72x64800
now when I find the minimum value with row and column indices I can locate the row element easily but how can I locate the column element in variables?
for example
after dong calculations for A,B,C I added them all and got a resultant in from of <72x(720x90)> or can say a matrix of integers of size <72x64800> then I found the minimum value of resultant with row and column index using the code below.
[minimumValue,ind]=min(resultant(:));
[row,col]=find(result== minimumValue);
then row got 14 and column got 6840 value..
now I can trace row 14 of all A,B,C variables easily but how can I know that the resultant column 6480 belongs to which combination of A,B,C?
Instead of using find, use the ind output from the min function. This is the linear index for minimumValue. To do that you can use ind2sub:
[r,c] = ind2sub(size(resultant),ind);
It is not quite clear what do you mean by resultant = A+B+C since you clearly don't sum them if you get a bigger array (72x64800), on the other hand, this is not a simple concatenation ([A B C]) since this would result in a 72x811 array.
However, assuming this is a concatenation you can do the following:
% get the 2nd dimension size of all matrices:
cols = cellfun(#(x) size(x,2),{A,B,C})
% create a vector with reapiting matrices names for all their columns:
mats = repelem(['A' 'B' 'C'],cols);
% get the relevant matrix for the c column:
mats(c)
so mats(c) will be the matrix with the minimum value.
EDIT:
From your comment I understand that your code looks something like this:
% arbitrary data:
A = rand(72,1);
B = rand(72,720);
C = rand(72,90);
% initializing:
K = size(B,2);
N = size(C,2);
counter = 1;
resultant = zeros(72,K*N);
% summing:
for k = 1:K
for n = 1:N
resultant(:,counter) = A + B(:,k) + C(:,n);
counter = counter+1;
end
end
% finding the minimum value:
[minimumValue,ind] = min(resultant(:))
and from the start of the answer you know that you can do this:
[r,c] = ind2sub(size(resultant),ind)
to get the row and column of minimumValue in resultant. So, in the same way you can do:
[Ccol,Bcol] = ind2sub([N,K],c)
where Bcol and Ccol is the column in B and C, respectively, so that:
minimumValue == A(r) + B(r,Bcol) + C(r,Ccol)
To see how it's working imagine that the loop above fills a matrix M with the value of counter, and M has a size of N-by-K. Because we fill M with a linear index, it will be filled in a column-major way, so the row will correspond to the n iterator, and the column will correspond to the k iterator. Now c corresponds to the counter where we got the minimum value, and the row and column of counter in M tells us the columns in B and C, so we can use ind2sub again to get the subscripts of the position of counter. Off course, we don't really need to create M, because the values within it are just the linear indices themselves.
Related
I'm a total beginner when it comes to MATLAB, so I have a question for this. How am I supposed to write this code out and if by any chance, can someone be kind enough to write out the code because I've been struggling with this. Use loops to create 3 x 5 matrix in which the value of each element is half of its row number plus three times its column number. for instance, the value of element (2,5) is: 1/22+35
The nested loops control the indexing of the matrix. The outer for-loop traverses through the rows of the matrix and the inner for-loop traverses through the columns of the matrix.
The second part of this question requires using a combination of the looping/scanning variables Row and Column to set the value of the matrix:
Matrix Value = (Row ÷ 2) + (3 × Column)
Number_Of_Rows = 3;
Number_Of_Columns = 5;
Matrix = zeros(Number_Of_Rows,Number_Of_Columns);
%Running through the array indices using two loops%
for Row = 1: Number_Of_Rows
for Column = 1: Number_Of_Columns
%Evaluating the value based on the current row and column index%
Matrix(Row,Column) = (Row/2) + (3*Column);
end
end
Matrix
Result:
Looping Methodology:
Variable Matrix Opened In Workspace:
Here's an intuitive way to do this:
% Initialize row num and column num
row = 3;
column = 5;
% H is the matrix of desire
% Initialize it as a 3*5 zero matrix
H = zeros(3,5);
% Outer loop, over column index
% Remember Matlab's index start with 1 not 0
for c = 1:column
% Inner Loop, over row index
for r = 1:row
% The algorithm of each element in the matrix
H(r,c) = 0.5*r+3*c;
end
end
I have a source matrix A(m,n) for which I used "find" and now I have a list of desired indices [y,x].
I also have a 3D matrix with dimensions B(m,n,3).
I want to extract all the elements in B using the result from find.
So if find yields 4 pairs of results, I would like to have a 4x3 matrix with the contents of the Z dimension of B for the resulting indices.
I tried many things but keep failing:
A = rand(480,640);
[y,x] = find(A < 0.5);
o = B(y,x,:);
Requested 39024x39024x3 (34.0GB) array exceeds maximum array size preference.
I am clearly doing something wrong since B has dimensions (640,640,3).
With the way you are trying to index, matlab tries to index B with every combination of the elements in y and x resulting in a massive matrix. I've implemented a for loop to do what I think you are asking.
I would also note that in order to index across B the first two dimensions need to be the same size as A, Otherwise you will not be able to index B past the maximum row or column index in A.
A = rand(480,640);
B = rand(480,640,3);
[x,y] = find(A < 0.5);
o = zeros(size(x,1),1,3); % x and y are the same length so it doesn't matter
for i = 1:size(x,1)
o(i,1,:)= B(x(i),y(i),:);
end
o = reshape(o,size(x,1),3);
You can reshape B to a 2D matrix of size [m*n , 3] then use logical indexing to extract elements:
C = reshape(B, [], 3);
o = C(A<0.5, :);
So I want to concatenate an m x n matrix to obtain a 1 x mn matrix. The matrix I want to concatenate are generated from a while loop. Although the number of columns will always be 3, I however cannot tell how many rows there will be for each iteration. Also, the row sizes for each iteration may not always be the same.
The code runs in cases where the row sizes were all equal to 6, but in cases where they aren't equal I get an error:
Error using vertcat Dimensions of matrices being concatenated are not consistent.
parts of the code are as follows:
A = [];
B = [];
searchArea = 2;
for ii = 1: numel(velocity)
Do ....
while area(ii,:) < searchArea
Do ....
% COLLATE vectors for A
A = [A; [Ax(ii), Ay(ii), Az(ii)]];
Do ...
end
%# Copy the A into new variable (B) and Reshape into row vector so as to associate each row to its corresponding velocity
B = [B; reshape(A.',1,[])];
A = [];
end
Could someone please advice me on what I am doing wrong here. I would clarify further if there be need. Thanks guys!
If it's your intent that B ends up being a row vector, then you need to change this:
B = [B; reshape(A.',1,[])]; % Does vertical concatenation
to this:
B = [B reshape(A.',1,[])]; % Does horizontal concatenation (note there's no semicolon)
so that each row vector gotten from reshaping A gets added to the end of the row instead of as a new row (as the semicolon indicates).
I have two matrices A and B. A(:,1) corresponds to an x-coordinate, A(:,2) corresponds to a y-coordinate, and A(:,3) corresponds to a certain radius. All three values in a row describe the same circle. Now let's say...
A =
[1,4,3]
[8,8,7]
[3,6,3]
B =
[1,3,3]
[1, 92,3]
[4,57,8]
[5,62,1]
[3,4,6]
[9,8,7]
What I need is to be able to loop through matrix A and determine if there are any rows in matrix B that are "similar" as in the x value is within a range (-2,2) of the x value of A (Likewise with the y-coordinate and radius).If it satisfies all three of these conditions, it will be added to a new matrix with the values that were in A. So for example I would need the above data to return...
ans =
[1,4,3]
[8,8,7]
Please help and thank you in advance to anyone willing to take the time!
You can use ismembertol.
result = A(ismembertol(A,B,2,'ByRows',1,'DataScale',1),:)
Manual method
A = [1,4,3;
8,8,7;
3,6,3];
B = [1,3,3;
1,92,3;
4,57,8;
5,62,1;
3,4,6;
9,8,7]; % example matrices
t = 2; % desired threshold
m = any(all(abs(bsxfun(#minus, A, permute(B, [3 2 1])))<=t, 2), 3);
result = A(m,:);
The key is using permute to move the first dimension of B to the third dimension. Then bsxfun computes the element-wise differences for all pairs of rows in the original matrices. A row of A should be selected if all the absolute differences with respect to any column of B are less than the desired threshold t. The resulting variable m is a logical index which is used for selecting those rows.
Using pdist2 (Statistics and Machine Learning Toolbox)
m = any(pdist2(A, B, 'chebychev')<=t, 2);
result = A(m,:);
Ths pdist2 function with the chebychev option computes the maximum coordinate difference (Chebychev distance, or L∞ metric) between pairs of rows.
With for loop
It should work:
A = [1,4,3;
8,8,7;
3,6,3]
B = [1,3,3;
1,92,3;
4,57,8;
5,62,1;
3,4,6;
9,8,7]
index = 1;
for i = 1:size(A,1)
C = abs(B - A(i,:));
if any(max(C,[],2)<=2)
out(index,:) = A(i,:);
index = index + 1
end
end
For each row of A, computes the absolute difference between B and that row, then checks if there exists a row in which the maximum is less than 2.
Without for loop
ind = any(max(abs(B - permute(A,[3 2 1])),[],2)<=2);
out = A(ind(:),:);
I have a matrix A which holds integers in a bounded range (0..255) and I need to build a table mapping a value (0..255) to all the coordinates in the matrix which hold this value.
What is the best way to achieve this? - I thought about using containers.Map for the task but Map doesn't support multiple values per key. I could have used lists but that would seem inefficient as I would have to create a new list on each iteration.
A vectorized solution, which gives the same output as the solution from Mikhail, is to sort all the pixel values in your image using the SORT function, convert the linear indices returned from SORT into subscripted indices using the function IND2SUB, and collect them together into a single cell array using the functions ACCUMARRAY and MAT2CELL:
A = randi([0 255],[5 5],'uint8'); %# A sample matrix
[values,indices] = sort(double(A(:))); %# Sort all the pixel values
[y,x] = ind2sub(size(A),indices); %# Convert linear index to subscript
counts = accumarray(values+1,1,[256 1]); %# Count number of each value
map = mat2cell([y x],counts); %# Create a 256-by-1 cell array
Now, for a given integer value iValue you can get the N-by-2 matrix containing the y (first column) and x (second column) coordinates for the N pixels in the image with that value by doing the following:
key = double(iValue)+1; %# Need to use double to avoid integer saturation
points = map{key}; %# An N-by-2 coordinate matrix
In addition, just in case you're interested, you could also make map a structure array with fields x and y using the function STRUCT:
map = struct('x',mat2cell(x,counts),'y',mat2cell(y,counts));
And you can then access the x and y coordinates for pixels with a value iValue as follows:
key = double(iValue)+1;
x = map(key).x;
y = map(key).y
What about using a cell array? You can index it with integers. For example:
map = {[1,1;13,56], [], [4,5]};
In this example index 0 is in the matrix in 1,1 and 13,56, index 1 in none and index 2 in 4,5
Your cell would have 256 elements (mine has 3) and to acces you would simply add 1 to the index.
You could also store indices linearly so the code to fill the table would be:
for ii = 0:255
map{ii+1} = find( mat(:)==ii )
end
Well, I wrote the following and it seems to work in reasonable time. I think the thing that does the trick is preallocating the cell arrays based on the histogram for each value:
[H, W] = size(A);
histogram = hist(A, 256);
AGT = arrayfun(#(avg) {0 cell(1, histogram(avg))}, 1:256, 'UniformOutput', false);
for y = 1:H
for x = 1:W
idx = A(y, x) + 1;
count = AGT{idx}{1};
AGT{idx}{2}{count + 1} = [y x];
AGT{idx}{1} = count + 1;
end
end
Accessing the table is a bit annoyting though :
AGT{200}{2}{:}
to access all coordinates with value 200.