I have two vectors xx and yy holding the x and y indices of certain pixels respectively in matrix A . What I want to do is to check the values of the pixels with those indices and count how many of those pixels have the value 0. For example, if xx=[1 2 3] and y=[2 5 8], I want to check how many of these pixels(x,y) (1,2), (2,5), (3,8) have the value 0. I can do this with for loops but I think it can be done easier in Matlab, so if anyone could please advise.
The following should work:
sum(A(sub2ind(size(A),xx,yy)) == 0)
First, you convert the row and column indices into single indices into the matrix A. Then, you check where A is zero for these indices (which will result in ones). Then you simply sum up the ones.
A dirtier way than sub2ind is
sum( A( [1 size(A,1)]*( [ yy; xx ] - 1 ) + 1 ) == 0 )
You can check here and see that the dirty method is ~x4 times faster than sub2ind. So, if you are in need for speed, use the dirty method ;)
Related
What's the best Matlab/Octave idiom, given idx a vector of indices, to get the sorted vector of idx +/-1 ?
I have an n x 7 data matrix, column 3 is an integer label, and I'm interested in viewing the neighborhood of discontinuities on it.
Hence I get the corresponding indices:
idx = find(diff(data(:,3)) > 0)
5297
6275
6832
...
20187
Then if I want to view that neighborhood +/- 1 on my column (e.g. on the (mx2) matrix [idx-1; idx+1]), I need to form the vector of idx-1, idx+1 either concatenated in-order, or resorted.
I found some clunky ways of doing this, what's the proper way?
(I tried all of the octave chapter on Rearranging Matrices)
% WAY 1: this works, but is ugly - a needless O(n) sort
sort([idx-1; idx+1])
% horzcat,vertcat,vec only stack it vertically
horzcat([idx-1; idx+1])
horzcat([idx-1; idx+1]')
% WAY 2?
%One of vec([idx-1; idx+1]) or vec([idx-1; idx+1]') should work? but doesn't, they always stack columnwise
horzcat([idx-1; idx+1]')
ans =
Columns 1 through ...
5297 6275 6832 ... 20187 5299 6277 6834 ... 20189
% TRY 3...
reshape([idx-1; idx+1], [36,1]) doesn't work either
You would expect there are only two ways to unstack a 2xm matrix, but ...
You can do this with implicit singleton expansion (R2016b or newer MATLAB, native to Octave)
idx = [2, 6, 9]; % some vector of integers
% Use reshape with [] to tell MATLAB "however many rows it takes"
neighbours = reshape( idx + [-1;1], [], 1 );
>> neighbours = [1; 3; 6; 8; 8; 10];
If you don't know whether idx is a row or column, you can be more robust by using
neighbours = reshape( idx(:)' + [-1,1], [], 1)
If you don't want to use implicit expansion (and again coping with either row or column idx), you can use reshape like so
neighbours = reshape( [idx(:)-1, idx(:)+1]', [], 1 )
Note: you may also want to wrap the whole thing in a call to unique. In my example, you get the index 8 twice, I'm not sure if this is desirable or not in your situation.
However, unique performs a sort (unless you use the 'stable' flag but that can make it even slower), so you might as well use your original approach if you want to remove duplicates:
% Remove duplicates and sort the result using unique
neighbours = unique( [idx-1, idx+1] );
Hmm, I finally found this octave matrix manipulation:
vec([idx-1, idx+1]')
ans =
5297
5299
6275
6277
6832
6834
...
20187
20189
Adapting Wolfie's solution into the shortest Octave-only code:
[idx-1, idx+1]' (:)
( idx(:)' + [-1; 1] )(:)
idx = ( find(diff(data(:,3)) > 0 )' + [-1; 1] )(:) works as a one-liner
... and [idx , data(idx,3)] displays the indices and data, side-by-side
Can anybody help me to find out the method to calculate the elements of different sized matrix in Matlab ?
Let say that I have 2 matrices with numbers.
Example:
A=[1 2 3;
4 5 6;
7 8 9]
B=[10 20 30;
40 50 60]
At first,we need to find maximum number in each column.
In this case, Ans=[40 50 60].
And then,we need to find ****coefficient** (k).
Coefficient(k) is equal to 1 divided by quantity of column of matrix A.
In this case, **coefficient (k)=1/3=0.33.
I wanna create matrix C filling with calculation.
Example in MS Excel.
H4 = ABS((C2-C6)/C9)*0.33+ABS((D2-D6)/D9)*0.33+ABS((E2-E6)/E9)*0.33
I4 = ABS((C3-C6)/C9)*0.33+ABS((D3-D6)/D9)*0.33+ABS((E3-E6)/E9)*0.33
J4 = ABS((C4-C6)/C9)*0.33+ABS((D4-D6)/D9)*0.33+ABS((E4-E6)/E9)*0.33
And then (Like above)
H5 = ABS((C2-C7)/C9)*0.33+ABS((D2-D7)/D9)*0.33+ABS((E2-E7)/E9)*0.33
I5 = ABS((C3-C7)/C9)*0.33+ABS((D3-D7)/D9)*0.33+ABS((E3-E7)/E9)*0.33
J5 = ABS((C4-C7)/C9)*0.33+ABS((D4-D7)/D9)*0.33+ABS((E4-E7)/E9)*0.33
C =
0.34 =|(1-10)|/40*0.33+|(2-20)|/50*0.33+|(3-30)|/60*0.33
0.28 =|(4-10)|/40*0.33+|(5-20)|/50*0.33+|(6-30)|/60*0.33
0.22 =|(7-10)|/40*0.33+|(8-20)|/50*0.33+|(9-30)|/60*0.33
0.95 =|(1-40)|/40*0.33+|(2-50)|/50*0.33+|(3-60)|/60*0.33
0.89 =|(4-40)|/40*0.33+|(5-50)|/50*0.33+|(6-60)|/60*0.33
0.83 =|(7-40)|/40*0.33+|(8-50)|/50*0.33+|(9-60)|/60*0.33
Actually A is a 15x4 matrix and B is a 5x4 matrix.
Perhaps,the matrices dimensions are more than this matrices (variables).
How can i write this in Matlab?
Thanks you!
You can do it like so. Let's assume that A and B are defined as you did before:
A = vec2mat(1:9, 3)
B = vec2mat(10:10:60, 3)
A =
1 2 3
4 5 6
7 8 9
B =
10 20 30
40 50 60
vec2mat will transform a vector into a matrix. You simply specify how many columns you want, and it will automatically determine the right amount of rows to transform the vector into a correctly shaped matrix (thanks #LuisMendo!). Let's also define more things based on your post:
maxCol = max(B); %// Finds maximum of each column in B
coefK = 1 / size(A,2); %// 1 divided by number of columns in A
I am going to assuming that coefK is multiplied by every element in A. You would thus compute your desired matrix as so:
cellMat = arrayfun(#(x) sum(coefK*(bsxfun(#rdivide, ...
abs(bsxfun(#minus, A, B(x,:))), maxCol)), 2), 1:size(B,1), ...
'UniformOutput', false);
outputMatrix = cell2mat(cellMat).'
You thus get:
outputMatrix =
0.3450 0.2833 0.2217
0.9617 0.9000 0.8383
Seems like a bit much to chew right? Let's go through this slowly.
Let's start with the bsxfun(#minus, A, B(x,:)) call. What we are doing is taking the A matrix and subtracting with a particular row in B called x. In our case, x is either 1 or 2. This is equal to the number of rows we have in B. What is cool about bsxfun is that this will subtract every row in A by this row called by B(x,:).
Next, what we need to do is divide every single number in this result by the corresponding columns found in our maximum column, defined as maxCol. As such, we will call another bsxfun that will divide every element in the matrix outputted in the first step by their corresponding column elements in maxCol.
Once we do this, we weight all of the values of each row by coefK (or actually every value in the matrix). In our case, this is 1/3.
After, we then sum over all of the columns to give us our corresponding elements for each column of the output matrix for row x.
As we wish to do this for all of the rows, going from 1, 2, 3, ... up to as many rows as we have in B, we apply arrayfun that will substitute values of x going from 1, 2, 3... up to as many rows in B. For each value of x, we will get a numCol x 1 vector where numCol is the total number of columns shared by A and B. This code will only work if A and B share the same number of columns. I have not placed any error checking here. In this case, we have 3 columns shared between both matrices. We need to use UniformOutput and we set this to false because the output of arrayfun is not a single number, but a vector.
After we do this, this returns each row of the output matrix in a cell array. We need to use cell2mat to transform these cell array elements into a single matrix.
You'll notice that this is the result we want, but it is transposed due to summing along the columns in the second step. As such, simply transpose the result and we get our final answer.
Good luck!
Dedication
This post is dedicated to Luis Mendo and Divakar - The bsxfun masters.
Assuming by maximum number in each column, you mean columnwise maximum after vertically concatenating A and B, you can try this one-liner -
sum(abs(bsxfun(#rdivide,bsxfun(#minus,permute(A,[3 1 2]),permute(B,[1 3 2])),permute(max(vertcat(A,B)),[1 3 2]))),3)./size(A,2)
Output -
ans =
0.3450 0.2833 0.2217
0.9617 0.9000 0.8383
If by maximum number in each column, you mean columnwise maximum of B, you can try -
sum(abs(bsxfun(#rdivide,bsxfun(#minus,permute(A,[3 1 2]),permute(B,[1 3 2])),permute(max(B),[1 3 2]))),3)./size(A,2)
The output for this case stays the same as the previous case, owing to the values of A and B.
In MATLAB have a large matrix with transition probabilities transition_probs, and an adjacency matrix adj_mat. I want to compute the cumulative sum of the transition matrix along the columns and then element wise multiply it against the adjacency matrix which acts as a mask in this way:
cumsumTransitionMat = cumsum(transition_probs,2) .* adj_mat;
I get a MEMORY error because with the cumsum all the entries of the matrix are then non-zero.
I would like to avoid this problem by only having the cumulative sum entries where there are non zero entries in the first place. How can this be done without the use of a for loop?
when CUMSUM is applied on rows, for each row it will go and fill with values starting with the first nonzero column it finds up until the last column, thats what it does by definition.
The worst case in terms of storage is when the sparse matrix contains values at the first column, the best case is when all nonzero values occur at the last column. Example:
% worst case
>> M = sparse([ones(5,1) zeros(5,4)]);
>> MM = cumsum(M,2); % completely dense matrix
>> nnz(MM)
ans =
25
% best case
>> MM = cumsum(fliplr(M),2);
If the resulting matrix does not fit in memory, I dont see what else you can do, except maybe use a for-loop over the rows, and process the matrix is smaller batches...
Note that you cannot apply the masking operation before computing the cumulative sum, since this will alter the results. So you cant say cumsum(transition_probs .* adj_mat, 2).
You can apply cumsum on the non-zero elements only. Here is some code:
A = sparse(round(rand(100,1))); %some sparse data
A_cum = A; %instantiate A_cum by copy A
idx_A = find(A); %find non-zeros
A_cum(idx_A) = cumsum(A(idx_A)); %cumsum on non-zeros elements only
You can check the output with
B = cumsum(A);
A_cum B
1 1
0 1
0 1
2 2
3 3
4 4
5 5
0 5
0 5
6 6
and isequal(A_cum(find(A_cum)), B(find(A_cum))) gives 1.
I have written a for loop code and I want to write in more succinct way without using a for loop, but instead use matrix conditional.
I am teaching myself matlab and I would appreciate any feedback.
I want to create a new matrix, the first column is y, and the second column is filled with zero except for the y's whose indices are contained in the indices matrix. And in the latter case, add 1 instead of 0.
Thanks.
y=[1;2;3;4;5;6;7];
indices=[1;3;5];
[m,n]=size(y);
tem=zeros(m,1);
data=[y,tem];
[r,c]=size(indices);
for i=1:r
a=indices(i);
data(a,2 )=1;
end
Output:
data =
1 1
2 0
3 1
4 0
5 1
6 0
7 0
A shorter alternative:
data = [y(:), full(sparse(indices, 1, 1, numel(y), 1))];
The resulting matrix data is composed of two column vectors: y(:) and a sparse array, with "1"s at the positions corresponding to indices.
Using proper initialization and sparse matrices can be really useful in MATLAB.
How about
data = zeros( m, 2 );
data(:,1) = y;
data( indices, 2 ) = 1;
In Matlab, by the function min（）， I can only get one single minimum element of a vector, even if there can be several equal minimum elements. I was wondering how to get the indices of all minimum elements in a vector?
For example,
v=[1,1];
I would like to get the indices 1 and 2, both of which index the smallest elements 1.
Thanks and regards!
You can use find to find the min values:
find(v == min(v))
v = [1 2 3 1 5];
find( v == min(v) )
ans = 1 4
At least in Octave (don't have matlab), this returns the indexes of all minimums in v