I'm struggling to vectorise a function which performs a somewhat pairwise difference between two vectors x = 2xN and v = 2xM, for some arbitrary N, M. I have this to work when N = 1, although, I would like to vectorise this function to apply to inputs with N arbitrary.
Indeed, what I want this function to do is for each column of x find the normed difference between x(:,column) (a 2x1) and v (a 2xM).
A similar post is this, although I haven't been able to generalise it.
Current implementation
function mat = vecDiff(x,v)
diffVec = bsxfun(#minus, x, v);
mat = diffVec ./ vecnorm(diffVec);
Example
x =
1
1
v =
1 3 5
2 4 6
----
vecDiff(x,v) =
0 -0.5547 -0.6247
-1.0000 -0.8321 -0.7809
Your approach can be adapted as follows to suit your needs:
Permute the dimensions of either x or v so that its number of columns becomes the third dimension. I'm choosing v in the code below.
This lets you exploit implicit expansion (or equivalently bsxfun) to compute a 2×M×N array of differences, where M and N are the numbers of columns of x and v.
Compute the vector-wise (2-)norm along the first dimension and use implicit expansion again to normalize this array:
x = [1 4 2 -1; 1 5 3 -2];
v = [1 3 5; 2 4 6];
diffVec = x - permute(v, [1 3 2]);
diffVec = diffVec./vecnorm(diffVec, 2, 1);
You may need to apply permute differently if you want the dimensions of the output in another order.
Suppose your two input matrices are A (a 2 x N matrix) and B (a 2 x M matrix), where each column represents a different observation (note that this is not the traditional way to represent data).
Note that the output will be of the size N x M x 2.
out = zeros(N, M, 2);
We can find the distance between them using the builtin function pdist2.
dists = pdist2(A.', B.'); (with the transpositions required for the orientation of the matrices)
To get the individual x and y distances, the easiest way I can think of is using repmat:
xdists = repmat(A(1,:).', 1, M) - repmat(B(1,:), N, 1);
ydists = repmat(A(2,:).', 1, M) - repmat(B(2,:), N, 1);
And we can then normalise this by the distances found earlier:
out(:,:,1) = xdists./dists;
out(:,:,2) = ydists./dists;
This returns a matrix out where the elements at position (i, j, :) are the components of the normed distance between A(:,i) and B(:,j).
Related
Consider three row vectors in Matlab, A, B, C, each with size 1xJ. I want to construct a matrix D of size Kx3 listing every triplets (a,b,c) such that:
a is the position in A of A(a).
b is the position in B of B(b).
A(a)-B(b) is an element of C.
c is the position in C of A(a)-B(b).
A(a) and B(b) are different from Inf, -Inf.
For example,
A=[-3 3 0 Inf -Inf];
B=[-2 2 0 Inf -Inf];
C=[Inf -Inf -1 1 0];
D=[1 1 3; %-3-(-2)=-1
2 2 4; % 3-2=1
3 3 5]; % 0-0=0
I would like this code to be efficient, because in my real example I have to repeat it many times.
This question relates to my previous question here, but now I'm looking for the positions of the elements.
You can use combvec (or any number of alternatives) to get all pairings of indices a and b for the corresponding arrays A and B. Then it's simply a case of following your criteria
Find the differences
Check which differences are in C
Remove elements you don't care about
Like so:
% Generate all index pairings
D = combvec( 1:numel(A), 1:numel(B) ).';
% Calculate deltas
delta = A(D(:,1)) - B(D(:,2));
delta = delta(:); % make it a column
% Get delta index in C (0 if not present)
[~,D(:,3)] = ismember(delta,C);
% If A or B are inf then the delta is Inf or NaN, remove these
idxRemove = isinf(delta) | isnan(delta) | D(:,3) == 0;
D(idxRemove,:) = [];
For your example, this yields the expected results from the question.
You said that A and B are at most 7 elements long, so you have up to 49 pairings to check. This isn't too bad, but readers should be careful that the pairings can grow quickly for larger inputs.
Assume we have a 1-d Matrix, with random length:
M = [102,4,12,6,8,3,4,65,23,43,111,4]
Moreover I have a vector with values that are linked to the index of M:
V = [1,5]
What i want is a simple code of:
counter = 1;
NewM = zeros(length(V)*3,1);
for i = 1:length(V)
NewM(counter:(counter+2)) = M(V(i):(V(i)+2))
counter = counter+3;
end
So, the result will be
NewM = [102,4,12,8,3,4]
In other words, I want the V to V+2 values from M in a new array.
I'm pretty sure this can be done easier, but I'm struggeling with how to implement it in arrayfun /bsxfun...
bsxfun(#(x,y) x(y:(y+2)),M,V)
With bsxfun it's about getting the Vi on one dimension and the +0, +1 +2 on the other:
M = [102,4,12,6,8,3,4,65,23,43,111,4];
V = [1,5];
NewM = M( bsxfun(#plus, V(:), 0:2) )
NewM =
102 4 12
8 3 4
Or if you want a line:
NewM = reshape(NewM.', 1, [])
NewM =
102 4 12 8 3 4
Using arrayfun (note that M is used as an "external" matrix entity in this scope, whereas the arrayfun anonymous function parameter x correspond to the elements in V)
NewM = cell2mat(arrayfun(#(x) M(x:x+2), V, 'UniformOutput', false));
With result
NewM =
102 4 12 8 3 4
One fully vectorized solution is to expand V to create the correct indexing vector. In your case:
[1,2,3,5,6,7]
You can expand V to replicate each of it's elements n times and then add a repeating vector of 0:(n-1):
n = 3;
idx = kron(V, ones(1,n)) + mod(0:numel(V)*n-1,n)
So here kron(V, ones(1,n)) will return [1,1,1,5,5,5] and mod(0:numel(V)*n-1,n) will return [0,1,2,0,1,2] which add up to the required indexing vector [1,2,3,5,6,7]
and now it's just
M(idx)
Note a slightly faster alternative to kron is reshape(repmat(V,n,1),1,[])
Suppose I have a matrix A [m x 1], where m is not necessarily even. I to create a matrix B also [m x 1] which tells me the decile of the elements in A (i.e. matrix B has numbers from 1 to 10).
I know I can use the function sort(A) to get the position of the elements in A and from there I can manually get deciles. Is there another way of doing it?
I think one possibility would be B = ceil(10 * tiedrank(A) / length(A) . What do you think? Are there any issues with this?
Also, more generally, if I have a matrix A [m x n] and I want to create a matrix B also [m x n], in which each column of B should have the decile of the corresponding column in A , is there a way of doing it without a for loop through the columns?
Hope the problem at hand is clear. So far I have been doing it using the sort function and then manually assigning the deciles, but it is very inefficient.
This is how I would do it:
N = 10;
B = ceil(sum(bsxfun(#le, A(:), A(:).'))*N/numel(A));
This counts, for each element, how many elements are less than or equal to it; and then rounds the results to 10 values.
Depending on how you define deciles, you may want to change #le to #lt, or ceil to floor. For numel(A) multiple of N, the above definition gives exactly numel(A)/N values in each of the N quantiles. For example,
>> A = rand(1,8)
A =
0.4387 0.3816 0.7655 0.7952 0.1869 0.4898 0.4456 0.6463
>> N = 4;
>> B = ceil(sum(bsxfun(#le, A(:), A(:).'))*N/numel(A))
B =
2 1 4 4 1 3 2 3
This question already has answers here:
Create a zero-filled 2D array with ones at positions indexed by a vector
(4 answers)
Closed 6 years ago.
I have a vector v of size (m,1) whose elements are integers picked from 1:n. I want to create a matrix M of size (m,n) whose elements M(i,j) are 1 if v(i) = j, and are 0 otherwise. I do not want to use loops, and would like to implement this as a simple vector-matrix manipulation only.
So I thought first, to create a matrix with repeated elements
M = v * ones(1,n) % this is a (m,n) matrix of repeated v
For example v=[1,1,3,2]'
m = 4 and n = 3
M =
1 1 1
1 1 1
3 3 3
2 2 2
then I need to create a comparison vector c of size (1,n)
c = 1:n
1 2 3
Then I need to perform a series of logical comparisons
M(1,:)==c % this results in [1,0,0]
.
M(4,:)==c % this results in [0,1,0]
However, I thought it should be possible to perform the last steps of going through each single row in compact matrix notation, but I'm stumped and not knowledgeable enough about indexing.
The end result should be
M =
1 0 0
1 0 0
0 0 1
0 1 0
A very simple call to bsxfun will do the trick:
>> n = 3;
>> v = [1,1,3,2].';
>> M = bsxfun(#eq, v, 1:n)
M =
1 0 0
1 0 0
0 0 1
0 1 0
How the code works is actually quite simple. bsxfun is what is known as the Binary Singleton EXpansion function. What this does is that you provide two arrays / matrices of any size, as long as they are broadcastable. This means that they need to be able to expand in size so that both of them equal in size. In this case, v is your vector of interest and is the first parameter - note that it's transposed. The second parameter is a vector from 1 up to n. What will happen now is the column vector v gets replicated / expands for as many values as there are n and the second vector gets replicated for as many rows as there are in v. We then do an eq / equals operator between these two arrays. This expanded matrix in effect has all 1s in the first column, all 2s in the second column, up until n. By doing an eq between these two matrices, you are in effect determining which values in v are equal to the respective column index.
Here is a detailed time test and breakdown of each function. I placed each implementation into a separate function and I also let n=max(v) so that Luis's first code will work. I used timeit to time each function:
function timing_binary
n = 10000;
v = randi(1000,n,1);
m = numel(v);
function luis_func()
M1 = full(sparse(1:m,v,1));
end
function luis_func2()
%m = numel(v);
%n = 3; %// or compute n automatically as n = max(v);
M2 = zeros(m, n);
M2((1:m).' + (v-1)*m) = 1;
end
function ray_func()
M3 = bsxfun(#eq, v, 1:n);
end
function op_func()
M4= ones(1,m)'*[1:n] == v * ones(1,n);
end
t1 = timeit(#luis_func);
t2 = timeit(#luis_func2);
t3 = timeit(#ray_func);
t4 = timeit(#op_func);
fprintf('Luis Mendo - Sparse: %f\n', t1);
fprintf('Luis Mendo - Indexing: %f\n', t2);
fprintf('rayryeng - bsxfun: %f\n', t3);
fprintf('OP: %f\n', t4);
end
This test assumes n = 10000 and the vector v is a 10000 x 1 vector of randomly distributed integers from 1 up to 1000. BTW, I had to modify Luis's second function so that the indexing will work as the addition requires vectors of compatible dimensions.
Running this code, we get:
>> timing_binary
Luis Mendo - Sparse: 0.015086
Luis Mendo - Indexing: 0.327993
rayryeng - bsxfun: 0.040672
OP: 0.841827
Luis Mendo's sparse code wins (as I expected), followed by bsxfun, followed by indexing and followed by your proposed approach using matrix operations. The timings are in seconds.
Assuming n equals max(v), you can use sparse:
v = [1,1,3,2];
M = full(sparse(1:numel(v),v,1));
What sparse does is build a sparse matrix using the first argument as row indices, the second as column indices, and the third as matrix values. This is then converted into a full matrix with full.
Another approach is to define the matrix containing initially zeros and then use linear indexing to fill in the ones:
v = [1,1,3,2];
m = numel(v);
n = 3; %// or compute n automatically as n = max(v);
M = zeros(m, n);
M((1:m) + (v-1)*m) = 1;
I think I've also found a way to do it, and it would be nice if somebody could tell me which of the methods shown is faster for very large vectors and matrices. The additional method I thought of is the following
M= ones(1,m)'*[1:n] == v * ones(1,n)
I am writing a script that operates on matrices, and I have run into the problem of needing to add the sum of the diagonals of a previous matrix to the diagonal elements of a new matrix. The code I have so far for this particular function (described in more detail below) is:
t = 1;
for k = (m-1):-1:-(m-1)
C = bsxfun(#plus, diag(B, k), d);
g(t) = sum(diag(B, k));
t = t + 1;
end
where d is a 1x3 array, and C is supposed to be a 3x3 array; however, C is being output as a 1x3 array in such a way that the first diagonal is being summed and added to d, then the main diagonal is being summed and added to d, and the final diagonal is being summed and added to d.
Is there a way I can get the values of C to be such that the first diagonal is the sum of it's individual elements added to the last element of d, the main diagonal's individual elements added to the middle element of d, and the bottom diagonal's elements added to the first element of d? (while still working for any array size?)
Here is a picture that describes what I'm trying to achieve:
Thanks!
You can use toeplitz to generate a matrix containing the values that need to be added to your original matrix:
M = [5 5 5; 7 7 7; 9 9 9]; %// data matrix
v = [1 11 4 3 2]; %// data vector
S = toeplitz(v);
S = S(1:(numel(v)+1)/2, (numel(v)+1)/2:end);
result = M+S;
Or, as noted by #thewaywewalk, you can do this more directly as follows:
M = [5 5 5; 7 7 7; 9 9 9]; %// data matrix
v = [1 11 4 3 2]; %// data vector
result = M + toeplitz(v(size(M,1):-1:1), v(size(M,2):end));
Assuming B to be a square shaped matrix, listed in this post would be one bsxfun based vectorized approach. Here's the implementation -
N = size(B,1) %// Store size of B for later usage
%// Find a 2D grid of all indices with kth column representing kth diagonal of B
idx = bsxfun(#plus,[N-numel(B)+1:N+1:N]',[0:2*N-2]*N) %//'
%// Mask of all valid indices as we would see many from the 2D grid
%// going out of bounds of 2D array, B
mask = idx>numel(B) | idx<1
%// Set all out-of-bounds indices to one, so that in next step
%// we could index into B in a vectorized manner and sum those up with d
idx(mask)=1
sum1 = bsxfun(#plus,B(idx),d(:).') %//'
%// Store the summations at proper places in B with masking again
B(idx(~mask)) = sum1(~mask)
Sample run -
B =
1 9 0
7 9 4
6 8 7
d =
4 9 5 8 2
B =
6 17 2
16 14 12
10 17 12
Code:
The following code adds the sums of the diagonals of A to the corresponding diagonals in the matrix B. The code works for matrices A, B of equal size, not necessarily square.
A = magic(4);
B = magic(4);
D = bsxfun(#minus, size(A,2)+(1:size(A,1)).', 1:size(A,2)); %'
sumsDiagsA = accumarray(D(:), A(:)); %// Compute sums of diagonals (your 'd')
B = B + sumsDiagsA(D); %// Add them to the matrix
Explanation:
First we build a matrix that numbers all diagonals beginning from the rightmost diagonal:
>> D = bsxfun(#minus, size(A,2)+(1:size(A,1)).', 1:size(A,2))
D =
4 3 2 1
5 4 3 2
6 5 4 3
7 6 5 4
Then we compute sumsDiagsA as the sum of the diagonals via accumarray:
sumsDiagsA = accumarray(D(:), A(:));
The variable sumsDiagsA is what you refer to as d in your code.
Now we use indexing to the vector containing the sums and add them to the matrix B:
C = B + sumsDiagsA(D);
Assuming you have already computed your vector d, you don't need the accumarray-step and all you need to do is:
D = bsxfun(#minus, size(B,2)+(1:size(B,1)).', 1:size(B,2)); %'
C = B + d(D);