I have two vectors, and I'm trying to find ALL coincidences of one on the other within a certain tolerance without using a for loop.
By tolerance I mean for example if I have the number 3, with tolerance 2, I will want to keep values within 3±2, so (1,2,3,4,5).
A = [5 3 4 2]; B = [2 4 4 4 6 8];
I want to obtain a cell array containing on each cell the numbers of all the coincidences with a tolerance of 1 (or more) units. (A = B +- 1)
I have a solution with zero units (A = B), which would look something like this:
tol = 0;
[tf, ia] = ismembertol(B,A,tol,'DataScale',1); % For tol = 0, this is equivalent to using ismember
idx = 1:numel(B);
ib = accumarray(nonzeros(ia), idx(tf), [], #(x){x}) % This gives the cell array
The output is:
ib =
[]
[]
[2 3 4]
[1]
Which is as desired.
If I change the tolerance to 1, the code doesn't work as intended. It outputs instead:
tol = 1
[tf, ia] = ismembertol(B,A,tol,'DataScale',1); % For tolerance = 1, this is equivalent to using ismember
idx = 1:numel(B);
ib = accumarray(nonzeros(ia), idx(tf), [], #(x){x}) % This gives the cell array
ib =
[5]
[2 3 4]
[]
[1]
When I would expect to obtain:
ib =
[2 3 4 5]
[1 2 3 4]
[2 3 4]
[1]
What am I doing wrong? Is there an alternative solution?
Your problem is that, in the current state of your code, ismembertol only outputs 1 index per element of B found in A, so you lose information in the cases where an element can be found several times within tolerance.
As per the documentation You can use the 'OutputAllIndices',true value pair argument syntax, to output what you want in ia with just a call to ismembertol:
A = [5 3 4 2]; B = [2 4 4 4 6 8];
tol = 0;
[tf, ia] = ismembertol(A,B,tol,'DataScale',1,'OutputAllIndices',true);
celldisp(ia) % tol = 0
ia{1} =
0
ia{2} =
0
ia{3} =
2
3
4
ia{4} =
1
celldisp(ia) % tol = 1
ia{1} =
2
3
4
5
ia{2} =
1
2
3
4
ia{3} =
2
3
4
ia{4} =
1
Here is a manual approach, just to provide another method. It computes an intermediate matrix of all absolute differences (using implicit expansion), and from the row and column indices of the entries that are less than the tolerance it builds the result:
A = [5 3 4 2];
B = [2 4 4 4 6 8];
tol = 1;
[ii, jj] = find(abs(A(:).'-B(:))<=tol);
ib = accumarray(jj, ii, [numel(A) 1], #(x){x});
Note that this approach
may be memory-intensive, because of the intermediate matrix;
can be made to work in old Matlab versions, because it doesn't use ismembertol; but then implicit expansion has to be replaced by explicitly calling bsxfun:
[ii, jj] = find(abs(bsxfun(#minus, A(:).', B(:)))<=tol);
ib = accumarray(jj, ii, [numel(A) 1], #(x){x});
Related
I have a question that seems like it should have a simple answer that can avoid for loops.
Suppose I have an N x 4 array defined in MATLAB:
A = [1 2 3 4; 1 2 3 4; 1 2 3 4; 1 2 3 4; 1 2 3 4; 1 2 3 4];
In this example, N = 6, but it is arbitrary. I want to re-arrange A into a new array, B, which is 2 x 2 x N array of the form:
B(:,:,1) = [1 2; 3 4];
B(:,:,2) = [1 2; 3 4];
...
B(:,:,N) = [1 2; 3 4];
This seems like a simple problem and I tried a variety of things such as:
B = reshape(A',2,2,N);
However, this results in
B(:,:,1) = [1 3; 2 4];
B(:,:,2) = [1 3; 2 4];
...
B(:,:,N) = [1 3; 2 4];
I feel like there must be a simple way of doing this in one line using some combination of "reshape", "permute" and/or "transpose" that I am missing. Any suggestions are appreciated.
You are only missing a final permute. This is needed because Matlab is column-major, so it fills the new array down, then across:
B = permute(reshape(A.', 2,2,N), [2 1 3]);
I am trying to create all possible 1xM vectors (word) from a 1xN vector (alphabet) in MATLAB. N is > M. For example, I want to create all possible 2x1 "words" from a 4x1 "alphabet" alphabet = [1 2 3 4];
I expect a result like:
[1 1]
[1 2]
[1 3]
[1 4]
[2 1]
[2 2]
...
I want to make M an input to my routine and I do not know it beforehand. Otherwise, I could easily do this using nested for-loops. Anyway to do this?
Try
[d1 d2] = ndgrid(alphabet);
[d2(:) d1(:)]
To parameterize on M:
d = cell(M, 1);
[d{:}] = ndgrid(alphabet);
for i = 1:M
d{i} = d{i}(:);
end
[d{end:-1:1}]
In general, and in languages that don't have ndgrid in their library, the way to parameterize for-loop nesting is using recursion.
[result] = function cartesian(alphabet, M)
if M <= 1
result = alphabet;
else
recursed = cartesian(alphabet, M-1)
N = size(recursed,1);
result = zeros(M, N * numel(alphabet));
for i=1:numel(alphabet)
result(1,1+(i-1)*N:i*N) = alphabet(i);
result(2:M,1+(i-1)*N:i*N) = recursed; % in MATLAB, this line can be vectorized with repmat... but in MATLAB you'd use ndgrid anyway
end
end
end
To get all k-letter combinations from an arbitrary alphabet, use
n = length(alphabet);
aux = dec2base(0:n^k-1,n)
aux2 = aux-'A';
ind = aux2<0;
aux2(ind) = aux(ind)-'0'
aux2(~ind) = aux2(~ind)+10;
words = alphabet(aux2+1)
The alphabet may consist of up to 36 elements (as per dec2base). Those elements may be numbers or characters.
How this works:
The numbers 0, 1, ... , n^k-1 when expressed in base n give all groups of k numbers taken from 0,...,n-1. dec2base does the conversion to base n, but gives the result in form of strings, so need to convert to the corresponding number (that's part with aux and aux2). We then add 1 to make the numbers 1,..., n. Finally, we index alphabet with that to use the real letters of numbers of the alphabet.
Example with letters:
>> alphabet = 'abc';
>> k = 2;
>> words
words =
aa
ab
ac
ba
bb
bc
ca
cb
cc
Example with numbers:
>> alphabet = [1 3 5 7];
>> k = 2;
>> words
words =
1 1
1 3
1 5
1 7
3 1
3 3
3 5
3 7
5 1
5 3
5 5
5 7
7 1
7 3
7 5
7 7
use ndgrid function in Matlab
[a,b] = ndgrid(alphabet)
I have two arrays:
A=[1 1 2 2 3 3 3];
B=[1 3];
Is there any function that can remove elements which are contained in B from A?
The result should be
C=[1 2 2 3 3];
The order is not important, but if there is more specific elements like two times 1 in A, then I need operation that removes (from A) only as many of these specific elements is in B (in this case only one of 1 and one of 3; meaning other 1 and 3 should remain in final product C). This function should be analogous to setdiff, with the difference that it should take care of multiple instances of array elements. This analogy can hold because my B only contains elements that are in A.
For loop solution:
C = A;
for ii = 1:length(B)
C(find(C == B(ii), 1,'first')) = [];
end
Result
C =
1 2 2 3 3
Here's a vectorized solution using accumarray and repelem:
maxValue = max([A B]);
counts = accumarray(A(:), 1, [maxValue 1])-accumarray(B(:), 1, [maxValue 1]);
C = repelem(1:maxValue, max(counts, 0));
And the result for your sample data A = [1 1 2 2 3 3 3]; B = [1 3];:
C =
1 2 2 3 3
This will even work for cases where there are values in B not in A (like B = [1 4];) or more of a given value in B than in A (like B = [1 1 1];).
Note: The above works sinceA and B contain integers. If they were to contain floating-point values, you could map the unique values to integers first using unique and ismember. Let's say we had the following sample data:
A = [0 0 pi pi 2*pi 2*pi 2*pi];
B = [0 2*pi];
Here's a variant of the above code that can handle this:
uniqueValues = unique([A B]);
[~, A] = ismember(A, uniqueValues);
[~, B] = ismember(B, uniqueValues);
maxValue = max([A B]);
counts = accumarray(A(:), 1, [maxValue 1])-accumarray(B(:), 1, [maxValue 1]);
C = uniqueValues(repelem(1:maxValue, max(counts, 0)));
And the results:
C =
0 3.1416 3.1416 6.2832 6.2832 % [0 pi pi 2*pi 2*pi]
I have two arrays. One is a list of lengths within the other. For example
zarray = [1 2 3 4 5 6 7 8 9 10]
and
lengths = [1 3 2 1 3]
I want to average (mean) over parts the first array with lengths given by the second. For this example, resulting in:
[mean([1]),mean([2,3,4]),mean([5,6]),mean([7]),mean([8,9,10])]
I am trying to avoid looping, for the sake of speed. I tried using mat2cell and cellfun as follows
zcell = mat2cell(zarray,[1],lengths);
zcellsum = cellfun('mean',zcell);
But the cellfun part is very slow. Is there a way to do this without looping or cellfun?
Here is a fully vectorized solution (no explicit for-loops, or hidden loops with ARRAYFUN, CELLFUN, ..). The idea is to use the extremely fast ACCUMARRAY function:
%# data
zarray = [1 2 3 4 5 6 7 8 9 10];
lengths = [1 3 2 1 3];
%# generate subscripts: 1 2 2 2 3 3 4 5 5 5
endLocs = cumsum(lengths(:));
subs = zeros(endLocs(end),1);
subs([1;endLocs(1:end-1)+1]) = 1;
subs = cumsum(subs);
%# mean of each part
means = accumarray(subs, zarray) ./ lengths(:)
The result in this case:
means =
1
3
5.5
7
9
Speed test:
Consider the following comparison of the different methods. I am using the TIMEIT function by Steve Eddins:
function [t,v] = testMeans()
%# generate test data
[arr,len] = genData();
%# define functions
f1 = #() func1(arr,len);
f2 = #() func2(arr,len);
f3 = #() func3(arr,len);
f4 = #() func4(arr,len);
%# timeit
t(1) = timeit( f1 );
t(2) = timeit( f2 );
t(3) = timeit( f3 );
t(4) = timeit( f4 );
%# return results to check their validity
v{1} = f1();
v{2} = f2();
v{3} = f3();
v{4} = f4();
end
function [arr,len] = genData()
%#arr = [1 2 3 4 5 6 7 8 9 10];
%#len = [1 3 2 1 3];
numArr = 10000; %# number of elements in array
numParts = 500; %# number of parts/regions
arr = rand(1,numArr);
len = zeros(1,numParts);
len(1:end-1) = diff(sort( randperm(numArr,numParts) ));
len(end) = numArr - sum(len);
end
function m = func1(arr, len)
%# #Drodbar: for-loop
idx = 1;
N = length(len);
m = zeros(1,N);
for i=1:N
m(i) = mean( arr(idx+(0:len(i)-1)) );
idx = idx + len(i);
end
end
function m = func2(arr, len)
%# #user1073959: MAT2CELL+CELLFUN
m = cellfun(#mean, mat2cell(arr, 1, len));
end
function m = func3(arr, len)
%# #Drodbar: ARRAYFUN+CELLFUN
idx = arrayfun(#(a,b) a-(0:b-1), cumsum(len), len, 'UniformOutput',false);
m = cellfun(#(a) mean(arr(a)), idx);
end
function m = func4(arr, len)
%# #Amro: ACCUMARRAY
endLocs = cumsum(len(:));
subs = zeros(endLocs(end),1);
subs([1;endLocs(1:end-1)+1]) = 1;
subs = cumsum(subs);
m = accumarray(subs, arr) ./ len(:);
if isrow(len)
m = m';
end
end
Below are the timings. Tests were performed on a WinXP 32-bit machine with MATLAB R2012a. My method is an order of magnitude faster than all other methods. For-loop is second best.
>> [t,v] = testMeans();
>> t
t =
0.013098 0.013074 0.022407 0.00031807
| | | \_________ #Amro: ACCUMARRAY (!)
| | \___________________ #Drodbar: ARRAYFUN+CELLFUN
| \______________________________ #user1073959: MAT2CELL+CELLFUN
\__________________________________________ #Drodbar: FOR-loop
Furthermore all results are correct and equal -- differences are in the order of eps the machine precision (caused by different ways of accumulating round-off errors), therefore considered rubbish and simply ignored:
%#assert( isequal(v{:}) )
>> maxErr = max(max( diff(vertcat(v{:})) ))
maxErr =
3.3307e-16
Here is a solution using arrayfun and cellfun
zarray = [1 2 3 4 5 6 7 8 9 10];
lengths = [1 3 2 1 3];
% Generate the indexes for the elements contained within each length specified
% subset. idx would be {[1], [4, 3, 2], [6, 5], [7], [10, 9, 8]} in this case
idx = arrayfun(#(a,b) a-(0:b-1), cumsum(lengths), lengths,'UniformOutput',false);
means = cellfun( #(a) mean(zarray(a)), idx);
Your desired output result:
means =
1.0000 3.0000 5.5000 7.0000 9.0000
Following #tmpearce comment I did a quick time performance comparison between above's solution, from which I create a function called subsetMeans1
function means = subsetMeans1( zarray, lengths)
% Generate the indexes for the elements contained within each length specified
% subset. idx would be {[1], [4, 3, 2], [6, 5], [7], [10, 9, 8]} in this case
idx = arrayfun(#(a,b) a-(0:b-1), cumsum(lengths), lengths,'UniformOutput',false);
means = cellfun( #(a) mean(zarray(a)), idx);
and a simple for loop alternative, function subsetMeans2.
function means = subsetMeans2( zarray, lengths)
% Method based on single loop
idx = 1;
N = length(lengths);
means = zeros( 1, N);
for i = 1:N
means(i) = mean( zarray(idx+(0:lengths(i)-1)) );
idx = idx+lengths(i);
end
Using the next test scrip, based on TIMEIT, that allows checking performance varying the number of elements on the input vector and sizes of elements per subset:
% Generate some data for the performance test
% Total of elements on the vector to test
nVec = 100000;
% Max of elements per subset
nSubset = 5;
% Data generation aux variables
lenghtsGen = randi( nSubset, 1, nVec);
accumLen = cumsum(lenghtsGen);
maxIdx = find( accumLen < nVec, 1, 'last' );
% % Original test data
% zarray = [1 2 3 4 5 6 7 8 9 10];
% lengths = [1 3 2 1 3];
% Vector to test
zarray = 1:nVec;
lengths = [ lenghtsGen(1:maxIdx) nVec-accumLen(maxIdx)] ;
% Double check that nVec is will be the max index
assert ( sum(lengths) == nVec)
t1(1) = timeit(#() subsetMeans1( zarray, lengths));
t1(2) = timeit(#() subsetMeans2( zarray, lengths));
fprintf('Time spent subsetMeans1: %f\n',t1(1));
fprintf('Time spent subsetMeans2: %f\n',t1(2));
It turns out that the non-vectorised version without arrayfun and cellfun is faster, presumably due to the extra overhead of those functions
Time spent subsetMeans1: 2.082457
Time spent subsetMeans2: 1.278473
Let's say I have a one-dimensional array:
a = [1, 2, 3];
Is there a built-in Matlab function that takes an array and an integer n and replicates each
element of the array n times?
For example calling replicate(a, 3) should return [1,1,1,2,2,2,3,3,3].
Note that this is not at all the same as repmat. I can certainly implement replicate by doing repmat on each element and concatenating the result, but I am wondering if there is a built in function that is more efficient.
I'm a fan of the KRON function:
>> a = 1:3;
>> N = 3;
>> b = kron(a,ones(1,N))
b =
1 1 1 2 2 2 3 3 3
You can also look at this related question (which dealt with replicating elements of 2-D matrices) to see some of the other solutions involving matrix indexing. Here's one such solution (inspired by Edric's answer):
>> b = a(ceil((1:N*numel(a))/N))
b =
1 1 1 2 2 2 3 3 3
a = [1 2 3];
N = 3;
b = reshape(repmat(a,N,1), 1, [])
As of R2015a, there is a built-in and documented function to do this, repelem:
repelem Replicate elements of an array.
W = repelem(V,N), with vector V and scalar N, creates a vector W where each element of V is repeated N times.
The second argument can also be a vector of the same length as V to specify the number of replications for each element. For 2D replication:
B = repelem(A,N1,N2)
No need for kron or other tricks anymore!
UPDATE: For a performance comparison with other speedy methods, please see the Q&A Repeat copies of array elements: Run-length decoding in MATLAB.
>> n=3;
>> a(floor((0:size(a,2)*n-1)/n)+1)
ans =
1 1 1 2 2 2 3 3 3
Some exotic alternatives. Admittedly more funny than useful:
Assign the (first) result of meshgrid to a vector:
b = NaN(1,numel(a)*n); %// pre-shape result
b(:) = meshgrid(a,1:n);
Build a matrix that multiplied by a gives the result:
b = a * fliplr(sortrows(repmat(eye(numel(a)),n,1))).';
Use ind2sub to generate the indices:
[~, ind] = ind2sub([n 1],1:numel(a)*n);
b = a(ind);
If you have the image processing toolbox, there is another alternative:
N = 3;
imresize(a, [1 N*numel(a)],'nearest')
% To get b = [1 1 1 2 2 2 3 3 3]
N = 3;
a = [1 2 3];
temp_a = a(ones(N,1),:);
b = reshape(temp_a,1,numel(temp_a));
% To get b = [1 2 3 1 2 3 1 2 3]
N = 3;
a = [1 2 3];
temp_a = a(ones(N,1),:);
b = reshape(temp_a',1,numel(temp_a));