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I am trying to subtract vectors stored in two different arrays. I want to subtract every row vector in matrix B from every row vector in matrix A such that the results of row vector 1 of A minus each of the row vectors of B is stored in a an array, the results of row vector 2 of A minus all row vectors B is stored in a different array...and so on until the end. Matrix A is a 63x2 array while matrix B is a 61x2 array. My final goal is to comapre all the results and identify and store all vectors that have duplicates.

A = [0 0

-5.22988566179964 6.05378360734033

5.82743694158990 -5.48096512411759

-7.71867012579003 13.6568061570767

-6.41307857048505 -4.78251223195356

-12.8832160382871 3.72429135615449

13.0437173772689 -8.93426740941455

-3.95560029264112 20.7165020025679

-14.9311651399118 17.1180076350708

-12.8261571409701 -9.56502446390712

-20.5365464147745 1.39479910496865

20.2599978129478 -12.3875696947115

-1.17023252546704 28.2159504090567

-22.1436601540336 20.5792091130649

-17.3827161983918 -16.1405677537219

-28.1898767912619 -0.934693146217198

23.2616199344730 -19.8031117948934

4.31055626391175 34.0435531963286

27.4257074918059 -8.83051968124259

-29.3561551681553 24.0404105910590

-19.3637625207825 -23.8914035453345

-35.8432071677493 -3.26418539740304

23.0577732626258 -27.8005142815998

9.79134505329053 39.8711559836006

10.4567903913941 28.9226347191244

34.5914171706640 -5.27346966777369

-31.5385456076644 31.7369794350011

-21.3448088431732 -31.6422393369470

-43.4965375442368 -5.59367764858889

25.6191596676504 -35.3793862084867

16.3972779876710 44.3835444937432

-35.1703582141579 38.8650883841765

-23.3258551655640 -39.3930751285596

-51.1498679207242 -7.92316989977473

28.1805460726749 -42.9582581353737

23.0032109220515 48.8959330038858

37.7741257337733 2.06617379783899

-29.3310994911081 -31.1740922588744

17.7048992093770 -36.5474963910512

-38.8021708206513 45.9931973333520

-22.3878218988045 -47.3378906450113

35.8328768192608 -45.2910320517349

29.7346992697278 53.2187867632789

18.1810613102127 55.2792607505272

40.9568342968826 9.40581726345168

-42.4339834271448 53.1213062825274

-22.9362553876826 -55.3190698002663

43.4852075658468 -47.6238059680961

36.8803922974807 49.6216947220428

-46.6615356421781 1.75362029283338

-32.2411590073453 50.5706555784546

-30.3866778744434 -47.4731795247682

32.3995579608740 -52.5168414608354

13.3589116983739 61.6625884971687

48.2086638919891 6.02780490060624

-49.3011842938828 49.0174602503434

-17.7011930022771 -61.3683773491444

46.9026731045917 -40.3904852029502

44.0260853252335 46.0246026808068

-49.8265337401194 9.10091823425565

-38.3629147966204 -48.0893325587636

55.4604934870957 2.64979253776079

51.1717783529864 42.4275106395707];

B = [5.82743694158990 -5.48096512411759

-2.48878446399039 7.60302254973639

-6.41307857048505 -4.78251223195356

-7.65333037648742 -2.32949225118584

7.21628043567895 -3.45330228529695

3.76306983314891 7.05969584549112

-7.21249501412177 3.46120147799410

-6.41307857048505 -4.78251223195356

-7.65333037648742 -2.32949225118584

7.21628043567895 -3.45330228529695

2.78536776717408 7.49944840648882

-7.21249501412177 3.46120147799410

-4.55655905742170 -6.57554328981479

-7.65333037648742 -2.32949225118584

3.00162212152522 -7.41554210018192

5.48078878937879 5.82760278727195

7.16570967885811 3.55705001346891

-7.21249501412177 3.46120147799410

-1.98104632239072 -7.75083579161255

-7.65333037648742 -2.32949225118584

-0.203846671847199 -7.99740248670635

5.48078878937879 5.82760278727195

6.14623412748235 -5.12091847720419

7.16570967885811 3.55705001346891

-2.18239043950905 7.69656884394205

-1.98104632239072 -7.75083579161255

-7.65333037648742 -2.32949225118584

2.56138640502452 -7.57887192688698

6.60593293438051 4.51238851014260

-3.63181260649347 7.12810894917545

-1.98104632239072 -7.75083579161255

-7.65333037648742 -2.32949225118584

2.56138640502452 -7.57887192688698

6.60593293438051 4.51238851014260

3.18270856310927 7.33964346561268

-7.98629064793488 0.468147078072581

-7.91426045827336 -1.16811018256442

-3.63181260649347 7.12810894917545

0.938033266759421 -7.94481551645176

7.65233074658597 -2.33277391636118

6.73148834767623 4.32285375939311

-4.82214961183880 6.38332774664147

3.18270856310927 7.33964346561268

-3.63181260649347 7.12810894917545

-0.548433488878012 -7.98117915525501

7.65233074658597 -2.33277391636118

7.14569302775288 -3.59709204123606

-3.16499809794131 7.34729794142227

6.56101181330606 4.57745824510266

-7.99885597563890 -0.135288879756905

-3.43331885838680 -7.22580940910052

-4.82214961183880 6.38332774664147

7.25182959510656 -3.37801236284544

-6.86720086673803 -4.10384603218402

5.23506238540546 -6.04930754887804

3.41746553874489 7.23332076514592

7.14569302775288 -3.59709204123606

-3.16499809794131 7.34729794142227

-7.97623692217691 -0.616153033995415

7.25182959510656 -3.37801236284544

7.14569302775288 -3.59709204123606];

SizeA = size(A,1);

SizeB = size(B,1);

for i = 1:SizeA

for j = 1:SizeB

vectortest(i,:) = vein_node_array(i,:)-Btest(j,:);

end

end

Star Strider
on 26 Oct 2020 at 19:03

See if this does what you want:

C1 = A(:,1).' - B(:,1); % Col #1 In ‘A’ - Col #1 In ‘B’

C2 = A(:,2).' - B(:,2); % Col #2 In ‘A’ - Col #2 In ‘B’

[Dupr,Dupc] = find((C1 == 0) & (C2 == 0));

Dupt = array2table([Dupc,Dupr], 'VariableNames',{'RowA','RowB'})

producing:

Dupt =

3×2 table

RowA RowB

____ ____

3 1

5 3

5 8

This works by creating two matrices formed by subtracting column vectors in ‘B’ from row vectors in ‘A’ (created by the transposition) for each column of both matrices. The elements of ‘C1’ and ‘C2’ are both equal to 0 if the respective rows of the ‘A’ and ‘B’ are duplicates.

In ‘Dupt’, the respective rows of matrix ‘A’ and matrix ‘B’ are duplicates, as demonstrated by:

A_dup = A(Dupc,:)

B_dup = B(Dupr,:)

producing:

A_dup =

5.827436941589900 -5.480965124117590

-6.413078570485050 -4.782512231953560

-6.413078570485050 -4.782512231953560

B_dup =

5.827436941589900 -5.480965124117590

-6.413078570485050 -4.782512231953560

-6.413078570485050 -4.782512231953560

These are by design exact matches. It might be possible to use a tolerance to find near matches, however I will defer that for the time being.

Star Strider
on 27 Oct 2020 at 21:35

O.K.

I’m beginning to understand what you’re doing. I need to think about this a bit.

Star Strider
on 29 Oct 2020 at 19:36

My apologies for the delay. It took me a while to figure out an acceptable approach to this problem.

This seems to work, at least with the ‘A’ matrix to identify an approximate graph of the connections between the stars:

Mdl = ExhaustiveSearcher(A);

[Idx,D] = rangesearch(Mdl, A, 9);

figure

hold on

for k = 1:numel(Idx)

xv = [repmat(A(Idx{k}(1),1),numel(Idx{k}),1), A(Idx{k}(1,:),1)];

yv = [repmat(A(Idx{k}(1),2),numel(Idx{k}),1), A(Idx{k}(1,:),2)];

plot(xv.', yv.','-p')

end

hold off

grid

axis('equal')

producing:

It works by using a -nearest-neighbour ExhaustiveSearcher exhaustive search, then uses the rangesearch function to identify the elements within a given distance (I empirically chose 9), returning the indices (in ‘Idx’ and the distances (in ‘D’ that I returned simply for informaiton). I then plotted them in the loop. The ‘xv’ and ‘yv’ matrices use the ‘Idx’ cell array elements to index into ‘A’ to plot each segment. Those take the ‘x’ vectors of each ‘Idx’ element for the first and subsequent columns of the indexed elements of ‘A’ and do the same for the ‘y’ vectors, so that the first value is replicated in the first column and the second column are the referenced neighbours. (This is a bit difficult to describe. Looking at the ‘xv’ and ‘yv’ matrices will help in understanding them.) They are then transposed so that they plot individual lines rather than one continuous line.

I did not try it on ‘B’.

I am not certain what you are doing, however this should make it easier for you to identiffy the branch-points and the properties of the stars connected to them.

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