![]() ![]() For those elements of A that do not occur in S, ismember returns 0. Returns index vector loc containing the highest index in S for each element in A that is a member of S. When A and S are matrices with the same number of columns returns a vector containing 1 where the rows of A are also rows of S and 0 otherwise. To specify an absolute tolerance, specify both tol and the 'DataScale' Name-Value pair. Two values, u and v, are within tolerance if abs(u-v) < tolmax(abs(A)). In set theoretic terms, k is 1 where A S. If two elements in A are within tolerance of each other, then uniquetol considers them to be equal. Returns a vector the same length as A containing logical true ( 1) where the elements of A are in the set S, and logical false ( 0) elsewhere. =min(sum((d2_-repmat(p1,npt,1)).Ismember (MATLAB Functions) MATLAB Function Reference % We want to find/build a permutation of do2 that minimizes the distances [irs) ĭisp('checking for wrong matches: should all be empty.')įunction =sortpermByPairwiseDistance(pl1,pl2) Otherwise I could use the distance matrix and just look for all entries below a tolerance, e.g., 1E-10. Some data sets have points matching perfectly, others have some distance between matching points. It does not have to be sorted according to distance. I want to know the pairings, which for my data match more or less uniquely. List all distinct pairs (black,coloured) formed by points in the first and points in the other set, sorted by their distance?Īlmost. My goal is to find the row j of array B which equals the row i of array A. Once I rotate them by a given angle I obtain a set of points B whose coordinates should match the ones of A. ![]() I have a array of points A whose coordinates form a ring. Irs, ics = I could not use BitSet() or Set(), since I need to know the order of the points, which I use later for mapping some fields. I am looking for a way to couple the 'rows' option with a set tolerance. Lk = 4 # 1 relative set, 3 working data sets # get distance matrix A i relative points, j points to be mapped ![]() This transformation introduces round-off differences to B. (MS Windows historically required both Carriage Return and Line Feed to mark the end of a line, but these days also supports using only Line Feed to mark the end of a line Linux has only ever supported Line Feed to mark the end of a line in old old times, Mac supported only using Carriage. Obtain a second matrix, B, by transforming and untransforming A. Text files are stored as streams of bytes, with operating-system-specific bytes indicating end-of-line. # pick pairs of smallest distance and return corresponding points (or their order) By default, ismembertol looks for elements that are within tolerance, but it also can find rows of a matrix that are within tolerance. ismembertol treats rows that are within tolerance as equal and thus determines that all of the rows in A are members of B. Use ismembertol to perform the row comparison using a small tolerance. Just in case anybody is interested, the solution looks like this: using Random lia ismember (A,B, 'rows') lia 5x1 logical array 0 0 0 0 1. Now Julia outperforms MATLAB by almost an order of magnitude. I tried to do the same in MATLAB, but maybe in this case I have done it less efficiently. UMFPACK also responds to spumoni, as do the majority of the. ![]() Now you just need to filter out which rows/cols have already been seen, perhaps something like julia> is, js = BitSet(), BitSet() Julia> CartesianIndices(A0)ĬartesianIndex(3, 2) # first, eliminate 3,2 Can’t you avoid this entirely, perhaps call sortperm(vec(A)) which takes 240ms, but only 3 times? A sketch: julia> A0 = rand(Int8, 3,3) For timetables, ismember takes row times into account to determine equality. If A and B are tables or timetables, then ismember returns a logical value for each row. Elsewhere, the array contains logical 0 ( false ). Sorry for self promotion, but it's a big addition to MATLAB after years of people asking. The accepted solutions for integer-valued data are great, but for floats, jump to this answer. This takes 7.4ms, times 1500 times 3 gives about 30s, which is how long (a slightly improved) version takes to run. Lia ismember (A,B) returns an array containing logical 1 ( true) where the data in A is found in B. For floating point data where a tolerance may be desired, note that version R2015a has added new functionality to handle this with a single built-in function. Any suggestions how I can speed up the code?Īs you say, all the time is in argmin(A). In reality Lp is greater than 1500, which is a real problem for me. I also tried findmin(A), but the performance is the same. # pick pairs of smallest distance & clear these pairs from Aįinding the minimum takes almost all the time and I was wondering if Julia with argmin(A) is maybe faster than MATLAB’s min(A). # create test data: coordinates of relative points and points to be mappedĪ = A + (pload*ones(1,Lj)-ones(Li,1)*pload').^2 I ported a code from MATLAB to Julia, because it takes too long right now: using Random ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |