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Usage in your case: boolarray numpy.in1d(array1, array2) Note: It also accepts lists as inputs. In contrast to other answers, numpy has the built-in numpy.in1d for doing that. For those elements of A that do not occur in S, ismember returns 0. ismember (MATLAB Functions) Detect members of set Syntax tf ismember (A, S) tf ismember (A, S, 'rows') tf, loc ismember (A, S. def ismember(A, B): return np.sum(a B) for a in A This should very much behave like the corresponding MALTAB function. 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. In set theoretic terms, k is 1 where A S. tf ismember(A, S) returns a vector the same length as A, containing logical 1 ( true ) where the elements of A are in the set S, and logical 0 ( false ). 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. We and our partners store and/or access information on a device, such as cookies and process personal data, such as unique identifiers and standard information sent by a device for personalised ads and content, ad and content measurement, and audience insights, as well as to develop and improve products. Try and see what works for you.Ismember (MATLAB Functions) MATLAB Function Reference If you have a sorted B, it's even faster since the two built-in functions assume the second argument ( Bs) is sorted and waste no time with checks. Is it worth doing it this way, with a penalty for sort and two effective ismember calls? Maybe not, but I think it's an interesting solution.

Testing operation with unsorted B: B(4:5) = B()įirstInds = builtin('_ismemberfirst',A,Bs) Looking closer at the third element: > allInds The first two cells are empty arrays, as expected. Learn more about ismember, cell arrays, indexing, loops are fast. Įach cell has the indexes in B (if any) of each element of A. We can quickly do this lookup and package each range of indexes with arrayfun, keeping in mind that the computationally intensive task of actually finding the indexes is already done: allInds = tf ismember(A,S) 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 ). We are able to ignore sortInds in the above example since B is already sorted, but an unsorted B is handled by simply looking up the locations in the unsorted array. def ismember(A, B): return np.sum(a B) for a in A This should very much behave like the corresponding MALTAB function. ismember (MATLAB Functions) MATLAB Function Reference ismember Detect members of a specific set Syntax tf ismember(A,S) tf ismember(A,S,'rows') tf, loc ismember(A,S.) Description tf ismember(A,S) In set theoretic terms, kis 1 where AS. The value 4 ( A(3)) occurs at locations 2:4 (i.e. There is no occurrence of A(1) or A(2) (5 or 3) in B, so those indexes are 0. The heavy lifting is now done - We have the first and last indexes in B for each element in A without having to do any looping. The thinking goes as follows: In a sorted B, what if you had the first and last indexes of each matching element? It turns out there are two helper functions used by ismember (if you have R2012b+, I think) that will give you both of these indexes: _ismemberfirst (a builtin) and ismembc2.įor the example data A = B = in the question, here is the implementation: = sort(B) % nop for this B, but required in generalįirstInds = builtin('_ismemberfirst',A,Bs) % newish version required

for each element of A, find the indexes of all corresponding elements in B). those without using iterations of find) involve swapping the inputs to ismember and grouping like indexes with accumarray, as in Eitan's answer, or vectorizing the find with bsxfun as in Luis Mendo's answer, IMHO. However, for those interested in a solution with undocumented functionality, and an admittedly hackish approach, here is another way to do it (i.e. This function is used within the stock Matlab ismember and setxor functions for fast processing of the core ismember functionality in regular cases: arrays of sorted, non-sparse, non-NaN data in which we’re only interested in the logical membership information (not the index locations of the found members). those without using iterations of find) involve swapping the inputs to ismember and grouping like indexes with accumarray, as in Eitan's answer, or vectorizing the find with bsxfun as in Luis Mendo's answer, IMHO.