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`[index, distance] = nn_search(pointset, atria, query_points, k)``[index, distance] = nn_search(pointset, atria, query_points, k, epsilon)``[index, distance] = nn_search(pointset, atria, query_indices, k, exclude)``[index, distance] = nn_search(pointset, atria, query_indices, k, exclude, epsilon)`

**Input arguments:**

`pointset`- a`N`by`D`double matrix containing the coordinates of the point set, organized as`N`points of dimension`D``atria`- output of (cf. Section 6.13)`nn_prepare`for pointset`query_points`- a`R`by`D`double matrix containing the coordinates of the query points, organized as`R`points of dimension`D``query_indices`- query points are taken out of the pointset,`query_indices`is a vector of length`R`which contains the indices of the query points (indices may vary from 1 to N)`k`- number of nearest neighbors to be determined`epsilon`- (optional) relative error for approximate nearest neighbors queries, defaults to 0 (= exact search)`exclude`- in case the query points are taken out of the pointset, exclude specifies a range of indices which are omitted from search. For example if the index of the query point is 124 and exclude is set to 3, points with indices 121 to 127 are omitted from search. Using exclude = 0 means: exclude self-matches

**Output arguments:**

`index`- a matrix of size`R`by`k`which contains the indices of the nearest neighbors. Each row of index contains`k`indices of the nearest neighbors to the corresponding query point.`distance`- a matrix of size`R`by`k`which contains the distances of the nearest neighbors to the corresponding query points, sorted in increasing order.

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