K-NEAREST NEIGHBOURS AND RADIUS (RANGE) SEARCH 1.0

When the dataset is small, when you have to run only a few number of search, or when the dimensions of points is large, the brute search method is still faster than kd-trees data structure. Computing the distances one by one take a minor time than building the tree.Some of this problems have became less gravous since the introduction of GLTree (file id 22190) which allows for a very fast tree construction.Despite this, very small dataset are still terrain for brute search algorithms.I saw many k-neighbours utilities on FEX, but all them were m-coded. I think such a brute calculation is not an m-code job. So I developed my own Nearest Neighbour finder. It is nothing special it just computes all the distances and take the ones required from the input parameters, but of course the mex implementation make it even faster than vectorized m-code.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	22190 Which,  Algorithmsi,  All The,  All Them,  Allows For,  And Take,  Are Still,  Became Less,  Brute Calculation,  Brute Search,  Dataset,  Dimensions,  Distances,  Fast Tree,  Input Parameters,  Mcode,  Small Dataset,  When You Have,  Which Allows For
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