KNNSEARCH search for K nearest neighbors
   and related statistics 

  Usage: 
     IDX = knnsearch(X,Y);
	  finds for each element (row) in Y, the nearest 
 	  of all elements in X, such that 
         IDX(k) points to X(IDX(k),:) that is nearest to Y(k,:)
 	  IDX has as many elements as Y has rows
     [IDX,DIST] = knnsearch(X,Y);
     ... = knnsearch(...,'k',k);
 		search for k nearest neighbors (default: k=2)
     ... = knnsearch(...,'Scale',Scale);
 		Scaling vector of 'seuclidian' metric
		default value is std(X)
     ... = knnsearch(...,'Cov',Cov);
 	    Cov is the covariance matrix used for Mahalanobis distance
	    default value is cov(X)
     ... = knnsearch(...,'Distance',Distance);
 	the following distance metrics are currently supported:
 	   'euclidean' [1], 
 	   'seuclidean', (scaled euclidian)
	   'minkowski' [3], 
          'cityblock' or 'manhattan' [4], 
          'hamming' [5],
          'mahalanobis' [6],
          'cosine' [7]
               (one minus the cosine of the angle between the two samples),
          'correlation'
               (one minus the linear correlation between each pair f data vectors),
          'spearman'
               (one minus the rank correlation between each pair of data vectors),

 SEE ALSO: corrcoef, spearman, rankcorr, cov, std

 Reference(s):
  [1] https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
  [2] https://en.wikipedia.org/wiki/Euclidean_distance
  [3] https://en.wikipedia.org/wiki/Minkowski_distance
  [4] https://en.wikipedia.org/wiki/Taxicab_geometry
  [5] https://en.wikipedia.org/wiki/Hamming_distance
  [6] https://en.wikipedia.org/wiki/Mahalanobis_distance
  [7] https://en.wikipedia.org/wiki/Cosine_similarity

Package: nan