Creates multiple empty trees to handle dynamic support */

Make sure the auxiliary list \a vind has the same size than the current * dataset, and re-generate if size has changed. */

Must return the number of data points

Must return the number of data points

Must return the number of data points

Returns the dim'th component of the idx'th point in the class: Since this is inlined and the "dim" argument is typically an immediate value, the "if/e

Returns the dim'th component of the idx'th point in the class: Since this is inlined and the "dim" argument is typically an immediate value, the "if/e

Returns the dim'th component of the idx'th point in the class: Since this is inlined and the "dim" argument is typically an immediate value, the "if/e

* Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1]. * Their indices are stored inside the result object. \sa radiusSearch,

Loads a previous index from a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * the index object must be c

Loads a previous index from a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * the index object must be c

Loads a previous index from a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * the index object must be c

Assignment operator definiton */

* Subdivide the list of points by a plane perpendicular on the axis * corresponding to the 'cutfeat' dimension at 'cutval' position. * * On

Query for the \a num_closest closest points to a given point (entered as * query_point[0:dim-1]). Note that this is a short-cut method for * ind

* Find all the neighbors to \a query_point[0:dim-1] within a maximum * radius. The output is given as a vector of pairs, of which the first * el

* Find all the neighbors to \a query_point[0:dim-1] within a maximum * radius. The output is given as a vector of pairs, of which the first * el

Remove a point from the set (Lazy Deletion) */

Stores the index in a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * when loading the index object it m

Stores the index in a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * when loading the index object it m

Stores the index in a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so * when loading the index object it m

Returns number of points in dataset */

* Computes the inde memory usage * Returns: memory used by the index */

Returns the length of each point in the dataset */

* Find the worst result (farthest neighbor) without copying or sorting * Pre-conditions: size() > 0 */

* Destructor. Frees all the memory allocated in this pool. */