* Compares the entry id of the result * @return true iff this.id == id */

* Compares the entry ids * @param eid * @return true iff this entry id is the same as eid */

* Compares the scores of two results * @return true iff this.score > r.score */

operator[]

return the optimizer operating on

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

* called by the optimizer if verbose. re-implement, if you want to print something */

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 index-

* convert from radian to degree */

* 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 elemen

check range (may be private because it is static)

where does the row at block-row r start?

where does the row at block-row r start?

rows of the matrix

rows of the matrix

how many rows does the block at block-row r has?

how many rows does the block at block-row r has?

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 must

* Returns the score of two vectors * @param a vector * @param b vector * @return score between vectors * @note the vectors must be already

* sets the initial estimate from an array of double * Implement setEstimateDataImpl() * @return true on success */

* writes the estimater to an array of double * @returns true on success */

true => this node should be considered fixed during the optimization