front() and back()

Creates multiple empty trees to handle dynamic support */

Creates multiple empty trees to handle dynamic support */

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. */

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

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

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:

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

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

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

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

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

* 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

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

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 const

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 const

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

Load points (from test or validation split) on which the metrics should be evaluated

Runs the loss on outputs of the model :param outputs: logits :param labels: labels :return: loss

Computes different metrics from confusion matrices. :param confusions: ([..., n_c, n_c] np.int32). Can be any dimension, the confusion matr

array type accessor

array type accessor

Assignment operator definiton */

Assignment operator definiton */

Assignment operator definiton */

operator[]

Picker callback: this get called when on pick events.

Manual pinning of the memory

Manual pinning of the memory

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

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

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

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-

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 eleme

* 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

* 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

* 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

* 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