opperations

Dropout layer. Args: inputs: tensor is_training: boolean tf.Variable scope: string keep_prob: float in [0,1] noise

front() and back()

Fully connected layer with non-linear operation. Args: inputs: 2-D tensor BxN num_outputs: int Returns: Variable tensor o

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

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 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,

* 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,

:param support_pts: points you have, B*N1*3 :param query_pts: points you want to know the neighbour index, B*N2*3 :param k: N

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

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

2D max pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Va

3D max pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns:

array type accessor

array type accessor

Assignment operator definiton */

Assignment operator definiton */

Assignment operator definiton */

operator[]

* 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

* 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

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 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

* 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