>>> from mlpack import kfn
This program will calculate the k-furthest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.
For example, the following will calculate the 5 furthest neighbors of eachpoint in 'input' and store the distances in 'distances' and the neighbors in 'neighbors':
>>> kfn(k=5, reference=input)
>>> distances = output['distances']
>>> neighbors = output['neighbors']
The output files are organized such that row i and column j in the neighbors output matrix corresponds to the index of the point in the reference set which is the j'th furthest neighbor from the point in the query set with index i. Row i and column j in the distances output file corresponds to the distance between those two points.
- algorithm (string): Type of neighbor search: 'naive', 'single_tree', 'dual_tree', 'greedy'. Default value dual_tree.
- copy_all_inputs (bool): If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code.
- epsilon (float): If specified, will do approximate furthest neighbor search with given relative error. Must be in the range [0,1). Default value 0.
- input_model (mlpack.KFNModelType): Pre-trained kFN model.
- k (int): Number of furthest neighbors to find. Default value 0.
- leaf_size (int): Leaf size for tree building (used for kd-trees, vp trees, random projection trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, and octrees). Default value 20.
- percentage (float): If specified, will do approximate furthest neighbor search. Must be in the range (0,1] (decimal form). Resultant neighbors will be at least (p*100) % of the distance as the true furthest neighbor. Default value 1.
- query (numpy matrix or arraylike, float dtype): Matrix containing query points (optional).
- random_basis (bool): Before tree-building, project the data onto a random orthogonal basis.
- reference (numpy matrix or arraylike, float dtype): Matrix containing the reference dataset.
- seed (int): Random seed (if 0, std::time(NULL) is used). Default value 0.
- tree_type (string): Type of tree to use: 'kd', 'vp', 'rp', 'max-rp', 'ub', 'cover', 'r', 'r-star', 'x', 'ball', 'hilbert-r', 'r-plus', 'r-plus-plus', 'oct'. Default value kd.
- true_distances (numpy matrix or arraylike, float dtype): Matrix of true distances to compute the effective error (average relative error) (it is printed when -v is specified).
- true_neighbors (numpy matrix or arraylike, int/long dtype): Matrix of true neighbors to compute the recall (it is printed when -v is specified).
- verbose (bool): Display informational messages and the full list of parameters and timers at the end of execution.
The return value from the binding is a dict containing the following elements:
- distances (numpy matrix, float dtype): Matrix to output distances into.
- neighbors (numpy matrix, int dtype): Matrix to output neighbors into.
- output_model (mlpack.KFNModelType): If specified, the kFN model will be output here.