krann | R Documentation |

An implementation of rank-approximate k-nearest-neighbor search (kRANN) using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use.

```
krann(
alpha = NA,
first_leaf_exact = FALSE,
input_model = NA,
k = NA,
leaf_size = NA,
naive = FALSE,
query = NA,
random_basis = FALSE,
reference = NA,
sample_at_leaves = FALSE,
seed = NA,
single_mode = FALSE,
single_sample_limit = NA,
tau = NA,
tree_type = NA,
verbose = getOption("mlpack.verbose", FALSE)
)
```

`alpha` |
The desired success probability. Default value "0.95" (numeric). |

`first_leaf_exact` |
The flag to trigger sampling only after exactly exploring the first leaf. Default value "FALSE" (logical). |

`input_model` |
Pre-trained kNN model (RAModel). |

`k` |
Number of nearest neighbors to find. Default value "0" (integer). |

`leaf_size` |
Leaf size for tree building (used for kd-trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, and octrees). Default value "20" (integer). |

`naive` |
If true, sampling will be done without using a tree. Default value "FALSE" (logical). |

`query` |
Matrix containing query points (optional) (numeric matrix). |

`random_basis` |
Before tree-building, project the data onto a random orthogonal basis. Default value "FALSE" (logical). |

`reference` |
Matrix containing the reference dataset (numeric matrix). |

`sample_at_leaves` |
The flag to trigger sampling at leaves. Default value "FALSE" (logical). |

`seed` |
Random seed (if 0, std::time(NULL) is used). Default value "0" (integer). |

`single_mode` |
If true, single-tree search is used (as opposed to dual-tree search. Default value "FALSE" (logical). |

`single_sample_limit` |
The limit on the maximum number of samples (and hence the largest node you can approximate). Default value "20" (integer). |

`tau` |
The allowed rank-error in terms of the percentile of the data. Default value "5" (numeric). |

`tree_type` |
Type of tree to use: 'kd', 'ub', 'cover', 'r', 'x', 'r-star', 'hilbert-r', 'r-plus', 'r-plus-plus', 'oct'. Default value "kd" (character). |

`verbose` |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical). |

This program will calculate the k rank-approximate-nearest-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. You must specify the rank approximation (in success probability).

A list with several components:

`distances` |
Matrix to output distances into (numeric matrix). |

`neighbors` |
Matrix to output neighbors into (integer matrix). |

`output_model` |
If specified, the kNN model will be output here (RAModel). |

mlpack developers

```
# For example, the following will return 5 neighbors from the top 0.1% of the
# data (with probability 0.95) for each point in "input" and store the
# distances in "distances" and the neighbors in "neighbors.csv":
## Not run:
output <- krann(reference=input, k=5, tau=0.1)
distances <- output$distances
neighbors <- output$neighbors
## End(Not run)
# Note that tau must be set such that the number of points in the
# corresponding percentile of the data is greater than k. Thus, if we choose
# tau = 0.1 with a dataset of 1000 points and k = 5, then we are attempting
# to choose 5 nearest neighbors out of the closest 1 point -- this is invalid
# and the program will terminate with an error message.
#
# The output matrices are organized such that row i and column j in the
# neighbors output file corresponds to the index of the point in the
# reference set which is the i'th nearest neighbor from the point in the
# query set with index j. Row i and column j in the distances output file
# corresponds to the distance between those two points.
```

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