Description Usage Arguments Details Value Author(s) See Also Examples

Build a vantage point tree in preparation for a nearest-neighbors search.

1 2 3 4 5 | ```
buildVptree(
X,
transposed = FALSE,
distance = c("Euclidean", "Manhattan", "Cosine")
)
``` |

`X` |
A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions). |

`transposed` |
Logical scalar indicating whether |

`distance` |
String specifying the type of distance to use. |

This function is automatically called by `findVptree`

and related functions.
However, it can be called directly by the user to save time if multiple queries are to be performed to the same `X`

.

Points in `X`

are reordered to improve data locality during the nearest-neighbor search.
Specifically, points in the same cluster are contiguous and ordered by increasing distance from the cluster center.

The function also reports a list containing four vectors of equal length describing the structure of the VP tree. Each parallel element specifies a node:

The first integer vector specifies the column index of

`data`

of the current node.The second integer vector specifies the column index of the left child of the current node,

The third integer vector specifies the column index of the right child of the current node.

The fourth numeric vector specifies the radius of the current node.

All indices here are zero-based, with child values set to -1 for leaf nodes.

A VptreeIndex object containing indexing structures for the VP-tree search.

Aaron Lun

VptreeIndex, for details on the output class.

`findVptree`

and `queryVptree`

, for dependent functions.

1 2 3 | ```
Y <- matrix(rnorm(100000), ncol=20)
out <- buildVptree(Y)
out
``` |

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