LazySearch

Exact nearest neighbors using a lazy search estrategy.

Parameters

• window_size

  Type → int

  Default → 50

  Size of the sliding window use to search neighbors with.

• min_distance_keep

  Type → float

  Default → 0.0

  The minimum distance (similarity) to consider adding a point to the window. E.g., a value of 0.0 will add even exact duplicates.

• dist_func

  Type → DistanceFunc | FunctionWrapper | None

  Default → None

  A distance function which accepts two input items to compare. If not set, use the Minkowski distance with p=2.

Methods

append

Add a point to the window, optionally with extra metadata.

Parameters

• item — 'typing.Any'
• extra — 'typing.Any | None' — defaults to None
• kwargs

search

Find the n_neighbors closest points to item, along with their distances.

Parameters

• item — 'typing.Any'
• n_neighbors — 'int'
• kwargs

update

Update the window with a new point, only added if > min distance.

If min distance is 0, we do not need to do the calculation. The item (and extra metadata) will not be added to the window if it is too close to an existing point.

Parameters

• item — 'typing.Any'
• n_neighbors — 'int' — defaults to 1
• extra — 'typing.Any | None' — defaults to None

Returns

A boolean (true/false) to indicate if the point was added.

Notes

Updates are by default stored by the FIFO (first in first out) method, which means that when the size limit is reached, old samples are dumped to give room for new samples. This is circular, meaning that older points are dumped first. This also gives the implementation a temporal aspect, because older samples are replaced with newer ones.

The parameter min_dinstance_keep controls the addition of new items to the window - items that are far enough away (> min_distance_keep) are added to the window. Thus a value of 0 indicates that we add all points, and increasing from 0 makes it less likely we will keep a new item.