emd.cycles.kdt_match#
- emd.cycles.kdt_match(x, y, K=15, distance_upper_bound=inf)[source]#
Find unique nearest-neighbours between two n-dimensional feature sets.
Useful for matching two sets of cycles on one or more features (ie amplitude and average frequency).
Rows in x are matched to rows in y. As such - it is good to have (many) more rows in y than x if possible.
This uses a k-dimensional tree to query for the K nearest neighbours and returns the closest unique neighbour. If no unique match is found - the row is not returned. Increasing K will find more matches but allow matches between more distant observations.
Not advisable for use with more than a handful of features.
- Parameters:
- xndarray
[ num observations x num features ] array to match to
- yndarray
[ num observations x num features ] array of potential matches
- Kint
number of potential nearest-neigbours to query
- Returns:
- ndarray
indices of matched observations in x
- ndarray
indices of matched observations in y