knn.from.data: get information about approximate k nearest neighbors from a...
In donelsonsmith/umap_R: Uniform Manifold Approximation and Projection

This implementation uses a randomization scheme and thus produces results that are nondeterministic and only approximately correct. The algorithm is roughly inspired by Dong et al, but there are differences. This is a rough implementation and improvements are possible.

1 2	knn.from.data(dT, k, metric.function, subsample.k = 0.5, fix.observations = NULL)

`dT`	matrix with data (observations in columns, features in rows)
`k`	integer, number of neighbors
`metric.function`	function that returns a metric distance
`subsample.k`	numeric, used for internal tuning of implementation
`fix.observations`	integer, number of observations in dT that will appear in knn

list with two components; indexes - identifies, for each point in dataset, the set of k neighbors distances - provides distances from each point to those neighbors num.computed - for diagnostics only, gives the number of distances computed internally avg.

donelsonsmith/umap_R documentation built on Nov. 4, 2019, 10:58 a.m.