umap(data,
dimension = 2,
numberOfNeighbors = 15,
localConnectivity = 1,
KnnIter = 64,
bandwidth = 1,
customNumberOfEpochs = NULL,
customMapCutoff = NULL,
debug = FALSE,
KDsearch = FALSE,
spectral.cos = TRUE,
setOpMixRatio = 1,
minDist = 0.10000000149011612,
spread = 1,
repulsionStrength = 1,
learningRate = 1);
data
data must be normalized! matrix value could be a dataframe object, or clr type INumericMatrix.
dimension
default 2, The dimension of the space to embed into. [as integer]
numberOfNeighbors
default 15, The size of local neighborhood (in terms of number of neighboring sample points)
used for manifold approximation. [as integer]
customMapCutoff
cutoff value in range [0,1]. [as double]
customNumberOfEpochs
default None, The number of training epochs to be used in optimizing the low dimensional embedding.
Larger values result in more accurate embeddings. [as integer]
KDsearch
knn search via KD-tree?. [as boolean]
localConnectivity
default 1, The local connectivity required -- i.e. the number of nearest neighbors that should
be assumed to be connected at a local level. [as double]
setOpMixRatio
default 1.0, The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use
a pure fuzzy union, while 0.0 will use a pure fuzzy intersection. [as double]
minDist
default 0.1, The effective minimum distance between embedded points. [as double]
spread
default 1.0, The effective scale of embedded points. In combination with min_dist this determines
how clustered/clumped the embedded points are. [as double]
learningRate
default 1.0, The initial learning rate for the embedding optimization. [as double]
repulsionStrength
default 1.0, Weighting applied to negative samples in low dimensional embedding optimization. [as double]
env
[as Environment]
this function returns data object of type list. the list data also has some specificied data fields:
.