GetKnnRandomProjection: Efficient Knn-search in high-dimensional search (upto 1000...
In poipou/CytoSEE: automatic computation and evaluation of cytometry data
Description
Usage
Arguments
Value
Description
export
Usage
1
GetKnnRandomProjection(X, num.tree = 50, K)
Arguments
X
A data matrix (columns are features and rows are data points)
num.tree
The number of trees for random projection
K
A integer to specify the number of neighbours in building the Knn graph.
Default to K=\log_2(N), where N is the number of data points
Value
A list containg knn.index, knn.dist, the dimensionality D,
and the projected trees: tree.index
poipou/CytoSEE documentation built on May 13, 2019, 6:15 p.m.
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Description Usage Arguments Value
Description
export
Usage
1 | GetKnnRandomProjection(X, num.tree = 50, K)
|
Arguments
X |
A data matrix (columns are features and rows are data points) |
num.tree |
The number of trees for random projection |
K |
A integer to specify the number of neighbours in building the Knn graph. Default to K=\log_2(N), where N is the number of data points |
Value
A list containg knn.index, knn.dist, the dimensionality D, and the projected trees: tree.index
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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