find_neighbors: K Nearest Neighbour Search
In BenaroyaResearch/briDiscovr: Distribution analysis across clusters of a parent population overlaid with a rare subpopulation
find_neighbors R Documentation
K Nearest Neighbour Search
Description
Uses a kd-tree to find the p number of near neighbours for each point in an input/output dataset.
Usage
find_neighbors(data, k)
Arguments
data
matrix; input data matrix
k
integer; number of nearest neighbours
Details
Use the nn2 function from the RANN package, utilizes the Approximate Near Neighbor (ANN) C++ library,
which can give the exact near neighbours or (as the name suggests) approximate near neighbours
to within a specified error bound. For more information on the ANN library please
visit http://www.cs.umd.edu/~mount/ANN/.
Value
a n-by-k matrix of neighbor indices
Examples
iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
neighbors <- find_neighbors(data, k=10)
BenaroyaResearch/briDiscovr documentation built on March 15, 2024, 12:31 a.m.
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| find_neighbors | R Documentation |
K Nearest Neighbour Search
Description
Uses a kd-tree to find the p number of near neighbours for each point in an input/output dataset.
Usage
find_neighbors(data, k)
Arguments
data |
matrix; input data matrix |
k |
integer; number of nearest neighbours |
Details
Use the nn2 function from the RANN package, utilizes the Approximate Near Neighbor (ANN) C++ library, which can give the exact near neighbours or (as the name suggests) approximate near neighbours to within a specified error bound. For more information on the ANN library please visit http://www.cs.umd.edu/~mount/ANN/.
Value
a n-by-k matrix of neighbor indices
Examples
iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
neighbors <- find_neighbors(data, k=10)
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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