Description Usage Arguments Value Author(s) References Examples
View source: R/multiscaleSVDxpts.R
Exploit k-nearest neighbor algorithms to estimate a sparse similarity matrix. Critical to the validity of this function is the basic mathematical relationships between euclidean distance and correlation and between correlation and covariance. For applications of such matrices, one may see relevant publications by Mauro Maggioni and other authors.
1 2 3 4 5 6 7 8 9 10 11 12 |
x |
input matrix, should be n (samples) by p (measurements) |
k |
number of neighbors |
r |
radius of epsilon-ball |
sigma |
parameter for kernel PCA. |
kmetric |
similarity or distance metric determining k nearest neighbors |
eps |
epsilon error for rapid knn |
ncores |
number of cores to use |
sinkhorn |
boolean |
kPackage |
name of package to use for knn |
verbose |
verbose output |
matrix sparse p by p matrix is output with p by k nonzero entries
Avants BB
http://www.math.jhu.edu/~mauro/multiscaledatageometry.html
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Not run:
set.seed(120)
mat = matrix( rnorm(60), ncol=10 )
smat = sparseDistanceMatrix( mat, 2 )
r16 = antsImageRead( getANTsRData( 'r16' ) )
mask = getMask( r16 )
mat <- getNeighborhoodInMask(image = r16, mask = mask, radius = c(0,0),
physical.coordinates=TRUE, spatial.info=TRUE )
smat = sparseDistanceMatrix( t(mat$indices), 10 ) # close points
testthat::expect_is(smat, "Matrix")
testthat::expect_is(smat, "dgCMatrix")
testthat::expect_equal(sum(smat), 18017)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.