| sliced_Wd {LOMAR} | R Documentation |
sliced_Wd
Description
Compute sliced Wasserstein distance or kernel. Reference: Mathieu Carriere, Marco Cuturi, and Steve Oudot. Sliced Wasserstein kernel for persistence diagrams. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 664–673, 2017.
Usage
sliced_Wd(Dg1, Dg2, M = 10, sigma = 1, dimensions = NULL, return.dist = FALSE)
Arguments
Dg1 |
a persistence diagram as a n1 x 3 matrix where each row is a topological feature and the columns are dimension, birth and death of the feature. |
Dg2 |
another persistence diagram as a n2 x 3 matrix |
M |
number of slices (default: 10) |
sigma |
kernel bandwidth (default: 1) |
dimensions |
vector of the dimensions of the topological features to consider, if NULL (default) use all available dimensions |
return.dist |
logical (default: FALSE). Whether to return the kernel or distance value. |
Value
kernel or distance value
Examples
D1 <- matrix(c(0,0,0,1,1,0,0,0,1.5, 3.5,2,2.5,3, 4, 6), ncol = 3, byrow = FALSE)
D2 <- matrix(c(0,0,1,1,0, 0, 1.2, 2, 1.4, 3.2,4.6,6.5), ncol = 3, byrow = FALSE)
K <- sliced_Wd(Dg1 = D1, Dg2 = D2, M = 10, sigma = 1, return.dist = TRUE)