Combine two similarity graphs by treating them as fuzzy topological sets and forming the intersection.
Arguments
- x
A sparse matrix representing the first similarity graph in the intersection operation.
- y
A sparse matrix representing the second similarity graph in the intersection operation.
- weight
A value between
0 - 1
, controlling the relative influence ofx
andy
in the intersection. Default (0.5
) gives equal influence. Values smaller than0.5
put more weight onx
. Values greater than0.5
put more weight ony
.- n_threads
Number of threads to use when resetting the local metric. Default is half the number of concurrent threads supported by the system.
- verbose
If
TRUE
, log progress to the console.
Examples
# Form two different "views" of the same data
iris30 <- iris[c(1:10, 51:60, 101:110), ]
iris_sg12 <- similarity_graph(iris30[, 1:2], n_neighbors = 5)
iris_sg34 <- similarity_graph(iris30[, 3:4], n_neighbors = 5)
# Combine the two representations into one
iris_combined <- simplicial_set_intersect(iris_sg12, iris_sg34)
# Optimize the layout based on the combined view
iris_combined_umap <- optimize_graph_layout(iris_combined, n_epochs = 100)