morphInstances: Morphing of two networks with a convex combination of the...
In netgen: Network Generator for Combinatorial Graph Problems

Description Usage Arguments Value See Also Examples

This function takes two (clustered) networks with equal number of nodes and, if present, equal number of depots, and generates another instance by applying a convex combination to the coordinates of node pairs. The node pairs are determined by a point matching algorithm, which solves this assignement problem via a integer programming procedure. If both instances contain depots, point matching is done separately on depots and the remaining nodes.

1 2	morphInstances(x, y, alpha, point.matching = NULL, point.matching.algorithm = "push_relabel")

`x`	[`Network`] First network.
`y`	[`Network`] Second network.
`alpha`	[`numeric(1)`] Coeffiecient alpha for convex combination.
`point.matching`	[`matrix` \| NULL] Point matching which shall be used for morphing. If `NULL`, an optimal point matching is generated via function `getOptimalPointMatching`. Default is `NULL`. Currently it is just possible to pass a point matching for instances without depots.
`point.matching.algorithm`	[`function`] Algorithm used to find a point matching. . See argument `method` of `getOptimalPointMatching`.

[Network] Morphed network.

visualizeMorphing, visualizePointMatching

x = generateRandomNetwork(n.points = 40L, n.depots = 2L)
y = generateClusteredNetwork(n.points = 40L, n.cluster = 2L, n.depots = 2L)
z = morphInstances(x, y, alpha = 0.2, point.matching.algorithm = "push_relabel")
## Not run: 
library(gridExtra)
plot.list = list(autoplot(x), autoplot(z), autoplot(y))
plot.list$nrow = 1
do.call(grid.arrange, plot.list)

## End(Not run)