Predict method for Gromov-Wasserstein — predict.gromov

Transport new samples using the learned GW alignment.

Usage

# S3 method for class 'gromov_wasserstein'
predict(
  object,
  newdata,
  from,
  to,
  type = c("weights", "transport"),
  k = 5,
  ...
)

Arguments

object: A gromov_wasserstein object
newdata: New data from the source domain (samples x features)
from: Source domain index or name
to: Target domain index or name
type: Prediction type. `"weights"` (default) returns barycentric weights over target-domain samples. `"transport"` returns barycentric combinations of target-domain features when available.
k: Number of nearest neighbours used to build the barycentric combination (default: 5).
...: Reserved for future use.

Value

Matrix of barycentric weights (`type = "weights"`) or transported samples (`type = "transport"`).

Examples

# \donttest{
set.seed(1)
X1 <- matrix(rnorm(30), 10, 3)
X2 <- matrix(rnorm(30), 10, 3)
library(multidesign)
md1 <- multidesign(X1, data.frame(id = 1:10))
md2 <- multidesign(X2, data.frame(id = 1:10))
hd <- hyperdesign(list(d1 = md1, d2 = md2))
fit <- gromov_wasserstein(hd, epsilon = 0.5, max_iter = 10)
newX <- matrix(rnorm(6), 2, 3)
pred <- predict(fit, newX, from = 1, to = 2)
# }