# sample pair of graphs w. 10 vertices
set.seed(123)
cgnp_pair <- sample_correlated_gnp_pair(10, .9, .5)
A <- cgnp_pair$graph1
B <- cgnp_pair$graph2
seeds<-1:4

test_that("warning from gm convex",
  {
    expect_warning(
      tt <<- gm(A, B, seeds, method = "PATH"),
      "Frank-Wolfe iterations reach the maximum iteration, convergence may not occur.*"
    )
  }
)



test_that("matching correspondence between graph1 and graph2", {
  set.seed(123)
  cgnp_pair <- sample_correlated_gnp_pair(10, .9, .5)
  A <- cgnp_pair$graph1
  B <- cgnp_pair$graph2
  seeds <- 1:4

  expect_warning(
    tt <<- gm(A, B, seeds, method = "PATH"),
    "Frank-Wolfe iterations reach the maximum iteration, convergence may not occur.*"
  )
  expect_snapshot_output(print(tt))
  expect_snapshot_output(print(round(as.matrix(tt$soft), 4)))

})


# startm <- matrix(rnorm(100), 10)
# test_that("add similarity scores", {
#   expect_snapshot_value(
#     gm(A, B, seeds, similarity = startm, method = "PATH"),
#     "serialize"
#   )
# })

# # sample a pair of directed graphs
# set.seed(123)
# cgnp_pair <- sample_correlated_gnp_pair(n = 10, corr = .3, p = .5, directed = TRUE)
# A <- cgnp_pair$graph1
# B <- cgnp_pair$graph2
# seeds <- c(1,3,5)

# test_that("PATH for directed graphs", {
#   expect_snapshot_value(
#     gm(A, B, seeds, method = "PATH"),
#     "serialize"
#   )
# })


# set.seed(12)
# gp_list <- replicate(2, sample_correlated_gnp_pair(10, .5, .5), simplify = FALSE)
# A <- lapply(gp_list, function(gp)gp[[1]])
# B <- lapply(gp_list, function(gp)gp[[2]])
# seeds <- 1:3

# test_that("PATH multi-layer", {
#   expect_snapshot_value(
#     gm(A, B, seeds = 1:3, epsilon = 5, method = "PATH"),
#     "serialize"
#   )
# })