| 1 | # Helper to generate a random series of matrices to align |
| 2 | .generateMatrices = function(d, K, N, noise) |
| 3 | { |
| 4 | matrices = list( matrix(runif(d*K, min=-1, max=1),ncol=K) ) #reference |
| 5 | for (i in 2:(N+1)) |
| 6 | { |
| 7 | matrices[[i]] <- matrices[[1]][,sample(1:K)] |
| 8 | if (noise) |
| 9 | matrices[[i]] = matrices[[i]] + matrix(rnorm(d*K, sd=0.05), ncol=K) |
| 10 | } |
| 11 | matrices |
| 12 | } |
| 13 | |
| 14 | test_that("labelSwitchingAlign correctly aligns de-noised parameters", |
| 15 | { |
| 16 | N <- 30 #number of matrices |
| 17 | d_K_list <- list(c(2,2), c(5,3)) |
| 18 | for (i in 1:2) |
| 19 | { |
| 20 | d <- d_K_list[[i]][1] |
| 21 | K <- d_K_list[[i]][2] |
| 22 | |
| 23 | # 1] Generate matrix series |
| 24 | Ms <- .generateMatrices(d, K, N, noise=FALSE) |
| 25 | |
| 26 | # 2] Call align function with mode=approx1 |
| 27 | aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]], ls_mode="approx1") |
| 28 | |
| 29 | # 3] Check alignment |
| 30 | for (j in 1:N) |
| 31 | expect_equal(aligned[[j]], Ms[[1]]) |
| 32 | |
| 33 | # 2bis] Call align function with mode=approx2 |
| 34 | aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]], ls_mode="approx2") |
| 35 | |
| 36 | # 3bis] Check alignment |
| 37 | for (j in 1:N) |
| 38 | expect_equal(aligned[[j]], Ms[[1]]) |
| 39 | } |
| 40 | }) |
| 41 | |
| 42 | test_that("labelSwitchingAlign correctly aligns noisy parameters", |
| 43 | { |
| 44 | N <- 30 #number of matrices |
| 45 | d_K_list <- list(c(2,2), c(5,3)) |
| 46 | for (i in 1:2) |
| 47 | { |
| 48 | d <- d_K_list[[i]][1] |
| 49 | K <- d_K_list[[i]][2] |
| 50 | max_error <- d * 0.2 #TODO: what value to choose ? |
| 51 | |
| 52 | # 1] Generate matrix series |
| 53 | Ms <- .generateMatrices(d, K, N, noise=TRUE) |
| 54 | |
| 55 | # 2] Call align function |
| 56 | aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]], ls_mode="exact") |
| 57 | |
| 58 | # 3] Check alignment |
| 59 | for (j in 2:N) |
| 60 | expect_lt( norm(aligned[[j]] - Ms[[1]]), max_error ) |
| 61 | } |
| 62 | }) |