context("alignMatrices")

# Helper to generate a random series of matrices to align
.generateMatrices = function(d, K, N, noise)
{
  matrices = list( matrix(runif(d*K, min=-1, max=1),ncol=K) ) #reference
  for (i in 2:N)
  {
    matrices[[i]] <- matrices[[1]][,sample(1:K)]
    if (noise)
      matrices[[i]] = matrices[[i]] + matrix(rnorm(d*K, sd=0.05), ncol=K)
  }
  matrices
}

test_that("labelSwitchingAlign correctly aligns de-noised parameters",
{
  N = 30 #number of matrices
  d_K_list = list(c(2,2), c(5,3))
  for (i in 1:2)
  {
    d = d_K_list[[i]][1]
    K = d_K_list[[i]][2]

    # 1] Generate matrix series
    matrices_permut = .generateMatrices(d,K,N,noise=FALSE)

    # 2] Call align function with mode=approx1
    matrices_aligned =
      alignMatrices(matrices_permut[2:N], ref=matrices_permut[[1]], ls_mode="approx1")

    # 3] Check alignment
    for (j in 2:N)
      expect_equal(matrices_aligned[[j-1]], matrices_permut[[1]])

    # 2bis] Call align function with mode=approx2
    matrices_aligned =
      alignMatrices(matrices_permut[2:N], ref=matrices_permut[[1]], ls_mode="approx2")

    # 3bis] Check alignment
    for (j in 2:N)
      expect_equal(matrices_aligned[[j-1]], matrices_permut[[1]])
  }
})

test_that("labelSwitchingAlign correctly aligns noisy parameters",
{
  N = 30 #number of matrices
  d_K_list = list(c(2,2), c(5,3))
  for (i in 1:2)
  {
    d = d_K_list[[i]][1]
    K = d_K_list[[i]][2]
    max_error = d * 0.2 #TODO: what value to choose ?

    # 1] Generate matrix series
    matrices_permut = .generateMatrices(d,K,N,noise=TRUE)

    # 2] Call align function
    matrices_aligned = alignMatrices(matrices_permut, ref="mean", ls_mode="exact")

    # 3] Check alignment
    for (j in 2:N)
    {
      expect_that( norm(matrices_aligned[[j]] - matrices_permut[[1]]),
        is_less_than(max_error) )
    }
  }
})