tests/testthat/test_utils.R

In motifcluster: Motif-Based Spectral Clustering of Weighted Directed Networks

context("Utilities")

# a_b_one

test_that("a_b_one returns correct matrix", {

  a_dense <- matrix(-4:4, nrow = 3, byrow = TRUE)
  b_dense <- matrix(-1:7, nrow = 3, byrow = TRUE)
  a_sparse <- drop0(a_dense)
  b_sparse <- drop0(b_dense)

  ans <- drop0(matrix(c(0, 0, 0, -9, 0, 9, 36, 54, 72),
                      nrow = 3, byrow = TRUE))

  expect_equal(a_b_one(a_dense, b_dense), ans)
  expect_equal(a_b_one(a_sparse, b_sparse), ans)

})

# a_one_b

test_that("a_one_b returns correct matrix", {

  a_dense <- matrix(-4:4, nrow = 3, byrow = TRUE)
  b_dense <- matrix(-1:7, nrow = 3, byrow = TRUE)
  a_sparse <- drop0(a_dense)
  b_sparse <- drop0(b_dense)

  ans <- drop0(matrix(c(-24, -27, -24, -6, 0, 12, 12, 27, 48),
                      nrow = 3, byrow = TRUE))

  expect_equal(a_one_b(a_dense, b_dense), ans)
  expect_equal(a_one_b(a_sparse, b_sparse), ans)

})

# drop0_killdiag

test_that("drop0_killdiag returns correct matrix", {

  adj_mat_dense <- matrix(-1:7, nrow = 3)
  adj_mat_sparse <- drop0(adj_mat_dense)

  ans <- drop0(matrix(c(0, 0, 1, 2, 0, 4, 5, 6, 0), nrow = 3))

  expect_equal(drop0_killdiag(adj_mat_dense), ans)
  expect_equal(drop0_killdiag(adj_mat_sparse), ans)
})

# get_largest_component

test_that("get_largest_component returns correct indices", {

  adj_mat_dense <- matrix(c(
    0, 0, 0, 0, 0,
    0, 0, 1, 0, 0,
    0, 0, 0, 0, 2,
    3, 0, 0, 0, 0,
    0, 4, 0, 0, 0
  ), nrow = 5, byrow = TRUE)
  adj_mat_sparse <- drop0(adj_mat_dense)

  ans <- c(2, 3, 5)

  expect_equal(get_largest_component(adj_mat_dense), ans)
  expect_equal(get_largest_component(adj_mat_sparse), ans)
})