context("general_MT and general_diagnosis.MT work correctly")
test_that("general_MT", {
# 40 data for versicolor in the iris dataset
iris_versicolor <- iris[61:100, -5]
unit_space <- general_MT(unit_space_data = iris_versicolor,
generates_transform_function =
generates_normalization_function,
calc_A = function(x) solve(cor(x)),
includes_transformed_data = TRUE)
correct_distance <- c(1.351, 0.802, 1.164, 0.643, 1.122, 1.184, 0.910, 1.224,
1.691, 0.547, 1.413, 0.692, 1.151, 1.292, 0.771, 0.982,
1.092, 1.116, 0.487, 0.976, 0.621, 0.776, 0.423, 1.324,
1.188, 1.161, 0.959, 1.136, 0.721, 0.685, 1.094, 0.584,
0.310, 1.109, 0.430, 0.982, 0.488, 0.524, 1.572, 0.297)
expect_equal(round(as.vector(unit_space$distance), 3), correct_distance)
})
test_that("general_diagnosis.MT without passing newdata", {
# 40 data for versicolor in the iris dataset
iris_versicolor <- iris[61:100, -5]
unit_space <- general_MT(unit_space_data = iris_versicolor,
generates_transform_function =
generates_normalization_function,
calc_A = function(x) solve(cor(x)),
includes_transformed_data = TRUE)
diagnosis <- general_diagnosis.MT(unit_space = unit_space,
threshold = 4,
includes_transformed_newdata = TRUE)
correct_distance <- c(1.351, 0.802, 1.164, 0.643, 1.122, 1.184, 0.910, 1.224,
1.691, 0.547, 1.413, 0.692, 1.151, 1.292, 0.771, 0.982,
1.092, 1.116, 0.487, 0.976, 0.621, 0.776, 0.423, 1.324,
1.188, 1.161, 0.959, 1.136, 0.721, 0.685, 1.094, 0.584,
0.310, 1.109, 0.430, 0.982, 0.488, 0.524, 1.572, 0.297)
expect_equal(round(as.vector(diagnosis$distance), 3), correct_distance)
})
test_that("general_diagnosis.MT with passing newdata", {
# 40 data for versicolor in the iris dataset
iris_versicolor <- iris[61:100, -5]
unit_space <- general_MT(unit_space_data = iris_versicolor,
generates_transform_function =
generates_normalization_function,
calc_A = function(x) solve(cor(x)),
includes_transformed_data = TRUE)
correct_distance <- c(1.351, 0.802, 1.164, 0.643, 1.122, 1.184, 0.910, 1.224,
1.691, 0.547, 1.413, 0.692, 1.151, 1.292, 0.771, 0.982,
1.092, 1.116, 0.487, 0.976, 0.621, 0.776, 0.423, 1.324,
1.188, 1.161, 0.959, 1.136, 0.721, 0.685, 1.094, 0.584,
0.310, 1.109, 0.430, 0.982, 0.488, 0.524, 1.572, 0.297)
# 10 data for each kind (setosa, versicolor, virginica) in the iris dataset
iris_test <- iris[c(1:10, 51:60, 101:111), -5]
diagnosis <- general_diagnosis.MT(unit_space = unit_space,
newdata = iris_test,
threshold = 4,
includes_transformed_newdata = TRUE)
correct_distance <- c(5.061, 4.270, 4.561, 4.262, 5.186, 5.225, 4.590, 4.806,
4.017, 4.552, 1.483, 0.870, 1.152, 1.027, 0.819, 0.839,
0.925, 1.144, 0.996, 1.129, 3.375, 2.138, 2.268, 1.758,
2.566, 2.745, 2.403, 2.452, 2.254, 3.029, 1.872)
expect_equal(round(as.vector(diagnosis$distance), 3), correct_distance)
})
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