Nothing
size_of_rows <- 5
size_of_columns <- 10
n_clusters <- 3
my_medoids <- NULL
# test w ff input data
test.correct_ff_mediod_names <- function(){
# Generate random data
input_dataset <- matrix(rnorm(size_of_rows * size_of_columns), ncol=size_of_columns)
rnames <- c('v','w','x','y','z')
rownames(input_dataset) <- rnames
# Create different object types accepted by parallel pam
data_symmetric_matrix <- 1-cor(t(input_dataset))
data_distance_matrix <- as.dist(data_symmetric_matrix)
data_binary_file <- ff(data_symmetric_matrix, vmode="double", dim=c(size_of_rows, size_of_rows),
dimnames=list(rnames,rnames))
# Execute original version
original_pam_result <- pam(data_distance_matrix, n_clusters)
# Execute parallel version passing different object types on input
ppam_result_binary_file <- ppam(data_binary_file, n_clusters)
# Compare medoids
invisible(checkEquals(original_pam_result$medoids,
ppam_result_binary_file$medoids, " Medoids should have same names."))
}
test.symmetric_correct_mediod_names <- function(){
# Generate random data
input_dataset <- matrix(rnorm(size_of_rows * size_of_columns), ncol=size_of_columns)
rownames(input_dataset) <- c('v','w','x','y','z')
# Create different object types accepted by parallel pam
data_symmetric_matrix <- 1-cor(t(input_dataset))
data_distance_matrix <- as.dist(data_symmetric_matrix)
# Execute original version
original_pam_result <- pam(data_distance_matrix, n_clusters)
# Execute parallel version passing different object types on input
ppam_result_symmetric_matrix <- ppam(data_symmetric_matrix, n_clusters)
# Compare medoids
invisible(checkEqualsNumeric(original_pam_result$medoids,
ppam_result_symmetric_matrix$medoids, "Symmetric medoid labels are the same."))
invisible(checkEqualsNumeric(original_pam_result$id.med,
ppam_result_symmetric_matrix$id.med, "Symmetric meds are the same."))
}
test.distance_correct_mediod_names <- function(){
# Generate random data
input_dataset <- matrix(rnorm(size_of_rows * size_of_columns), ncol=size_of_columns)
rownames(input_dataset) <- c('v','w','x','y','z')
# Create different object types accepted by parallel pam
data_symmetric_matrix <- 1-cor(t(input_dataset))
data_distance_matrix <- as.dist(data_symmetric_matrix)
# Execute original version
original_pam_result <- pam(data_distance_matrix, n_clusters)
# Execute parallel version passing different object types on input
ppam_result_distance_matrix <- ppam(data_distance_matrix, n_clusters)
# Compare medoids
invisible(checkEqualsNumeric(original_pam_result$medoids,
ppam_result_distance_matrix$medoids, "Distance medoid labels are the same."))
invisible(checkEqualsNumeric(original_pam_result$id.med,
ppam_result_distance_matrix$id.med, "Distance med labels are the same."))
}
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