Generate synthetic clustered samples with isotropic Gaussian noise

This function generates synthetic datasets composed of n_clusters Gaussian clusters in n_dim-dimensional space. Cluster centroids are placed uniformly inside a hypercube of side cube_size, and samples are drawn with isotropic Gaussian noise of standard deviation std_dev.

Usage

create_synthetic_samples(
  n_samples,
  n_clusters,
  std_dev,
  n_dim,
  cube_size = 100,
  standardize = FALSE,
  center = TRUE,
  scale. = TRUE
)

Arguments

n_samples

Total number of samples to generate.

n_clusters

Number of clusters to simulate.

std_dev

Standard deviation of the Gaussian noise around each centroid.

n_dim

Number of dimensions (features).

cube_size

Side length of the hypercube where centroids are placed.

standardize

Logical; if TRUE, standardise the final dataset (mean 0, sd 1 per feature).

center, scale.

Logical arguments passed to scale() if standardize = TRUE.

Value

A data frame of size n_samples × n_dim containing the synthetic samples.

Details

The function is used internally to calibrate the compactness of real data by matching its AMD peak against synthetic datasets with varying noise levels.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
syn <- create_synthetic_samples(
  n_samples = 200,
  n_clusters = 4,
  std_dev = 5,
  n_dim = 10
)
head(syn)
} # }