Generate synthetic data for manifold learning

A unified wrapper function for generating various types of synthetic data to test manifold learning techniques and demonstrate differences.

Usage

manifold_synthetic_data(
  type = c("swiss_role", "clusters", "trajectory"),
  n_samples,
  dim = 32L,
  n_clusters = 15L,
  cell_trajectories = NULL,
  topology = c("bifurcation", "linear", "combination"),
  noise = 0.1,
  seed = 42L
)

Arguments

type

Character. Type of synthetic data to generate. One of:

• "swiss_role" - Swiss role manifold

• "clusters" - Clustered data

• "trajectory" - A trajectory-like data with branching. You can specify your own topology via cell_trajectories or use one of the pre- defined ones via topology.

n_samples

Integer. Number of data points to generate.

dim

Integer. Dimensionality of the data (used for "clusters" and "trajectory").

n_clusters

Integer. Number of clusters (used for "clusters" type). Default is 15L.

cell_trajectories

Optional list. Named list to use to provide your own topology for the "trajectory" version.

topology

String. One of c("bifurcation", "linear", "combination"). If cell trajectories is not NULL, this will be ignored.

noise

Numeric. Amount of noise to add (used for "swiss_role" and "tree"). must be any non 0 positive value. Default is 0.1.

seed

Integer. Seed for reproducibility.

Value

A list with the following elements:

• data - Numerical matrix with the generated data

• membership - Vector of cluster/branch assignments (NULL for swiss_role)

Examples

if (FALSE) { # \dontrun{

# Generate Swiss role data
swiss <- manifold_synthetic_data(
  "swiss_role",
  n_samples = 1000L
)

# Generate clustered data
clusters <- manifold_synthetic_data(
  "clusters",
  n_samples = 1000L,
  dim = 10L,
  n_clusters = 5L
)

# Generate trajectory-like data
trajectory <- manifold_synthetic_data(
  "trajectory",
  n_samples = 1000L,
  dim = 10L,
  cell_trajectories = NULL, # use a topology
  topology = "bifurcation"
)
} # }