run_adaptive_sampling: Performs adaptive Monte Carlo sampling

In topolow: Force-Directed Euclidean Embedding of Dissimilarity Data

Performs adaptive Monte Carlo sampling

Description

Performs adaptive Monte Carlo sampling to explore and refine the parameter space, running locally in parallel. Samples are drawn adaptively based on previously evaluated likelihoods to focus sampling in high-likelihood regions. Results from all parallel jobs accumulate in a single output file.

Usage

run_adaptive_sampling(
  initial_samples_file,
  scenario_name,
  dissimilarity_matrix,
  max_cores = NULL,
  num_samples = 10,
  mapping_max_iter = 1000,
  relative_epsilon = 1e-04,
  folds = 20,
  output_dir,
  verbose = FALSE
)

Arguments

initial_samples_file	Character. Path to a CSV file containing initial samples.
scenario_name	Character. Name for the output files.
dissimilarity_matrix	Matrix. The input dissimilarity matrix.
max_cores	Integer. Number of cores to use for parallel execution. If NULL, uses all available cores minus 1.
num_samples	Integer. Number of new samples to generate via adaptive sampling.
mapping_max_iter	Integer. Maximum number of map optimization iterations.
relative_epsilon	Numeric. Convergence threshold for relative change in error. Default is 1e-4.
folds	Integer. Number of cross-validation folds.
output_dir	Character. Required directory for output files.
verbose	Logical. Whether to print progress messages. Default is FALSE.

Value

No return value. Called for its side effect of writing results to a CSV file in output_dir.

Examples

# 1. Locate the example initial samples file included with the package
# In a real scenario, this file would be from an 'initial_parameter_optimization' run.
initial_file <- system.file(
  "extdata", "initial_samples_example.csv",
  package = "topolow"
)

# 2. Create a temporary directory for the function's output
# This function requires a writable directory for its results.
temp_out_dir <- tempdir()

# 3. Create a sample dissimilarity matrix for the function to use
dissim_mat <- matrix(runif(100, 1, 10), 10, 10)
diag(dissim_mat) <- 0

# 4. Run the adaptive sampling only if the example file is found
if (nzchar(initial_file)) {
  run_adaptive_sampling(
    initial_samples_file = initial_file,
    scenario_name = "adaptive_test_example",
    dissimilarity_matrix = dissim_mat,
    output_dir = temp_out_dir,
    max_cores = 1,
    num_samples = 1,
    verbose = FALSE
  )

  # 5. Verify output files were created
  print("Output files from adaptive sampling:")
  print(list.files(temp_out_dir, recursive = TRUE))

  # 6. Clean up the temporary directory
  unlink(temp_out_dir, recursive = TRUE)
}

topolow documentation built on Aug. 31, 2025, 1:07 a.m.