topolow (version 2.0.1)

run_adaptive_sampling: 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
)

Value

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

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.

Examples

# \donttest{
# 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)
}
# }