Assign observations to a user-selected number of configurations — assign

The official workflow supplies an amd_curve object. The function then extracts the exact fit that produced the AMD value at the explicitly requested c; no new clustering is performed.

Usage

assign_configurations(
  data,
  c,
  its = 50,
  iter_max = 100,
  m = 2,
  scale_data = FALSE,
  verbose = TRUE,
  seeds = NULL
)

Arguments

data: Preferably an object returned by compute_amd_curve(). A numeric matrix or data frame with observations in rows and variables in columns is accepted only through the provisional compatibility mode.
c: One integer greater than or equal to 2. The number of configurations to extract or fit. It must have been evaluated when data is an AMD curve.
its: Positive integer number of random starts in independent fitting mode (default: 50). Ignored when extracting from an AMD curve.
iter_max: Positive integer maximum number of fuzzy c-means iterations in independent fitting mode (default: 100). Ignored when extracting from an AMD curve.
m: Fuzziness parameter greater than one in independent fitting mode (default: 2). Ignored when extracting from an AMD curve.
scale_data: Logical; standardize variables before independent fitting. Ignored when extracting from an AMD curve.
verbose: Logical; print progress information in independent fitting mode. Ignored when extracting from an AMD curve.
seeds: Optional vector of positive integer seeds, one per random start in independent fitting mode. Supplied seeds make the result reproducible and do not alter the caller's random-number state. Ignored when extracting from an AMD curve. If NULL, seeds are sampled from the caller's active random-number generator and advance its state. Unused trailing seeds are ignored. Duplicate seeds reduce the number of unique starts and generate a warning.

Value

A list of class "amd_assignment". schema_version identifies the public object layout. The object contains the selected c (also in the compatibility alias c_opt), hard assignments, memberships, centers, AMD and objective values, selection metadata, and a starts data frame with the diagnostics for every random start. source distinguishes exact extraction from an AMD curve from an independent fit. Components backend and selection_rule identify how memberships were estimated and how the retained fit was selected. complete_rows and omitted_rows map fitted observations back to the input rows when incomplete rows were removed. settings records the fitting and preprocessing contract, while manifest_id links the object to amd_reproducibility_manifest(). In curve-extraction mode, settings retains the complete candidate range used to create the curve; in independent fitting mode it records the single fitted c. If converged starts have exactly tied minimum objectives, the first start is selected. The selected fit is always converged under the stated iteration rule.

Details

A provisional compatibility mode accepts a numeric matrix or data frame and independently estimates memberships with repeated fuzzy c-means starts. It is not the package's recommended scientific workflow and may be deprecated or removed in a future version.

The function never chooses c automatically. In particular, passing an AMD curve still requires an explicit c, so the user can incorporate the shape of the curve rather than being forced to accept its numerical maximum.

Future automation may assist with interpreting the curve and proposing c, but it must be validated against known truth and must not silently replace inspection of the complete curve.

AMD is defined independently of fuzzy c-means. Fuzzy c-means is only the membership-estimation backend used by the current implementation of compute_amd_curve() and by the provisional compatibility mode.

Examples

# \donttest{
set.seed(1)
X <- rbind(
  matrix(rnorm(100, mean = -2), ncol = 2),
  matrix(rnorm(100, mean = 2), ncol = 2)
)
curve <- compute_amd_curve(
  X, its = 5, nin = 2, nsp = 4, seeds = 1:15, verbose = FALSE
)

# Exact fit underlying the selected point of the inspected curve
extracted <- assign_configurations(curve, c = 2)

# }