AINet

R6 implementation of the Artificial Immune Network algorithm. This is the core bHIVE algorithm using C++ backends for performance-critical operations. Supports composable modules for somatic hypermutation, idiotypic network regulation, germinal center selection, and more.

Super class

bHIVE::ImmuneAlgorithm -> AINet

Methods

Public methods

• AINet$new()

• AINet$fit()

• AINet$clone()

Inherited methods

• bHIVE::ImmuneAlgorithm$predict()
• bHIVE::ImmuneAlgorithm$print()
• bHIVE::ImmuneAlgorithm$summary()

---

Method new()

Create a new AINet algorithm instance.

Usage

AINet$new(
  nAntibodies = 20,
  beta = 5,
  epsilon = 0.01,
  maxIter = 50,
  k = 3,
  affinityFunc = "gaussian",
  distFunc = "euclidean",
  affinityParams = list(alpha = 1, c = 1, p = 2, Sigma = NULL),
  mutationDecay = 1,
  mutationMin = 0.01,
  maxClones = Inf,
  stopTolerance = 0,
  noImprovementLimit = Inf,
  initMethod = "sample",
  consolidate = TRUE,
  consolidationSteps = 10L,
  scale = c("none", "zscore", "robust", "arcsinh"),
  scaleCofactor = 5,
  targetK = NULL,
  epsilonQuantile = NULL,
  coverageBoost = FALSE,
  coverageQuantile = 0.05,
  shm = NULL,
  init = NULL,
  activation = NULL,
  idiotypic = NULL,
  germinalCenter = NULL,
  microenvironment = NULL,
  memory = NULL,
  classSwitcher = NULL,
  verbose = TRUE
)

Arguments

nAntibodies

Integer. Initial antibody population size.

beta

Numeric. Clone multiplier.

epsilon

Numeric. Suppression distance threshold.

maxIter

Integer. Maximum iterations.

k

Integer. Top-k antibodies to clone per data point.

affinityFunc

Character. Affinity function name.

distFunc

Character. Distance function name.

affinityParams

List. Parameters for affinity/distance functions.

mutationDecay

Numeric. Per-iteration mutation rate decay.

mutationMin

Numeric. Minimum mutation rate.

maxClones

Numeric. Maximum clones per antibody.

stopTolerance

Numeric. Early stopping tolerance.

noImprovementLimit

Integer. Early stopping patience.

initMethod

Character. Initialization method.

consolidate

Logical. For clustering, run Lloyd-style consolidation (an M-step) after affinity maturation so antibodies are pulled onto the data manifold and become true data-space prototypes. Has no effect on classification. Default TRUE.

consolidationSteps

Integer. Maximum consolidation iterations.

scale

Character. Per-feature input scaling applied at fit() and re-applied to new data at predict(). One of "none" (default, no transform), "zscore" (center/SD), "robust" (median / IQR, outlier-tolerant), or "arcsinh" (inverse hyperbolic sine with cofactor scaleCofactor, the standard mass-cytometry transform). Because epsilon, mutation scale, and all distances live in feature units, scaling makes the same defaults behave consistently across datasets of different magnitude.

scaleCofactor

Numeric. Cofactor for scale = "arcsinh" (asinh(x / cofactor)). Default 5 (CyTOF convention; use ~150 for fluorescence flow).

targetK

Integer or NULL. If set, force the clustering solution to exactly targetK clusters. Affinity maturation still discovers where prototypes belong, but the final consolidation seeds a K-means (Lloyd) refinement at exactly targetK centroids: surviving antibodies are agglomerated (if more than K) or split with k-means++ (if fewer than K) before refinement. This decouples the reported cluster count from the emergent suppression dynamics. NULL (default) keeps the emergent, self-selected K. Ignored for classification.

epsilonQuantile

Numeric in (0, 1) or NULL. If set, the suppression threshold is recomputed each iteration as this quantile of the pairwise distances among the current antibodies, making suppression scale-free and adaptive instead of using the fixed epsilon. NULL (default) uses the fixed epsilon.

coverageBoost

Logical. Clustering only. After maturation, find data points that no surviving antibody covers well (max affinity in the bottom coverageQuantile tail) and seed fresh antibodies there with k-means++. Counters the clonal-selection bias toward dense regions, which otherwise leaves rare populations unrepresented. Pairs naturally with targetK: the extra seeds give the forced-K refinement candidate prototypes for sparse populations. Default FALSE.

coverageQuantile

Numeric in (0, 1). Affinity-coverage tail that defines "poorly covered" points for coverageBoost. Default 0.05.

shm

An SHMEngine instance or NULL for default uniform mutation.

init

A VDJLibrary instance or NULL for default initialization.

activation

An ActivationGate instance or NULL.

idiotypic

An IdiotypicNetwork instance or NULL.

germinalCenter

A GerminalCenter instance or NULL.

microenvironment

A Microenvironment instance or NULL.

memory

A MemoryPool instance or NULL.

classSwitcher

A ClassSwitcher instance or NULL.

verbose

Logical. Print progress.

---

Method fit()

Fit the AINet algorithm to data.

Usage

AINet$fit(X, y = NULL, task = NULL, ...)

Arguments

X

Numeric matrix or data frame (n x d).

y

Optional factor target for classification.

task

Character: "clustering" or "classification". Inferred from y if NULL.

...

Additional arguments (currently unused).

Returns

Invisible self, with result populated.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

AINet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Clustering with Iris data
data(iris)
X <- as.matrix(iris[, 1:4])
model <- AINet$new(nAntibodies = 15, maxIter = 10, verbose = FALSE)
model$fit(X, task = "clustering")
table(model$result$assignments)
#> 
#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 
#> 10 10 23  7 14 10 10  8 12  9 18 10  3  6 

# Classification
model2 <- AINet$new(nAntibodies = 20, maxIter = 10, verbose = FALSE)
model2$fit(X, iris$Species, task = "classification")
mean(model2$result$assignments == as.character(iris$Species))
#> [1] 0.8933333

# Predict on new data
preds <- model2$predict(X[1:10, ])