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",
  shm = NULL,
  init = NULL,
  activation = NULL,
  idiotypic = NULL,
  germinalCenter = NULL,
  microenvironment = NULL,
  memory = 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.

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.

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 target: factor (classification) or numeric (regression).

task

Character: "clustering", "classification", or "regression". 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 
#> 14 12 22  7 14  8 12  4 14 11 11 14  1  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, ])