bHIVE

B-cell Hybrid Immune Variant Engine

Introduction

bHIVE is an R package implementing an Artificial Immune Network (AI-Net) algorithm. Inspired by principles of immunology, the bHIVE algorithm evolves a population of "antibodies" to model and analyze datasets. It supports three tasks:

• Clustering: Groups data points based on similarity.
• Classification: Assigns labels to data points based on learned patterns.
• Regression: Predicts continuous outcomes based on input features.

The AI-Net algorithm uses clonal selection and mutation, network suppression, and affinity/distance metrics to iteratively refine the antibody population.

Algorithm Basis

The algorithm operates in the following steps:

1. Affinity Calculation: For a data point x and an antibody a, the affinity is defined as:

   Affinity(x, a) = f(x, a; params)

   where f is a similarity kernel such as Gaussian (RBF), Laplace, or cosine similarity.

2. Clonal Expansion and Mutation: Top-matching antibodies are cloned and mutated:

   a' = a + ε

   where ε is sampled from a distribution scaled by affinity and iteration parameters.

3. Network Suppression: Antibodies that are too similar (within a threshold ε) are suppressed to maintain diversity:

   Distance(a_i, a_j) < ε ⇒ Suppress a_j

4. Assignment: Data points are assigned to antibodies using affinity (for classification/regression) or distance (for clustering).

Installation

To install the bHIVE from GitHub, use:

# Install bHIVE from GitHub
devtools::install_github("ncborcherding/bHIVE")

Examples

Clustering

# Load the Iris dataset
data(iris)
X <- as.matrix(iris[, 1:4])  # Use numeric features only

# Run bHIVE for clustering
res <- bHIVE(X = X, 
             task = "clustering", 
             nAntibodies = 30, 
             beta = 5, 
             epsilon = 0.01, 
             maxIter = 20, 
             k = 3)

# View clustering results
table(res$assignments)

Classification

# Load the Iris dataset
data(iris)
X <- as.matrix(iris[, 1:4])  # Use numeric features
y <- iris$Species           # Classification labels

# Run bHIVE for classification
res <- bHIVE(X = X, 
             y = y, 
             task = "classification", 
             nAntibodies = 30, beta = 5, 
             epsilon = 0.01, 
             maxIter = 20, 
             k = 3)

# Compare predicted labels with actual labels
table(Predicted = res$assignments, Actual = y)

Regression

# Load the Iris dataset
data(iris)
X <- as.matrix(iris[, 2:4])  # Use other features as predictors
y <- iris$Sepal.Length       # Regression target

# Run bHIVE for regression
res <- bHIVE(X = X, 
             y = y, 
             task = "regression",
             nAntibodies = 30, 
             beta = 5, 
             epsilon = 0.01, 
             maxIter = 20, 
             k = 3)

# Compare predicted values with actual values
cor(res$assignments, y)

Bug Reports/New Features

If you run into any issues or bugs please submit a GitHub issue with details of the issue.

If possible please include a reproducible example.

Any requests for new features or enhancements can also be submitted as GitHub issues.

Contributing

We welcome contributions to the bHIVE project! To contribute:

• Fork the repository.
• Create a feature branch (git checkout -b feature-branch).
• Commit your changes (git commit -m "Add new feature").
• Push to the branch (git push origin feature-branch).
• Open a pull request.