API Overview

Browse the public API by task. Each name links to one reference location containing all of its documented methods. For help choosing an algorithm, start with the model guide.

ART Models and Options

ARTMAP Models and Options

Training

Classification and Evaluation

Data Preparation

Advanced Configuration

ARTSCENE Feature Extraction

Package

AdaptiveResonance.AdaptiveResonance — Module

Main module for AdaptiveResonance.jl, a Julia package of adaptive resonance theory algorithms.

This module exports all of the ART modules, options, and utilities used by the AdaptiveResonance.jl package. For full usage, see the official guide at https://ap6yc.github.io/AdaptiveResonance.jl/dev/man/guide/.

Basic Usage

Install and import the package in a script with

using Pkg
Pkg.add("AdaptiveResonance")
using AdaptiveResonance

then create an ART module with default options

my_art = DDVFA()

or custom options via keyword arguments

my_art = DDVFA(rho_ub=0.45, rho_ub=0.7)

Train all models with train! and conduct inference with classify. In batch, samples are interpreted in the Julia column-major fashion with dimensions (n_dim, n_samples) (i.e., columns are samples).

Train unsupervised ART modules incrementally or in batch with optional labels as a keyword argument y

# Load your data somehow
samples, labels = load_some_data()

# Unsupervised batch
train!(my_art, samples)

# Supervised batch
train!(my_art, samples, y=labels)

# Unsupervised incremental
for ix in eachindex(labels)
    train!(my_art, samples[:, ix])
end

# Supervised incremental
for ix in eachindex(labels)
    train!(my_art, samples[:, ix], y=labels[ix])
end

Train supervised ARTMAP with positional arguments

my_artmap = SFAM()
train!(my_artmap, samples, labels)

With either module, conduct inference with classify(art, samples)

# Batch inference
y_hat = classify(my_art, test_samples)

# Incremental inference
for ix in eachindex(test_labels)
    y_hat[ix] = classify(my_artmap, test_samples[:, ix])
end

Imports

The following names are imported by the package as dependencies:

  • Base
  • Core
  • Distributed
  • DocStringExtensions
  • ElasticArrays
  • Logging
  • NumericalTypeAliases
  • Parameters
  • Pkg
  • ProgressBars
  • SharedArrays

Exports

The following names are exported and available when using the package:

source