Index

This page lists the core methods and types of the AdaptiveResonance.jl package. The Modules section lists the modules exported by the package including the AdaptiveResonance module itself. The Methods section lists the public methods for the package that use the modules in Types. Each of these entries link to the docstrings in the Docs section.

ART modules document their internal working parameters and references, while their hyperparameters/options are documented under their corresponding option structs opts_....

Index

This section enumerates the names exported by the package, each of which links to its corresponding Documentation.

Modules

Methods

Types

Constants

Docs

This section lists the documentation for every exported name of the AdaptiveResonance.jl package.

Modules

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

Functions

AdaptiveResonance.DAM — Method
DAM(opts::opts_SFAM) -> SFAM

Summary

Implements a Default ARTMAP module with specified options.

Default ARTMAP is a variant of SFAM, using the AdaptiveResonance.opts_SFAM options. This constructor sets the activation to :choice_by_difference in addition to the keyword argument options you provide.

Arguments

Method List / Definition Locations

DAM(opts)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/variants.jl:41.

source
AdaptiveResonance.DAM — Method
DAM(; kwargs...) -> SFAM

Summary

Constructs a Default ARTMAP module using a SFAM module using Default ARTMAP's choice-by-difference activation function.

Default ARTMAP is a variant of SFAM, using the AdaptiveResonance.opts_SFAM options. This constructor sets the activation to :choice_by_difference in addition to the keyword argument options you provide.

Arguments

References:

  1. G. P. Amis and G. A. Carpenter, 'Default ARTMAP 2,' IEEE Int. Conf. Neural Networks - Conf. Proc., vol. 2, no. September 2007, pp. 777-782, Mar. 2007, doi: 10.1109/IJCNN.2007.4371056.

Method List / Definition Locations

DAM(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/variants.jl:29.

source
AdaptiveResonance.GammaNormalizedFuzzyART — Method
GammaNormalizedFuzzyART(opts::opts_FuzzyART)

Summary

Implements a Gamma-Normalized FuzzyART module with specified options.

GammaNormalizedFuzzyART is a variant of FuzzyART, using the AdaptiveResonance.opts_FuzzyART options. This constructor passes gamma_normalization=true, which internally uses match=:gamma_match and activation=:gamma_activation in addition to the keyword argument options you provide.

Arguments

Method List / Definition Locations

GammaNormalizedFuzzyART(opts)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/variants.jl:39.

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AdaptiveResonance.GammaNormalizedFuzzyART — Method
GammaNormalizedFuzzyART(; kwargs...) -> FuzzyART

Summary

Constructs a Gamma-Normalized FuzzyART module as a variant of FuzzyART by using the gamma_normalization option.

GammaNormalizedFuzzyART is a variant of FuzzyART, using the AdaptiveResonance.opts_FuzzyART options. This constructor passes gamma_normalization=true, which internally uses match=:gamma_match and activation=:gamma_activation in addition to the keyword argument options you provide.

Arguments

Method List / Definition Locations

GammaNormalizedFuzzyART(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/variants.jl:26.

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AdaptiveResonance.classify — Method
classify(
    art::ARTModule,
    x::AbstractMatrix{T} where T<:Real;
    preprocessed,
    get_bmu
) -> Any

Summary

Predict categories of 'x' using the ART model.

Returns predicted categories 'y_hat.'

Arguments

  • art::ARTModule: ART or ARTMAP module to use for batch inference.
  • x::RealMatrix: the 2-D dataset containing columns of samples with rows of features.
  • preprocessed::Bool=false: flag, if the data has already been complement coded or not.
  • get_bmu::Bool=false, flag, if the model should return the best-matching-unit label in the case of total mismatch.

Examples

julia> my_DDVFA = DDVFA()
DDVFA
    opts: opts_DDVFA
    ...
julia> x, y = load_data()
julia> train!(my_DDVFA, x)
julia> y_hat = classify(my_DDVFA, y)

Method List / Definition Locations

classify(art, x; preprocessed, get_bmu)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:554.

source
AdaptiveResonance.classify — Method

Summary

Predict categories of a single sample of features 'x' using the ART model.

Returns predicted category 'y_hat.'

Arguments

  • art::ARTModule: ART or ARTMAP module to use for batch inference.
  • x::RealVector: the single sample of features to classify.
  • preprocessed::Bool=false: optional, flag if the data has already been complement coded or not.
  • get_bmu::Bool=false: optional, flag if the model should return the best-matching-unit label in the case of total mismatch.

Method List / Definition Locations

classify(art, x; preprocessed, get_bmu)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/distributed/modules/DDVFA.jl:335.

classify(art, x; preprocessed, get_bmu)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/DVFA.jl:337.

classify(art, x; preprocessed, get_bmu)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/FuzzyART.jl:370.

classify(art, x; preprocessed, get_bmu)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/SFAM.jl:293.

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AdaptiveResonance.complement_code — Method
complement_code(
    data::AbstractArray{T} where T<:Real;
    config
) -> Any

Summary

Normalizes the data x to [0, 1] and returns the augmented vector [x, 1 - x].

Arguments

  • data::RealArray: the 1-D or 2-D data to be complement coded.
  • config::DataConfig=DataConfig(): the data configuration for the ART/ARTMAP module.

Method List / Definition Locations

complement_code(data; config)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:402.

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AdaptiveResonance.data_setup! — Method
data_setup!(
    art::ARTModule,
    data::AbstractMatrix{T} where T<:Real
)

Summary

Convenience method for setting up the DataConfig of an ART module in advance.

Arguments

  • art::ARTModule: the ART/ARTMAP module to manually configure the data config for.
  • data::RealArray: the 2-D batch of data used to create the data config.

Method List / Definition Locations

data_setup!(art, data)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:295.

source
AdaptiveResonance.data_setup! — Method
data_setup!(
    config::DataConfig,
    data::AbstractMatrix{T} where T<:Real
)

Summary

Sets up the data config for the ART module before training.

This function crucially gets the original and complement-coded dimensions of the data, and it infers the bounds of the data (minimums and maximums) by the largest and smallest values along each feature dimension.

Arguments

  • config::DataConfig: the ART/ARTMAP module's data configuration object.
  • data::RealMatrix: the 2-D batch of data to use for creating the data configuration.

Method List / Definition Locations

data_setup!(config, data)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:268.

source
AdaptiveResonance.get_data_characteristics — Method
get_data_characteristics(
    data::AbstractMatrix{T} where T<:Real;
    config
) -> NTuple{4, Any}

Summary

Get the characteristics of the data, taking account if a data config is passed.

If no DataConfig is passed, then the data characteristics come from the array itself. Otherwise, use the config for the statistics of the data and the data array for the number of samples.

Arguments

  • data::RealMatrix: the 2-D data to be complement coded.
  • config::DataConfig=DataConfig(): the data configuration for the ART/ARTMAP module.

Method List / Definition Locations

get_data_characteristics(data; config)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:310.

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AdaptiveResonance.linear_normalization — Method
linear_normalization(
    data::AbstractMatrix{T} where T<:Real;
    config
) -> Any

Summary

Normalize the data to the range [0, 1] along each feature.

Arguments

  • data::RealMatrix: the 2-D batch of data to normalize.
  • config::DataConfig=DataConfig(): the data configuration from the ART/ARTMAP module.

Method List / Definition Locations

linear_normalization(data; config)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:369.

source
AdaptiveResonance.linear_normalization — Method
linear_normalization(
    data::AbstractVector{T} where T<:Real;
    config
) -> Vector{Float64}

Summary

Normalize the data to the range [0, 1] along each feature.

Arguments

  • data::RealVector: the 1-D sample of data to normalize.
  • config::DataConfig=DataConfig(): the data configuration from the ART/ARTMAP module.

Method List / Definition Locations

linear_normalization(data; config)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:339.

source
AdaptiveResonance.opts_DAM — Method
opts_DAM(; kwargs...) -> opts_SFAM

Summary

Implements a Default ARTMAP module's options.

Default ARTMAP is a variant of SFAM, using the AdaptiveResonance.opts_SFAM options. This constructor sets the activation to :choice_by_difference in addition to the keyword argument options you provide.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Method List / Definition Locations

opts_DAM(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/variants.jl:52.

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AdaptiveResonance.opts_GammaNormalizedFuzzyART — Method
opts_GammaNormalizedFuzzyART(; kwargs...)

Summary

Implements a Gamma-Normalized FuzzyART module's options.

GammaNormalizedFuzzyART is a variant of FuzzyART, using the AdaptiveResonance.opts_FuzzyART options. This constructor passes gamma_normalization=true, which internally uses match=:gamma_match and activation=:gamma_activation in addition to the keyword argument options you provide.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Method List / Definition Locations

opts_GammaNormalizedFuzzyART(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/variants.jl:50.

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AdaptiveResonance.train! — Function
train!(
    art::ARTMAP,
    x::AbstractMatrix{T} where T<:Real,
    y::AbstractVector{T} where T<:Integer
) -> Any
train!(
    art::ARTMAP,
    x::AbstractMatrix{T} where T<:Real,
    y::AbstractVector{T} where T<:Integer,
    preprocessed::Bool
) -> Any

Summary

train!(art::ARTMAP, x::RealMatrix, y::IntegerVector, preprocessed::Bool=false)

Train the ARTMAP model on a batch of data 'x' with supervisory labels 'y.'

Arguments

  • art::ARTMAP: the supervised ARTMAP model to train.
  • x::RealMatrix: the 2-D dataset containing columns of samples with rows of features.
  • y::IntegerVector: labels for supervisory training.
  • preprocessed::Bool=false: flag, if the data has already been complement coded or not.

Method List / Definition Locations

train!(art, x, y)
train!(art, x, y, preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/common.jl:23.

source
AdaptiveResonance.train! — Method
train!(
    art::ART,
    x::AbstractMatrix{T} where T<:Real;
    y,
    preprocessed
) -> Any

Summary

Train the ART model on a batch of data 'x' with optional supervisory labels 'y.'

Arguments

  • art::ART: the unsupervised ART model to train.
  • x::RealMatrix: the 2-D dataset containing columns of samples with rows of features.
  • y::IntegerVector=Int[]: optional, labels for simple supervisory training.
  • preprocessed::Bool=false: optional, flag if the data has already been complement coded or not.

Method List / Definition Locations

train!(art, x; y, preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/common.jl:21.

source
AdaptiveResonance.train! — Method

Summary

Train the ART model on a single sample of features 'x' with an optional supervisory label.

Arguments

  • art::ART: the unsupervised ART model to train.
  • x::RealVector: the single sample feature vector to train upon.
  • y::Integer=0: optional, a label for simple supervisory training.
  • preprocessed::Bool=false: optional, flag if the data has already been complement coded or not.

Method List / Definition Locations

train!(art, x; y, preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/distributed/modules/DDVFA.jl:266.

train!(art, x; y, preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/DVFA.jl:260.

train!(art, x; y, preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/FuzzyART.jl:292.

source
AdaptiveResonance.train! — Method

Summary

Train the supervised ARTMAP model on a single sample of features 'x' with supervisory label 'y'.

Arguments

  • art::ARTMAP: the supervised ART model to train.
  • x::RealVector: the single sample feature vector to train upon.
  • y::Integer: the label for supervisory training.
  • preprocessed::Bool=false: optional, flag if the data has already been complement coded or not.

Method List / Definition Locations

train!(art, x, y; preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/SFAM.jl:227.

source

Types

AdaptiveResonance.ART — Type
abstract type ART <: ARTModule

Summary

Abstract supertype for all default unsupervised ART modules.

Fields

source
AdaptiveResonance.DDVFA — Type
mutable struct DDVFA <: ART

Summary

Distributed Dual Vigilance Fuzzy ARTMAP module struct.

For module options, see AdaptiveResonance.opts_DDVFA.

References

  1. L. E. Brito da Silva, I. Elnabarawy, and D. C. Wunsch, 'Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence,' Neural Networks, vol. 121, pp. 208-228, 2020, doi: 10.1016/j.neunet.2019.08.033.
  2. G. Carpenter, S. Grossberg, and D. Rosen, 'Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,' Neural Networks, vol. 4, no. 6, pp. 759-771, 1991.

Fields

  • opts::opts_DDVFA: DDVFA options struct.
  • subopts::opts_FuzzyART: FuzzyART options struct used for all F2 nodes.
  • config::DataConfig: Data configuration struct.
  • threshold::Float64: Operating module threshold value, a function of the vigilance parameter.
  • F2::Vector{FuzzyART}: List of F2 nodes (themselves FuzzyART modules).
  • labels::Vector{Int64}: Incremental list of labels corresponding to each F2 node, self-prescribed or supervised.
  • n_categories::Int64: Number of total categories.
  • epoch::Int64: Current training epoch.
  • T::Vector{Float64}: DDVFA activation values.
  • M::Vector{Float64}: DDVFA match values.
  • stats::Dict{String, Any}: Runtime statistics for the module, implemented as a dictionary containing entries at the end of each training iteration. These entries include the best-matching unit index and the activation and match values of the winning node.
source
AdaptiveResonance.DDVFA — Method
DDVFA(; kwargs...) -> DDVFA

Summary

Implements a DDVFA learner with optional keyword arguments.

Arguments

Examples

By default:

julia> DDVFA()
DDVFA
    opts: opts_DDVFA
    subopts: opts_FuzzyART
    ...

or with keyword arguments:

julia> DDVFA(rho_lb=0.4, rho_ub = 0.75)
DDVFA
    opts: opts_DDVFA
    subopts: opts_FuzzyART
    ...

Method List / Definition Locations

DDVFA(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/distributed/modules/DDVFA.jl:198.

source
AdaptiveResonance.DVFA — Type
mutable struct DVFA <: AdaptiveResonance.AbstractFuzzyART

Summary

Dual Vigilance Fuzzy ARTMAP module struct.

For module options, see AdaptiveResonance.opts_DVFA.

References:

  1. L. E. Brito da Silva, I. Elnabarawy and D. C. Wunsch II, 'Dual Vigilance Fuzzy ART,' Neural Networks Letters. To appear.
  2. G. Carpenter, S. Grossberg, and D. Rosen, 'Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,' Neural Networks, vol. 4, no. 6, pp. 759-771, 1991.

Fields

  • opts::opts_DVFA: DVFA options struct.
  • config::DataConfig: Data configuration struct.
  • threshold_ub::Float64: Operating upper bound module threshold value, a function of the upper bound vigilance parameter.
  • threshold_lb::Float64: Operating lower bound module threshold value, a function of the lower bound vigilance parameter.
  • labels::Vector{Int64}: Incremental list of labels corresponding to each F2 node, self-prescribed or supervised.
  • W::ElasticArrays.ElasticMatrix{Float64, V} where V<:DenseVector{Float64}: Category weight matrix.
  • T::Vector{Float64}: Activation values for every weight for a given sample.
  • M::Vector{Float64}: Match values for every weight for a given sample.
  • n_categories::Int64: Number of category weights (F2 nodes).
  • n_clusters::Int64: Number of labeled clusters, may be lower than n_categories
  • epoch::Int64: Current training epoch.
  • stats::Dict{String, Any}: Runtime statistics for the module, implemented as a dictionary containing entries at the end of each training iteration. These entries include the best-matching unit index and the activation and match values of the winning node.
source
AdaptiveResonance.DVFA — Method
DVFA(; kwargs...) -> DVFA

Summary

Implements a DVFA learner with optional keyword arguments.

Arguments

Examples

By default:

julia> DVFA()
DVFA
    opts: opts_DVFA
    ...

or with keyword arguments:

julia> DVFA(rho=0.7)
DVFA
    opts: opts_DVFA
    ...

Method List / Definition Locations

DVFA(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/DVFA.jl:181.

source
AdaptiveResonance.DataConfig — Type
mutable struct DataConfig

Summary

Container to standardize training/testing data configuration.

This container declares if a data configuration has been setup, what the original and complement coded dimensions are, and what the minimums and maximums of the values along each feature dimension are.

Fields

  • setup::Bool: Flag if data has been setup yet or not.
  • mins::Vector{Float64}: List of minimum values for each feature.
  • maxs::Vector{Float64}: List of maximum values for each feature.
  • dim::Int64: Dimensionality of the feature vectors (i.e., number of features).
  • dim_comp::Int64: Complement coded feature dimensionality, twice the size of dim.
source
AdaptiveResonance.DataConfig — Method
DataConfig(
    mins::AbstractVector{T} where T<:Real,
    maxs::AbstractVector{T} where T<:Real
) -> DataConfig

Summary

Convenience constructor for DataConfig, requiring only mins and maxs of the features.

This constructor is used when the mins and maxs differ across features. The dimension is inferred by the length of the mins and maxs.

Arguments

  • mins::RealVector: a vector of minimum values for each feature dimension.
  • maxs::RealVector: a vector of maximum values for each feature dimension.

Method List / Definition Locations

DataConfig(mins, maxs)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:79.

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AdaptiveResonance.DataConfig — Method
DataConfig(min::Real, max::Real, dim::Integer) -> DataConfig

Summary

Convenience constructor for DataConfig, requiring only a global min, max, and dim.

This constructor is used in the case that the feature mins and maxs are all the same respectively.

Arguments

  • min::Real: the minimum value across all features.
  • max::Real: the maximum value across all features.
  • dim::Integer: the dimension of the features, which must be provided because it cannot be inferred from just the minimum or maximum values.

Method List / Definition Locations

DataConfig(min, max, dim)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/lib/common.jl:104.

source
AdaptiveResonance.FAM — Type
mutable struct FAM <: ARTMAP

Summary

Fuzzy ARTMAP struct.

For module options, see AdaptiveResonance.opts_FAM.

References

  1. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 698-713, 1992, doi: 10.1109/72.159059.

Fields

  • opts::opts_FAM: Fuzzy ARTMAP options struct.
  • config::DataConfig: Data configuration struct.
  • W::ElasticArrays.ElasticMatrix{Float64, V} where V<:DenseVector{Float64}: Category weight matrix.
  • labels::Vector{Int64}: Incremental list of labels corresponding to each F2 node, self-prescribed or supervised.
  • n_categories::Int64: Number of category weights (F2 nodes).
  • epoch::Int64: Current training epoch.
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AdaptiveResonance.FuzzyART — Type
mutable struct FuzzyART <: AdaptiveResonance.AbstractFuzzyART

Summary

Gamma-Normalized Fuzzy ART learner struct

For module options, see AdaptiveResonance.opts_FuzzyART.

References

  1. G. Carpenter, S. Grossberg, and D. Rosen, 'Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,' Neural Networks, vol. 4, no. 6, pp. 759-771, 1991.

Fields

  • opts::opts_FuzzyART: FuzzyART options struct.
  • config::DataConfig: Data configuration struct.
  • threshold::Float64: Operating module threshold value, a function of the vigilance parameter.
  • labels::Vector{Int64}: Incremental list of labels corresponding to each F2 node, self-prescribed or supervised.
  • T::Vector{Float64}: Activation values for every weight for a given sample.
  • M::Vector{Float64}: Match values for every weight for a given sample.
  • W::ElasticArrays.ElasticMatrix{Float64, V} where V<:DenseVector{Float64}: Category weight matrix.
  • n_instance::Vector{Int64}: Number of weights associated with each category.
  • n_categories::Int64: Number of category weights (F2 nodes).
  • epoch::Int64: Current training epoch.
  • stats::Dict{String, Any}: Runtime statistics for the module, implemented as a dictionary containing entries at the end of each training iteration. These entries include the best-matching unit index and the activation and match values of the winning node.
source
AdaptiveResonance.FuzzyART — Method
FuzzyART(
    opts::opts_FuzzyART,
    sample::AbstractVector{T} where T<:Real;
    preprocessed
) -> FuzzyART

Summary

Create and initialize a FuzzyART with a single sample in one step.

Principally used as a method for initialization within DDVFA.

Arguments

  • opts::opts_FuzzyART: the FuzzyART options contains.
  • sample::RealVector: the sample to use as a basis for setting up the FuzzyART.
  • preprocessed::Bool=false: flag for if the sample is already complement coded and normalized.

Method List / Definition Locations

FuzzyART(opts, sample; preprocessed)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/FuzzyART.jl:239.

source
AdaptiveResonance.FuzzyART — Method
FuzzyART(; kwargs...) -> FuzzyART

Summary

Implements a Fuzzy ART learner with optional keyword arguments.

Arguments

Examples

By default:

julia> FuzzyART()
FuzzyART
    opts: opts_FuzzyART
    ...

or with keyword arguments:

julia> FuzzyART(rho=0.7)
FuzzyART
    opts: opts_FuzzyART
    ...

Method List / Definition Locations

FuzzyART(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ART/single/modules/FuzzyART.jl:184.

source
AdaptiveResonance.SFAM — Type
mutable struct SFAM <: ARTMAP

Summary

Simple Fuzzy ARTMAP struct.

For module options, see AdaptiveResonance.opts_SFAM.

References

  1. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 698-713, 1992, doi: 10.1109/72.159059.

Fields

  • opts::opts_SFAM: Simplified Fuzzy ARTMAP options struct.
  • config::DataConfig: Data configuration struct.
  • W::ElasticArrays.ElasticMatrix{Float64, V} where V<:DenseVector{Float64}: Category weight matrix.
  • labels::Vector{Int64}: Incremental list of labels corresponding to each F2 node, self-prescribed or supervised.
  • n_categories::Int64: Number of category weights (F2 nodes).
  • epoch::Int64: Current training epoch.
  • T::Vector{Float64}: DDVFA activation values.
  • M::Vector{Float64}: DDVFA match values.
  • stats::Dict{String, Any}: Runtime statistics for the module, implemented as a dictionary containing entries at the end of each training iteration. These entries include the best-matching unit index and the activation and match values of the winning node.
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AdaptiveResonance.SFAM — Method
SFAM(; kwargs...) -> SFAM

Summary

Implements a Simple Fuzzy ARTMAP learner with optional keyword arguments.

Arguments

Examples

By default:

julia> SFAM()
SFAM
    opts: opts_SFAM
    ...

or with keyword arguments:

julia> SFAM(rho=0.6)
SFAM
    opts: opts_SFAM
    ...

Method List / Definition Locations

SFAM(; kwargs...)

defined at /home/runner/work/AdaptiveResonance.jl/AdaptiveResonance.jl/src/ARTMAP/SFAM.jl:158.

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AdaptiveResonance.opts_DDVFA — Type
mutable struct opts_DDVFA <: ARTOpts

Summary

Distributed Dual Vigilance Fuzzy ART options struct.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Fields

  • rho_lb::Float64: Lower-bound vigilance parameter: rho_lb ∈ [0, 1]. Default: 0.7

  • rho_ub::Float64: Upper bound vigilance parameter: rho_ub ∈ [0, 1]. Default: 0.85

  • alpha::Float64: Choice parameter: alpha > 0. Default: 0.001

  • beta::Float64: Learning parameter: beta ∈ (0, 1]. Default: 1.0

  • gamma::Float64: Pseudo kernel width: gamma >= 1. Default: 3.0

  • gamma_ref::Float64: Reference gamma for normalization: 0 <= gamma_ref < gamma. Default: 1.0

  • similarity::Symbol: Similarity method (activation and match): similarity ∈ [:single, :average, :complete, :median, :weighted, :centroid]. Default: :single

  • max_epoch::Int64: Maximum number of epochs during training: max_epochs ∈ (1, Inf). Default: 1

  • display::Bool: Display flag for progress bars. Default: false

  • gamma_normalization::Bool: Flag to normalize the threshold by the feature dimension. Default: true

  • uncommitted::Bool: Flag to use an uncommitted node when learning.

    If true, new weights are created with ones(dim) and learn on the complement-coded sample. If false, fast-committing is used where the new weight is simply the complement-coded sample. Default: false

  • activation::Symbol: Selected activation function. Default: :gamma_activation

  • match::Symbol: Selected match function. Default: :gamma_match

  • update::Symbol: Selected weight update function. Default: :basic_update

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AdaptiveResonance.opts_DVFA — Type
mutable struct opts_DVFA <: ARTOpts

Summary

Dual Vigilance Fuzzy ART options struct.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Fields

  • rho_lb::Float64: Lower-bound vigilance parameter: rho_lb ∈ [0, 1]. Default: 0.55

  • rho_ub::Float64: Upper bound vigilance parameter: rho_ub ∈ [0, 1]. Default: 0.75

  • alpha::Float64: Choice parameter: alpha > 0. Default: 0.001

  • beta::Float64: Learning parameter: beta ∈ (0, 1]. Default: 1.0

  • max_epoch::Int64: Maximum number of epochs during training. Default: 1

  • display::Bool: Display flag for progress bars. Default: false

  • uncommitted::Bool: Flag to use an uncommitted node when learning.

    If true, new weights are created with ones(dim) and learn on the complement-coded sample. If false, fast-committing is used where the new weight is simply the complement-coded sample. Default: false

  • activation::Symbol: Selected activation function. Default: :basic_activation

  • match::Symbol: Selected match function. Default: :unnormalized_match

  • update::Symbol: Selected weight update function. Default: :basic_update

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AdaptiveResonance.opts_FAM — Type
mutable struct opts_FAM <: ARTOpts

Summary

Implements a Fuzzy ARTMAP learner's options.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Fields

  • rho::Float64: Vigilance parameter: rho ∈ [0, 1]. Default: 0.6

  • alpha::Float64: Choice parameter: alpha > 0. Default: 1.0e-7

  • epsilon::Float64: Match tracking parameter: epsilon ∈ (0, 1). Default: 0.001

  • beta::Float64: Learning parameter: beta ∈ (0, 1]. Default: 1.0

  • max_epochs::Int64: Maximum number of epochs during training: max_epochs ∈ [1, Inf) Default: 1

  • uncommitted::Bool: Uncommitted node flag. Default: true

  • display::Bool: Display flag for progress bars. Default: false

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AdaptiveResonance.opts_FuzzyART — Type
mutable struct opts_FuzzyART <: ARTOpts

Summary

Gamma-Normalized Fuzzy ART options struct.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Fields

  • rho::Float64: Vigilance parameter: rho ∈ [0, 1]. Default: 0.6

  • alpha::Float64: Choice parameter: alpha > 0. Default: 0.001

  • beta::Float64: Learning parameter: beta ∈ (0, 1]. Default: 1.0

  • gamma::Float64: Pseudo kernel width: gamma >= 1. Default: 3.0

  • gamma_ref::Float64: Reference gamma for normalization: 0 <= gamma_ref < gamma. Default: 1.0

  • max_epoch::Int64: Maximum number of epochs during training: max_epochs ∈ (1, Inf). Default: 1

  • display::Bool: Display flag for progress bars. Default: false

  • gamma_normalization::Bool: Flag to normalize the threshold by the feature dimension.

    NOTE: this flag overwrites the activation and match settings here to their gamma-normalized equivalents along with adjusting the thresold. Default: false

  • uncommitted::Bool: Flag to use an uncommitted node when learning.

    If true, new weights are created with ones(dim) and learn on the complement-coded sample. If false, fast-committing is used where the new weight is simply the complement-coded sample. Default: false

  • activation::Symbol: Selected activation function. Default: :basic_activation

  • match::Symbol: Selected match function. Default: :basic_match

  • update::Symbol: Selected weight update function. Default: :basic_update

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AdaptiveResonance.opts_SFAM — Type
mutable struct opts_SFAM <: ARTOpts

Summary

Implements a Simple Fuzzy ARTMAP learner's options.

These options are a Parameters.jl struct, taking custom options keyword arguments. Each field has a default value listed below.

Fields

  • rho::Float64: Vigilance parameter: rho ∈ [0, 1]. Default: 0.75

  • alpha::Float64: Choice parameter: alpha > 0. Default: 1.0e-7

  • epsilon::Float64: Match tracking parameter: epsilon ∈ (0, 1). Default: 0.001

  • beta::Float64: Learning parameter: beta ∈ (0, 1]. Default: 1.0

  • max_epoch::Int64: Maximum number of epochs during training: max_epoch ∈ [1, Inf). Default: 1

  • display::Bool: Display flag for progress bars. Default: false

  • uncommitted::Bool: Flag to use an uncommitted node when learning.

    If true, new weights are created with ones(dim) and learn on the complement-coded sample. If false, fast-committing is used where the new weight is simply the complement-coded sample. Default: false

  • match::Symbol: Selected match function. Default: :basic_match

  • activation::Symbol: Selected activation function. Default: :basic_activation

  • update::Symbol: Selected weight update function. Default: :basic_update

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Constants

AdaptiveResonance.ADAPTIVERESONANCE_VERSION — Constant

ADAPTIVERESONANCE_VERSION

Description

A constant that contains the version of the installed AdaptiveResonance.jl package.

This value is computed at compile time, so it may be used to programmatically verify the version of AdaptiveResonance that is installed in case a compat entry in your Project.toml is missing or otherwise incorrect.

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