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Welcome to AdaptiveResonanceLib, a comprehensive and modular Python library for Adaptive Resonance Theory (ART) algorithms. Based on scikit-learn, our library offers a wide range of ART models designed for both researchers and practitioners in the field of machine learning and neural networks. Whether you're working on classification, clustering, or pattern recognition, AdaptiveResonanceLib provides the tools you need to implement ART algorithms efficiently and effectively.
AdaptiveResonanceLib includes implementations for the following ART models:
To install AdaptiveResonanceLib, simply use pip:
pip install artlib
Or to install directly from the most recent source:
pip install git+https://github.com/NiklasMelton/AdaptiveResonanceLib.git@develop
Ensure you have Python 3.9 or newer installed.
Here are some quick examples to get you started with AdaptiveResonanceLib:
from artlib import FuzzyART
import numpy as np
# Your dataset
train_X = np.array([...]) # shape (n_samples, n_features)
test_X = np.array([...])
# Initialize the Fuzzy ART model
model = FuzzyART(rho=0.7, alpha = 0.0, beta=1.0)
# Prepare Data
train_X_prep = model.prepare_data(train_X)
test_X_prep = model.prepare_data(test_X)
# Fit the model
model.fit(train_X_prep)
# Predict data labels
predictions = model.predict(test_X_prep)
from artlib import GaussianART, SimpleARTMAP
import numpy as np
# Your dataset
train_X = np.array([...]) # shape (n_samples, n_features)
train_y = np.array([...]) # shape (n_samples, ), must be integers
test_X = np.array([...])
# Initialize the Gaussian ART model
sigma_init = np.array([0.5]*train_X.shape[1]) # variance estimate for each feature
module_a = GaussianART(rho=0.0, sigma_init=sigma_init)
# Initialize the SimpleARTMAP model
model = SimpleARTMAP(module_a=module_a)
# Prepare Data
train_X_prep = model.prepare_data(train_X)
test_X_prep = model.prepare_data(test_X)
# Fit the model
model.fit(train_X_prep, train_y)
# Predict data labels
predictions = model.predict(test_X_prep)
from artlib import FuzzyART, HypersphereART, FusionART
import numpy as np
# Your dataset
train_X = np.array([...]) # shape (n_samples, n_features_X)
train_y = np.array([...]) # shape (n_samples, n_features_y)
test_X = np.array([...])
# Initialize the Fuzzy ART model
module_x = FuzzyART(rho=0.0, alpha = 0.0, beta=1.0)
# Initialize the Hypersphere ART model
r_hat = 0.5*np.sqrt(train_X.shape[1]) # no restriction on hyperpshere size
module_y = HypersphereART(rho=0.0, alpha = 0.0, beta=1.0, r_hat=r_hat)
# Initialize the FusionARTMAP model
gamma_values = [0.5, 0.5] # eqaul weight to both channels
channel_dims = [
2*train_X.shape[1], # fuzzy ART complement codes data so channel dim is 2*n_features
train_y.shape[1]
]
model = FusionART(
modules=[module_x, module_y],
gamma_values=gamma_values,
channel_dims=channel_dims
)
# Prepare Data
train_Xy = model.join_channel_data(channel_data=[train_X, train_y])
train_Xy_prep = model.prepare_data(train_Xy)
test_Xy = model.join_channel_data(channel_data=[train_X], skip_channels=[1])
test_Xy_prep = model.prepare_data(test_Xy)
# Fit the model
model.fit(train_X_prep, train_y)
# Predict y-channel values
pred_y = model.predict_regression(test_Xy_prep, target_channels=[1])
For more detailed documentation, including the full list of parameters for each model, visit our Read the Docs page.
For examples of how to use each model in AdaptiveResonanceLib, check out the /examples
directory in our repository.
We welcome contributions to AdaptiveResonanceLib! If you have suggestions for improvements, or if you'd like to add more ART models, please see our CONTRIBUTING.md
file for guidelines on how to contribute.
You can also join our Discord server and participate directly in the discussion.
AdaptiveResonanceLib is open source and available under the MIT license. See the LICENSE
file for more info.
For questions and support, please open an issue in the GitHub issue tracker or message us on our Discord server. We'll do our best to assist you.
Happy Modeling with AdaptiveResonanceLib!
If you use this project in your research, please cite it as:
Melton, N. (2024). AdaptiveResonanceLib (Version 0.1.3)
FAQs
A Python library for Adaptive Resonance Theory (ART) algorithms.
We found that artlib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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