Merge pull request #101 from NiklasMelton/readthedocs

Readthedocs and numpy formatted docstrings

README.md

@@ -3,6 +3,7 @@
 
 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.
 
+<!-- START available_models -->
 ## Available Models
 
 AdaptiveResonanceLib includes implementations for the following ART models:
@@ -17,7 +18,6 @@ AdaptiveResonanceLib includes implementations for the following ART models:
     - Fuzzy ART
     - Quadratic Neuron ART
     - Dual Vigilance ART
-    - Gram ART
 - #### Metric Informed 
     - CVI ART  
     - iCVI Fuzzy ART  
@@ -35,13 +35,20 @@ AdaptiveResonanceLib includes implementations for the following ART models:
     - Fusion ART
 - #### Reinforcement Learning
     - FALCON
-
+    - TD-FALCON
 - #### Biclustering
     - Biclustering ARTMAP
+
+<!-- END available_models -->
 
+<!-- START comparison_of_elementary_models -->
 ## Comparison of Elementary Models
-![Comparison of Elementary Images](./img/comparison_of_elementary_methods.jpg?raw=true")
 
+[comment]: <> (![Comparison of Elementary Images]&#40;https://github.com/NiklasMelton/AdaptiveResonanceLib/raw/main/docs/_static/comparison_of_elementary_methods.jpg?raw=true"&#41;)
+![Comparison of Elementary Images](https://github.com/NiklasMelton/AdaptiveResonanceLib/raw/main/img/comparison_of_elementary_methods.jpg?raw=true")
+<!-- END comparison_of_elementary_models -->
+
+<!-- START installation -->
 ## Installation
 
 To install AdaptiveResonanceLib, simply use pip:
@@ -57,7 +64,9 @@ pip install artlib
 ```
 
 Ensure you have Python 3.9 or newer installed.
+<!-- END installation -->
 
+<!-- START quick-start -->
 ## Quick Start
 
 Here's a quick example of how to use AdaptiveResonanceLib with the Fuzzy ART model:
@@ -80,28 +89,38 @@ model.fit(train_X)
 predictions = model.predict(test_X)
 ```
 
-Replace `params` with the parameters appropriate for your use case.
+<!-- END quick-start -->
 
+<!-- START documentation -->
 ## Documentation
 
 For more detailed documentation, including the full list of parameters for each model, visit our [documentation page](https://github.com/NiklasMelton/AdaptiveResonanceLib).
+<!-- END documentation -->
 
+<!-- START examples -->
 ## Examples
 
 For examples of how to use each model in AdaptiveResonanceLib, check out the `/examples` directory in our repository.
+<!-- END examples -->
 
+<!-- START contributing -->
 ## Contributing
 
 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](https://discord.gg/E465HBwEuN) and participate directly in the discussion.
+<!-- END contributing -->
 
+<!-- START license -->
 ## License
 
 AdaptiveResonanceLib is open source and available under the MIT license. See the `LICENSE` file for more info.
+<!-- END license -->
 
+<!-- START contact -->
 ## Contact
 
 For questions and support, please open an issue in the GitHub issue tracker or message us on our [Discord server](https://discord.gg/E465HBwEuN). We'll do our best to assist you.
 
 Happy Modeling with AdaptiveResonanceLib!
+<!-- END contact -->

artlib/__init__.py

@@ -1,3 +1,18 @@
+"""
+Adaptive Resonance Theory (ART) is a cognitive and neural network model that explains how the brain learns to recognize
+patterns while maintaining stability in the face of new, potentially conflicting information. ART networks are known for
+their ability to perform unsupervised learning and adaptively categorize data without forgetting previously learned
+patterns, a feature known as "plasticity-stability balance."
+
+The ART modules provided here support classification, clustering, and reinforcement learning tasks by dynamically
+adjusting to incoming data streams. They also offer advanced capabilities, including hierarchical clustering,
+topological clustering, data fusion, and regression, enabling flexible exploration of complex data structures.
+
+`Adaptive Resonance Theory <https://en.wikipedia.org/wiki/Adaptive_resonance_theory>`_
+
+"""
+
+
 from artlib.common.BaseART import BaseART
 from artlib.common.BaseARTMAP import BaseARTMAP
 from artlib.common.utils import normalize, compliment_code, de_compliment_code, de_normalize
@@ -6,7 +21,6 @@
 from artlib.elementary.ART1 import ART1
 from artlib.elementary.ART2 import ART2A
 from artlib.elementary.BayesianART import BayesianART
-from artlib.elementary.DualVigilanceART import DualVigilanceART
 from artlib.elementary.EllipsoidART import EllipsoidART
 from artlib.elementary.GaussianART import GaussianART
 from artlib.elementary.FuzzyART import FuzzyART
@@ -28,6 +42,7 @@
 from artlib.biclustering.BARTMAP import BARTMAP
 
 from artlib.topological.TopoART import TopoART
+from artlib.topological.DualVigilanceART import DualVigilanceART
 
 __all__ = [
     "BaseART",
@@ -51,5 +66,7 @@
     "FusionART",
     "BARTMAP",
     "iCVIFuzzyART",
-    "CVIART"
+    "CVIART",
+    "FALCON",
+    "TD_FALCON"
 ]