AIS-Package · Joao-Paulo-Silva · Apr 21, 2026 · Apr 10, 2026 · Apr 10, 2026 · Apr 21, 2026
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
@@ -0,0 +1,9 @@
+## Description
+
+Describe the changes introduced by this Pull Request.
+
+## Checklist
+
+- [ ] My code follows the [Project Standards](https://github.com/AIS-Package/aisp/blob/main/CONTRIBUTING.md#project-standards).
+- [ ] Alls new and existing test passed.
+- [ ] I have added docstrings to all new or modified functions and class.
diff --git a/README.md b/README.md
@@ -1,103 +1,200 @@
 <div align = center>
 
-|    <img src='https://ais-package.github.io/assets/images/logo-7b415c6841a3ed8a760eff38ecd996b8.svg'/>   |     <h1 class='text-title' align=center>**Artificial Immune Systems Package.**</h1>  |
-|:-------------:|:-------------:|
+<img alt="Artificial Immune Systems Package" src='https://ais-package.github.io/assets/images/logo-7b415c6841a3ed8a760eff38ecd996b8.svg'/>
+
+# Artificial Immune Systems Package
+
+A Python package for Artificial Immune Systems algorithms
 
 </div>
 
 ---
 
-#### Select the language / Selecione o Idioma
+## Language
 
-<div class='language-options'>
+* [Português](https://github.com/AIS-Package/aisp/blob/main/docs/pt-br/README.md)
 
-* [English.](#english)
-* [Português.](https://ais-package.github.io/pt-br/docs/intro)
+## Documentation
 
-</div>
+* [Official Docs](https://ais-package.github.io/docs/intro)
+* [Github Wiki](https://github.com/AIS-Package/aisp/wiki)
 
-#### Package documentation / Documentação do pacote
+---
 
-* [Docs.](https://ais-package.github.io/docs/intro)
+## Introduction
 
-* [Wiki Github.](https://github.com/AIS-Package/aisp/wiki)
+**AISP** is a python package that implements artificial immune systems techniques, distributed under the GNU Lesser
+General Public License v3.0 (LGPLv3).
 
----
+The package started in **2022** as a research package at the Federal Institute of Northern Minas Gerais - Salinas
+campus (**IFNMG - Salinas**).
 
-<section id='english'>
+Artificial Immune Systems (AIS) are inspired by the vertebrate immune system, creating metaphors that apply the
+ability to detect and catalog pathogens, among other features of this system.
 
-#### Summary
+### What can you do with AISP?
 
-> 1. [Introduction.](#introduction)
-> 2. [Installation.](#installation)
->    1. [Dependencies](#dependencies)
->    2. [User installation](#user-installation)
-> 3. [Examples.](#examples)
+AISP provides implementations of bio-inspired algorithms for:
+
+- **Anomaly detection:** Identify abnormal patterns in data.
+- **Classification:** Classify data with multiple classes.
+- **Optimization:** Find optimal solutions for objective functions.
+- **Clustering:** Group data without supervision.
 
 ---
-<section id='introduction'>
 
-#### Introduction
+## Implemented Algorithms
 
-The **AISP** is a python package that implements artificial immune systems techniques, distributed under the GNU Lesser General Public License v3.0 (LGPLv3).
+### Negative Selection (`aisp.nsa`)
 
-The package started in **2022** as a research package at the Federal Institute of Northern Minas Gerais - Salinas campus (**IFNMG - Salinas**).
+- **BNSA** - Binary Negative Selection Algorithm  
+- **RNSA** - Real-Valued Negative Selection Algorithm  
 
-Artificial Immune Systems (AIS) are inspired by the vertebrate immune system, creating metaphors that apply the ability to detect and catalog pathogens, among other features of this system.
+### Clonal Selection (`aisp.csa`)
 
-##### Algorithms implemented
+- **AIRS** - Artificial Immune Recognition System  
+- **CLONALG** - Clonal Selection Algorithm  
 
-> * [x] [**Negative Selection.**](https://ais-package.github.io/docs/aisp-techniques/negative-selection/)
-> * [x] [**Clonal Selection Algorithms.**](https://ais-package.github.io/docs/aisp-techniques/clonal-selection-algorithms/)
->   * [AIRS - Artificial Immune Recognition System](https://ais-package.github.io/docs/aisp-techniques/clonal-selection-algorithms/airs/)
->   * [CLONALG - Clonal Selection Algorithm](https://ais-package.github.io/docs/aisp-techniques/clonal-selection-algorithms/clonalg)
-> * [ ] *Danger Theory.*
-> * [x] [*Immune Network Theory.*](https://ais-package.github.io/docs/aisp-techniques/immune-network-theory/)
->   * [AiNet - Artificial Immune Network para Clustering and Compression](https://ais-package.github.io/docs/aisp-techniques/immune-network-theory/ainet)
+### Immune Network Theory (`aisp.ina`)
 
-</section>
+- **AiNet** - Artificial Immune Network for clustering and data compression
 
-<section id='installation'>
+### Module in Development
 
-#### **Installation**
+#### Danger Theory (`aisp.dta`)
 
-The module requires installation of [python 3.10](https://www.python.org/downloads/) or higher.
+- **DCA** - Dendritic Cell Algorithm *(planned)*
 
-<section id='dependencies'>
+## API overview
 
-##### **Dependencies:**
+All algorithms follow a simple and consistent interface:
+
+- `fit(X, y, verbose: bool = True)`: trains the model for classification tasks.
+- `fit(X, verbose: bool = True)`: trains the model for clustering tasks.
+- `predict(X)`: makes predictions based on new data.
+- `optimize(max_iters: int =..., n_iter_no_change: int =..., verbose: bool = True)`: run the optimization algorithms
+
+---
+
+## Installation
+
+The module requires installation of [python 3.10](https://www.python.org/downloads/) or higher.
+
+### Dependencies
 
 <div align = center>
 
-|    Packages   |     Version   |
-|:-------------:|:-------------:|
-|    numpy      |    ≥ 1.22.4   |
-|    scipy      |    ≥ 1.8.1    |
-|    tqdm       |    ≥ 4.64.1   |
-|    numba      |    ≥ 0.59.0   |
+| Packages |  Version  |
+|:--------:|:---------:|
+|  numpy   | ≥ 1.22.4  |
+|  scipy   |  ≥ 1.8.1  |
+|   tqdm   | ≥ 4.64.1  |
+|  numba   | ≥ 0.59.0  |
 
 </div>
 
-</section>
-<section id='user-installation'>
-
-##### **User installation**
+### User installation
 
 The simplest way to install AISP is using ``pip``:
 
 ```Bash
 pip install aisp
 ```
 
-</section>
+---
+
+## Quick Start
+
+Below are minimal examples demonstrating how to use AISP for different tasks.
+
+### Classification with RNSA
+
+```python
+import numpy as np
+from aisp.nsa import RNSA
+
+# Generating training data
+np.random.seed(1)
+class_a = np.random.uniform(high=0.5, size=(50, 2))
+class_b = np.random.uniform(low=0.51, size=(50, 2))
+x_train = np.vstack((class_a, class_b))
+y_train = ['a'] * 50 + ['b'] * 50
+
+# Training the model
+model = RNSA(N=150, r=0.3, seed=1)
+model.fit(x_train, y_train, verbose=False)
+
+# Predict
+x_test = [
+    [0.15, 0.45],  # Expected: 'a'
+    [0.85, 0.65],  # Expected: 'b'
+]
+
+y_pred = model.predict(x_test)
+print(y_pred)
+```
+
+### Clustering with AiNet
+
+```python
+import numpy as np
+from aisp.ina import AiNet
+
+np.random.seed(1)
+# Generating training data
+a = np.random.uniform(high=0.4, size=(50, 2))
+b = np.random.uniform(low=0.6, size=(50, 2))
+x_train = np.vstack((a, b))
+
+# Training the model
+model = AiNet(
+    N=150,
+    mst_inconsistency_factor=1,
+    seed=1,
+    affinity_threshold=0.85,
+    suppression_threshold=0.7
+)
+
+model.fit(x_train, verbose=False)
+
+# Predict cluster labels
+x_test = [
+    [0.15, 0.45],
+    [0.85, 0.65],
+]
+
+y_pred = model.predict(x_test)
+print(y_pred)
+```
+
+### Optimization with CLONALG
+
+```python
+import numpy as np
+from aisp.csa import Clonalg
+
+# Define search space
+bounds = {'low': -5.12, 'high': 5.12}
 
-</section>
-<section id='examples'>
+# Objective function (Rastrigin)
+def rastrigin(x):
+    x = np.clip(x, bounds['low'], bounds['high'])
+    return 10 * len(x) + np.sum(x**2 - 10 * np.cos(2 * np.pi * x))
 
-#### Examples
+# Initialize optimizer
+model = Clonalg(problem_size=2, rate_hypermutation=0.5, bounds=bounds, seed=1)
+model.register('affinity_function', rastrigin)
+
+# Run optimization
+population = model.optimize(100, 50, False)
+
+print(model.best_solution, model.best_cost) # Best solution
+```
 
 ---
 
+## Examples
+
 Explore the example notebooks available in the [AIS-Package/aisp repository](https://github.com/AIS-Package/aisp/tree/main/examples).
 These notebooks demonstrate how to utilize the package's functionalities in various scenarios, including applications of the RNSA,
 BNSA and AIRS algorithms on datasets such as Iris, Geyser, and Mushrooms.
@@ -106,6 +203,4 @@ You can run the notebooks directly in your browser without any local installatio
 
 [![Launch on Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AIS-Package/aisp/HEAD?labpath=%2Fexamples)
 
-> 💡 **Tip**: Binder may take a few minutes to load the environment, especially on the first launch.
-</section>
-</section>
+💡 **Tip**: Binder may take a few minutes to load the environment, especially on the first launch.
diff --git a/aisp/__init__.py b/aisp/__init__.py
@@ -26,10 +26,12 @@
 https://ais-package.github.io/docs/intro
 """
 
+from importlib.metadata import version
+
 from . import csa
 from . import ina
 from . import nsa
 
 __author__ = "AISP Development Team"
-__version__ = "0.5.1"
+__version__ = version('aisp')
 __all__ = ["csa", "nsa", "ina"]
diff --git a/aisp/base/core/_base.py b/aisp/base/core/_base.py
@@ -1,7 +1,8 @@
 """Base class for parameter introspection compatible with the scikit-learn API."""
-
 from __future__ import annotations
 
+from inspect import signature
+
 
 class Base:
     """
@@ -43,7 +44,7 @@ def get_params(self, deep: bool = True) -> dict:  # pylint: disable=W0613
             Dictionary containing the object's attributes that do not start with "_".
         """
         return {
-            key: value
-            for key, value in self.__dict__.items()
-            if not key.startswith("_")
+            key: getattr(self, key)
+            for key, _ in signature(self.__init__).parameters.items()
+            if key != "self" and not key.startswith("_") and hasattr(self, key)
         }
diff --git a/aisp/base/core/_classifier.py b/aisp/base/core/_classifier.py
@@ -45,11 +45,11 @@ def fit(
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data used for training the model.
+            Training input samples. Each row corresponds to a samples and column to feature.
         y : Union[npt.NDArray, list]
-            Corresponding labels or target values for the input data.
+            Target vector of shape (n_samples,). Must contain the same number of samples as `X`.
         verbose : bool, default=True
-            Flag to enable or disable detailed output during training.
+            If True, prints training progress information.
 
         Returns
         -------
@@ -67,7 +67,7 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Input samples. Must have the same number of features used during training.
 
         Returns
         -------
@@ -83,8 +83,8 @@ def score(
         """
         Score function calculates forecast accuracy.
 
-        Details
-        -------
+        Notes
+        -----
         This function performs the prediction of X and checks how many elements are equal
         between vector y and y_predicted. This function was added for compatibility with some
         scikit-learn functions.
@@ -116,11 +116,11 @@ def _slice_index_list_by_class(self, y: npt.NDArray) -> dict:
         Parameters
         ----------
         y : npt.NDArray
-            Receives a y ``n_samples`` array with the output classes of the ``X`` sample array.
+            Receives a y `n_samples` array with the output classes of the `X` sample array.
 
         Returns
         -------
         indices_by_class : dict
-            A dictionary with the list of array positions(``y``), with the classes as key.
+            A dictionary with the list of array positions(`y`), with the classes as key.
         """
         return slice_index_list_by_class(self.classes, y)
diff --git a/aisp/base/core/_clusterer.py b/aisp/base/core/_clusterer.py
@@ -50,9 +50,9 @@ def fit(self, X: Union[npt.NDArray, list], verbose: bool = True) -> BaseClustere
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data used for training the model.
+            Training input samples. Each row corresponds to a samples and column to feature.
         verbose : bool, default=True
-            Flag to enable or disable detailed output during training.
+            If True, prints training progress information.
 
         Returns
         -------
@@ -70,7 +70,7 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Input samples. Must have the same number of features used during training.
 
         Returns
         -------
@@ -87,9 +87,9 @@ def fit_predict(self, X: Union[npt.NDArray, list], verbose: bool = True) -> npt.
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Training input samples. Each row corresponds to a samples and column to feature.
         verbose : bool, default=True
-            Flag to enable or disable detailed output during training.
+            If True, prints training progress information.
 
         Returns
         -------