cluster-ga 0.2

This Python script implements a genetic algorithm for clustering data. The algorithm optimizes the cluster assignments of data points using a genetic approach, aiming to improve the silhouette score. The silhouette score is a measure of how well-defined the clusters are in the data.

Genetic Clustering Algorithm

This Python script implements a genetic algorithm for clustering data. The algorithm optimizes the cluster assignments of data points using a genetic approach, aiming to improve the silhouette score. The silhouette score is a measure of how well-defined the clusters are in the data.

Table of Contents

• Getting Started
  • Installation
• Usage
• Algorithm Overview
  • Genetic Class
  • Cluster Class
• Parameters
• Results
• License
• Acknowledgments

Getting Started

Installation

2. Install the required dependencies:

pip install cluster_ga

Usage

from sklearn import datasets
import numpy as np
import pandas as pd
from cluster_ga.cluster import cluster

# this is a for test

iris = datasets.load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
x = np.array(iris_df[["petal length (cm)", "petal width (cm)"]])
y = iris.target

# Instantiate and fit the model
model = cluster(x, y, 500, 0.9,150) 
model.fit()


# show fitness plot
model.show_plot()

Algorithm Overview

The genetic clustering algorithm consists of the following components:

Genetic Class

Defines the genetic operations such as mutation, generation, and fitness calculation.

Cluster Class

Manages the clustering process, including the initialization of populations, evolution, and convergence.

Parameters

• size_population: Number of individuals in the population.
• goal: The desired fitness score to achieve.
• repeat: Number of generations to run the algorithm.
• is_mutation: Boolean flag to enable or disable mutation.

Results

The script outputs the progress of the algorithm, including the generation number and the fitness score achieved. Additionally, a plot of the fitness scores over generations is displayed at the end of the execution.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• This implementation is inspired by genetic algorithms and clustering techniques.
• Special thanks to the scikit-learn library for providing the silhouette score metric.