Skip to main content

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

  1. 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.

result

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.

Release files for cluster-ga 0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cluster-ga 0.2
File Size Uploaded
cluster_ga-0.2.tar.gz 2.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for cluster-ga 0.2
File Interpreter ABI Platform
cluster_ga-0.2-py3-none-any.whl Python 3 none any Details

Total release size: 4.5 kB

Release files / cluster_ga-0.2.tar.gz

Download URL cluster_ga-0.2.tar.gz
Size 2.3 kB
Tags Source
SHA-256 checksum
How to use checksums
4c9c697596164c6cb7804f59ab7038e588fa6e9da22f6a0701bf9b4591b0096a
BLAKE2b-256 checksum
How to use checksums
c08e26f449e38a61905030c840d37f096b5e13fdacfb2ce38de91eeb00b9ebfa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.0.1 CPython/3.13.0

Release files / cluster_ga-0.2-py3-none-any.whl

Download URL cluster_ga-0.2-py3-none-any.whl
Size 2.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
9f71d16e3b23270029a45bfe4201897840d1200278c6d30c7cf492db44c55c76
BLAKE2b-256 checksum
How to use checksums
d88667ac2c5ca334781835fc422ad5c4d4b7ce143667cb3f4c5021ce84e5621a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.0.1 CPython/3.13.0

Release history Release notifications | RSS feed

This release

0.2 This release

2 release files

0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page