UVCEML
A Python package that provides various machine learning algorithms, including Artificial Neural Networks (ANNs), Bayesian Networks, K-Means clustering, K-Nearest Neighbors (KNN), and decision trees (ID3). It also includes algorithms like Candidate Elimination, Find-S, and Naive Bayes, along with Local Weighted Regression.
Features
- ANN: Implementation of an Artificial Neural Network with a single hidden layer.
- Bayesian Network: Build a Bayesian Network and perform inference using the
pgmpylibrary. - K-Means Clustering: Perform K-Means clustering using
scikit-learn. - KNN: K-Nearest Neighbors classification using
scikit-learn. - Candidate Elimination Algorithm: Perform hypothesis elimination to find the most specific/general hypothesis.
- Find-S Algorithm: Find the maximally specific hypothesis from training examples.
- ID3 Decision Tree: Build a decision tree using the ID3 algorithm.
- Naive Bayes Classifier: A simple Naive Bayes classifier.
- Local Weighted Regression: Perform regression using locally weighted linear regression.
Installation
You can install the package using pip:
pip install uvceml
Usage
Importing the package
import uvceml
Example Usage
- Artificial Neural Network (ANN)
from uvceml import ANN
ANN()
- Bayesian Network
from uvceml import bayes_network
input_file = 'path_to_csv_file.csv'
bayes_network(input_file)
- K-Means Clustering
from uvceml import k_means
k_means()
- KNN
from uvceml import knn
knn()
- Candidate Elimination
from uvceml import candidate
input_file = 'path_to_csv_file.csv'
candidate(input_file)
- Find-S Algorithm
from uvceml import find_s
input_file = 'path_to_csv_file.csv'
find_s(input_file)
- ID3 Decision Tree
from uvceml import ID3
input_file = 'path_to_csv_file.csv'
ID3(input_file)
- Naive Bayes Classifier
from uvceml import NaiveBayes
input_file = 'path_to_csv_file.csv'
NaiveBayes(input_file)
- Local Weighted Regression
from uvceml import regression
input_file = 'path_to_csv_file.csv'
regression(input_file)
Dependencies
This package requires the following libraries:
numpy pandas scikit-learn pgmpy matplotlib You can install these using pip: pip install numpy pandas scikit-learn pgmpy matplotlib
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contributing
Feel free to contribute by submitting a pull request or opening an issue.
Happy coding!
Release files for uvceml 1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| uvceml-1.0.tar.gz | 5.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| uvceml-1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.7 kB
Release files / uvceml-1.0.tar.gz
| Download URL | uvceml-1.0.tar.gz |
|---|---|
| Size | 5.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
f076baf0a9733d4f2c4d807de96e12f7757fe9ab8cb7fa88e284ecd044e693da
|
|
BLAKE2b-256 checksum How to use checksums |
e6ae66829206cfe792e89a0bf4d9cbd5d5add55ea5d3ea125a73d10b1ce37e23
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.6
|
Release files / uvceml-1.0-py3-none-any.whl
| Download URL | uvceml-1.0-py3-none-any.whl |
|---|---|
| Size | 5.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b3a65be6dcae343c5bf1736365db3cda5f31404469ca0bff549d07549410f6ba
|
|
BLAKE2b-256 checksum How to use checksums |
cc085b40c4ac4b32a49771ecee723161538410bb70b5ab66abff80e5557dfd9b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.6
|