utelearn

utelearn is a custom machine learning library built from scratch for educational purposes at UTE.

Installation

You can install the package directly from PyPI:

pip install utelearn

Features

KMeans Clustering: Custom implementation of the KMeans clustering algorithm using NumPy.
Built-in support for data visualization with Matplotlib.

Quick Start

Here is a quick example of how to use the KMeans model from utelearn:

import numpy as np
import matplotlib.pyplot as plt
from utelearn.kmeans import KMeans

# 1. Tạo dữ liệu mẫu ngẫu nhiên (2 cụm rõ rệt)
np.random.seed(42)
X = np.vstack([
    np.random.randn(50, 2) + np.array([2, 2]),
    np.random.randn(50, 2) + np.array([-2, -2])
])

# 2. Khởi tạo và huấn luyện mô hình K-Means với k=2
kmeans = KMeans(k=2, max_iters=100)
kmeans.fit(X)

# 3. In kết quả trung tâm cụm (centroids)
print("Tọa độ các tâm cụm (Centroids):")
print(kmeans.centroids)

# 4. Dự đoán nhãn cho các điểm dữ liệu (nếu lớp KMeans có hàm predict)
# Hoặc gán nhãn dựa vào khoảng cách gần nhất tới các centroids
labels = np.argmin([np.linalg.norm(X - c, axis=1) for c in kmeans.centroids], axis=0)

# 5. Vẽ biểu đồ trực quan
plt.figure(figsize=(8, 6))
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', marker='o', alpha=0.7, edgecolors='k', label='Dữ liệu mẫu')
plt.scatter(kmeans.centroids[:, 0], kmeans.centroids[:, 1], c='red', marker='X', s=250, edgecolors='k', label='Tâm cụm (Centroids)')

plt.title("Trực quan hóa thuật toán K-Means - Utelearn", fontsize=14)
plt.xlabel("Trục X", fontsize=12)
plt.ylabel("Trục Y", fontsize=12)
plt.legend()
plt.grid(True, linestyle='--', alpha=0.5)
plt.show()

Author

Dung Cai (dung.cai@hcmute.edu.vn)

Metadata

Release files for utelearn 0.2.2026

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

Source distribution (sdist)

Source distribution for utelearn 0.2.2026
File	Size	Uploaded
utelearn-0.2.2026.tar.gz	2.9 kB	Oct 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for utelearn 0.2.2026
File	Interpreter	ABI	Platform	Reset
utelearn-0.2.2026-py3-none-any.whl 3.0 kB Oct 5, 2026	Python 3	none	any	Details

Total release size: 6.0 kB

Release files / utelearn-0.2.2026.tar.gz

Download URL	utelearn-0.2.2026.tar.gz
Size	2.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`224a12ba51104592d55bd392687257dad91731277221f928e6fc5c60aa3889c3`
BLAKE2b-256 checksum How to use checksums	`2d8581b005c3d714d67b4045038f4b14f5f24b153e430456be4aac1577413205`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.4`

Release files / utelearn-0.2.2026-py3-none-any.whl

Download URL	utelearn-0.2.2026-py3-none-any.whl
Size	3.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`03489ea9b7ccf705127194b342acff822b03d1748552586d01d7aff4b792eb85`
BLAKE2b-256 checksum How to use checksums	`27b2b5ebfd34325f95be817f363070a570dd3a091aef94eda201c808a3951462`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.4`