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 & KNN 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()

K-Nearest Neighbors (KNN)

1. KNN Regression & Classification Example

import numpy as np
from utelearn import knn

# Generate synthetic training data
X_train = np.random.rand(100, 1)
y_train = 4 * X_train**2 + 3 * X_train + 2 + np.random.randn(100, 1) * 0.1

# Generate test points
X_test = np.linspace(0, 1, 10).reshape(-1, 1)

# Perform KNN regression with k=5
predictions = knn.knn_regression(X_train, y_train, X_test, k=5)
print("Regression Predictions:", predictions)

# Generate synthetic training and test features/labels
X_train = np.random.rand(100, 2)
y_train = np.random.randint(0, 3, size=100) # 3 classes
X_test = np.random.rand(10, 2)

# Perform KNN classification with k=5
predictions = knn.knn_classify(X_train, y_train, X_test, k=5, n_classes=3)
print("Classification Predictions:", predictions)

## Author

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

Metadata

Release files for utelearn 10.1.2026

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 10.1.2026
File	Size	Uploaded
utelearn-10.1.2026.tar.gz	4.2 kB	Oct 6, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for utelearn 10.1.2026
File	Interpreter	ABI	Platform	Reset
utelearn-10.1.2026-py3-none-any.whl 4.4 kB Oct 6, 2026	Python 3	none	any	Details

Total release size: 8.7 kB

Release files / utelearn-10.1.2026.tar.gz

Download URL	utelearn-10.1.2026.tar.gz
Size	4.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`1049750e0750a3b7737c753d2ef4edec82d1c2d04c7195e5f070a5bdb56b1a89`
BLAKE2b-256 checksum How to use checksums	`0159ef83da7d408602b2a3991af43d8d6d0aeca3a986b61988c394b2d4d23767`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.4`

Release files / utelearn-10.1.2026-py3-none-any.whl

Download URL	utelearn-10.1.2026-py3-none-any.whl
Size	4.4 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5dce963b74e6ba950f70caeeeb6f0c6fd22a6d855afd7864b6ac17bd5c53ae41`
BLAKE2b-256 checksum How to use checksums	`7d45c86366b6c00d9e652a667a98e6f97226479853dbb99f9c3882cf9f962faf`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.4`