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 are quick examples of how to use the machine learning libs from utelearn:

K-Means

Kmeans Clustering Example

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)

K-Nearest Neighbors (KNN)

1. KNN Regression & Classification Example using Points Lib

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from utelearn import knn
from utelearn import points as point

# 1. Configuration parameters
n_training_points = 100
n_test_points = 500
n_classes = 3
dimension = 2
k_neighbors = 5

# 2. Generate Training and Test Data using utelearn's points module (Spiral Distribution)
training_data = point.Spiral(n_training_points, n_classes, dimension)
test_data = point.Spiral(n_test_points, n_classes, dimension)

# 3. Perform KNN Classification using utelearn's knn module
print("Running KNN classification on test data using utelearn...")
predicted_labels = knn.knn_classify(
    X_train=training_data.P, 
    y_train=training_data.L, 
    X_test=test_data.P, 
    k=k_neighbors, 
    n_classes=n_classes
)

# 4. Plotting the Results
plt.figure(figsize=(15, 5))

# Subplot 1: Reference Training Data
plt.subplot(1, 3, 1)
plt.scatter(
    training_data.P[:, 0], 
    training_data.P[:, 1], 
    c=training_data.L, 
    s=20, 
    cmap=mcolors.ListedColormap(["red", "purple", "green"])
)
plt.title(f"Reference Data ({n_training_points * n_classes} points)")
plt.grid(True, alpha=0.3)

# Subplot 2: Unclassified Test Data
plt.subplot(1, 3, 2)
plt.scatter(
    test_data.P[:, 0], 
    test_data.P[:, 1], 
    color="blue", 
    s=15
)
plt.title("Unclassified Test Data")
plt.grid(True, alpha=0.3)

# Subplot 3: Classified Test Data via utelearn KNN
plt.subplot(1, 3, 3)
plt.scatter(
    test_data.P[:, 0], 
    test_data.P[:, 1], 
    c=predicted_labels, 
    s=15, 
    cmap=mcolors.ListedColormap(["red", "purple", "green"])
)
plt.title(f"Test Data After KNN (k={k_neighbors})")
plt.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Author

• Dung Cai (dung.cai@hcmute.edu.vn)