vicentin 1.1.2

Personal collection of algorithms.

Vicentin

A comprehensive Python library for mathematical optimization, deep learning, computer vision, and classic algorithms. This library is designed with a dual-backend architecture, offering seamless switching between NumPy for transparency and PyTorch for hardware acceleration and automatic differentiation.

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Table of Contents

• Introduction
• Features
• Installation
• Usage
• Pre-commit Setup
• License

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Introduction

vicentin is a Python package that contains my personal implementations of a variety of algorithms, data structures, and optimization techniques. It serves as a collection of theoretical and practical programming concepts.

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Features

• Data Structures: Queue, Stack, Tree, Graph, Heap, Priority Queue, Trie, Union Find
• Dynamic Programming: Knapsack, Matrix Multiplication, Rod Cutting, Edit Distance
• Graph Algorithms: Minimum Spanning Tree (MST), Shortest Path, Negative Cycle Detection
• Image & Video Processing: Optical Flow (Horn-Schunck), Differentiation, Regularization, Image-to-Graph
• Optimization: Gradient Descent, Newton's Method, Barrier Method, Proximal Gradient Descent, ISTA, Projected Gradient Descent, Newton-Raphson (Root Finding)
• Deep Learning:
  • Models: Autoencoders (AE), Variational Autoencoders (VAE)
  • Trainers: Standard, Supervised, GAN, Distillation
  • Losses: Beta-VAE, Wasserstein GAN
• Sorting: Heap Sort
• Mathematical Tools: Polynomial Operations

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Installation

1️⃣ Clone the Repository

git clone https://github.com/your-username/vicentin.git
cd vicentin

2️⃣ Set Up a Virtual Environment

python -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

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Usage

Heap data structure

# Example: Using the heap data structure
from vicentin.data_structures.heap import Heap

heap = Heap()
heap.insert(5)
heap.insert(2)
heap.insert(8)

print(heap.extract_min())  # Output: 2

Newton's Method

import torch
from vicentin.optimization.minimization import newton_method

def objective(x):
    return torch.sum(x**2)

x0 = torch.tensor([10.0, 10.0])
A = torch.tensor([[1.0, 1.0]])
b = torch.tensor([1.0])

# Backend (Torch) is automatically detected from x0
x_opt = newton_method(objective, x0, equality=(A, b))
print(f"Optimal solution: {x_opt}")

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Pre-commit Setup

This repository uses pre-commit to enforce coding standards, automatic formatting and automatic version bumping before commits.

1️⃣ Install pre-commit

pip install pre-commit

2️⃣ Install Hooks

pre-commit install

3️⃣ Use commitizen to commit

cz commit

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License

This project is licensed under the MIT License.