https://github.com/tomcat-42/daa

A library for design and analisys of algorithms in c++ (include python bindings)
https://github.com/tomcat-42/daa

Last synced: 25 days ago
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A library for design and analisys of algorithms in c++ (include python bindings)

• Host: GitHub
• URL: https://github.com/tomcat-42/daa
• Owner: Tomcat-42
• Created: 2022-11-13T20:37:36.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-07-10T11:39:47.000Z (over 1 year ago)
• Last Synced: 2024-11-19T19:00:23.026Z (about 2 months ago)
• Language: C++
• Size: 74.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Design and Analysis of Algorithms Library in C++ with Python bindings

This is a library for designing and analyzing algorithms, implemented in C++. It

also has Python bindings via pybind11. The library includes implementations for

common problems like the 0-1 knapsack and sorting algorithms.

## Features

Implementation of the 0-1 knapsack problem using dynamic programming

Implementation of common sorting algorithms like quicksort and mergesort Python

bindings using pybind11 for easy use in Python projects Well-commented and

easily understandable C++ code Description

This library provides a set of algorithms implemented in C++ that are designed

for solving various problems related to designing and analyzing algorithms. It

includes common algorithms for sorting, such as quicksort and mergesort, as well

as an implementation for the 0-1 knapsack problem, which is solved using dynamic

programming.

One of the main benefits of this library is that it includes Python bindings via

pybind11, which means that the C++ code can be easily used in Python projects.

This can save time and effort when implementing algorithms in Python, as users

can take advantage of the well-tested and optimized C++ implementations provided

by this library.

The C++ code is well-commented and easy to understand, making it a good resource

for learning about algorithms and how they can be implemented. The code is also

highly modular, which means that users can easily modify and extend it to suit

their needs.

## Installation

To use this library, users can either build it from source or install it using a

package manager such as pip. The library can be built from source by cloning the

repository and running CMake to generate the build files. Once the build files

have been generated, the library can be built using the standard CMake build

process.

To install the library using pip, users can run the following command:

```python

pip install pydaa

```

Once the library has been installed, it can be imported into Python projects

using the standard import statement.

## Conclusion

The Design and Analysis of Algorithms Library is a useful resource for anyone

looking to implement common algorithms in their projects. With its Python

bindings and well-commented C++ code, it provides a straightforward and

accessible way to learn about algorithms and their implementation.