JAJA - C++

JAJA is an open-source C++ library for linear algebra and machine learning algorithms. It provides fast and efficient implementations of a variety of mathematical operations, including matrix multiplication, matrix decomposition, and optimization algorithms. JAJA also includes tools for data preprocessing, feature engineering, and model evaluation.

Features

• Fast and efficient linear algebra operations, including matrix multiplication, matrix decomposition, and linear regression.
• Optimization algorithms for gradient descent, stochastic gradient descent, and Newton's method.
• Tools for data preprocessing, feature engineering, and model evaluation.
• Support for both dense and sparse matrices.
• Easy-to-use API for simple and complex mathematical operations.
• Designed for high-performance computing on multicore and distributed systems.

Getting Started

To begin using JAJA, you can either download the source code from the official website or install it using a package manager. JAJA is compatible with Windows, Linux, and macOS operating systems.

Installation

Linux and macOS

To install JAJA on Linux or macOS, follow these steps:

1. Install the required dependencies:

   $ sudo apt-get install build-essential cmake git libeigen3-dev
   

2. Clone the JAJA repository:

   $ git clone https://github.com/jaja-cpp/jaja.git
   

3. Build and install JAJA:

   $ cd jaja
   $ mkdir build && cd build
   $ cmake ..
   $ make
   $ sudo make install
   

Windows

To install JAJA on Windows, follow these steps:

1. Install Visual Studio with C++ Development Workload and CMake.

2. Download and install Boost library from here.

3. Clone the JAJA repository:

   $ git clone https://github.com/jaja-cpp/jaja.git
   

4. Open the JAJA project in Visual Studio and build it.

Usage

Here is an example of how to use JAJA to solve a linear regression problem:

#include <jaja/jaja.hpp>

using namespace jaja;

int main()
{
    // Generate a sample dataset
    Matrix<double> X = {{1, 1}, {1, 2}, {1, 3}, {1, 4}};
    Vector<double> y = {2, 4, 6, 8};

    // Fit a linear regression model
    LinearRegressor<double> regressor;
    regressor.fit(X, y);

    // Predict new values
    Vector<double> y_pred = regressor.predict(X);

    // Print the results
    std::cout << "Coefficients: " << regressor.coef() << std::endl;
    std::cout << "Intercept: " << regressor.intercept() << std::endl;
    std::cout << "Predicted values: " << y_pred << std::endl;

    return 0;
}

Contribution

JAJA is an open-source project and contributions are welcome. You can contribute by submitting bug reports, feature requests, or by contributing code. See the contributing guidelines for more details.

License

JAJA is released under the MIT License.