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libuaic

uAIC is a small machine learning library for C.

Features

  • Designed to be easy to use and integrate into your C projects.
  • Provides a range of functionalities for data preprocessing, model training, and prediction.
  • Supports supervised learning tasks.
  • Offers high-performance capabilities to handle large datasets.
  • Includes comprehensive documentation and examples to help you get started quickly.

Installation

To use uAIC in your C projects, follow these steps:

  1. Clone the repository: git clone https://github.com/CodenameSource/libuaic.git
  2. Navigate to the project directory: cd libuaic
  3. Compile the library: make lib
  4. Link the library with your project: gcc -o myproject myproject.c -I./include -L./lib -luaic
  5. Run your project: ./myproject

Usage

Once you have linked the library with your project, you can start using uAIC. Here's a simple example to demonstrate how to train a linear regression model:

int main()
{
    DataFrame test = {0}, X = {0}, Y = {0};
    UAI_MUST(df_load_csv(&test, "csv/houses.csv", ','));
    df_set_header(&test, true);

    df_to_double(&test, DATACELL_CONVERT_STRICT);
    df_normalize(&test);

    srand(time(NULL));

    UAI_MUST(df_create_vsplit(&test, &Y, 1, DATAFRAME_SAMPLE_SEQ));
    UAI_MUST(df_create_vsplit(&test, &X, 4, DATAFRAME_SAMPLE_SEQ));

    LinearRegressor *reg = lr_init();
    lr_fit(reg, &X, &Y, 2000, 0.015);

    for (size_t r=0; r < Y.rows; ++r)
        printf("%lf\n", uai_denormalize_value(lr_predict(reg, X.data[r], X.cols), Y.data[0][0].min, Y.data[0][0].delta));

    lr_destroy(reg);

    df_destroy(&test);
    df_destroy(&X);
    df_destroy(&Y);
}

For more detailed information on the available functionalities and how to use them, refer to the documentation.

Example code

Extensive example code is provided in the examples/ directory. You can build and run the examples using:

$ make

All the binaries will be built in ./examples/*.out.

The following examples are available:

  • dataframe/load_csv
  • dataframe/convert
  • dataframe/export_csv
  • dataframe/split
  • dataframe/fill
  • dataframe/resize
  • dataframe/scale_data
  • logistic_regression/logistic_regression
  • classification/decision_tree
  • linear_regression/linear_regression

Contributing

Contributions are welcome! If you would like to contribute to uAIC, please follow the guidelines outlined in CONTRIBUTING.md.

License

This project is licensed under the GPL License.