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C-ML is a lightweight machine learning library written in C. It provides implementations for various neural network components.

Features

• Layers: Dense, Dropout, Flatten, Pooling, Max-Pooling
• Activations: ReLU, Sigmoid, Tanh, Softmax, ELU, Leaky ReLU, Linear
• Loss Functions: Mean Squared Error, Binary Cross-Entropy, Focal Loss, etc.
• Metrics: Accuracy, Precision, Recall, F1 Score, etc.
• Optimizers: SGD, Adam, RMSprop
• Preprocessing: Label Encoding, One-Hot Encoding, Standard Scaler, Min-Max Scaler
• Regularizers: L1, L2, Combined L1-L2

Prerequisites

• GCC (GNU Compiler Collection)
• make build tool

Build Instructions

1. Clone the repository:

   git clone https://github.com/jaywyawhare/C-ML.git
   cd C-ML

2. Build the project:

   make

3. Run the example program:

   ./bin/main

4. Run the tests:

   make test

5. Run the examples:

   make examples

6. Clean the build artifacts:

   make clean

Example Usage

The main.c file demonstrates how to use the library to create a simple neural network with a dense layer, ReLU activation, and mean squared error loss.

#include <stdio.h>
#include <stdlib.h>
#include "include/Core/training.h"
#include "include/Core/dataset.h"

int main()
{
    NeuralNetwork *network = create_neural_network(2);
    build_network(network, OPTIMIZER_ADAM, 0.1f, LOSS_MSE, 0.0f, 0.0f);
    model_add(network, LAYER_DENSE, ACTIVATION_RELU, 2, 4, 0.0f, 0, 0);
    model_add(network, LAYER_DENSE, ACTIVATION_TANH, 4, 4, 0.0f, 0, 0);
    model_add(network, LAYER_DENSE, ACTIVATION_SIGMOID, 4, 1, 0.0f, 0, 0);

    float X_data[4][2] = {{0.0f, 0.0f}, {0.0f, 1.0f}, {1.0f, 0.0f}, {1.0f, 1.0f}};
    float y_data[4][1] = {{0.0f}, {1.0f}, {1.0f}, {1.0f}};

    Dataset *dataset = dataset_create();
    dataset_load_arrays(dataset, (float *)X_data, (float *)y_data, 4, 2, 1);

    summary(network);

    train_network(network, dataset, 30);
    test_network(network, dataset->X, dataset->y, dataset->num_samples, NULL);

    dataset_free(dataset);
    free_neural_network(network);

    return 0;
}

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

License

This project is licensed under the DBaJ-NC-CFL License.