Perceptron Implementation in C++

This C++ program demonstrates a basic implementation of a perceptron trained using the loss function and the perceptron trick. The perceptron is a fundamental building block of neural networks and is capable of binary classification.

Getting Started

Prerequisites

• C++ Compiler

Usage

1. Clone the repository:

   git clone https://github.com/your-username/perceptron-cpp.git

2. Compile the C++ program:

   g++ perceptron.cpp -o perceptron

3. Run the executable:

   ./perceptron

Input Data

The program expects two CSV files:

1. train.csv - Training data containing input features and corresponding binary labels.
2. test.csv - Testing data for evaluating the trained perceptron.

Code Overview

• split_nums: Function to split a string into a vector of doubles.
• printCols: Function to print input features and labels.
• sum: Function to calculate the sum of two vectors, including a bias term.
• classifier: Function to classify input using the trained perceptron.

The program performs training using the perceptron trick and then tests the perceptron on a separate dataset.

Parameters

• Learning Rate: 0.1
• Epochs: 1000

Considerations

• This is a very basic single neuron perceptron with random weights. The sample data given is also very small (~100 rows). So the current outputs may vary a lot. Thus, it is for learning purposes only.

Contributing

Feel free to contribute by opening issues or submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Inspired by the concept of perceptrons and neural networks.