🧠 Perceptron Implementation

This project implements a Perceptron, which is a fundamental algorithm used in machine learning for binary classification tasks. The Perceptron learns from a set of training data and adjusts its weights to classify new data points. It was made for the NAI (tools of AI) class at Pjait.

📋 Perceptron Class

• Functionality: The Perceptron class provides methods for training the Perceptron using training data, adjusting weights, classifying vectors, and testing the Perceptron's accuracy.
• Training: The trainPerceptron() method iteratively adjusts weights until the Perceptron is trained for the given training data.
• Classification: The classifyVector() method predicts the class of a data vector based on learned weights.
• Testing: The testPerceptronForListOfData() method evaluates the Perceptron's accuracy on a test dataset.

🚀 Main Class

• Initialization: The Main class initializes the Perceptron with training data and trains it.
• Testing: It then tests the trained Perceptron on a test dataset and prints the number of correctly classified examples and the experiment's accuracy.
• User Input: The getVectorInput() method allows users to input their own data vectors and see the predicted class label.

📁 Project Structure

• Perceptron.java: Contains the implementation of the Perceptron algorithm.
• Main.java: Entry point of the application, responsible for initializing, training, testing, and user interaction.
• DataReader.java: Utility class for reading data from files.
• Data.java: Class representing individual data instances.
• DataInput.java: Utility class for user input.

🛠️ Dependencies

• Java 8 or higher