https://github.com/pereirar3/simpleperceptron
Activity carried out in the AI discipline
https://github.com/pereirar3/simpleperceptron
perceptron python tree-decision-classifier
Last synced: 3 months ago
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Activity carried out in the AI discipline
- Host: GitHub
- URL: https://github.com/pereirar3/simpleperceptron
- Owner: pereiraR3
- Created: 2025-05-01T22:54:42.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-05T17:48:31.000Z (4 months ago)
- Last Synced: 2025-06-05T18:41:01.653Z (4 months ago)
- Topics: perceptron, python, tree-decision-classifier
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Artificial Intelligence Activity - Algorithm Implementations
This repository contains Python implementations of two fundamental Artificial Intelligence algorithms, developed as part of an assignment for the AI course. The implemented algorithms are:
1. **Decision Tree:** A supervised learning algorithm used for classification and regression tasks.
2. **Perceptron:** A fundamental algorithm for linear classification, serving as a building block for more complex neural networks.
## Repository Structure
The repository is organized as follows:
```
├── README.md # This file, containing information about the repository
└── scripts/
├── decision_tree.py # Python script implementing the Decision Tree
└── perceptron.py # Python script implementing the Perceptron
```
## Script Descriptions
### `decision_tree.py`
This script implements a Decision Tree for classification tasks. Key features of the implementation include:
* **Entropy Calculation:** Used to measure the impurity of a dataset.
* **Data Splitting:** Function to partition the dataset based on an attribute and a threshold value.
* **Information Gain:** Metric used to determine the best attribute for splitting.
* **Recursive Tree Building:** The tree is built recursively by splitting the data until a stopping criterion is met (node purity or max depth).
* **Tree Representation:** Uses a class `DecisionTree` with an internal `_DecisionNode` class to represent tree nodes.
* **Functions for Training, Printing, and Classification:** Methods to train the tree on data, display its structure, and classify new instances.
### `perceptron.py`
This script implements a Perceptron, a linear learning algorithm for binary classification. Key features of the implementation include:
* **Step Activation Function:** Used to produce a binary output.
* **Iterative Training:** The Perceptron's weights are updated iteratively based on the error between predicted and expected outputs.
* **Learning Rate and Threshold:** Parameters controlling the learning process and classification decision.
* **Perceptron Representation:** Uses a `Perceptron` class to encapsulate weights, threshold, and training/prediction methods.
* **Functions for Training and Prediction:** Methods to train the Perceptron on labeled data and to predict the class of new instances.
## How to Run the Scripts
To run the scripts, make sure you have Python 3 installed, along with the `numpy` and `pandas` libraries (required for the Decision Tree).
1. **Clone the repository** (if you haven't already).
2. **Navigate to the `scripts` directory:**
```bash
cd scripts
```
3. **Run the scripts with the Python interpreter:**
```bash
python decision_tree.py
python perceptron.py
```
The scripts will execute the algorithm implementations using sample data and display the results in your terminal.
---
**Author:** Anthony Ricardo Rodrigues Rezende
**Date:** May 4, 2025
**Course:** Artificial Intelligence
**Institution:** Federal University of Mato Grosso