https://github.com/gbroques/perceptron

Perceptron learning algorithm implemented in Python.
https://github.com/gbroques/perceptron

machine-learning perceptron perceptron-learning-algorithm python3

Last synced: 3 months ago
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Perceptron learning algorithm implemented in Python.

• Host: GitHub
• URL: https://github.com/gbroques/perceptron
• Owner: gbroques
• License: mit
• Created: 2018-04-23T14:22:45.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T02:14:40.000Z (almost 3 years ago)
• Last Synced: 2025-01-12T09:42:37.322Z (10 months ago)
• Topics: machine-learning, perceptron, perceptron-learning-algorithm, python3
• Language: Python
• Size: 130 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Perceptron

[![Build Status](https://travis-ci.org/gbroques/perceptron.svg?branch=master)](https://travis-ci.org/gbroques/perceptron)

[![Coverage Status](https://coveralls.io/repos/github/gbroques/perceptron/badge.svg?branch=master)](https://coveralls.io/github/gbroques/perceptron?branch=master)

Perceptron learning algorithm implemented in Python.

![perceptron](perceptron.png)

## Getting Started

This project depends upon the popular numerical processing library [NumPy](http://www.numpy.org/) for lightning-fast vector arithmetic, and other packages for unit testing.

### Prerequisites

To install NumPy, it's recommended you use Python's offical package manager **pip**.

To ensure pip is installed on your machine, run the command:

```

$ pip --version

```

pip should come installed with Python depending upon your version.

For more details, see [installation](https://pip.pypa.io/en/stable/installing/)

on pip's documentation.

### Installing

It's recommended you use `virtualenv` to create isolated Python environments.

You can find details on [virtualenv's documentation](https://virtualenv.pypa.io/en/stable/).

Once pip is installed, run:

```

$ pip install -r requirements.txt

```

This will install this project's dependencies on your machine.

## How to Run

```

$ python main.py

```

## Usage

API inspired by the popular machine learning library

[scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html).

```python

import numpy as np

from perceptron import Perceptron

# Training data for logical OR function

training_data = np.array([

    [0, 0, 0],

    [0, 1, 1],

    [1, 0, 1],

    [1, 1, 1]

])

design_matrix = training_data[:, :2]

target_values = training_data[:, -1]

perceptron = Perceptron(max_iter=100, learning_rate=0.2)

perceptron.fit(design_matrix, target_values)

predictions = perceptron.predict(design_matrix)

print(predictions)  # [0, 1, 1, 1]

```