Metadata-Version: 2.1
Name: PyPerceptron
Version: 1.0.1
Summary: A python implementation of the build block of the Neural Network, The Perceptron
Home-page: https://github.com/paolodelia99/Python-Perceptron
Author: Paolo D'Elia
Author-email: paolo.delia99@gmail.com
License: MIT
Project-URL: Demo, https://github.com/paolodelia99/Python-Perceptron/tree/master/demo
Project-URL: Repo, https://github.com/paolodelia99/Python-Perceptron
Description: # Python-Perceptron
        
        ![language](https://img.shields.io/badge/language-python-blue)
        ![license](https://img.shields.io/badge/license-MIT-orange)
        
        An Basic implementation of the perceptron, the build block a neural net.
        
        ![perceptron](./assets/img/perceptron_350_328.png)
        
        ## Usage
        
            pip install PyPerceptron
            
        ## Example
        
        Here's how to instanitate the Perceptron
        
        ```python
        from Perceptron.perceptron import Perceptron
        
        p = Perceptron(number_inputs, learning_rate, Activation_fn, Loss_fn)
        ```
            
        ```python
        from Perceptron.perceptron import Perceptron
        from Perceptron.functions.activationFunctions.heaviside import Heaviside
        from Perceptron.functions.lossFunctions.quadratic_loss import QuadraticLoss
        
        dataset = [[2.7810836, 2.550537003, 0],
                       [1.465489372, 2.362125076, 0],
                       [3.396561688, 4.400293529, 0],
                       [1.38807019, 1.850220317, 0],
                       [3.06407232, 3.005305973, 0],
                       [7.627531214, 2.759262235, 1],
                       [5.332441248, 2.088626775, 1],
                       [6.922596716, 1.77106367, 1],
                       [8.675418651, -0.242068655, 1],
                       [7.673756466, 3.508563011, 1]]
        
        
        p = Perceptron(2, 0.1, Heaviside(), QuadraticLoss()) # number of inputs, learning rate, activation function, loss funciton
        p.train(dataset, 3, 30)
        
        for d in dataset:
            assert p.evaluate(d[0], d[1]) == d[2]
        
        ```
        
        To find out more about the math behind the perceptron, check out the [notebook](./demo/What_is_a_perceptron.ipynb) with the fully explanation.
        
        If you wanna see more about how to use the perceptron checkout the [demos](./demo).
        
        ## Author
        
        Paolo D'Elia
        
        ## Contributing
        
        Feel free report issues and contribute to the project, making it better.
        
        ## License 
        
        MIT
        
Keywords: Perceptron,Neural Net,Machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
