Metadata-Version: 1.1
Name: genepy
Version: 1.0.0b1
Summary: Simple genetic programming library using tree representation for individuals
Home-page: https://github.com/amstuta/genetic.py
Author: Arthur Amstutz
Author-email: arthur.amstutz@gmail.com
License: MIT
Description: # Evolutionary Algorithm \- Genetic Programming
        
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/amstuta/genetic.py/blob/master/LICENSE.md)
        
        This package is a high-level library implementing a
        genetic programming algorithm using a tree representation for individuals. It
        is compatible with Python 2.7 and all the following versions and uses only the
        standard library (no dependencies).
        
        It can be used to solve linear and nonlinear regression problems and is
        usable through a simple interface:
        - A fit function that takes as inputs the training examples, target values and the number of iterations
        - A predict function that takes as parameter an input for prediction
        
        An example of usage can be found in the example.py script. This example requires to
        have the libraries numpy and matplotlib installed.
        
        ### Installation
        
        The package can be installed simply using Pypi:
        ```sh
        pip install genetic
        ```
        
        ### Usage
        
        To use it, you need to give the algorithm:
        - A fitness function that takes as parameters:
            - An instance of the class Tree
            - The training examples: array-like object of shape [n_samples, n_features]
            - The target values: array-like object of shape [n_samples]
        - The function set that can be used in the tree
        
        Basic example:
        ```python
        from operator import add, sub, mul
        from genetic.core import EvolutionaryAlgorithm
        
        # Load your dataset
        train_features, train_targets = ...
        test_features, test_targets   = ...
        
        # Define a fitness function
        def compute_fitness(tree, features, targets):
            ...
            return fitness
        
        # Define the parameters of the algorithm
        parameters = {
          'min_depth':        2,
          'max_depth':        5,
          'nb_trees':         50,
          'max_const':        100,
          'func_ratio':       0.5,
          'var_ratio':        0.5,
          'crossover_prob':   0.8,
          'mutation_prob':    0.2,
          'iterations':       80,
          'functions':        [add,sub,mul],
          'fitness_function': compute_fitness
        }
        
        # Create object and train algorithm
        ea = EvolutionaryAlgorithm(**parameters)
        ea.fit(train_features, train_targets)
        
        # Make a prediction
        predicted = ea.predict(test_features[0])
        ```
        
Keywords: genetic evolution intelligence data learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
