semvec/pypi-packages huggingface dataset:
gmlp 0.2
Genetic Algorithm for Python
Requires Python: >=2.7

# Genetic Machine Learning for Python
GMLP or *Genetic Machine Learning for Python*, is a user friendly python machine learning package. GMLP is intuitive and can be used for lots of Machine Learning Projects.
## What is GMLP?
GMLP helps you with your genetic programming! You can turn __90__ lines of code into __18 lines of code!__
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## Examples
GMLP can be used for evolutionary neural networks, and genetic programming!
Here are some Examples:
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```python
# GMLP Example 1. - Phrase Guesser.
# This Evolutionary Neural Network will have you put in a phrase and the E.N.N will guess it.
import random

import gmlp as gp
# Importing our modules.

# Our characters that we will be using.
characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ,.?!: '

# We make our characters into a list.
characters = list(characters)

# The phrase.
phrase = "Hello World!"

# Making the phrase into numbers by using ord() so working with the phrase will be easier.
ord_phrase = [ord(target) for target in phrase]

# Having the population generate random letters and turn them into numbers using the ord() function.
starting_population = [[ord(random.choice(characters))for gene in range(len(ord_phrase))]for pop_size in range(10000)]

print("Our goal phrase is:", phrase)
# Setting up the enviroment.
env = gp.Enviroment(ord_phrase, .9)

# Our population.
population = env.generate_population(settings=starting_population)

# Our fitnesses.
scores = gp.calculate_fitness(population, env.goal)

# Our maximum number of generations.
max_generations = 500

for generation in range(max_generations):
	# Calculating the scores for the population.
	scores = gp.calculate_fitness(population, env.goal)

	# The best score is the minimum of the scores because the scores are how far you are away from your goal.
	best = min(scores)

	# getting the score index of the best score.
	best_score = scores[scores.index(best)]

	# the population organism of the best score index.
	Output = population[scores.index(best)]

	print("Generation:%1s, Best Score:%2s, Output->%3s"%(generation, str(best_score), ''.join([chr(c) for c in Output])))

	# Our new population is the mutated population of the crossover of the fittest population .
	population = gp.ValueEncodingMut(env.crossover(env.tournament_selection(population, scores, 3), ord_phrase), .15)

	# If we are 0% away from our goal we print the output and phrase and break the loop.
	if min(scores) == 0:
		print(f'Our Output -> {"".join([chr(c) for c in Output])}')
		print(f'Our Phrase -> {phrase}')
		break
```
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## Game
This Feature is new to GMLP and is still in progress
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