44 lines
No EOL
1.5 KiB
Python
44 lines
No EOL
1.5 KiB
Python
import random
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import numpy as np
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# Let's solve the function f(x, y) = -2x^2 - 3(y - 4)^2
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def fitness(x):
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return -2 * (x[:, 0] ** 2) - 3 * (x[:, 1] - 4)**2
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class Population:
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def __init__(self, initial_guess, population_size, fitness_fn, learning_rate = 1e-4, sigma = 0.1):
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self.current_solution = initial_guess
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self.population_size = population_size
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self.sigma = sigma
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self.learning_rate = learning_rate
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assert self.population_size > 0
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assert self.sigma >= 0
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self.calculate_fitness = fitness_fn
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def __iter__(self):
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return self
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# This function is suppose to take us to the next generation
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def __next__(self):
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white_noise = np.random.randn(self.population_size, *self.current_solution.shape)
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noise = self.sigma * white_noise
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candidate_solutions = self.current_solution + noise
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fitness_values = self.calculate_fitness(candidate_solutions)
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# Mean shift and scale
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fitness_values = (fitness_values - np.mean(fitness_values)) / (np.std(fitness_values) + np.finfo('float').eps)
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new_solution = self.current_solution + self.learning_rate * np.mean(white_noise.T * fitness_values, axis = 1) / self.sigma
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self.current_solution = new_solution
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return new_solution
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def item(self):
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return self.current_solution
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def test():
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guess = np.random.randn(2)
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p = Population(guess, 100, fitness)
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for i in range(10000):
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next(p)
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return p.item() |