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Sleeping
Daniel Varga
big code drop with different main_* entry points, tuned evolution, and less logging.
4e673fc
import numpy as np | |
import matplotlib.pyplot as plt | |
DO_VIS = True | |
# in-place | |
def clip_params(population, clipper_function): | |
for i, individual in enumerate(population): | |
population[i] = clipper_function(individual) | |
def evolution_strategies_optimizer(objective_function, clipper_function, | |
init_mean, init_scale, | |
number_of_generations, | |
population_size): | |
# Initialize parameters | |
mutation_scale = 0.1 | |
selection_ratio = 0.5 | |
selected_size = int(population_size * selection_ratio) | |
# Initialize population (randomly) | |
population = np.random.normal(loc=init_mean, scale=init_scale, size=(population_size, len(init_mean))) | |
clip_params(population, clipper_function) | |
for generation in range(number_of_generations): | |
# Evaluate fitness | |
fitness = np.array([objective_function(individual) for individual in population]) | |
# Select the best individuals | |
selected_indices = np.argsort(fitness)[:selected_size] | |
selected = population[selected_indices] | |
# Reproduce (mutate) | |
offspring = selected + np.random.normal(loc=0, scale=init_scale * mutation_scale, size=(selected_size, len(init_mean))) | |
clip_params(offspring, clipper_function) # in-place | |
# Logging | |
best_fitness = fitness[selected_indices[0]] | |
best_solution = selected[0].copy() | |
print(f"Generation {generation + 1}: Best Fitness = {best_fitness}", f"Best solution so far: {best_solution}") | |
# Replacement: Here we simply generate new candidates around the selected ones | |
population[:selected_size] = selected | |
population[selected_size:] = offspring | |
print(f"Best solution found: {best_solution} with loss {best_fitness}") | |
return best_solution | |
def toy_objective_function(x): | |
return (x[..., 0] - 3)**2 + (x[..., 1] + 2)**2 | |
def toy_clipper_function(x): | |
return x | |
def main(): | |
init_mean = np.array([0.0, 0.0]) | |
init_scale = np.array([10.0, 10.0]) | |
best_solution = evolution_strategies_optimizer(toy_objective_function, toy_clipper_function, init_mean, init_scale) | |
if __name__ == '__main__': | |
main() | |