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Enable flexible random initialization of fitness function at each generation #5

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Enable flexible random initialization of fitness function at each generation #5

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15 changes: 11 additions & 4 deletions es/separable_natural_es.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,9 @@
import numpy as np
from . import lib

def optimize(func, mu, sigma,
def optimize(func,
mu, sigma,
func_wrapper=None,
learning_rate_mu=None, learning_rate_sigma=None, population_size=None,
max_iter=2000,
fitness_shaping=True, mirrored_sampling=True, record_history=False,
Expand Down Expand Up @@ -45,11 +47,16 @@ def optimize(func, mu, sigma,
z = np.vstack([z, mu - sigma * s])
s = np.vstack([s, -s])

if parallel_threads is None:
fitness = np.fromiter((func(zi) for zi in z), np.float)
if func_wrapper is not None:
func_i = func_wrapper(func)
else:
func_i = func

if parallel_threads is None:
fitness = np.fromiter((func_i(zi) for zi in z), np.float)
elif isinstance(parallel_threads, int):
pool = mp.Pool(processes=parallel_threads)
fitness = np.fromiter(pool.map(func, z), np.float)
fitness = np.fromiter(pool.map(func_i, z), np.float)
pool.close()
pool.join()

Expand Down