for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from .simulated_annealing import SimulatedAnnealingOptimizer
class StochasticTunnelingOptimizer(SimulatedAnnealingOptimizer):
def __init__(self, _main_args_, _opt_args_):
super().__init__(_main_args_, _opt_args_)
def _accept(self, _p_):
f_stun = 1 - np.exp(-self._opt_args_.gamma * self._score_norm(_p_))
return np.exp(-f_stun / self.temp)
SimonBlanke /
Hyperactive
