def _adapt_params(self, stepsize, stepsize_bar, h_bar, mu, index_i, alpha, n_alpha=1):
        """
        Run tha adaptation for stepsize for better proposals of position
        """
        gamma = 0.05  # free parameter that controls the amount of shrinkage towards mu
        t0 = 10.0  # free parameter that stabilizes the initial iterations of the algorithm
        kappa = 0.75
        # See equation (6) section 3.2.1 for details

        estimate = 1.0 / (index_i + t0)
        h_bar = (1 - estimate) * h_bar + estimate * (self.delta - alpha / n_alpha)

        stepsize = np.exp(mu - sqrt(index_i) / gamma * h_bar)
        i_kappa = index_i ** (-kappa)
        stepsize_bar = np.exp(i_kappa * np.log(stepsize) + (1 - i_kappa) * np.log(stepsize_bar))

        return stepsize, stepsize_bar, h_bar
def _adapt_params ( self , stepsize , stepsize_bar , h_bar , mu , index_i , alpha , n_alpha = 1 ) : gamma = 0.05 t0 = 10.0 kappa = 0.75 estimate = 1.0 / ( index_i + t0 ) h_bar = ( 1 - estimate ) * h_bar + estimate * ( self . delta - alpha / n_alpha ) stepsize = np . exp ( mu - sqrt ( index_i ) / gamma * h_bar ) i_kappa = index_i ** ( - kappa ) stepsize_bar = np . exp ( i_kappa * np . log ( stepsize ) + ( 1 - i_kappa ) * np . log ( stepsize_bar ) ) return stepsize , stepsize_bar , h_bar
Run tha adaptation for stepsize for better proposals of position