bb9894245d4f138871a2a8207fc62ece26b2c79a,mloop/learners.py,GaussianProcessLearner,fit_gaussian_process,#GaussianProcessLearner#,1304

Before Change

                self.noise_level = self.cost_scaler.var_
            // Cost variance"s scaling factor is square of costs"s scaling factor.
            self.scaled_noise_level = self.noise_level / self.cost_scaler.scale_**2
            self.gaussian_process.kernel.set_params(k2__noise_level=self.scaled_noise_level)
        self.gaussian_process.fit(self.all_params,self.scaled_costs)
        
        if self.update_hyperparameters:
            

After Change

                // self.noise_level is overwritten.
                self.noise_level = cost_scaling_factor**2
            if np.any(np.isnan(self.noise_level_bounds)):
                self.noise_level_bounds = np.array([1e-5, 1e5]) * cost_scaling_factor**2
            // Cost variance"s scaling factor is square of costs"s scaling factor.
            self.scaled_noise_level = self.noise_level / cost_scaling_factor**2
            self.scaled_noise_level_bounds = self.noise_level_bounds / cost_scaling_factor**2

Instances