bayesian_optimizer.py 文件源码

def expected_improvement(self, optimiser, x):
        mu,std = optimiser.predict([x], return_std=True)
        current_best = max([score for score, params in self.hyperparam_history])
        gamma = (current_best - mu[0])/std[0]
        exp_improv = std[0] * (gamma * norm.cdf(gamma) + norm.pdf(gamma))
        return -1 * exp_improv