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
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