16 lines
532 B
Python
16 lines
532 B
Python
import numpy as np
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from scipy.stats import norm
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def ExpectedImprovement(gp, X, nr_test, nr_weights, kappa=2.576, seed=None, lower=-1.0, upper=1.0):
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y_hat = gp.predict(X)
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best_y = max(y_hat)
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rng = np.random.default_rng(seed=seed)
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X_test = rng.uniform(lower, upper, (nr_test, nr_weights))
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mu, sigma = gp.predict(X_test, return_std=True)
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z = (mu - best_y - kappa) / sigma
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ei = (mu - best_y - kappa) * norm.cdf(z) + sigma * norm.pdf(z)
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idx = np.argmax(ei)
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X_next = X_test[idx, :]
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return X_next
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