import numpy as np
from scipy.stats import norm

def ExpectedImprovement(gp, X, nr_test, nr_weights, kappa=2.576, seed=None, lower=-1.0, upper=1.0):
    y_hat = gp.predict(X)
    best_y = max(y_hat)
    rng = np.random.default_rng(seed=seed)
    X_test = rng.uniform(lower, upper, (nr_test, nr_weights))
    mu, sigma = gp.predict(X_test, return_std=True)
    z = (mu - best_y - kappa) / sigma
    ei = (mu - best_y - kappa) * norm.cdf(z) + sigma * norm.pdf(z)

    idx = np.argmax(ei)
    X_next = X_test[idx, :]
    return X_next