for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from scipy.stats import norm
def normalize(array):
num = array - array.min()
den = array.max() - array.min()
if den == 0:
return np.random.random_sample(array.shape)
else:
return ((num / den) + 0) / 1
class ExpectedImprovement:
def __init__(self, surrogate_model, position_l, xi):
self.surrogate_model = surrogate_model
self.position_l = position_l
self.xi = xi
def calculate(self, X_sample, Y_sample):
mu, sigma = self.surrogate_model.predict(self.position_l, return_std=True)
# TODO mu_sample = self.surrogate_model.predict(X_sample)
mu = mu.reshape(-1, 1)
sigma = sigma.reshape(-1, 1)
# with normalization this is always 1
Y_sample = normalize(np.array(Y_sample)).reshape(-1, 1)
imp = mu - np.max(Y_sample) - self.xi
Z = np.divide(imp, sigma, out=np.zeros_like(sigma), where=sigma != 0)
exploit = imp * norm.cdf(Z)
explore = sigma * norm.pdf(Z)
aqu_func = exploit + explore
aqu_func[sigma == 0.0] = 0.0
return aqu_func[:, 0]
SimonBlanke /
Gradient-Free-Optimizers
