import numpy as np
import GPy
import GPyOpt
X = np.array	([[5.5, 2.36, 2.1, 1.1],
	[2.7, 1.8, 3.3, 2.3],
	[6.9, 0.68, 2.7, 3.5],
	[8.3, 2.92, 0.9, 5.9],
	[4.1, 1.24, 1.5, 4.7],
	[6.7, 0.77, 2.7, 3.3],
	[6.5, 1.36, 2.0, 2.2]])
Y = -np.array([67, 31, 68, 48, 66, 70, 78])[:, np.newaxis]
initial_x = X
initial_y = Y
bounds =	[{‘name’: ‘current’, ‘type’: ‘continuous’, ‘domain’: (2,9)},
	{‘name’: ‘Init_molarity’, ‘type’: ‘continuous’, ‘domain’: (0.4,3.2)},
	{‘name’: ‘electrolyte’, ‘type’: ‘continuous’, ‘domain’: (0.6,3.6)},
	{‘name’: ‘time’, ‘type’: ‘continuous’, ‘domain’: (0.5,6.5)}]
myBopt = GPyOpt.methods.BayesianOptimization(f=None,
		domain=bounds,
		X = initial_x,
		Y = initial_y,
		acquisition_type=‘EI’,
		)
next_x = myBopt.suggest_next_locations()
print(next_x)