import numpy as np
import GPy
import GPyOpt
X = np.array	([[6.5, 1.2, 2.4, 3.5],
	[4.5, 2.4, 2.4, 1.5],
	[5.5, 1.2, 3.0, 5.5],
	[5.5, 2.4, 1.2, 3.5],
	[6.5, 1.8, 3.0, 1.5],
	[4.5, 1.8, 1.2, 5.5],
	[5.8, 2.2, 1.9, 3.4],
	[6.2, 2.5, 1.5, 3.2]])
Y = -np.array([65, 51, 39, 76, 66, 52, 78, 74])[:, 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)