#######################################
# pyGPGO examples
# franke: optimizes Franke's function.
#######################################
import numpy as np
from pyGPGO.covfunc import matern32
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO
import matplotlib.pyplot as plt
from matplotlib import cm
def f(x, y):
# Franke's function (https://www.mathworks.com/help/curvefit/franke.html)
one = 0.75 * np.exp(-(9 * x - 2)**2/4 - (9 * y - 2)**2/4)
two = 0.75 * np.exp(-(9 * x + 1)**2/49 - (9 * y + 1)/10)
three = 0.5 * np.exp(-(9 * x - 7)**2/4 - (9*y - 3)**2/4)
four = 0.25 * np.exp(-(9 * x - 4)**2 - (9*y - 7)**2)
return one + two + three - four
def plotFranke():
"""
Plots Franke's function
"""
x = np.linspace(0, 1, num=1000)
y = np.linspace(0, 1, num=1000)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
if __name__ == '__main__':
plotFranke()
cov = matern32() # Using a matern v=3/2 covariance kernel
gp = GaussianProcess(cov) # A Gaussian Process regressor without hyperparameter optimization
acq = Acquisition(mode='ExpectedImprovement') # Expected Improvement acquisition function
param = {'x': ('cont', [0, 1]),
'y': ('cont', [0, 1])} # Specify parameter space
np.random.seed(1337)
gpgo = GPGO(gp, acq, f, param) # Call GPGO class
gpgo.run(max_iter=10) # 10 iterations
gpgo.getResult() # Get your result
