def part_1(max_iter):
# Plot the function
param = OrderedDict()
param['x'] = ('cont', [-2, 2])
param['y'] = ('cont', [-2, 2])
# squared exponential kernel function
plt.suptitle("Convergence Rate, True Optimum = 0")
np.random.seed(20)
plt.subplot(131)
sqexp = squaredExponential()
gp = GaussianProcess(sqexp)
acq = Acquisition(mode='ExpectedImprovement')
gpgo = GPGO(gp, acq, Part_1a.f, param, n_jobs=-1)
gpgo.run(max_iter=max_iter)
plot_convergence(gpgo, "Squared Exponential Kernel")
# matern52 kernel function
np.random.seed(20)
plt.subplot(132)
matern = matern52()
gp = GaussianProcess(matern)
acq = Acquisition(mode='ExpectedImprovement')
gpgo = GPGO(gp, acq, Part_1a.f, param, n_jobs=-1)
gpgo.run(max_iter=max_iter)
plot_convergence(gpgo, "Matern52 Kernel")
# rational quadratic kernel function
np.random.seed(20)
plt.subplot(133)
ratq = rationalQuadratic()
gp = GaussianProcess(ratq)
acq = Acquisition(mode='ExpectedImprovement')
gpgo = GPGO(gp, acq, Part_1a.f, param, n_jobs=-1)
gpgo.run(max_iter=max_iter)
plot_convergence(gpgo, "Rational Quadratic Kernel")
plt.show()
import numpy as np
from pyGPGO.covfunc import squaredExponential, matern, matern32, matern52, \
gammaExponential, rationalQuadratic, expSine, dotProd
covfuncs = [
squaredExponential(),
matern(),
matern32(),
matern52(),
gammaExponential(),
rationalQuadratic(),
expSine(),
dotProd()
]
grad_enabled = [
squaredExponential(),
matern32(),
matern52(),
gammaExponential(),
rationalQuadratic(),
expSine()
]
# Some kernels do not have gradient computation enabled, such is the case
# of the generalised matérn kernel.
#
# All (but the dotProd kernel) have a characteristic length-scale l that
# we test for here.