def test2DpyEI(self):
f = lambda x: sum(sin(x))
bounds = [[0., 5.], [0., 5.]]
X = lhcSample(bounds, 5, seed=24)
Y = [f(x) for x in X]
kernel = GaussianKernel_ard(array([1.0, 1.0]))
GP = GaussianProcess(kernel, X, Y)
maxei = maximizeEI(GP, bounds)
if False:
figure(1)
c0 = [(i/50.)*(bounds[0][1]-bounds[0][0])+bounds[0][0] for i in xrange(51)]
c1 = [(i/50.)*(bounds[1][1]-bounds[1][0])+bounds[1][0] for i in xrange(51)]
z = array([[GP.ei(array([i, j])) for i in c0] for j in c1])
ax = plt.subplot(111)
cs = ax.contour(c0, c1, z, 10, alpha=0.5, cmap=cm.Blues_r)
plot([x[0] for x in X], [x[1] for x in X], 'ro')
for i in xrange(len(X)):
annotate('%2f'%Y[i], X[i])
plot(maxei[1][0], maxei[1][1], 'ko')
show()
def test2DpyEI(self):
f = lambda x: sum(sin(x))
bounds = [[0., 5.], [0., 5.]]
X = lhcSample(bounds, 5, seed=24)
Y = [f(x) for x in X]
kernel = GaussianKernel_ard(array([1.0, 1.0]))
GP = GaussianProcess(kernel, X, Y)
maxei = maximizeEI(GP, bounds)
if False:
figure(1)
c0 = [(i / 50.) * (bounds[0][1] - bounds[0][0]) + bounds[0][0]
for i in range(51)]
c1 = [(i / 50.) * (bounds[1][1] - bounds[1][0]) + bounds[1][0]
for i in range(51)]
z = array([[GP.ei(array([i, j])) for i in c0] for j in c1])
ax = plt.subplot(111)
cs = ax.contour(c0, c1, z, 10, alpha=0.5, cmap=cm.Blues_r)
plot([x[0] for x in X], [x[1] for x in X], 'ro')
for i in range(len(X)):
annotate('%2f' % Y[i], X[i])
plot(maxei[1][0], maxei[1][1], 'ko')
show()
def test1DcEI(self):
f = lambda x: float(sin(x*5.))
X = lhcSample([[0., 1.]], 5, seed=22)
Y = [f(x) for x in X]
kernel = GaussianKernel_ard(array([1.0]))
GP = GaussianProcess(kernel)
GP.addData(X, Y)
# should use optimizeGP.cpp
maxei = maximizeEI(GP, [[0., 1.]])
if False:
figure(1)
plot(X, Y, 'ro')
plot([x/100 for x in xrange(100)], [GP.ei(x/100) for x in xrange(100)])
plot(maxei[1][0], maxei[0], 'ko')
show()