def testFromEmptyGP(self):
# test a GP that has no data to start
f = lambda x: float(sin(x * 10) + x)
kernel = GaussianKernel_iso(array([1.0]))
GP = GaussianProcess(kernel)
for x in arange(0., 1., .1):
GP.addData(array([x]), f(x))
for x in arange(1., 2., .1):
GP.addData(array([x]), f(x))
self.failUnlessEqual(len(GP.X), 20)
def testFromEmptyGP(self):
# test a GP that has no data to start
f = lambda x: float(sin(x*10)+x)
kernel = GaussianKernel_iso(array([1.0]))
GP = GaussianProcess(kernel)
for x in arange(0., 1., .1):
GP.addData(array([x]), f(x))
for x in arange(1., 2., .1):
GP.addData(array([x]), f(x))
self.failUnlessEqual(len(GP.X), 20)
def testTraining(self):
# test that sequential training gives the same result as batch
tf = Shekel5()
X = lhcSample(tf.bounds, 25, seed=1)
Y = [tf.f(x) for x in X]
# GP1 adds all data during initialization
GP1 = GaussianProcess(GaussianKernel_iso([.1]), X, Y, noise=.2)
# GP2 adds data one at a time
GP2 = GaussianProcess(GaussianKernel_iso([.1]), noise=.2)
# GP3 uses addData()
GP3 = GaussianProcess(GaussianKernel_iso([.1]), noise=.2)
# GP4 adds using various methods
GP4 = GaussianProcess(GaussianKernel_iso([.1]),
X[:10],
Y[:10],
noise=.2)
for x, y in zip(X, Y):
GP2.addData(x, y)
for i in xrange(0, 25, 5):
GP3.addData(X[i:i + 5], Y[i:i + 5])
GP4.addData(X[10], Y[10])
GP4.addData(X[11:18], Y[11:18])
for i in xrange(18, 25):
GP4.addData(X[i], Y[i])
self.failUnless(all(GP1.R == GP2.R))
self.failUnless(all(GP1.R == GP3.R))
self.failUnless(all(GP1.R == GP4.R))
testX = lhcSample(tf.bounds, 25, seed=2)
for x in testX:
mu1, s1 = GP1.posterior(x)
mu2, s2 = GP2.posterior(x)
mu3, s3 = GP3.posterior(x)
mu4, s4 = GP4.posterior(x)
self.failUnlessEqual(mu1, mu2)
self.failUnlessEqual(mu1, mu3)
self.failUnlessEqual(mu1, mu4)
self.failUnlessEqual(s1, s2)
self.failUnlessEqual(s1, s3)
self.failUnlessEqual(s1, s4)
def testTraining(self):
# test that sequential training gives the same result as batch
tf = Shekel5()
X = lhcSample(tf.bounds, 25, seed=1)
Y = [tf.f(x) for x in X]
# GP1 adds all data during initialization
GP1 = GaussianProcess(GaussianKernel_iso([.1]), X, Y, noise=.2)
# GP2 adds data one at a time
GP2 = GaussianProcess(GaussianKernel_iso([.1]), noise=.2)
# GP3 uses addData()
GP3 = GaussianProcess(GaussianKernel_iso([.1]), noise=.2)
# GP4 adds using various methods
GP4 = GaussianProcess(GaussianKernel_iso([.1]), X[:10], Y[:10], noise=.2)
for x, y in zip(X, Y):
GP2.addData(x, y)
for i in xrange(0, 25, 5):
GP3.addData(X[i:i+5], Y[i:i+5])
GP4.addData(X[10], Y[10])
GP4.addData(X[11:18], Y[11:18])
for i in xrange(18, 25):
GP4.addData(X[i], Y[i])
self.failUnless(all(GP1.R==GP2.R))
self.failUnless(all(GP1.R==GP3.R))
self.failUnless(all(GP1.R==GP4.R))
testX = lhcSample(tf.bounds, 25, seed=2)
for x in testX:
mu1, s1 = GP1.posterior(x)
mu2, s2 = GP2.posterior(x)
mu3, s3 = GP3.posterior(x)
mu4, s4 = GP4.posterior(x)
self.failUnlessEqual(mu1, mu2)
self.failUnlessEqual(mu1, mu3)
self.failUnlessEqual(mu1, mu4)
self.failUnlessEqual(s1, s2)
self.failUnlessEqual(s1, s3)
self.failUnlessEqual(s1, s4)