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)
def testShekelClass(self): S = Shekel5() # get 50 latin hypercube samples X = lhcSample(S.bounds, 50, seed=2) Y = [S.f(x) for x in X] hyper = [.2, .2, .2, .2] noise = 0.1 gkernel = GaussianKernel_ard(hyper) # print gkernel.sf2 GP = GaussianProcess(gkernel, X, Y, noise=noise) # let's take a look at the trained GP. first, make sure variance at # the samples is determined by noise mu, sig2 = GP.posteriors(X) for m, s, y in zip(mu, sig2, Y): # print m, s self.failUnless(s < 1 / (1 + noise)) self.failUnless(abs(m - y) < 2 * noise) # now get some test samples and see how well we are fitting the function testX = lhcSample(S.bounds, 50, seed=3) testY = [S.f(x) for x in X] for tx, ty in zip(testX, testY): m, s = GP.posterior(tx) # prediction should be within one stdev of mean self.failUnless(abs(ty - m) / sqrt(s) < 1)
def testShekelClass(self): S = Shekel5() # get 50 latin hypercube samples X = lhcSample(S.bounds, 50, seed=2) Y = [S.f(x) for x in X] hyper = [.2, .2, .2, .2] noise = 0.1 gkernel = GaussianKernel_ard(hyper) # print gkernel.sf2 GP = GaussianProcess(gkernel, X, Y, noise=noise) # let's take a look at the trained GP. first, make sure variance at # the samples is determined by noise mu, sig2 = GP.posteriors(X) for m, s, y in zip(mu, sig2, Y): # print m, s self.failUnless(s < 1/(1+noise)) self.failUnless(abs(m-y) < 2*noise) # now get some test samples and see how well we are fitting the function testX = lhcSample(S.bounds, 50, seed=3) testY = [S.f(x) for x in X] for tx, ty in zip(testX, testY): m, s = GP.posterior(tx) # prediction should be within one stdev of mean self.failUnless(abs(ty-m)/sqrt(s) < 1)