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[beckdaniel] merge in warped gp changes
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commit
87539b1c1b
4 changed files with 183 additions and 57 deletions
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@ -289,6 +289,64 @@ class MiscTests(unittest.TestCase):
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m.optimize()
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print(m)
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def test_warped_gp_identity(self):
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"""
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A WarpedGP with the identity warping function should be
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equal to a standard GP.
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"""
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k = GPy.kern.RBF(1)
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m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
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m.optimize()
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preds = m.predict(self.X)
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warp_k = GPy.kern.RBF(1)
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warp_f = GPy.util.warping_functions.IdentityFunction()
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warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k, warping_function=warp_f)
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warp_m.optimize()
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warp_preds = warp_m.predict(self.X)
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np.testing.assert_almost_equal(preds, warp_preds)
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@unittest.skip('Comment this to plot the modified sine function')
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def test_warped_gp_sine(self):
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"""
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A test replicating the sine regression problem from
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Snelson's paper.
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"""
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X = (2 * np.pi) * np.random.random(151) - np.pi
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Y = np.sin(X) + np.random.normal(0,0.1,151)
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Y = np.exp(Y) - 5
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#Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y]) + 0
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#np.seterr(over='raise')
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import matplotlib.pyplot as plt
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warp_k = GPy.kern.RBF(1)
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warp_f = GPy.util.warping_functions.TanhWarpingFunction_d(n_terms=2)
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warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
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#warp_m['.*variance.*'].constrain_fixed(0.25)
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#warp_m['.*lengthscale.*'].constrain_fixed(1)
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#warp_m['warp_tanh.d'].constrain_fixed(1)
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#warp_m.randomize()
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#warp_m['.*warp_tanh.psi*'][:,0:2].constrain_bounded(0,100)
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#warp_m['.*warp_tanh.psi*'][:,0:1].constrain_fixed(1)
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#print(warp_m.checkgrad())
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#warp_m.plot()
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#plt.show()
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warp_m.optimize_restarts(parallel=True, robust=True)
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#print(warp_m.checkgrad())
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print(warp_m)
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print(warp_m['.*warp.*'])
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warp_m.predict_in_warped_space = False
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warp_m.plot()
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warp_m.predict_in_warped_space = True
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warp_m.plot()
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warp_f.plot(X.min()-10, X.max()+10)
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plt.show()
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class GradientTests(np.testing.TestCase):
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def setUp(self):
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######################################
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