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refactored the numeric inverse into the mother class, to test Identity and Log

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This commit is contained in:
beckdaniel 2016-03-02 17:05:22 +00:00
parent 7bee3daac8
commit 6bea908234
2 changed files with 43 additions and 48 deletions
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21
GPy/testing/model_tests.py
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@ -300,12 +300,12 @@ class MiscTests(unittest.TestCase):
preds = m.predict(self.X)
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.IdentityFunction()
warp_f = GPy.util.warping_functions.IdentityFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k, warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X)
np.testing.assert_almost_equal(preds, warp_preds)
np.testing.assert_almost_equal(preds, warp_preds, decimal=4)
def test_warped_gp_log(self):
"""
@ -316,21 +316,16 @@ class MiscTests(unittest.TestCase):
Y = np.abs(self.Y)
logY = np.log(Y)
m = GPy.models.GPRegression(self.X, logY, kernel=k)
#m.optimize()
m['Gaussian_noise.variance'] = 1e-4
m.optimize()
preds = m.predict(self.X)[0]
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.LogFunction()
warp_f = GPy.util.warping_functions.LogFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, Y, kernel=warp_k, warping_function=warp_f)
warp_m.optimize()
warp_m['.*'] = 1.0
warp_m['Gaussian_noise.variance'] = 1e-4
warp_preds = warp_m.predict(self.X, median=True)[0]
#print np.exp(preds)
#print warp_preds
np.testing.assert_almost_equal(np.exp(preds), warp_preds)
np.testing.assert_almost_equal(np.exp(preds), warp_preds, decimal=4)
@unittest.skip('Comment this to plot the modified sine function')
def test_warped_gp_sine(self):
@ -354,17 +349,11 @@ class MiscTests(unittest.TestCase):
print(warp_m['.*warp.*'])
warp_m.predict_in_warped_space = False
warp_m.plot()
import ipdb; ipdb.set_trace()
warp_m.predict_in_warped_space = True
warp_m.plot()
m.plot()
warp_f.plot(X.min()-10, X.max()+10)
plt.show()
class GradientTests(np.testing.TestCase):