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moved cubic sine from tests to examples

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This commit is contained in:
beckdaniel 2016-03-07 10:46:42 +00:00
parent 76f3ff65a1
commit 2ecd13b08d
2 changed files with 27 additions and 28 deletions
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26
GPy/examples/regression.py
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@ -550,3 +550,29 @@ def parametric_mean_function(max_iters=100, optimize=True, plot=True):
return m
def warped_gp_cubic_sine(max_iters=100):
"""
A test replicating the sine regression problem from
Snelson's paper.
"""
X = (2 * np.pi) * np.random.random(151) - np.pi
Y = np.sin(X) + np.random.normal(0,0.2,151)
Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
m = GPy.models.GPRegression(X[:, None], Y[:, None])
m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
print(warp_m)
print(warp_m['.*warp.*'])
warp_m.predict_in_warped_space = False
warp_m.plot(title="Warped GP - Latent space")
warp_m.predict_in_warped_space = True
warp_m.plot(title="Warped GP - Warped space")
m.plot(title="Standard GP")
#warp_f.plot(X.min()-10, X.max()+10)
warp_m.plot_warping()
pb.show()

29
GPy/testing/model_tests.py
View file

@ -311,6 +311,7 @@ class MiscTests(unittest.TestCase):
"""
A WarpedGP with the log warping function should be
equal to a standard GP with log labels.
Note that we predict the median here.
"""
k = GPy.kern.RBF(1)
Y = np.abs(self.Y)
@ -327,34 +328,6 @@ class MiscTests(unittest.TestCase):
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):
"""
A test replicating the sine regression problem from
Snelson's paper.
"""
X = (2 * np.pi) * np.random.random(151) - np.pi
Y = np.sin(X) + np.random.normal(0,0.2,151)
Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
import matplotlib.pyplot as plt
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
m = GPy.models.GPRegression(X[:, None], Y[:, None])
m.optimize_restarts(parallel=False, robust=True, num_restarts=5)
warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5)
print(warp_m)
print(warp_m['.*warp.*'])
warp_m.predict_in_warped_space = False
warp_m.plot()
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):
def setUp(self):