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This commit is contained in:
beckdaniel 2016-01-14 15:28:48 +00:00
parent 4c843adc4c
commit 24e9d68a19
3 changed files with 10 additions and 5 deletions
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2
GPy/models/warped_gp.py
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@ -160,7 +160,7 @@ class WarpedGP(GP):
mu_star, var_star = self._raw_predict(x_test) mu_star, var_star = self._raw_predict(x_test)
fy = self.warping_function.f(y_test) fy = self.warping_function.f(y_test)
ll_lpd = self.likelihood.log_predictive_density(fy, mu_star, var_star, Y_metadata=Y_metadata) ll_lpd = self.likelihood.log_predictive_density(fy, mu_star, var_star, Y_metadata=Y_metadata)
return ll_lpd * self.warping_function.fgrad_y(y_test) return ll_lpd - np.log(self.warping_function.fgrad_y(y_test))
if __name__ == '__main__': if __name__ == '__main__':

4
GPy/testing/model_tests.py
View file

@ -307,7 +307,7 @@ class MiscTests(unittest.TestCase):
np.testing.assert_almost_equal(preds, warp_preds) np.testing.assert_almost_equal(preds, warp_preds)
@unittest.skip('Comment this to plot the modified sine function') #@unittest.skip('Comment this to plot the modified sine function')
def test_warped_gp_sine(self): def test_warped_gp_sine(self):
""" """
A test replicating the sine regression problem from A test replicating the sine regression problem from
@ -321,7 +321,6 @@ class MiscTests(unittest.TestCase):
warp_k = GPy.kern.RBF(1) warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.TanhWarpingFunction_d(n_terms=2) warp_f = GPy.util.warping_functions.TanhWarpingFunction_d(n_terms=2)
warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f) warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
warp_m['.*noise.variance.*'].constrain_fixed(0.1)
m = GPy.models.GPRegression(X[:, None], Y[:, None]) m = GPy.models.GPRegression(X[:, None], Y[:, None])
m.optimize_restarts(parallel=False, robust=True, num_restarts=5) m.optimize_restarts(parallel=False, robust=True, num_restarts=5)
@ -330,6 +329,7 @@ class MiscTests(unittest.TestCase):
print(warp_m['.*warp.*']) print(warp_m['.*warp.*'])
warp_m.predict_in_warped_space = False warp_m.predict_in_warped_space = False
warp_m.plot() warp_m.plot()
import ipdb; ipdb.set_trace()
warp_m.predict_in_warped_space = True warp_m.predict_in_warped_space = True
warp_m.plot() warp_m.plot()
m.plot() m.plot()

9
GPy/util/warping_functions.py
View file

@ -152,7 +152,7 @@ class TanhWarpingFunction(WarpingFunction):
class TanhWarpingFunction_d(WarpingFunction): class TanhWarpingFunction_d(WarpingFunction):
def __init__(self, n_terms=3): def __init__(self, n_terms=3, initial_y=None):
"""n_terms specifies the number of tanh terms to be used""" """n_terms specifies the number of tanh terms to be used"""
self.n_terms = n_terms self.n_terms = n_terms
self.num_parameters = 3 * self.n_terms + 1 self.num_parameters = 3 * self.n_terms + 1
@ -165,6 +165,7 @@ class TanhWarpingFunction_d(WarpingFunction):
self.d = Param('%s' % ('d'), 1.0, Logexp()) self.d = Param('%s' % ('d'), 1.0, Logexp())
self.link_parameter(self.psi) self.link_parameter(self.psi)
self.link_parameter(self.d) self.link_parameter(self.d)
self.initial_y = initial_y
def f(self, y): def f(self, y):
""" """
@ -187,7 +188,7 @@ class TanhWarpingFunction_d(WarpingFunction):
z += a*np.tanh(b*(y+c)) z += a*np.tanh(b*(y+c))
return z return z
def f_inv(self, z, max_iterations=1000, y=None): def f_inv(self, z, max_iterations=100, y=None):
""" """
calculate the numerical inverse of f calculate the numerical inverse of f
@ -195,12 +196,15 @@ class TanhWarpingFunction_d(WarpingFunction):
""" """
z = z.copy() z = z.copy()
if y is None: if y is None:
# The idea here is to initialize y with +1 where # The idea here is to initialize y with +1 where
# z is positive and -1 where it is negative. # z is positive and -1 where it is negative.
# For negative z, Newton-Raphson diverges # For negative z, Newton-Raphson diverges
# if we initialize y with a positive value (and vice-versa). # if we initialize y with a positive value (and vice-versa).
y = ((z > 0) * 1.) - (z <= 0) y = ((z > 0) * 1.) - (z <= 0)
if self.initial_y is not None:
y *= self.initial_y
it = 0 it = 0
update = np.inf update = np.inf
@ -213,6 +217,7 @@ class TanhWarpingFunction_d(WarpingFunction):
it += 1 it += 1
if it == max_iterations: if it == max_iterations:
print("WARNING!!! Maximum number of iterations reached in f_inv ") print("WARNING!!! Maximum number of iterations reached in f_inv ")
print("Sum of updates: %.4f" % np.sum(update))
return y return y
def fgrad_y(self, y, return_precalc=False): def fgrad_y(self, y, return_precalc=False):