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some cleaning on warped gp

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beckdaniel 2016-02-24 11:28:05 +00:00
parent 9039fae29e
commit 50f72f0294
1 changed files with 12 additions and 18 deletions
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30
GPy/models/warped_gp.py
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@ -9,17 +9,18 @@ from GPy.util.warping_functions import TanhWarpingFunction
from GPy import kern from GPy import kern
class WarpedGP(GP): class WarpedGP(GP):
"""
This defines a GP Regression model that applies a
warping function to the output.
"""
def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3): def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3):
if kernel is None: if kernel is None:
kernel = kern.RBF(X.shape[1]) kernel = kern.RBF(X.shape[1])
if warping_function == None: if warping_function == None:
self.warping_function = TanhWarpingFunction(warping_terms) self.warping_function = TanhWarpingFunction(warping_terms)
self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1) * 1) self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1) * 1)
else: else:
self.warping_function = warping_function self.warping_function = warping_function
self.scale_data = False self.scale_data = False
if self.scale_data: if self.scale_data:
Y = self._scale_data(Y) Y = self._scale_data(Y)
@ -27,7 +28,6 @@ class WarpedGP(GP):
self.Y_untransformed = Y.copy() self.Y_untransformed = Y.copy()
self.predict_in_warped_space = True self.predict_in_warped_space = True
likelihood = likelihoods.Gaussian() likelihood = likelihoods.Gaussian()
GP.__init__(self, X, self.transform_data(), likelihood=likelihood, kernel=kernel) GP.__init__(self, X, self.transform_data(), likelihood=likelihood, kernel=kernel)
self.link_parameter(self.warping_function) self.link_parameter(self.warping_function)
@ -40,29 +40,22 @@ class WarpedGP(GP):
return (Y + 0.5) * (self._Ymax - self._Ymin) + self._Ymin return (Y + 0.5) * (self._Ymax - self._Ymin) + self._Ymin
def parameters_changed(self): def parameters_changed(self):
"""
Notice that we update the warping function gradients here.
"""
self.Y[:] = self.transform_data() self.Y[:] = self.transform_data()
super(WarpedGP, self).parameters_changed() super(WarpedGP, self).parameters_changed()
Kiy = self.posterior.woodbury_vector.flatten() Kiy = self.posterior.woodbury_vector.flatten()
self.warping_function.update_grads(self.Y_untransformed, Kiy) self.warping_function.update_grads(self.Y_untransformed, Kiy)
#grad_y = self.warping_function.fgrad_y(self.Y_untransformed)
#grad_y_psi, grad_psi = self.warping_function.fgrad_y_psi(self.Y_untransformed,
# return_covar_chain=True)
#djac_dpsi = ((1.0 / grad_y[:, :, None, None]) * grad_y_psi).sum(axis=0).sum(axis=0)
#dquad_dpsi = (Kiy[:, None, None, None] * grad_psi).sum(axis=0).sum(axis=0)
#warping_grads = -dquad_dpsi + djac_dpsi
#self.warping_function.psi.gradient[:] = warping_grads[:, :-1]
#self.warping_function.d.gradient[:] = warping_grads[0, -1]
def transform_data(self): def transform_data(self):
Y = self.warping_function.f(self.Y_untransformed.copy()).copy() Y = self.warping_function.f(self.Y_untransformed.copy()).copy()
return Y return Y
def log_likelihood(self): def log_likelihood(self):
"""
Notice we add the jacobian of the warping function here.
"""
ll = GP.log_likelihood(self) ll = GP.log_likelihood(self)
jacobian = self.warping_function.fgrad_y(self.Y_untransformed) jacobian = self.warping_function.fgrad_y(self.Y_untransformed)
return ll + np.log(jacobian).sum() return ll + np.log(jacobian).sum()
@ -148,7 +141,8 @@ class WarpedGP(GP):
def log_predictive_density(self, x_test, y_test, Y_metadata=None): def log_predictive_density(self, x_test, y_test, Y_metadata=None):
""" """
Calculation of the log predictive density Calculation of the log predictive density. Notice we add
the jacobian of the warping function here.
.. math: .. math:
p(y_{*}|D) = p(y_{*}|f_{*})p(f_{*}|\mu_{*}\\sigma^{2}_{*}) p(y_{*}|D) = p(y_{*}|f_{*})p(f_{*}|\mu_{*}\\sigma^{2}_{*})