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implemented variance using gauss-hermite

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beckdaniel 2015-08-04 18:46:10 +01:00
parent 3aa563d5ba
commit 2426580d4d
2 changed files with 30 additions and 7 deletions
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36
GPy/models/warped_gp.py
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@ -64,11 +64,17 @@ class WarpedGP(GP):
def log_likelihood(self):
ll = GP.log_likelihood(self)
jacobian = self.warping_function.fgrad_y(self.Y_untransformed)
print np.log(jacobian)
return ll + np.log(jacobian).sum()
def plot_warping(self):
self.warping_function.plot(self.Y_untransformed.min(), self.Y_untransformed.max())
def _get_warped_term(self, mean, var, gh_samples, pred_init=None):
arg1 = gh_samples.dot(var.T) * np.sqrt(2)
arg2 = np.ones(shape=gh_samples.shape).dot(mean.T)
return self.warping_function.f_inv(arg1 + arg2, y=pred_init)
def _get_warped_mean(self, mean, var, pred_init=None, deg_gauss_hermite=100):
"""
Calculate the warped mean by using Gauss-Hermite quadrature.
@ -76,9 +82,17 @@ class WarpedGP(GP):
gh_samples, gh_weights = np.polynomial.hermite.hermgauss(deg_gauss_hermite)
gh_samples = gh_samples[:,None]
gh_weights = gh_weights[None,:]
arg1 = gh_samples.dot(var.T) * np.sqrt(2)
arg2 = np.ones(shape=gh_samples.shape).dot(mean.T)
return gh_weights.dot(self.warping_function.f_inv(arg1 + arg2, y=pred_init)) / np.sqrt(np.pi)
return gh_weights.dot(self._get_warped_term(mean, var, gh_samples)) / np.sqrt(np.pi)
def _get_warped_variance(self, mean, var, pred_init=None, deg_gauss_hermite=100):
gh_samples, gh_weights = np.polynomial.hermite.hermgauss(deg_gauss_hermite)
gh_samples = gh_samples[:,None]
gh_weights = gh_weights[None,:]
arg1 = gh_weights.dot(self._get_warped_term(mean, var, gh_samples,
pred_init=pred_init) ** 2) / np.sqrt(np.pi)
arg2 = self._get_warped_mean(mean, var, pred_init=pred_init,
deg_gauss_hermite=deg_gauss_hermite)
return arg1 - (arg2 ** 2)
def predict(self, Xnew, which_parts='all', pred_init=None, full_cov=False, Y_metadata=None,
median=False, deg_gauss_hermite=100):
@ -91,16 +105,24 @@ class WarpedGP(GP):
if self.predict_in_warped_space:
if median:
pred = self.warping_function.f_inv(mean, y=pred_init)
#print 'MEDIAN!'
wmean = self.warping_function.f_inv(mean, y=pred_init)
else:
pred = self._get_warped_mean(mean, var, pred_init=pred_init,
#print 'MEAN!'
wmean = self._get_warped_mean(mean, var, pred_init=pred_init,
deg_gauss_hermite=deg_gauss_hermite).T
#var = self.warping_function.f_inv(var)
wvar = self._get_warped_variance(mean, var, pred_init=pred_init,
deg_gauss_hermite=deg_gauss_hermite).T
var = self.warping_function.f_inv(var)
else:
wmean = mean
#wvar = var
wvar = self.warping_function.f_inv(var)
if self.scale_data:
pred = self._unscale_data(pred)
return pred, var
return wmean, wvar
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
"""

1
GPy/util/warping_functions.py
View file

@ -45,6 +45,7 @@ class WarpingFunction(Parameterized):
plt.xlabel('y')
plt.ylabel('f(y)')
plt.title('warping function')
plt.show()
class TanhWarpingFunction(WarpingFunction):