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added a new test which tries to replicate Snelson's toy 1D but NR seems to diverge...

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beckdaniel 2015-08-05 19:05:48 +01:00
parent 76bc0bec25
commit 995de0f399
4 changed files with 88 additions and 25 deletions
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27
GPy/models/warped_gp.py
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@ -69,27 +69,27 @@ class WarpedGP(GP):
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)
def _get_warped_term(self, mean, std, gh_samples, pred_init=None):
arg1 = gh_samples.dot(std.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):
def _get_warped_mean(self, mean, std, pred_init=None, deg_gauss_hermite=100):
"""
Calculate the warped mean by using Gauss-Hermite quadrature.
"""
gh_samples, gh_weights = np.polynomial.hermite.hermgauss(deg_gauss_hermite)
gh_samples = gh_samples[:,None]
gh_weights = gh_weights[None,:]
return gh_weights.dot(self._get_warped_term(mean, var, gh_samples)) / np.sqrt(np.pi)
return gh_weights.dot(self._get_warped_term(mean, std, gh_samples)) / np.sqrt(np.pi)
def _get_warped_variance(self, mean, var, pred_init=None, deg_gauss_hermite=100):
def _get_warped_variance(self, mean, std, 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,
arg1 = gh_weights.dot(self._get_warped_term(mean, std, gh_samples,
pred_init=pred_init) ** 2) / np.sqrt(np.pi)
arg2 = self._get_warped_mean(mean, var, pred_init=pred_init,
arg2 = self._get_warped_mean(mean, std, pred_init=pred_init,
deg_gauss_hermite=deg_gauss_hermite)
return arg1 - (arg2 ** 2)
@ -103,20 +103,20 @@ class WarpedGP(GP):
mean, var = self.likelihood.predictive_values(mu, var)
if self.predict_in_warped_space:
std = np.sqrt(var)
if median:
#print 'MEDIAN!'
wmean = self.warping_function.f_inv(mean, y=pred_init)
wmean = self.warping_function.f_inv(mean, y=pred_init)
else:
#print 'MEAN!'
wmean = self._get_warped_mean(mean, var, pred_init=pred_init,
wmean = self._get_warped_mean(mean, std, 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,
wvar = self._get_warped_variance(mean, std, pred_init=pred_init,
deg_gauss_hermite=deg_gauss_hermite).T
else:
wmean = mean
#wvar = var
wvar = self.warping_function.f_inv(var)
wvar = var
if self.scale_data:
pred = self._unscale_data(pred)
@ -138,9 +138,12 @@ class WarpedGP(GP):
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
a, b = self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)
if not self.predict_in_warped_space:
return [a, b]
#print a.shape
new_a = self.warping_function.f_inv(a)
new_b = self.warping_function.f_inv(b)
return [new_a, new_b]
#return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)