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added predict_quantiles method to warped gps

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beckdaniel 2015-07-30 17:33:42 +01:00
parent 45321f536b
commit 00a209f4cb
1 changed files with 22 additions and 1 deletions
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23
GPy/models/warped_gp.py
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@ -69,7 +69,7 @@ class WarpedGP(GP):
def plot_warping(self): def plot_warping(self):
self.warping_function.plot(self.Y_untransformed.min(), self.Y_untransformed.max()) self.warping_function.plot(self.Y_untransformed.min(), self.Y_untransformed.max())
def predict(self, Xnew, which_parts='all', pred_init=None, Y_metadata=None, full_cov=False): def predict(self, Xnew, which_parts='all', pred_init=None, full_cov=False, Y_metadata=None):
# normalize X values # normalize X values
# Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale # Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
mu, var = GP._raw_predict(self, Xnew) mu, var = GP._raw_predict(self, Xnew)
@ -86,6 +86,27 @@ class WarpedGP(GP):
return mean, var return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
"""
Get the predictive quantiles around the prediction at X
:param X: The points at which to make a prediction
:type X: np.ndarray (Xnew x self.input_dim)
:param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
:type quantiles: tuple
:returns: list of quantiles for each X and predictive quantiles for interval combination
:rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)]
"""
m, v = self._raw_predict(X, full_cov=False)
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)
#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)
if __name__ == '__main__': if __name__ == '__main__':
X = np.random.randn(100, 1) X = np.random.randn(100, 1)
Y = np.sin(X) + np.random.randn(100, 1)*0.05 Y = np.sin(X) + np.random.randn(100, 1)*0.05