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minor bugfixes in plotting: quantiles are now computed using predict_kw

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correctly
This commit is contained in:
James Hensman 2015-08-18 08:00:47 +01:00
parent 097b048100
commit ea3bfbb597
4 changed files with 9 additions and 5 deletions
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6
GPy/core/gp.py
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@ -249,7 +249,7 @@ class GP(Model):
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata)
return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, kern=None):
"""
Get the predictive quantiles around the prediction at X
@ -257,10 +257,12 @@ class GP(Model):
: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
:param kern: optional kernel to use for prediction
:type predict_kw: dict
:returns: list of quantiles for each X and predictive quantiles for interval combination
:rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]
"""
m, v = self._raw_predict(X, full_cov=False)
m, v = self._raw_predict(X, full_cov=False, kern=kern)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)