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This commit is contained in:
Alan Saul 2015-07-22 18:32:12 +01:00
parent 8fbfb915b0
commit af20bed747
1 changed files with 3 additions and 3 deletions
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6
GPy/core/gp.py
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@ -244,7 +244,7 @@ class GP(Model):
mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var)
# now push through likelihood
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata)
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):
@ -261,7 +261,7 @@ class GP(Model):
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)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
def predictive_gradients(self, Xnew):
"""
@ -331,7 +331,7 @@ class GP(Model):
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
fsim = self.posterior_samples_f(X, size, full_cov=full_cov)
Ysim = self.likelihood.samples(fsim, Y_metadata)
Ysim = self.likelihood.samples(fsim, Y_metadata=Y_metadata)
return Ysim
def plot_f(self, plot_limits=None, which_data_rows='all',