apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-08 19:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

plotting fix

Browse source
This commit is contained in:
James Hensman 2014-03-13 16:44:39 +00:00
parent 328e0124c7
commit c302e515e2
5 changed files with 6 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

4
GPy/core/gp.py
View file

@ -148,7 +148,7 @@ class GP(Model):
return Ysim
def posterior_samples(self,X,size=10, full_cov=True, Y_metadata=None):
def posterior_samples(self, X, size=10, full_cov=False, Y_metadata=None):
"""
Samples the posterior GP at the points X.
@ -163,7 +163,7 @@ class GP(Model):
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
Ysim = self.likelihood.noise_model.samples(Ysim, Y_metadata)
Ysim = self.likelihood.samples(Ysim, Y_metadata)
return Ysim

2
GPy/likelihoods/gaussian.py
View file

@ -94,7 +94,7 @@ class Gaussian(Likelihood):
return self.variance + sigma**2
def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
return [stats.norm.ppf(q)*np.sqrt(var) + mu for q in quantiles]
return [stats.norm.ppf(q/100.)*np.sqrt(var) + mu for q in quantiles]
def pdf_link(self, link_f, y, extra_data=None):
"""

2
GPy/likelihoods/likelihood.py
View file

@ -397,7 +397,7 @@ class Likelihood(Parameterized):
return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles]
def samples(self, gp):
def samples(self, gp, Y_metadata=None):
"""
Returns a set of samples of observations based on a given value of the latent variable.

2
GPy/likelihoods/student_t.py
View file

@ -263,7 +263,7 @@ class StudentT(Likelihood):
def conditional_variance(self, gp):
return self.deg_free/(self.deg_free - 2.)
def samples(self, gp):
def samples(self, gp, Y_metadata=None):
"""
Returns a set of samples of observations based on a given value of the latent variable.

3
GPy/plotting/matplot_dep/models_plots.py
View file

@ -86,8 +86,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
upper = m + 2*np.sqrt(v)
else:
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
for d in which_data_ycols: