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James Hensman 2013-01-31 15:30:58 +00:00
parent 4211cad89d
commit 3a558d8244
1 changed files with 1 additions and 18 deletions
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19
GPy/models/GP.py
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@ -29,7 +29,6 @@ class GP(model):
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
#TODO: when using EP, predict needs to return 3 values otherwise it just needs 2. At the moment predict returns 3 values in any case.
def __init__(self, X, kernel, likelihood, normalize_X=False, Xslices=None):
@ -164,7 +163,7 @@ class GP(model):
"""
#normalise X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
mu, var, phi = self._raw_predict(Xnew, slices, full_cov=full_cov)
mu, var = self._raw_predict(Xnew, slices, full_cov=full_cov)
#now push through likelihood TODO
@ -208,25 +207,9 @@ class GP(model):
if self.X.shape[1]==1:
Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
<<<<<<< HEAD
m,v = self._raw_predict(Xnew,slices=which_functions,full_cov=False)
lower, upper = m.flatten() - 2.*np.sqrt(v) , m.flatten()+ 2.*np.sqrt(v)
gpplot(Xnew,m,lower,upper)
=======
m,v = self.predict(Xnew,slices=which_functions,full_cov=full_cov)
if self.EP:
pb.subplot(211)
gpplot(Xnew,m,v)
if samples: #NOTE why don't we put samples as a parameter of gpplot
s = np.random.multivariate_normal(m.flatten(),np.diag(v.flatten()),samples)
pb.plot(Xnew.flatten(),s.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
pb.plot(Xorig,Yorig,'kx',mew=1.5)
pb.xlim(xmin,xmax)
if self.EP:
phi_m, phi_v, phi_l, phi_u = self.likelihood.predictive_values(m,v)
>>>>>>> 9feae765dc2253edaa37b25e3417a364e5b9acdc
pb.plot(X,Y,'kx',mew=1.5)
pb.xlim(xmin,xmax)
elif self.X.shape[1]==2: