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Log-likelihood,predictions and plotting are working.

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Ricardo 2013-01-29 23:54:02 +00:00
parent bb1e0021d7
commit d1a0883c12
4 changed files with 64 additions and 56 deletions
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16
GPy/models/GP.py
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@ -196,7 +196,6 @@ class GP(model):
This is to allow for different normalisations of the output dimensions.
"""
#normalise X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
mu, var, phi = self._raw_predict(Xnew, slices, full_cov)
@ -224,13 +223,18 @@ class GP(model):
if full_cov:
Kxx = self.kern.K(_Xnew, slices1=slices,slices2=slices)
var = Kxx - np.dot(KiKx.T,Kx)
if self.EP:
raise NotImplementedError, "full_cov = True not implemented for EP"
#var = np.diag(var)[:,None]
#phi = self.likelihood.predictive_mean(mu,var)
else:
Kxx = self.kern.Kdiag(_Xnew, slices=slices)
var = Kxx - np.sum(np.multiply(KiKx,Kx),0)
phi = None if not self.EP else self.likelihood.predictive_mean(mu,var)
if self.EP:
phi = self.likelihood.predictive_mean(mu,var)
return mu, var, phi
def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None):
def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
"""
:param samples: the number of a posteriori samples to plot
:param which_data: which if the training data to plot (default all)
@ -268,13 +272,13 @@ class GP(model):
if self.X.shape[1]==1:
Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
m,v,phi = self.predict(Xnew,slices=which_functions)
m,v,phi = 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),samples)
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)
@ -288,7 +292,7 @@ class GP(model):
resolution = 50 or resolution
xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
Xtest = np.vstack((xx.flatten(),yy.flatten())).T
zz,vv,phi = self.predict(Xtest,slices=which_functions)
zz,vv,phi = self.predict(Xtest,slices=which_functions,full_cov=full_cov)
zz = zz.reshape(resolution,resolution)
pb.contour(xx,yy,zz,vmin=zz.min(),vmax=zz.max(),cmap=pb.cm.jet)
pb.scatter(Xorig[:,0],Xorig[:,1],40,Yorig,linewidth=0,cmap=pb.cm.jet,vmin=zz.min(),vmax=zz.max())