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Bug found and fixed in plots for normalized X.

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This commit is contained in:
Ricardo Andrade 2013-02-08 14:00:13 +00:00
parent 2422945f6c
commit 6f55358a45
2 changed files with 17 additions and 7 deletions
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21
GPy/models/GP.py
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@ -30,7 +30,7 @@ class GP(model):
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
#FIXME normalize vs normalise
def __init__(self, X, likelihood, kernel, normalize_X=False, Xslices=None):
# parse arguments
@ -216,9 +216,14 @@ class GP(model):
gpplot(Xnew,m,m-2*np.sqrt(np.diag(v)[:,None]),m+2*np.sqrt(np.diag(v))[:,None])
for i in range(samples):
pb.plot(Xnew,Ysim[i,:],Tango.coloursHex['darkBlue'],linewidth=0.25)
pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
pb.xlim(xmin,xmax)
ymin,ymax = min(np.append(self.likelihood.Y,m-2*np.sqrt(np.diag(v)[:,None]))), max(np.append(self.likelihood.Y,m+2*np.sqrt(np.diag(v)[:,None])))
ymin, ymax = ymin - 0.1*(ymax - ymin), ymax + 0.1*(ymax - ymin)
pb.ylim(ymin,ymax)
if hasattr(self,'Z'):
pb.plot(self.Z,self.Z*0+pb.ylim()[0],'r|',mew=1.5,markersize=12)
elif self.X.shape[1] == 2:
resolution = resolution or 50
Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits,resolution)
@ -239,16 +244,22 @@ class GP(model):
which_data = slice(None)
if self.X.shape[1] == 1:
Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
Xu = self.X * self._Xstd + self._Xmean #NOTE self.X are the normalized values now
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
m, var, lower, upper = self.predict(Xnew, slices=which_functions)
gpplot(Xnew,m, lower, upper)
pb.plot(self.X[which_data],self.likelihood.data[which_data],'kx',mew=1.5)
pb.plot(Xu[which_data],self.likelihood.data[which_data],'kx',mew=1.5)
ymin,ymax = min(np.append(self.likelihood.data,lower)), max(np.append(self.likelihood.data,upper))
ymin, ymax = ymin - 0.1*(ymax - ymin), ymax + 0.1*(ymax - ymin)
pb.xlim(xmin,xmax)
pb.ylim(ymin,ymax)
if hasattr(self,'Z'):
Zu = self.Z*self._Xstd + self._Xmean
pb.plot(Zu,Zu*0+pb.ylim()[0],'r|',mew=1.5,markersize=12)
elif self.X.shape[1]==2:
elif self.X.shape[1]==2: #FIXME
resolution = resolution or 50
Xnew, xx, yy, xmin, xmax = x_frame2D(self.X, plot_limits,resolution)
x, y = np.linspace(xmin[0],xmax[0],resolution), np.linspace(xmin[1],xmax[1],resolution)

3
GPy/models/sparse_GP.py
View file

@ -198,7 +198,7 @@ class sparse_GP(GP):
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
if full_cov:
Kxx = self.kern.K(Xnew)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE thiswon't work for plotting
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
@ -211,7 +211,6 @@ class sparse_GP(GP):
"""
GP.plot(self,*args,**kwargs)
if self.Q==1:
pb.plot(self.Z,self.Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
if self.has_uncertain_inputs:
pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten()))
if self.Q==2: