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Few more fix to the plotings and predictions

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Nicolas 2013-02-05 18:57:30 +00:00
parent 642b1449e1
commit a1568ca1c8
1 changed files with 19 additions and 8 deletions
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27
GPy/models/GP.py
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@ -137,7 +137,8 @@ class GP(model):
else:
Kxx = self.kern.Kdiag(_Xnew, slices=slices)
var = Kxx - np.sum(np.multiply(KiKx,Kx),0)
return mu, var[:,None]
var = var[:,None]
return mu, var
def predict(self,Xnew, slices=None, full_cov=False):
@ -171,12 +172,12 @@ class GP(model):
mu, var = self._raw_predict(Xnew, slices, full_cov)
#now push through likelihood TODO
mean, _5pc, _95pc = self.likelihood.predictive_values(mu, var)
mean, _025pm, _975pm = self.likelihood.predictive_values(mu, var)
return mean, var, _5pc, _95pc
return mean, var, _025pm, _975pm
def plot_internal(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
def plot_f(self, samples=0, plot_limits=None, which_data='all', which_functions='all', resolution=None, full_cov=False):
"""
Plot the GP's view of the world, where the data is normalised and the likelihood is Gaussian
@ -203,8 +204,17 @@ class GP(model):
if self.X.shape[1] == 1:
Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
m,v = self._raw_predict(Xnew, slices=which_functions)
gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v))
if samples == 0:
m,v = self._raw_predict(Xnew, slices=which_functions)
gpplot(Xnew,m,m-2*np.sqrt(v),m+2*np.sqrt(v))
pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
else:
m,v = self._raw_predict(Xnew, slices=which_functions,full_cov=True)
Ysim = np.random.multivariate_normal(m.flatten(),v,samples)
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)
elif self.X.shape[1] == 2:
@ -220,6 +230,7 @@ class GP(model):
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
# TODO include samples
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
if which_data=='all':
@ -230,10 +241,10 @@ class GP(model):
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)
ymin,ymax = lower.min(),upper.max() #self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2
ymin,ymax = lower.min(),upper.max()
pb.xlim(xmin,xmax)
pb.ylim(ymin,ymax)
elif self.X.shape[1]==2:
resolution = resolution or 50
Xnew, xx, yy, xmin, xmax = x_frame2D(self.X, plot_limits,resolution)