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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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25
GPy/models/GP.py
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@ -137,7 +137,8 @@ class GP(model):
else: else:
Kxx = self.kern.Kdiag(_Xnew, slices=slices) Kxx = self.kern.Kdiag(_Xnew, slices=slices)
var = Kxx - np.sum(np.multiply(KiKx,Kx),0) 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): 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) mu, var = self._raw_predict(Xnew, slices, full_cov)
#now push through likelihood TODO #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 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: if self.X.shape[1] == 1:
Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits) Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
m,v = self._raw_predict(Xnew, slices=which_functions) if samples == 0:
gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v)) 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.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
pb.xlim(xmin,xmax) pb.xlim(xmin,xmax)
elif self.X.shape[1] == 2: 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" 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): 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': if which_functions=='all':
which_functions = [True]*self.kern.Nparts which_functions = [True]*self.kern.Nparts
if which_data=='all': if which_data=='all':
@ -230,7 +241,7 @@ class GP(model):
m, var, lower, upper = self.predict(Xnew, slices=which_functions) m, var, lower, upper = self.predict(Xnew, slices=which_functions)
gpplot(Xnew,m, lower, upper) gpplot(Xnew,m, lower, upper)
pb.plot(self.X[which_data],self.likelihood.data[which_data],'kx',mew=1.5) 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.xlim(xmin,xmax)
pb.ylim(ymin,ymax) pb.ylim(ymin,ymax)