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Small fixes to ploting

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Nicolas 2013-02-05 14:23:51 +00:00
parent 5b19d8609a
commit 642b1449e1
1 changed files with 10 additions and 8 deletions
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18
GPy/models/GP.py
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@ -153,7 +153,8 @@ class GP(model):
:param full_cov: whether to return the folll covariance matrix, or just the diagonal :param full_cov: whether to return the folll covariance matrix, or just the diagonal
:type full_cov: bool :type full_cov: bool
:rtype: posterior mean, a Numpy array, Nnew x self.D :rtype: posterior mean, a Numpy array, Nnew x self.D
:rtype: posterior variance, a Numpy array, Nnew x Nnew x (self.D) :rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.D
.. Note:: "slices" specifies how the the points X_new co-vary wich the training points. .. Note:: "slices" specifies how the the points X_new co-vary wich the training points.
@ -167,12 +168,12 @@ class GP(model):
""" """
#normalise X values #normalise X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
mu, var = self._raw_predict(Xnew, slices, full_cov=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, _5pc, _95pc = self.likelihood.predictive_values(mu, var)
return mean, _5pc, _95pc return mean, var, _5pc, _95pc
def plot_internal(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False): def plot_internal(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
@ -206,9 +207,9 @@ class GP(model):
gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v)) gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v))
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:
resolution = resolution or 50 resolution = resolution or 50
Xnew, xmin, xmax,xx,yy = x_frame2D(self.X, plot_limits,resolution) Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits,resolution)
m,v = self._raw_predict(Xnew, slices=which_functions) m,v = self._raw_predict(Xnew, slices=which_functions)
m = m.reshape(resolution,resolution).T m = m.reshape(resolution,resolution).T
pb.contour(xx,yy,m,vmin=m.min(),vmax=m.max(),cmap=pb.cm.jet) pb.contour(xx,yy,m,vmin=m.min(),vmax=m.max(),cmap=pb.cm.jet)
@ -226,17 +227,18 @@ 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, 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 = self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2 ymin,ymax = lower.min(),upper.max() #self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2
pb.xlim(xmin,xmax) pb.xlim(xmin,xmax)
pb.ylim(ymin,ymax) pb.ylim(ymin,ymax)
elif self.X.shape[1]==2: elif self.X.shape[1]==2:
resolution = resolution or 50 resolution = resolution or 50
Xnew, xx, yy, xmin, xmax = x_frame2D(self.X, plot_limits,resolution) 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) x, y = np.linspace(xmin[0],xmax[0],resolution), np.linspace(xmin[1],xmax[1],resolution)
m,lower,upper = self.predict(Xnew, slices=which_functions) m, var, lower, upper = self.predict(Xnew, slices=which_functions)
m = m.reshape(resolution,resolution).T m = m.reshape(resolution,resolution).T
pb.contour(x,y,m,vmin=m.min(),vmax=m.max(),cmap=pb.cm.jet) pb.contour(x,y,m,vmin=m.min(),vmax=m.max(),cmap=pb.cm.jet)
Yf = self.likelihood.Y.flatten() Yf = self.likelihood.Y.flatten()