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Merge branch 'newGP' of github.com:SheffieldML/GPy into newGP

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James Hensman 2013-02-01 17:13:10 +00:00
commit f7d2fc6ca4
7 changed files with 147 additions and 184 deletions
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68
GPy/models/GP.py
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@ -7,7 +7,7 @@ import pylab as pb
from .. import kern
from ..core import model
from ..util.linalg import pdinv,mdot
from ..util.plot import gpplot, Tango
from ..util.plot import gpplot,x_frame, Tango
from ..likelihoods import EP
class GP(model):
@ -175,37 +175,7 @@ class GP(model):
return mean, _5pc, _95pc
def _x_frame(self,plot_limits=None,which_data='all',which_functions='all',resolution=None):
"""
Internal helper function for making plots, return a set of new input values to plot as well as lower and upper limits
"""
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
if which_data=='all':
which_data = slice(None)
X = self.X[which_data,:]
Y = self.likelihood.Y[which_data,:]
if plot_limits is None:
xmin,xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
elif len(plot_limits)==2:
xmin, xmax = plot_limits
else:
raise ValueError, "Bad limits for plotting"
if self.X.shape[1]==1:
Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
elif self.X.shape[1]==2:
resolution = resolution or 50
xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
Xnew = np.vstack((xx.flatten(),yy.flatten())).T
else:
raise NotImplementedError, "Cannot plot GPs with more than two input dimensions"
return Xnew, xmin, xmax
def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
def plot_GP(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
@ -223,21 +193,29 @@ class GP(model):
- In higher dimensions, we've no implemented this yet !TODO!
Can plot only part of the data and part of the posterior functions using which_data and which_functions
"""
"""
Plot the data's view of the world, with non-normalised values and GP predictions passed through the likelihood
"""
Xnew, xmin, xmax = self._x_frame()
m,v = self._raw_predict(Xnew)
if isinstance(self.likelihood,EP):
pb.subplot(211)
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
if which_data=='all':
which_data = slice(None)
Xnew, xmin, xmax = x_frame(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))
pb.plot(self.X,self.likelihood.Y,'kx',mew=1.5)
pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
pb.xlim(xmin,xmax)
if isinstance(self.likelihood,EP):
pb.subplot(212)
phi_m,phi_l,phi_u = self.likelihood.predictive_values(m,v)
gpplot(Xnew,phi_m,phi_l,phi_u)
pb.plot(self.X,self.likelihood.data,'kx',mew=1.5)
pb.xlim(xmin,xmax)
def plot_output(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
if which_data=='all':
which_data = slice(None)
Xnew, xmin, xmax = x_frame(self.X, plot_limits=plot_limits)
m, 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 = self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2
pb.xlim(xmin,xmax)
pb.ylim(ymin,ymax)