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added optional number of contour levels to the 2D plotting in GP.plot

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James Hensman 2013-03-01 11:13:55 +00:00
parent b47161e874
commit 4674ed4558
1 changed files with 13 additions and 2 deletions
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15
GPy/models/GP.py
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@ -60,6 +60,12 @@ class GP(model):
model.__init__(self)
def dL_dZ(self):
"""
TODO: one day we might like to learn Z by gradient methods?
"""
return np.zeros_like(self.Z)
def _set_params(self,p):
self.kern._set_params_transformed(p[:self.kern.Nparam])
#self.likelihood._set_params(p[self.kern.Nparam:]) # test by Nicolas
@ -236,7 +242,12 @@ class GP(model):
else:
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,levels=20):
"""
TODO: Docstrings!
:param levels: for 2D plotting, the number of contour levels to use
"""
# TODO include samples
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
@ -265,7 +276,7 @@ class GP(model):
x, y = np.linspace(xmin[0],xmax[0],resolution), np.linspace(xmin[1],xmax[1],resolution)
m, var, lower, upper = self.predict(Xnew, slices=which_functions)
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,levels=levels)
Yf = self.likelihood.Y.flatten()
pb.scatter(self.X[:,0], self.X[:,1], 40, Yf, cmap=pb.cm.jet,vmin=m.min(),vmax=m.max(), linewidth=0.)
pb.xlim(xmin[0],xmax[0])