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James Hensman 2013-03-11 13:26:39 +00:00
parent 95cedc7e4e
commit 6a330db253
7 changed files with 80 additions and 81 deletions
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15
GPy/models/GP.py
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@ -30,7 +30,6 @@ class GP(model):
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
#FIXME normalize vs normalise
def __init__(self, X, likelihood, kernel, normalize_X=False, Xslices=None):
# parse arguments
@ -41,7 +40,7 @@ class GP(model):
assert isinstance(kernel, kern.kern)
self.kern = kernel
#here's some simple normalisation for the inputs
#here's some simple normalization for the inputs
if normalize_X:
self._Xmean = X.mean(0)[None,:]
self._Xstd = X.std(0)[None,:]
@ -134,7 +133,7 @@ class GP(model):
def _raw_predict(self,_Xnew,slices=None, full_cov=False):
"""
Internal helper function for making predictions, does not account
for normalisation or likelihood
for normalization or likelihood
"""
Kx = self.kern.K(self.X,_Xnew, slices1=self.Xslices,slices2=slices)
mu = np.dot(np.dot(Kx.T,self.Ki),self.likelihood.Y)
@ -172,10 +171,10 @@ class GP(model):
- If a list of booleans, specifying which kernel parts are active
If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
This is to allow for different normalisations of the output dimensions.
This is to allow for different normalizations of the output dimensions.
"""
#normalise X values
#normalize X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
mu, var = self._raw_predict(Xnew, slices, full_cov)
@ -187,7 +186,7 @@ class GP(model):
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 normalized and the likelihood is Gaussian
:param samples: the number of a posteriori samples to plot
:param which_data: which if the training data to plot (default all)
@ -203,7 +202,7 @@ 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
Plot the data's view of the world, with non-normalized values and GP predictions passed through the likelihood
"""
if which_functions=='all':
which_functions = [True]*self.kern.Nparts
@ -221,7 +220,7 @@ class GP(model):
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(Xnew,Ysim[i,:],Tango.colorsHex['darkBlue'],linewidth=0.25)
pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
pb.xlim(xmin,xmax)
ymin,ymax = min(np.append(self.likelihood.Y,m-2*np.sqrt(np.diag(v)[:,None]))), max(np.append(self.likelihood.Y,m+2*np.sqrt(np.diag(v)[:,None])))