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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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4
GPy/likelihoods/Gaussian.py
View file

@ -8,7 +8,7 @@ class Gaussian(likelihood):
self.Z = 0. # a correction factor which accounts for the approximation made
N, self.D = data.shape
#normalisation
#normaliztion
if normalize:
self._mean = data.mean(0)[None,:]
self._std = data.std(0)[None,:]
@ -45,7 +45,7 @@ class Gaussian(likelihood):
def predictive_values(self,mu,var):
"""
Un-normalise the prediction and add the likelihood variance, then return the 5%, 95% interval
Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
"""
mean = mu*self._std + self._mean
true_var = (var + self._variance)*self._std**2

15
GPy/models/GP.py
View file

@ -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])))

4
GPy/models/sparse_GP.py
View file

@ -54,7 +54,7 @@ class sparse_GP(GP):
GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X, Xslices=Xslices)
#normalise X uncertainty also
#normalize X uncertainty also
if self.has_uncertain_inputs:
self.X_uncertainty /= np.square(self._Xstd)
@ -228,7 +228,7 @@ class sparse_GP(GP):
return dL_dZ
def _raw_predict(self, Xnew, slices, full_cov=False):
"""Internal helper function for making predictions, does not account for normalisation"""
"""Internal helper function for making predictions, does not account for normalization"""
Kx = self.kern.K(self.Z, Xnew)
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)

2
GPy/models/uncollapsed_sparse_GP.py
View file

@ -93,7 +93,7 @@ class uncollapsed_sparse_GP(sparse_GP):
return A+B+C+D+E
def _raw_predict(self, Xnew, slices,full_cov=False):
"""Internal helper function for making predictions, does not account for normalisation"""
"""Internal helper function for making predictions, does not account for normalization"""
Kx = self.kern.K(Xnew,self.Z)
mu = mdot(Kx,self.Kmmi,self.q_u_expectation[0])

132
GPy/util/Tango.py
View file

@ -25,7 +25,7 @@ def fewerXticks(ax=None,divideby=2):
ax.set_xticks(ax.get_xticks()[::divideby])
coloursHex = {\
colorsHex = {\
"Aluminium6":"#2e3436",\
"Aluminium5":"#555753",\
"Aluminium4":"#888a85",\
@ -54,9 +54,9 @@ coloursHex = {\
"mediumButter":"#edd400",\
"darkButter":"#c4a000"}
darkList = [coloursHex['darkBlue'],coloursHex['darkRed'],coloursHex['darkGreen'], coloursHex['darkOrange'], coloursHex['darkButter'], coloursHex['darkPurple'], coloursHex['darkChocolate'], coloursHex['Aluminium6']]
mediumList = [coloursHex['mediumBlue'], coloursHex['mediumRed'],coloursHex['mediumGreen'], coloursHex['mediumOrange'], coloursHex['mediumButter'], coloursHex['mediumPurple'], coloursHex['mediumChocolate'], coloursHex['Aluminium5']]
lightList = [coloursHex['lightBlue'], coloursHex['lightRed'],coloursHex['lightGreen'], coloursHex['lightOrange'], coloursHex['lightButter'], coloursHex['lightPurple'], coloursHex['lightChocolate'], coloursHex['Aluminium4']]
darkList = [colorsHex['darkBlue'],colorsHex['darkRed'],colorsHex['darkGreen'], colorsHex['darkOrange'], colorsHex['darkButter'], colorsHex['darkPurple'], colorsHex['darkChocolate'], colorsHex['Aluminium6']]
mediumList = [colorsHex['mediumBlue'], colorsHex['mediumRed'],colorsHex['mediumGreen'], colorsHex['mediumOrange'], colorsHex['mediumButter'], colorsHex['mediumPurple'], colorsHex['mediumChocolate'], colorsHex['Aluminium5']]
lightList = [colorsHex['lightBlue'], colorsHex['lightRed'],colorsHex['lightGreen'], colorsHex['lightOrange'], colorsHex['lightButter'], colorsHex['lightPurple'], colorsHex['lightChocolate'], colorsHex['Aluminium4']]
def currentDark():
return darkList[-1]
@ -76,85 +76,85 @@ def nextLight():
return lightList[-1]
def reset():
while not darkList[0]==coloursHex['darkBlue']:
while not darkList[0]==colorsHex['darkBlue']:
darkList.append(darkList.pop(0))
while not mediumList[0]==coloursHex['mediumBlue']:
while not mediumList[0]==colorsHex['mediumBlue']:
mediumList.append(mediumList.pop(0))
while not lightList[0]==coloursHex['lightBlue']:
while not lightList[0]==colorsHex['lightBlue']:
lightList.append(lightList.pop(0))
def setLightFigures():
mpl.rcParams['axes.edgecolor']=coloursHex['Aluminium6']
mpl.rcParams['axes.facecolor']=coloursHex['Aluminium2']
mpl.rcParams['axes.labelcolor']=coloursHex['Aluminium6']
mpl.rcParams['figure.edgecolor']=coloursHex['Aluminium6']
mpl.rcParams['figure.facecolor']=coloursHex['Aluminium2']
mpl.rcParams['grid.color']=coloursHex['Aluminium6']
mpl.rcParams['savefig.edgecolor']=coloursHex['Aluminium2']
mpl.rcParams['savefig.facecolor']=coloursHex['Aluminium2']
mpl.rcParams['text.color']=coloursHex['Aluminium6']
mpl.rcParams['xtick.color']=coloursHex['Aluminium6']
mpl.rcParams['ytick.color']=coloursHex['Aluminium6']
mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium6']
mpl.rcParams['axes.facecolor']=colorsHex['Aluminium2']
mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium6']
mpl.rcParams['figure.edgecolor']=colorsHex['Aluminium6']
mpl.rcParams['figure.facecolor']=colorsHex['Aluminium2']
mpl.rcParams['grid.color']=colorsHex['Aluminium6']
mpl.rcParams['savefig.edgecolor']=colorsHex['Aluminium2']
mpl.rcParams['savefig.facecolor']=colorsHex['Aluminium2']
mpl.rcParams['text.color']=colorsHex['Aluminium6']
mpl.rcParams['xtick.color']=colorsHex['Aluminium6']
mpl.rcParams['ytick.color']=colorsHex['Aluminium6']
def setDarkFigures():
mpl.rcParams['axes.edgecolor']=coloursHex['Aluminium2']
mpl.rcParams['axes.facecolor']=coloursHex['Aluminium6']
mpl.rcParams['axes.labelcolor']=coloursHex['Aluminium2']
mpl.rcParams['figure.edgecolor']=coloursHex['Aluminium2']
mpl.rcParams['figure.facecolor']=coloursHex['Aluminium6']
mpl.rcParams['grid.color']=coloursHex['Aluminium2']
mpl.rcParams['savefig.edgecolor']=coloursHex['Aluminium6']
mpl.rcParams['savefig.facecolor']=coloursHex['Aluminium6']
mpl.rcParams['text.color']=coloursHex['Aluminium2']
mpl.rcParams['xtick.color']=coloursHex['Aluminium2']
mpl.rcParams['ytick.color']=coloursHex['Aluminium2']
mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium2']
mpl.rcParams['axes.facecolor']=colorsHex['Aluminium6']
mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium2']
mpl.rcParams['figure.edgecolor']=colorsHex['Aluminium2']
mpl.rcParams['figure.facecolor']=colorsHex['Aluminium6']
mpl.rcParams['grid.color']=colorsHex['Aluminium2']
mpl.rcParams['savefig.edgecolor']=colorsHex['Aluminium6']
mpl.rcParams['savefig.facecolor']=colorsHex['Aluminium6']
mpl.rcParams['text.color']=colorsHex['Aluminium2']
mpl.rcParams['xtick.color']=colorsHex['Aluminium2']
mpl.rcParams['ytick.color']=colorsHex['Aluminium2']
def hex2rgb(hexcolor):
hexcolor = [hexcolor[1+2*i:1+2*(i+1)] for i in range(3)]
r,g,b = [int(n,16) for n in hexcolor]
return (r,g,b)
coloursRGB = dict([(k,hex2rgb(i)) for k,i in coloursHex.items()])
colorsRGB = dict([(k,hex2rgb(i)) for k,i in colorsHex.items()])
cdict_RB = {'red' :((0.,coloursRGB['mediumRed'][0]/256.,coloursRGB['mediumRed'][0]/256.),
(.5,coloursRGB['mediumPurple'][0]/256.,coloursRGB['mediumPurple'][0]/256.),
(1.,coloursRGB['mediumBlue'][0]/256.,coloursRGB['mediumBlue'][0]/256.)),
'green':((0.,coloursRGB['mediumRed'][1]/256.,coloursRGB['mediumRed'][1]/256.),
(.5,coloursRGB['mediumPurple'][1]/256.,coloursRGB['mediumPurple'][1]/256.),
(1.,coloursRGB['mediumBlue'][1]/256.,coloursRGB['mediumBlue'][1]/256.)),
'blue':((0.,coloursRGB['mediumRed'][2]/256.,coloursRGB['mediumRed'][2]/256.),
(.5,coloursRGB['mediumPurple'][2]/256.,coloursRGB['mediumPurple'][2]/256.),
(1.,coloursRGB['mediumBlue'][2]/256.,coloursRGB['mediumBlue'][2]/256.))}
cdict_RB = {'red' :((0.,colorsRGB['mediumRed'][0]/256.,colorsRGB['mediumRed'][0]/256.),
(.5,colorsRGB['mediumPurple'][0]/256.,colorsRGB['mediumPurple'][0]/256.),
(1.,colorsRGB['mediumBlue'][0]/256.,colorsRGB['mediumBlue'][0]/256.)),
'green':((0.,colorsRGB['mediumRed'][1]/256.,colorsRGB['mediumRed'][1]/256.),
(.5,colorsRGB['mediumPurple'][1]/256.,colorsRGB['mediumPurple'][1]/256.),
(1.,colorsRGB['mediumBlue'][1]/256.,colorsRGB['mediumBlue'][1]/256.)),
'blue':((0.,colorsRGB['mediumRed'][2]/256.,colorsRGB['mediumRed'][2]/256.),
(.5,colorsRGB['mediumPurple'][2]/256.,colorsRGB['mediumPurple'][2]/256.),
(1.,colorsRGB['mediumBlue'][2]/256.,colorsRGB['mediumBlue'][2]/256.))}
cdict_BGR = {'red' :((0.,coloursRGB['mediumBlue'][0]/256.,coloursRGB['mediumBlue'][0]/256.),
(.5,coloursRGB['mediumGreen'][0]/256.,coloursRGB['mediumGreen'][0]/256.),
(1.,coloursRGB['mediumRed'][0]/256.,coloursRGB['mediumRed'][0]/256.)),
'green':((0.,coloursRGB['mediumBlue'][1]/256.,coloursRGB['mediumBlue'][1]/256.),
(.5,coloursRGB['mediumGreen'][1]/256.,coloursRGB['mediumGreen'][1]/256.),
(1.,coloursRGB['mediumRed'][1]/256.,coloursRGB['mediumRed'][1]/256.)),
'blue':((0.,coloursRGB['mediumBlue'][2]/256.,coloursRGB['mediumBlue'][2]/256.),
(.5,coloursRGB['mediumGreen'][2]/256.,coloursRGB['mediumGreen'][2]/256.),
(1.,coloursRGB['mediumRed'][2]/256.,coloursRGB['mediumRed'][2]/256.))}
cdict_BGR = {'red' :((0.,colorsRGB['mediumBlue'][0]/256.,colorsRGB['mediumBlue'][0]/256.),
(.5,colorsRGB['mediumGreen'][0]/256.,colorsRGB['mediumGreen'][0]/256.),
(1.,colorsRGB['mediumRed'][0]/256.,colorsRGB['mediumRed'][0]/256.)),
'green':((0.,colorsRGB['mediumBlue'][1]/256.,colorsRGB['mediumBlue'][1]/256.),
(.5,colorsRGB['mediumGreen'][1]/256.,colorsRGB['mediumGreen'][1]/256.),
(1.,colorsRGB['mediumRed'][1]/256.,colorsRGB['mediumRed'][1]/256.)),
'blue':((0.,colorsRGB['mediumBlue'][2]/256.,colorsRGB['mediumBlue'][2]/256.),
(.5,colorsRGB['mediumGreen'][2]/256.,colorsRGB['mediumGreen'][2]/256.),
(1.,colorsRGB['mediumRed'][2]/256.,colorsRGB['mediumRed'][2]/256.))}
cdict_Alu = {'red' :((0./5,coloursRGB['Aluminium1'][0]/256.,coloursRGB['Aluminium1'][0]/256.),
(1./5,coloursRGB['Aluminium2'][0]/256.,coloursRGB['Aluminium2'][0]/256.),
(2./5,coloursRGB['Aluminium3'][0]/256.,coloursRGB['Aluminium3'][0]/256.),
(3./5,coloursRGB['Aluminium4'][0]/256.,coloursRGB['Aluminium4'][0]/256.),
(4./5,coloursRGB['Aluminium5'][0]/256.,coloursRGB['Aluminium5'][0]/256.),
(5./5,coloursRGB['Aluminium6'][0]/256.,coloursRGB['Aluminium6'][0]/256.)),
'green' :((0./5,coloursRGB['Aluminium1'][1]/256.,coloursRGB['Aluminium1'][1]/256.),
(1./5,coloursRGB['Aluminium2'][1]/256.,coloursRGB['Aluminium2'][1]/256.),
(2./5,coloursRGB['Aluminium3'][1]/256.,coloursRGB['Aluminium3'][1]/256.),
(3./5,coloursRGB['Aluminium4'][1]/256.,coloursRGB['Aluminium4'][1]/256.),
(4./5,coloursRGB['Aluminium5'][1]/256.,coloursRGB['Aluminium5'][1]/256.),
(5./5,coloursRGB['Aluminium6'][1]/256.,coloursRGB['Aluminium6'][1]/256.)),
'blue' :((0./5,coloursRGB['Aluminium1'][2]/256.,coloursRGB['Aluminium1'][2]/256.),
(1./5,coloursRGB['Aluminium2'][2]/256.,coloursRGB['Aluminium2'][2]/256.),
(2./5,coloursRGB['Aluminium3'][2]/256.,coloursRGB['Aluminium3'][2]/256.),
(3./5,coloursRGB['Aluminium4'][2]/256.,coloursRGB['Aluminium4'][2]/256.),
(4./5,coloursRGB['Aluminium5'][2]/256.,coloursRGB['Aluminium5'][2]/256.),
(5./5,coloursRGB['Aluminium6'][2]/256.,coloursRGB['Aluminium6'][2]/256.))}
cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1'][0]/256.),
(1./5,colorsRGB['Aluminium2'][0]/256.,colorsRGB['Aluminium2'][0]/256.),
(2./5,colorsRGB['Aluminium3'][0]/256.,colorsRGB['Aluminium3'][0]/256.),
(3./5,colorsRGB['Aluminium4'][0]/256.,colorsRGB['Aluminium4'][0]/256.),
(4./5,colorsRGB['Aluminium5'][0]/256.,colorsRGB['Aluminium5'][0]/256.),
(5./5,colorsRGB['Aluminium6'][0]/256.,colorsRGB['Aluminium6'][0]/256.)),
'green' :((0./5,colorsRGB['Aluminium1'][1]/256.,colorsRGB['Aluminium1'][1]/256.),
(1./5,colorsRGB['Aluminium2'][1]/256.,colorsRGB['Aluminium2'][1]/256.),
(2./5,colorsRGB['Aluminium3'][1]/256.,colorsRGB['Aluminium3'][1]/256.),
(3./5,colorsRGB['Aluminium4'][1]/256.,colorsRGB['Aluminium4'][1]/256.),
(4./5,colorsRGB['Aluminium5'][1]/256.,colorsRGB['Aluminium5'][1]/256.),
(5./5,colorsRGB['Aluminium6'][1]/256.,colorsRGB['Aluminium6'][1]/256.)),
'blue' :((0./5,colorsRGB['Aluminium1'][2]/256.,colorsRGB['Aluminium1'][2]/256.),
(1./5,colorsRGB['Aluminium2'][2]/256.,colorsRGB['Aluminium2'][2]/256.),
(2./5,colorsRGB['Aluminium3'][2]/256.,colorsRGB['Aluminium3'][2]/256.),
(3./5,colorsRGB['Aluminium4'][2]/256.,colorsRGB['Aluminium4'][2]/256.),
(4./5,colorsRGB['Aluminium5'][2]/256.,colorsRGB['Aluminium5'][2]/256.),
(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}
# cmap_Alu = mpl.colors.LinearSegmentedColormap('TangoAluminium',cdict_Alu,256)
# cmap_BGR = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_BGR,256)
# cmap_RB = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_RB,256)

2
GPy/util/datasets.py
View file

@ -46,7 +46,7 @@ def oil_100(seed=default_seed):
return {'X': X, 'Y': Y, 'info': "Subsample of the oil data extracting 100 values randomly without replacement."}
def pumadyn(seed=default_seed):
# Data is variance 1, no need to normalise.
# Data is variance 1, no need to normalize.
data = np.loadtxt(os.path.join(data_path, 'pumadyn-32nm/Dataset.data.gz'))
indices = np.random.permutation(data.shape[0])
indicesTrain = indices[0:7168]

2
GPy/util/plot.py
View file

@ -6,7 +6,7 @@ import Tango
import pylab as pb
import numpy as np
def gpplot(x,mu,lower,upper,edgecol=Tango.coloursHex['darkBlue'],fillcol=Tango.coloursHex['lightBlue'],axes=None,**kwargs):
def gpplot(x,mu,lower,upper,edgecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'],axes=None,**kwargs):
if axes is None:
axes = pb.gca()
mu = mu.flatten()